1,592
个编辑
更改
Moved page from wikipedia:en:Atmospheric model (history)
此词条暂由彩云小译翻译，翻译字数共2959，未经人工整理和审校，带来阅读不便，请见谅。
[[File:GFS 850 MB.PNG|right|250px|thumb|A 96-hour forecast of 850 [[millibar|mbar]] [[geopotential height]] and [[temperature]] from the [[Global Forecast System]]]]
An '''atmospheric model''' is a [[mathematical model]] constructed around the full set of [[primitive equations|primitive]] [[Dynamical systems theory|dynamical equations]] which govern atmospheric motions. It can supplement these equations with [[Parametrization (climate)|parameterizations]] for [[Turbulence|turbulent]] diffusion, [[radiation]], [[moist processes]] ([[clouds]] and [[precipitation (meteorology)|precipitation]]), [[heat transfer|heat exchange]], [[soil]], vegetation, surface water, the [[Kinematics|kinematic]] effects of [[terrain]], and convection. Most atmospheric models are numerical, i.e. they discretize equations of motion. They can predict microscale phenomena such as [[tornadoes]] and [[Eddy covariance|boundary layer eddies]], sub-microscale turbulent flow over buildings, as well as synoptic and global flows. The horizontal domain of a model is either ''global'', covering the entire [[Earth]], or ''regional'' (''limited-area''), covering only part of the Earth. The different types of models run are thermotropic, [[barotropic]], hydrostatic, and nonhydrostatic. Some of the model types make assumptions about the atmosphere which lengthens the time steps used and increases computational speed.
An atmospheric model is a mathematical model constructed around the full set of primitive dynamical equations which govern atmospheric motions. It can supplement these equations with parameterizations for turbulent diffusion, radiation, moist processes (clouds and precipitation), heat exchange, soil, vegetation, surface water, the kinematic effects of terrain, and convection. Most atmospheric models are numerical, i.e. they discretize equations of motion. They can predict microscale phenomena such as tornadoes and boundary layer eddies, sub-microscale turbulent flow over buildings, as well as synoptic and global flows. The horizontal domain of a model is either global, covering the entire Earth, or regional (limited-area), covering only part of the Earth. The different types of models run are thermotropic, barotropic, hydrostatic, and nonhydrostatic. Some of the model types make assumptions about the atmosphere which lengthens the time steps used and increases computational speed.
大气模式是围绕控制大气运动的一整套原始动力学方程建立的数学模式。它可以用湍流扩散、辐射、湿过程(云和降水)、热交换、土壤、植被、地表水、地形的运动学效应和对流等参数化来补充这些方程。大多数大气模型都是数字化的，例如。他们把运动方程分开。他们可以预测微尺度现象，例如龙卷风和边界层涡旋，建筑物上空的亚微尺度湍流，以及天气和全球气流。模型的水平域要么是全球性的，覆盖了整个地球; 要么是区域性的，只覆盖了地球的一部分。不同类型的模式运行是热力学，正压，流体静力学和非流体静力学。一些模型类型对大气层做出假设，从而延长了使用的时间步骤，提高了计算速度。
Forecasts are computed using mathematical equations for the physics and dynamics of the atmosphere. These equations are nonlinear and are impossible to solve exactly. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods. Global models often use [[spectral method]]s for the horizontal dimensions and [[Finite difference method|finite-difference methods]] for the vertical dimension, while regional models usually use finite-difference methods in all three dimensions. For specific locations, [[model output statistics]] use climate information, output from [[numerical weather prediction]], and current [[surface weather observation]]s to develop statistical relationships which account for model bias and resolution issues.
Forecasts are computed using mathematical equations for the physics and dynamics of the atmosphere. These equations are nonlinear and are impossible to solve exactly. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods. Global models often use spectral methods for the horizontal dimensions and finite-difference methods for the vertical dimension, while regional models usually use finite-difference methods in all three dimensions. For specific locations, model output statistics use climate information, output from numerical weather prediction, and current surface weather observations to develop statistical relationships which account for model bias and resolution issues.
预报是利用大气物理和动力学的数学方程式计算出来的。这些方程是非线性的，不可能精确求解。因此，数值方法可以得到近似解。不同的模型使用不同的求解方法。全球模型通常采用谱方法计算水平维数，有限差分方法计算垂直维数，而区域模型通常采用三维有限差分方法。对于特定的位置，模型输出统计使用气候信息、数值天气预报气象观测数据和当前地面天气观测数据来建立统计关系，以解释模型偏差和分辨率问题。
== Types ==
The main assumption made by the '''thermotropic''' model is that while the magnitude of the [[thermal wind]] may change, its direction does not change with respect to height, and thus the [[Baroclinity|baroclinicity]] in the atmosphere can be simulated using the {{convert|500|mb|inHg|adj=on |abbr=on |lk=on}} and {{convert|1000|mb|inHg|adj=on|abbr=on}} [[geopotential height]] surfaces and the average thermal wind between them.<ref>{{cite book|last= Gates|first=W. Lawrence|title=Results Of Numerical Forecasting With The Barotropic And Thermotropic Atmospheric Models|date=August 1955|publisher=Air Force Cambridge Research Laboratories|location=[[Hanscom Air Force Base]]|url=http://handle.dtic.mil/100.2/AD101943}}</ref><ref>{{cite journal |last=Thompson|first=P. D.|author2=W. Lawrence Gates|title=A Test of Numerical Prediction Methods Based on the Barotropic and Two-Parameter Baroclinic Models|journal=[[Journal of the Atmospheric Sciences|Journal of Meteorology]]| date=April 1956 |volume=13|issue=2|pages=127–141 |doi= 10.1175/1520-0469(1956)013<0127:ATONPM>2.0.CO;2 |issn=1520-0469|bibcode = 1956JAtS...13..127T |doi-access=free}}</ref>
The main assumption made by the thermotropic model is that while the magnitude of the thermal wind may change, its direction does not change with respect to height, and thus the baroclinicity in the atmosphere can be simulated using the and geopotential height surfaces and the average thermal wind between them.
= = = = = 由热带模式作出的主要假设是，虽然热风的大小可能改变，但其方向不随高度而改变，因此大气的斜压性可以利用位势高度表面和它们之间的平均热风来模拟。
'''Barotropic''' models assume the atmosphere is nearly [[barotropic]], which means that the direction and speed of the [[geostrophic wind]] are independent of height. In other words, no vertical [[wind shear]] of the geostrophic wind. It also implies that thickness contours (a proxy for temperature) are parallel to upper level height contours. In this type of atmosphere, [[high-pressure area|high]] and [[low-pressure area|low pressure area]]s are centers of warm and cold temperature anomalies. Warm-core highs (such as the [[subtropical ridge]] and Bermuda-Azores high) and [[cold-core low]]s have strengthening winds with height, with the reverse true for cold-core highs (shallow arctic highs) and warm-core lows (such as [[tropical cyclone]]s).<ref>{{cite book|title=Atmospheric Science: An Introductory Survey|author1=Wallace, John M. |author2=Peter V. Hobbs |name-list-style=amp |year=1977|isbn=978-0-12-732950-5|publisher=Academic Press, Inc.|pages=384–385}}</ref> A barotropic model tries to solve a simplified form of [[atmospheric dynamics]] based on the assumption that the atmosphere is in [[geostrophic balance]]; that is, that the [[Rossby number]] of the air in the atmosphere is small.<ref>{{cite book|last=Marshall|first=John|title=Atmosphere, ocean, and climate dynamics : an introductory text|year=2008|publisher=Elsevier Academic Press|location=Amsterdam|isbn=978-0-12-558691-7|author2=Plumb, R. Alan|pages=109–12|chapter=Balanced flow}}</ref> If the assumption is made that the atmosphere is [[divergence-free]], the [[curl (mathematics)|curl]] of the [[Euler equations (fluid dynamics)|Euler equations]] reduces into the [[barotropic vorticity equation]]. This latter equation can be solved over a single layer of the atmosphere. Since the atmosphere at a height of approximately {{convert|5.5|km|mi}} is mostly divergence-free, the barotropic model best approximates the state of the atmosphere at a [[geopotential height]] corresponding to that altitude, which corresponds to the atmosphere's {{convert|500|mb|inHg|abbr=on}} pressure surface.<ref name="Charney 1950"/><!-- also solved as a stream function, need to find a reference for that -->
Barotropic models assume the atmosphere is nearly barotropic, which means that the direction and speed of the geostrophic wind are independent of height. In other words, no vertical wind shear of the geostrophic wind. It also implies that thickness contours (a proxy for temperature) are parallel to upper level height contours. In this type of atmosphere, high and low pressure areas are centers of warm and cold temperature anomalies. Warm-core highs (such as the subtropical ridge and Bermuda-Azores high) and cold-core lows have strengthening winds with height, with the reverse true for cold-core highs (shallow arctic highs) and warm-core lows (such as tropical cyclones). A barotropic model tries to solve a simplified form of atmospheric dynamics based on the assumption that the atmosphere is in geostrophic balance; that is, that the Rossby number of the air in the atmosphere is small. If the assumption is made that the atmosphere is divergence-free, the curl of the Euler equations reduces into the barotropic vorticity equation. This latter equation can be solved over a single layer of the atmosphere. Since the atmosphere at a height of approximately is mostly divergence-free, the barotropic model best approximates the state of the atmosphere at a geopotential height corresponding to that altitude, which corresponds to the atmosphere's pressure surface.
正压模式假设大气接近正压，这意味着地转风的方向和速度与高度无关。换句话说，没有地转风的垂直风切变。这也意味着厚度等值线(代表温度)是平行于上层高度等值线。在这种类型的大气中，高压区和低压区是暖温和冷温异常的中心。温核高压(如副热带嵴线和百慕大-亚速尔群岛高压)和冷核低压具有随高度增强的风力，而冷核高压(北极浅层高压)和温核低压(如热带气旋)则相反。正压模式试图解决一个简化形式的大气动力学的基础上的假设，大气是地转平衡，即大气中的空气罗斯比数量很小。如果假设大气是无散度的，则欧拉方程的旋度降为正压涡度方程。后一个方程可以在一层大气上求解。由于大气在大约高度处基本上是无辐散的，正压模式最接近大气在相应于高度的位势高度处的状态，也就是相应于大气压力表面的状态。
[[Primitive equations#Vertical pressure.2C Cartesian tangential plane|'''Hydrostatic''' model]]s filter out vertically moving [[acoustic wave]]s from the vertical momentum equation, which significantly increases the time step used within the model's run. This is known as the [[hydrostatic equilibrium|hydrostatic approximation]]. Hydrostatic models use either pressure or [[sigma coordinate system|sigma-pressure]] vertical coordinates. Pressure coordinates intersect topography while sigma coordinates follow the contour of the land. Its hydrostatic assumption is reasonable as long as horizontal grid resolution is not small, which is a scale where the hydrostatic assumption fails. Models which use the entire vertical momentum equation are known as '''nonhydrostatic'''. A nonhydrostatic model can be solved anelastically, meaning it solves the complete [[continuity equation]] for air assuming it is incompressible, or elastically, meaning it solves the complete continuity equation for air and is fully compressible. Nonhydrostatic models use altitude or sigma altitude for their vertical coordinates. Altitude coordinates can intersect land while sigma-altitude coordinates follow the contours of the land.<ref>{{cite book|url=https://books.google.com/books?id=41qztAEACAAJ|pages=138–143|title=Fundamentals of atmospheric modeling|author=Jacobson, Mark Zachary|year=2005|publisher=Cambridge University Press|isbn=978-0-521-83970-9}}</ref>
Hydrostatic models filter out vertically moving acoustic waves from the vertical momentum equation, which significantly increases the time step used within the model's run. This is known as the hydrostatic approximation. Hydrostatic models use either pressure or sigma-pressure vertical coordinates. Pressure coordinates intersect topography while sigma coordinates follow the contour of the land. Its hydrostatic assumption is reasonable as long as horizontal grid resolution is not small, which is a scale where the hydrostatic assumption fails. Models which use the entire vertical momentum equation are known as nonhydrostatic. A nonhydrostatic model can be solved anelastically, meaning it solves the complete continuity equation for air assuming it is incompressible, or elastically, meaning it solves the complete continuity equation for air and is fully compressible. Nonhydrostatic models use altitude or sigma altitude for their vertical coordinates. Altitude coordinates can intersect land while sigma-altitude coordinates follow the contours of the land.
流体静力学模型从垂直动量方程中过滤出垂直运动的声波，这显著地增加了模型运行中使用的时间步长。这就是流体静力学近似。流体静力学模型使用压力或 sigma 压力的垂直坐标。压力坐标与地形相交，而 sigma 坐标跟随地形等高线。只要水平网格分辨率不小，其静力学假设是合理的，这是静力学假设失效的尺度。使用整个垂直动量方程的模型称为非静力学模型。非流体静力学模型可以用分弹性方法求解，这意味着它可以求解空气的完全连续性方程，前提是它是不可压缩的，或者是弹性的，这意味着它可以求解空气的完全连续性方程，并且是完全可压缩的。非静力学模型使用高度或西格玛高度作为其垂直坐标。高度坐标可以与地面相交，而 sigma 高度坐标则跟随地面的等高线。
== History ==
[[File:Two women operating ENIAC.gif|thumb|280px|The ENIAC main control panel at the [[Moore School of Electrical Engineering]]]]
{{Main|History of numerical weather prediction}}
The [[history of numerical weather prediction]] began in the 1920s through the efforts of [[Lewis Fry Richardson]] who utilized procedures developed by [[Vilhelm Bjerknes]].<ref name="Lynch JCP">{{cite journal|last=Lynch|author-link=Peter Lynch (meteorologist)|first=Peter|title=The origins of computer weather prediction and climate modeling|journal=[[Journal of Computational Physics]]|date=2008-03-20|volume=227|issue=7|pages=3431–44|doi= 10.1016/j.jcp.2007.02.034 |url=http://www.rsmas.miami.edu/personal/miskandarani/Courses/MPO662/Lynch,Peter/OriginsCompWF.JCP227.pdf|access-date= 2010-12-23 |bibcode=2008JCoPh.227.3431L|archive-url=https://web.archive.org/web/20100708191309/http://www.rsmas.miami.edu/personal/miskandarani/Courses/MPO662/Lynch,Peter/OriginsCompWF.JCP227.pdf|archive-date=2010-07-08|url-status=dead}}</ref><ref name="Lynch Ch1">{{cite book|last=Lynch |first= Peter |title=The Emergence of Numerical Weather Prediction|year=2006|publisher=[[Cambridge University Press]]|isbn=978-0-521-85729-1|pages=1–27 |chapter= Weather Prediction by Numerical Process}}</ref> It was not until the advent of the computer and [[computer simulation]] that computation time was reduced to less than the forecast period itself. [[ENIAC]] created the first computer forecasts in 1950,<ref name="Charney 1950">{{cite journal|last1= Charney|first1=Jule|last2=Fjörtoft|first2=Ragnar|last3=von Neumann|first3=John|title=Numerical Integration of the Barotropic Vorticity Equation|journal= Tellus|date=November 1950|volume=2|issue=4|doi=10.3402/tellusa.v2i4.8607|author-link1=Jule Charney|author-link3=John von Neumann|bibcode= 1950TellA...2..237C |pages=237–254|doi-access=free}}</ref><ref>{{cite book|title=Storm Watchers|page=[https://archive.org/details/stormwatcherstur00cox_df1/page/208 208]|year=2002|author=Cox, John D.|publisher=John Wiley & Sons, Inc.|isbn=978-0-471-38108-2|url=https://archive.org/details/stormwatcherstur00cox_df1/page/208}}</ref> and more powerful computers later increased the size of initial datasets and included more complicated versions of the equations of motion.<ref name="Harper BAMS">{{cite journal|last=Harper|first=Kristine|author2=Uccellini, Louis W.|author3= Kalnay, Eugenia|author4= Carey, Kenneth|author5= Morone, Lauren|title=2007: 50th Anniversary of Operational Numerical Weather Prediction|journal=[[Bulletin of the American Meteorological Society]]|date=May 2007|volume=88|issue=5|pages=639–650|doi=10.1175/BAMS-88-5-639 |bibcode=2007BAMS...88..639H |doi-access=free}}</ref> In 1966, [[West Germany]] and the United States began producing operational forecasts based on [[primitive equations|primitive-equation]] models, followed by the United Kingdom in 1972 and Australia in 1977.<ref name="Lynch JCP"/><ref name="Leslie BOM">{{cite journal|last=Leslie|first=L.M.|author2=Dietachmeyer, G.S.|title=Real-time limited area numerical weather prediction in Australia: a historical perspective|journal=Australian Meteorological Magazine|date=December 1992|volume=41|issue=SP|pages=61–77|url=http://www.bom.gov.au/amoj/docs/1992/leslie2.pdf|access-date=2011-01-03|publisher=[[Bureau of Meteorology]]}}</ref> The development of global [[Forecasting#Categories of forecasting methods|forecasting models]] led to the first climate models.<ref name="Phillips"/><ref name="Cox210"/> The development of limited area (regional) models facilitated advances in forecasting the tracks of [[tropical cyclone]] as well as [[air quality]] in the 1970s and 1980s.<ref name="Shuman W&F">{{cite journal|last=Shuman|first=Frederick G.|author-link=Frederick Gale Shuman|title=History of Numerical Weather Prediction at the National Meteorological Center|journal=[[Weather and Forecasting]]|date=September 1989|volume=4|issue=3|pages=286–296|doi= 10.1175/1520-0434(1989)004<0286:HONWPA>2.0.CO;2 |issn=1520-0434|bibcode=1989WtFor...4..286S|doi-access=free}}</ref><ref name="Steyn, D. G. 1991 241–242">{{cite book|title=Air pollution modeling and its application VIII, Volume 8|author=Steyn, D. G.|publisher=Birkhäuser|year=1991|pages=241–242|isbn= 978-0-306-43828-8}}</ref>
The history of numerical weather prediction began in the 1920s through the efforts of Lewis Fry Richardson who utilized procedures developed by Vilhelm Bjerknes. It was not until the advent of the computer and computer simulation that computation time was reduced to less than the forecast period itself. ENIAC created the first computer forecasts in 1950, and more powerful computers later increased the size of initial datasets and included more complicated versions of the equations of motion. In 1966, West Germany and the United States began producing operational forecasts based on primitive-equation models, followed by the United Kingdom in 1972 and Australia in 1977. The development of global forecasting models led to the first climate models. The development of limited area (regional) models facilitated advances in forecasting the tracks of tropical cyclone as well as air quality in the 1970s and 1980s.
数值天气预报的历史始于20世纪20年代，这得益于 Lewis Fry Richardson 的努力，他运用了威廉·皮耶克尼斯的方法。直到计算机和计算机模拟时代的到来，计算时间才减少到低于预测期本身。1950年 ENIAC 发明了第一台计算机预测系统，后来功能更强大的计算机增加了初始数据集的规模，并包含了更复杂的运动方程预测系统。1966年，西德和美国开始根据原始方程模型制作业务预报，1972年联合王国和1977年澳大利亚紧随其后。全球预报模型的发展导致了第一个气候模型的诞生。在20世纪70年代和80年代，有限区域(区域)模型的发展促进了热带气旋轨道和空气质量预报的进步。
Because the output of forecast models based on [[atmospheric dynamics]] requires corrections near ground level, [[model output statistics]] (MOS) were developed in the 1970s and 1980s for individual ''forecast points'' (locations).<ref name="MOS"/><ref name="L. Best, D. L. and S. P. Pryor 1983 1–90">{{cite book|title=Air Weather Service Model Output Statistics Systems|author1=L. Best, D. L. |author2=S. P. Pryor |name-list-style=amp |year=1983|pages=1–90|publisher=Air Force Global Weather Central}}</ref> Even with the increasing power of supercomputers, the [[forecast skill]] of numerical weather models only extends to about two weeks into the future, since the density and quality of observations—together with the [[Chaos theory|chaotic]] nature of the [[partial differential equation]]s used to calculate the forecast—introduce errors which double every five days.<ref name="Cox">{{cite book|title=Storm Watchers|pages=[https://archive.org/details/stormwatcherstur00cox_df1/page/222 222–224]|year=2002|author=Cox, John D.|publisher=John Wiley & Sons, Inc.|isbn=978-0-471-38108-2|url=https://archive.org/details/stormwatcherstur00cox_df1/page/222}}</ref><ref name="Klaus">Weickmann, Klaus, Jeff Whitaker, Andres Roubicek and Catherine Smith (2001-12-01). [http://www.cdc.noaa.gov/spotlight/12012001/ The Use of Ensemble Forecasts to Produce Improved Medium Range (3–15 days) Weather Forecasts.] [[Climate Diagnostics Center]]. Retrieved 2007-02-16.</ref> The use of model ensemble forecasts since the 1990s helps to define the forecast uncertainty and extend [[weather forecasting]] farther into the future than otherwise possible.<ref name="Toth">{{cite journal|last=Toth|first=Zoltan|author2=Kalnay, Eugenia|title=Ensemble Forecasting at NCEP and the Breeding Method |journal=[[Monthly Weather Review]]|date=December 1997|volume=125|issue=12|pages=3297–3319|doi=10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2|issn=1520-0493|bibcode=1997MWRv..125.3297T|author-link2=Eugenia Kalnay|citeseerx=10.1.1.324.3941}}</ref><ref name="ECens">{{cite web|url=http://ecmwf.int/products/forecasts/guide/The_Ensemble_Prediction_System_EPS_1.html |title=The Ensemble Prediction System (EPS) |publisher=[[ECMWF]] |access-date=2011-01-05 |archive-url=https://web.archive.org/web/20110125125209/http://ecmwf.int/products/forecasts/guide/The_Ensemble_Prediction_System_EPS_1.html |archive-date=25 January 2011 |url-status=dead }}</ref><ref name="RMS">{{cite journal|title=The ECMWF Ensemble Prediction System: Methodology and validation|journal=Quarterly Journal of the Royal Meteorological Society|date=January 1996|volume=122|issue=529|pages=73–119|author1=Molteni, F. |author2=Buizza, R. |author3=Palmer, T.N. |author4=Petroliagis, T. |doi=10.1002/qj.49712252905|bibcode=1996QJRMS.122...73M}}</ref>
Because the output of forecast models based on atmospheric dynamics requires corrections near ground level, model output statistics (MOS) were developed in the 1970s and 1980s for individual forecast points (locations). Even with the increasing power of supercomputers, the forecast skill of numerical weather models only extends to about two weeks into the future, since the density and quality of observations—together with the chaotic nature of the partial differential equations used to calculate the forecast—introduce errors which double every five days.Weickmann, Klaus, Jeff Whitaker, Andres Roubicek and Catherine Smith (2001-12-01). The Use of Ensemble Forecasts to Produce Improved Medium Range (3–15 days) Weather Forecasts. Climate Diagnostics Center. Retrieved 2007-02-16. The use of model ensemble forecasts since the 1990s helps to define the forecast uncertainty and extend weather forecasting farther into the future than otherwise possible.
由于基于大气动力学的预报模式的输出需要近地面水平的修正，因此在20世纪70年代和80年代发展了单个预报点(位置)的模式输出统计学(MOS)。即使超级计算机的能力越来越强，数值天气模式的预报技巧也只能延伸到未来两周左右，因为观测的密度和质量ーー以及用于计算预报的偏微分方程的混沌性ーー带来了每五天翻一番的误差。Weickmann，Klaus，Jeff Whitaker，Andres Roubicek 和 Catherine Smith (2001-12-01)。利用集合天气预报制作经改进的中期(3-15天)天气预报。气候诊断中心。检索2007-02-16。自20世纪90年代以来，模式集合预报的使用有助于确定预报的不确定性，并且比其他方式可能延长未来一个天气预报。
==Initialization==
{{transcluded section|source=Numerical weather prediction}}
{{#section-h:Numerical weather prediction|Initialization}}
==Computation==
[[File:NAM 500 MB.PNG|thumb|An example of 500 [[millibar|mbar]] [[geopotential height]] prediction from a numerical weather prediction model.]]
[[File:Supercomputing the Climate.ogv|thumb|Supercomputers are capable of running highly complex models to help scientists better understand Earth's climate.]]
thumb|Supercomputers are capable of running highly complex models to help scientists better understand Earth's climate.
超级计算机能够运行高度复杂的模型，以帮助科学家更好地了解地球的气候。
A model is a computer program that produces [[meteorological]] information for future times at given locations and altitudes. Within any model is a set of equations, known as the [[primitive equations]], used to predict the future state of the atmosphere.<ref>{{cite book|last=Pielke|first=Roger A.|title=Mesoscale Meteorological Modeling|year=2002|publisher=[[Academic Press]]|isbn=978-0-12-554766-6|pages=48–49}}</ref> These equations are initialized from the analysis data and rates of change are determined. These rates of change predict the state of the atmosphere a short time into the future, with each time increment known as a time step. The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time into the future. ''Time stepping'' is repeated until the solution reaches the desired forecast time. The length of the time step chosen within the model is related to the distance between the points on the computational grid, and is chosen to maintain [[numerical stability]].<ref>{{cite book|last=Pielke|first=Roger A.|title=Mesoscale Meteorological Modeling|year=2002|publisher=[[Academic Press]]|isbn=978-0-12-554766-6|pages=285–287}}</ref> Time steps for global models are on the order of tens of minutes,<ref>{{cite book|url=https://books.google.com/books?id=JZikIbXzipwC&pg=PA131|page=132|title=Computational Science – ICCS 2005: 5th International Conference, Atlanta, GA, USA, May 22–25, 2005, Proceedings, Part 1|author=Sunderam, V. S. |author2=G. Dick van Albada |author3=Peter M. A. Sloot |author4=J. J. Dongarra|year=2005|publisher=Springer|isbn=978-3-540-26032-5}}</ref> while time steps for regional models are between one and four minutes.<ref>{{cite book|url=https://books.google.com/books?id=UV6PnF2z5_wC&pg=PA276|page=276|title=Developments in teracomputing: proceedings of the ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology|author=Zwieflhofer, Walter |author2=Norbert Kreitz |author3=European Centre for Medium Range Weather Forecasts|year=2001|publisher=World Scientific|isbn=978-981-02-4761-4}}</ref> The global models are run at varying times into the future. The [[UKMET]] [[Unified model]] is run six days into the future,<ref name="models"/> the [[European Centre for Medium-Range Weather Forecasts]] model is run out to 10 days into the future,<ref>{{cite book|url=https://books.google.com/books?id=fhW5oDv3EPsC&pg=PA474|page=480|author=Holton, James R.|title=An introduction to dynamic meteorology, Volume 1|year=2004|publisher=Academic Press|isbn=978-0-12-354015-7}}</ref> while the [[Global Forecast System]] model run by the [[Environmental Modeling Center]] is run 16 days into the future.<ref>{{cite book|url=https://books.google.com/books?id=mTZvR3R6YdkC&pg=PA121|page=121|title=Famine early warning systems and remote sensing data|author=Brown, Molly E.|publisher=Springer|year=2008|isbn=978-3-540-75367-4}}</ref>
A model is a computer program that produces meteorological information for future times at given locations and altitudes. Within any model is a set of equations, known as the primitive equations, used to predict the future state of the atmosphere. These equations are initialized from the analysis data and rates of change are determined. These rates of change predict the state of the atmosphere a short time into the future, with each time increment known as a time step. The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time into the future. Time stepping is repeated until the solution reaches the desired forecast time. The length of the time step chosen within the model is related to the distance between the points on the computational grid, and is chosen to maintain numerical stability. Time steps for global models are on the order of tens of minutes, while time steps for regional models are between one and four minutes. The global models are run at varying times into the future. The UKMET Unified model is run six days into the future, the European Centre for Medium-Range Weather Forecasts model is run out to 10 days into the future, while the Global Forecast System model run by the Environmental Modeling Center is run 16 days into the future.
模型是一种计算机程序，可以在给定的地点和高度为未来时间生成气象信息。在任何模型中都有一组方程，被称为原始方程组方程，用来预测未来的大气状态。这些方程由分析数据初始化，并确定变化率。这些变化率可以预测未来一小段时间内大气层的状态，每一个时间增量称为时间步长。然后将这些方程式应用到这种新的大气状态，以发现新的变化速率，而这些新的变化速率预测了未来更远时间的大气层。重复时间步进，直到解到达预期的预测时间。在模型中选择的时间步长与计算网格中点之间的距离有关，选择这个时间步长是为了保持数值稳定性。全球模型的时间步长约为数十分钟，而区域模型的时间步长约为1至4分钟。全球模型在未来的不同时间运行。统一模型在未来6天内运行，欧洲中期天气预报中心模型在未来10天内运行，而由环境建模中心运行的全球预报系统模型在未来16天内运行。
The equations used are [[nonlinear system|nonlinear]] partial differential equations which are impossible to solve exactly through analytical methods,<ref name="finite">{{cite book|url=https://books.google.com/books?id=SH8R_flZBGIC&pg=PA165|title=Finite difference schemes and partial differential equations|author=Strikwerda, John C.|pages=165–170|year=2004|publisher=SIAM|isbn=978-0-89871-567-5}}</ref> with the exception of a few idealized cases.<ref>{{cite book|last=Pielke|first=Roger A.|title=Mesoscale Meteorological Modeling|year=2002|publisher=[[Academic Press]]|isbn=978-0-12-554766-6|pages=65}}</ref> Therefore, numerical methods obtain approximate solutions. Different models use different solution methods: some global models use [[spectral method]]s for the horizontal dimensions and [[finite difference method]]s for the vertical dimension, while regional models and other global models usually use finite-difference methods in all three dimensions.<ref name="finite"/> The visual output produced by a model solution is known as a [[prognostic chart]], or ''prog''.<ref>{{cite book|author=Ahrens, C. Donald|page=244|isbn=978-0-495-11558-8|year=2008|publisher=Cengage Learning|title=Essentials of meteorology: an invitation to the atmosphere|url=https://books.google.com/books?id=2Yn29IFukbgC&pg=PA244}}</ref>
The equations used are nonlinear partial differential equations which are impossible to solve exactly through analytical methods, with the exception of a few idealized cases. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods: some global models use spectral methods for the horizontal dimensions and finite difference methods for the vertical dimension, while regional models and other global models usually use finite-difference methods in all three dimensions. The visual output produced by a model solution is known as a prognostic chart, or prog.
所用的方程是非线性偏微分方程，除了少数理想化的情况外，不可能用解析方法精确求解。因此，数值方法可以得到近似解。不同的模型使用不同的求解方法: 一些全球模型使用谱方法求解水平维度，而差分方法求解垂直维度，而区域模型和其他全球模型通常使用三维有限差分方法。模型解决方案产生的可视化输出被称为预测图，或 prog。
== Parameterization ==
{{main|Parametrization (climate)}}
Weather and climate model gridboxes have sides of between {{convert|5|km|mi}} and {{convert|300|km|mi}}. A typical [[cumulus cloud]] has a scale of less than {{convert|1|km|mi}}, and would require a grid even finer than this to be represented physically by the equations of fluid motion. Therefore, the processes that such [[clouds]] represent are ''[[Parametrization (atmospheric modeling)|parameterized]]'', by processes of various sophistication. In the earliest models, if a column of air in a model gridbox was unstable (i.e., the bottom warmer than the top) then it would be overturned, and the air in that vertical column mixed. More sophisticated schemes add enhancements, recognizing that only some portions of the box might [[convection|convect]] and that entrainment and other processes occur. Weather models that have gridboxes with sides between {{convert|5|km|mi}} and {{convert|25|km|mi}} can explicitly represent convective clouds, although they still need to parameterize [[cloud microphysics]].<ref>{{cite journal|url=http://ams.confex.com/ams/pdfpapers/126017.pdf|title=3.7 Improving Precipitation Forecasts by the Operational Nonhydrostatic Mesoscale Model with the Kain-Fritsch Convective Parameterization and Cloud Microphysics|author1=Narita, Masami |author2=Shiro Ohmori |name-list-style=amp |date=2007-08-06|access-date=2011-02-15|publisher=[[American Meteorological Society]]|journal=12th Conference on Mesoscale Processes}}</ref> The formation of large-scale ([[stratus cloud|stratus]]-type) clouds is more physically based, they form when the [[relative humidity]] reaches some prescribed value. Still, sub grid scale processes need to be taken into account. Rather than assuming that clouds form at 100% relative humidity, the [[cloud fraction]] can be related to a critical relative humidity of 70% for stratus-type clouds, and at or above 80% for cumuliform clouds,<ref>{{cite web|url=http://www.atmos.washington.edu/~dargan/591/diag_cloud.tech.pdf |pages=4–5 |title=The Diagnostic Cloud Parameterization Scheme |author=Frierson, Dargan |publisher=[[University of Washington]] |date=2000-09-14 |access-date=2011-02-15 |archive-url=https://web.archive.org/web/20110401013742/http://www.atmos.washington.edu/~dargan/591/diag_cloud.tech.pdf |archive-date=1 April 2011 |url-status=dead }}</ref> reflecting the sub grid scale variation that would occur in the real world.
Weather and climate model gridboxes have sides of between and . A typical cumulus cloud has a scale of less than , and would require a grid even finer than this to be represented physically by the equations of fluid motion. Therefore, the processes that such clouds represent are parameterized, by processes of various sophistication. In the earliest models, if a column of air in a model gridbox was unstable (i.e., the bottom warmer than the top) then it would be overturned, and the air in that vertical column mixed. More sophisticated schemes add enhancements, recognizing that only some portions of the box might convect and that entrainment and other processes occur. Weather models that have gridboxes with sides between and can explicitly represent convective clouds, although they still need to parameterize cloud microphysics. The formation of large-scale (stratus-type) clouds is more physically based, they form when the relative humidity reaches some prescribed value. Still, sub grid scale processes need to be taken into account. Rather than assuming that clouds form at 100% relative humidity, the cloud fraction can be related to a critical relative humidity of 70% for stratus-type clouds, and at or above 80% for cumuliform clouds, reflecting the sub grid scale variation that would occur in the real world.
= = = 天气和气候模型网格参量化的边界在到之间。一个典型的积云的尺度小于，并且需要一个比这更精细的网格才能用流体运动方程来表示。因此，这些云所代表的过程是通过各种复杂的过程来参数化的。在最早的模型中，如果模型网格盒中的空气柱是不稳定的(即，底部比顶部暖) ，那么它将被推翻，并且垂直柱中的空气将混合。更复杂的方案增加了增强功能，认识到只有盒子的一部分可能会突起，并且夹带和其他过程会发生。具有边界在到之间的网格框的天气模型可以明确地表示对流云，尽管它们仍然需要将云的微物理参数化。大尺度云(层云类型)的形成更多的是基于物理上的，它们是在相对湿度达到某个规定值时形成的。不过，次级电网规模的过程仍然需要考虑。与其假设云的形成相对湿度是100% ，不如假设层云型云的形成临界相对湿度是70% ，而积云型云的形成率是80% 或以上，这反映了现实世界中亚网格尺度的变化。
The amount of solar radiation reaching ground level in rugged terrain, or due to variable cloudiness, is parameterized as this process occurs on the molecular scale.<ref>{{cite book|url=https://books.google.com/books?id=lMXSpRwKNO8C&pg=PA56|title=Parameterization schemes: keys to understanding numerical weather prediction models|author=Stensrud, David J.|page=6|year=2007|publisher=Cambridge University Press|isbn=978-0-521-86540-1}}</ref> Also, the grid size of the models is large when compared to the actual size and roughness of clouds and topography. Sun angle as well as the impact of multiple cloud layers is taken into account.<ref>{{cite book|url=https://books.google.com/books?id=vdg5BgBmMkQC&pg=PA226|author1=Melʹnikova, Irina N.|author2=Alexander V. Vasilyev |name-list-style=amp |pages=226–228|title=Short-wave solar radiation in the earth's atmosphere: calculation, oberservation, interpretation|year=2005|publisher=Springer|isbn=978-3-540-21452-6}}</ref> Soil type, vegetation type, and soil moisture all determine how much radiation goes into warming and how much moisture is drawn up into the adjacent atmosphere. Thus, they are important to parameterize.<ref>{{cite book|url=https://books.google.com/books?id=lMXSpRwKNO8C&pg=PA56|title=Parameterization schemes: keys to understanding numerical weather prediction models|author=Stensrud, David J.|pages=12–14|year=2007|publisher=Cambridge University Press|isbn=978-0-521-86540-1}}</ref>
The amount of solar radiation reaching ground level in rugged terrain, or due to variable cloudiness, is parameterized as this process occurs on the molecular scale. Also, the grid size of the models is large when compared to the actual size and roughness of clouds and topography. Sun angle as well as the impact of multiple cloud layers is taken into account. Soil type, vegetation type, and soil moisture all determine how much radiation goes into warming and how much moisture is drawn up into the adjacent atmosphere. Thus, they are important to parameterize.
当这个过程在分子尺度上发生时，在崎岖地形中到达地面的太阳辐射量，或者由于变化的云量，被参数化了。此外，网格大小的模型是大相比，实际大小和粗糙的云和地形。太阳的角度以及多个云层的影响被考虑在内。土壤类型、植被类型和土壤湿度都决定有多少辐射进入气候变暖，有多少湿气进入邻近的大气层。因此，将它们参数化很重要。
==Domains==
The horizontal domain of a model is either ''global'', covering the entire Earth, or ''regional'', covering only part of the Earth. Regional models also are known as ''limited-area'' models, or LAMs. Regional models use finer grid spacing to resolve explicitly smaller-scale meteorological phenomena, since their smaller domain decreases computational demands. Regional models use a compatible global model for initial conditions of the edge of their domain. Uncertainty and errors within LAMs are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as within the creation of the boundary conditions for the LAMs itself.<ref>{{cite book|url=https://books.google.com/books?id=6RQ3dnjE8lgC&pg=PA261|title=Numerical Weather and Climate Prediction|author=Warner, Thomas Tomkins |publisher=[[Cambridge University Press]]|year=2010|isbn=978-0-521-51389-0|page=259}}</ref>
The horizontal domain of a model is either global, covering the entire Earth, or regional, covering only part of the Earth. Regional models also are known as limited-area models, or LAMs. Regional models use finer grid spacing to resolve explicitly smaller-scale meteorological phenomena, since their smaller domain decreases computational demands. Regional models use a compatible global model for initial conditions of the edge of their domain. Uncertainty and errors within LAMs are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as within the creation of the boundary conditions for the LAMs itself.
一个模型的水平域要么是全球性的，覆盖整个地球; 要么是区域性的，只覆盖地球的一部分。区域模型也称为有限区域模型(LAMs)。区域模型使用更精细的网格间距来明确地解决较小尺度的气象现象，因为它们较小的区域减少了计算需求。区域模型使用一个兼容的全局模型来处理区域边缘的初始条件。区域模型边界条件的全局模型以及区域模型本身边界条件的创建都引入了区域模型内部的不确定性和误差。
The vertical coordinate is handled in various ways. Some models, such as Richardson's 1922 model, use geometric height (<math>z</math>) as the vertical coordinate. Later models substituted the geometric <math>z</math> coordinate with a pressure coordinate system, in which the [[geopotential height]]s of constant-pressure surfaces become [[dependent variable]]s, greatly simplifying the primitive equations.<ref name="Lynch Ch2">{{cite book|last=Lynch|first=Peter|title=The Emergence of Numerical Weather Prediction|year=2006|publisher=[[Cambridge University Press]]|isbn=978-0-521-85729-1|pages=45–46|chapter=The Fundamental Equations}}</ref> This follows since pressure decreases with height through the [[Earth's atmosphere]].<ref>{{cite book|author=Ahrens, C. Donald|page=10|isbn=978-0-495-11558-8|year=2008|publisher=Cengage Learning|title=Essentials of meteorology: an invitation to the atmosphere|url=https://books.google.com/books?id=2Yn29IFukbgC&pg=PA244}}</ref> The first model used for operational forecasts, the single-layer barotropic model, used a single pressure coordinate at the {{convert|500|mbar|inHg|adj=on}} level,<ref name="Charney 1950"/> and thus was essentially two-dimensional. High-resolution models—also called ''mesoscale models''—such as the [[Weather Research and Forecasting model]] tend to use normalized pressure coordinates referred to as ''[[sigma coordinates]]''.<ref>{{cite web|last=Janjic |first=Zavisa |title=Scientific Documentation for the NMM Solver |url=http://nldr.library.ucar.edu/collections/technotes/asset-000-000-000-845.pdf |publisher=[[National Center for Atmospheric Research]] |access-date=2011-01-03 |author2=Gall, Robert |author3=Pyle, Matthew E. |pages=12–13 |date=February 2010 |url-status=dead |archive-url=https://web.archive.org/web/20110823082059/http://nldr.library.ucar.edu/collections/technotes/asset-000-000-000-845.pdf |archive-date=2011-08-23 }}</ref>
The vertical coordinate is handled in various ways. Some models, such as Richardson's 1922 model, use geometric height (z) as the vertical coordinate. Later models substituted the geometric z coordinate with a pressure coordinate system, in which the geopotential heights of constant-pressure surfaces become dependent variables, greatly simplifying the primitive equations. This follows since pressure decreases with height through the Earth's atmosphere. The first model used for operational forecasts, the single-layer barotropic model, used a single pressure coordinate at the level, and thus was essentially two-dimensional. High-resolution models—also called mesoscale models—such as the Weather Research and Forecasting model tend to use normalized pressure coordinates referred to as sigma coordinates.
垂直坐标以各种方式处理。一些模型，如 Richardson 的1922模型，使用几何高度(z)作为垂直坐标。后来的模型用压力坐标系代替了几何 z 坐标，在压力坐标系中，等压面的位势高度变成了相关变量，极大地简化了原始方程组。这是因为通过地球大气层的压力随着高度的降低而降低。第一个用于业务预报的模式，即单层正压模式，在水平上使用单一的气压坐标，因此基本上是二维的。高分辨率模式ーー也称为中尺度模式ーー例如天气研究和预报模式往往使用称为 sigma 坐标的归一化气压坐标。
=== Global versions ===
=== Global versions ===
= = = 全球版本 = = =
Some of the better known global numerical models are:
Some of the better known global numerical models are:
一些比较著名的全球数值模型是:
* '''GFS''' [[Global Forecast System]] (previously AVN) – developed by [[NOAA]]
* '''NOGAPS''' – developed by the [[US Navy]] to compare with the GFS
* '''GEM''' [[Global Environmental Multiscale Model]] – developed by the [[Meteorological Service of Canada]] (MSC)
* '''IFS''' developed by the [[European Centre for Medium-Range Weather Forecasts]]
* '''UM''' [[Unified Model]] developed by the [[United Kingdom|UK]] [[Met Office]]
* '''ICON''' developed by the [[German Weather Service]], DWD, jointly with the Max-Planck-Institute (MPI) for Meteorology, Hamburg, NWP Global model of DWD
* '''ARPEGE''' developed by the French Weather Service, [[Météo-France]]
* '''IGCM''' [[Intermediate General Circulation Model]]<ref name="models">{{cite book|pages=295–301|url=https://books.google.com/books?id=6gFiunmKWWAC&pg=PA297|title=Global Perspectives on Tropical Cyclones: From Science to Mitigation|author1=Chan, Johnny C. L.|author2=Jeffrey D. Kepert |name-list-style=amp |year=2010|publisher=World Scientific|isbn=978-981-4293-47-1}}</ref>
* GFS Global Forecast System (previously AVN) – developed by NOAA
* NOGAPS – developed by the US Navy to compare with the GFS
* GEM Global Environmental Multiscale Model – developed by the Meteorological Service of Canada (MSC)
* IFS developed by the European Centre for Medium-Range Weather Forecasts
* UM Unified Model developed by the UK Met Office
* ICON developed by the German Weather Service, DWD, jointly with the Max-Planck-Institute (MPI) for Meteorology, Hamburg, NWP Global model of DWD
* ARPEGE developed by the French Weather Service, Météo-France
* IGCM Intermediate General Circulation Model
* GFS 全球预报系统(前身为 AVN) -- 由 NOAA 开发
* NOAA gap -- 由美国海军开发，用于与 GFS
* GEM 全球环境多尺度模式进行比较 -- 由加拿大气象服务中心开发
* GEM 全球环境多尺度模式——由英国气象服务中心开发
* UM 统一模式
* ICON，由德国气象局开发
* ICON，与德国汉堡马克斯-普朗克气象研究所(MPI)联合开发
* ARPEGE 全球模式，由法国气象服务中心开发，m é o-france
* IGCM 中级环流模式
=== Regional versions ===
=== Regional versions ===
= = = 区域版本 = = =
Some of the better known regional numerical models are:
Some of the better known regional numerical models are:
一些比较著名的区域数值模型是:
* '''WRF''' The [[Weather Research and Forecasting model]] was developed cooperatively by NCEP, NCAR, and the meteorological research community. WRF has several configurations, including:
** '''WRF-NMM''' The WRF Nonhydrostatic Mesoscale Model is the primary short-term weather forecast model for the U.S., replacing the Eta model.
** '''WRF-ARW''' Advanced Research WRF developed primarily at the U.S. [[National Center for Atmospheric Research]] (NCAR)
* '''[[North American Mesoscale Model|NAM]]''' The term North American Mesoscale model refers to whatever regional model [[National Centers for Environmental Prediction|NCEP]] operates over the North American domain. NCEP began using this designation system in January 2005. Between January 2005 and May 2006 the Eta model used this designation. Beginning in May 2006, NCEP began to use the WRF-NMM as the operational NAM.
* '''RAMS''' the [[Regional Atmospheric Modeling System]] developed at [[Colorado State University]] for numerical simulations of atmospheric meteorology and other environmental phenomena on scales from meters to hundreds of kilometers – now supported in the public domain
* '''MM5''' The [[MM5 (weather model)|Fifth Generation Penn State/NCAR Mesoscale Model]]
* '''ARPS''' the Advanced Region Prediction System developed at the [[University of Oklahoma]] is a comprehensive multi-scale nonhydrostatic simulation and prediction system that can be used for regional-scale weather prediction up to the tornado-scale simulation and prediction. Advanced radar data assimilation for thunderstorm prediction is a key part of the system..
* '''[[HIRLAM]]''' High Resolution Limited Area Model, is developed by the European NWP research consortia [http://hirlam.org/ HIRLAM ] co-funded by 10 European weather services. The meso-scale HIRLAM model is known as HARMONIE and developed in collaboration with Meteo France and ALADIN consortia.
* '''GEM-LAM''' Global Environmental Multiscale Limited Area Model, the high resolution {{convert|2.5|km|abbr=on}} GEM by the [[Meteorological Service of Canada]] (MSC)
* '''ALADIN''' The high-resolution limited-area hydrostatic and non-hydrostatic model developed and operated by several European and North African countries under the leadership of Météo-France<ref name="models"/>
* '''COSMO''' The COSMO Model, formerly known as LM, aLMo or LAMI, is a limited-area non-hydrostatic model developed within the framework of the Consortium for Small-Scale Modelling (Germany, Switzerland, Italy, Greece, Poland, Romania, and Russia).<ref>Consortium on Small Scale Modelling. [http://cosmo-model.cscs.ch/ Consortium for Small-scale Modeling.] Retrieved on 2008-01-13.</ref>
* '''[http://mesonh.aero.obs-mip.fr/ Meso-NH]''' The Meso-NH Model<ref>Lac, C., Chaboureau, P., Masson, V., Pinty, P., Tulet, P., Escobar, J., ... & Aumond, P. (2018). Overview of the Meso-NH model version 5.4 and its applications. Geoscientific Model Development, 11, 1929-1969.</ref> is a limited-area non-hydrostatic model developed jointly by the Centre National de Recherches Météorologiques and the Laboratoire d'Aérologie (France, Toulouse) since 1998.<ref>Lafore, Jean Philippe, et al. "The Meso-NH atmospheric simulation system. Part I: Adiabatic formulation and control simulations." Annales geophysicae. Vol. 16. No. 1. Copernicus GmbH, 1998.</ref> Its application is from mesoscale to centimetric scales weather simulations.
* WRF The Weather Research and Forecasting model was developed cooperatively by NCEP, NCAR, and the meteorological research community. WRF has several configurations, including:
** WRF-NMM The WRF Nonhydrostatic Mesoscale Model is the primary short-term weather forecast model for the U.S., replacing the Eta model.
** WRF-ARW Advanced Research WRF developed primarily at the U.S. National Center for Atmospheric Research (NCAR)
* NAM The term North American Mesoscale model refers to whatever regional model NCEP operates over the North American domain. NCEP began using this designation system in January 2005. Between January 2005 and May 2006 the Eta model used this designation. Beginning in May 2006, NCEP began to use the WRF-NMM as the operational NAM.
* RAMS the Regional Atmospheric Modeling System developed at Colorado State University for numerical simulations of atmospheric meteorology and other environmental phenomena on scales from meters to hundreds of kilometers – now supported in the public domain
* MM5 The Fifth Generation Penn State/NCAR Mesoscale Model
* ARPS the Advanced Region Prediction System developed at the University of Oklahoma is a comprehensive multi-scale nonhydrostatic simulation and prediction system that can be used for regional-scale weather prediction up to the tornado-scale simulation and prediction. Advanced radar data assimilation for thunderstorm prediction is a key part of the system..
* HIRLAM High Resolution Limited Area Model, is developed by the European NWP research consortia HIRLAM co-funded by 10 European weather services. The meso-scale HIRLAM model is known as HARMONIE and developed in collaboration with Meteo France and ALADIN consortia.
* GEM-LAM Global Environmental Multiscale Limited Area Model, the high resolution GEM by the Meteorological Service of Canada (MSC)
* ALADIN The high-resolution limited-area hydrostatic and non-hydrostatic model developed and operated by several European and North African countries under the leadership of Météo-France
* COSMO The COSMO Model, formerly known as LM, aLMo or LAMI, is a limited-area non-hydrostatic model developed within the framework of the Consortium for Small-Scale Modelling (Germany, Switzerland, Italy, Greece, Poland, Romania, and Russia).Consortium on Small Scale Modelling. Consortium for Small-scale Modeling. Retrieved on 2008-01-13.
* Meso-NH The Meso-NH ModelLac, C., Chaboureau, P., Masson, V., Pinty, P., Tulet, P., Escobar, J., ... & Aumond, P. (2018). Overview of the Meso-NH model version 5.4 and its applications. Geoscientific Model Development, 11, 1929-1969. is a limited-area non-hydrostatic model developed jointly by the Centre National de Recherches Météorologiques and the Laboratoire d'Aérologie (France, Toulouse) since 1998.Lafore, Jean Philippe, et al. "The Meso-NH atmospheric simulation system. Part I: Adiabatic formulation and control simulations." Annales geophysicae. Vol. 16. No. 1. Copernicus GmbH, 1998. Its application is from mesoscale to centimetric scales weather simulations.
* WRF 天气研究和预报模型是由 NCEP、 NCAR 和气象研究团体共同开发的。WRF 有几种配置，包括:
*
* WRF-nmm WRF 非静力中尺度模式是美国主要的短期天气预报模式，取代了 Eta 模式。
*
* WRF-ARW 高级研究 WRF 主要由美国国家大气研究中心(NCAR)
* NAM 术语“北美中尺度模式”指的是 NCEP 在北美地区运作的任何区域模式。NCEP 于2005年1月开始使用这一指定系统。2005年1月至2006年5月期间，埃塔模式使用了这一称号。从2006年5月开始，NCEP 开始使用 WRF-NMM 作为业务不结盟运动。
* RAMS 区域大气模拟系统是科罗拉多州立大学开发的区域大气模拟系统，用于从数米到数百公里范围内的大气气象和其他环境现象的数值模拟，现已得到公共领域的支持
* mm5第五代宾夕法尼亚州立大学/NCAR 中尺度模式
* ARPS 奥克拉荷马大学开发的高级区域预报系统是一个综合性的多尺度非静力模拟和预报系统，可用于区域尺度的天气预报，直至龙卷尺度的模拟和预报。用于雷暴预报的先进雷达数据同化是该系统的关键部分。.
* HIRLAM 高分辨率有限区域模型，由欧洲数值天气预报研究联盟 HIRLAM 开发，由10个欧洲气象部门共同资助。中尺度的 HIRLAM 模型被称为 HARMONIE，是与 Meteo France 和 ALADIN 联盟合作开发的。
* GEM-LAM 全球环境多尺度有限区域模型，由加拿大气象局提供的高分辨率 GEM
* ALADIN 高分辨率有限区域静水压和非静水压模型，由几个欧洲和北非国家在 Météo-France 领导下开发和运行
* COSMO 模型，以前称为 COSMO，aLMo 或 LAMI，是在小尺度模型联合会(德国、瑞士、意大利、希腊、波兰、罗马尼亚和俄罗斯)框架内开发的有限区域非静水压模型。小尺度模型联合会。小规模建模联合会。2008-01-13.
* Meso-NH The Meso-NH ModelLac，c. ，Chaboureau，p. ，Masson，v. ，Pinty，p. ，Tulet，p. ，Escobar，j. ，... & Aumond，p. (2018).Meso-NH 模型版本5.4及其应用的概述。Geoscientific Model Development, 11, 1929-1969. is a limited-area non-hydrostatic model developed jointly by the Centre National de Recherches Météorologiques and the Laboratoire d'Aérologie (France, Toulouse) since 1998.Lafore, Jean Philippe, et al.”Meso-NH 大气模拟系统。第一部分: 绝热制定和控制模拟地球物理年鉴。第一卷。16.没有。1.Copernicus GmbH, 1998.它的应用是从中尺度到厘米尺度的天气模拟。
==Model output statistics==
{{Main|Model output statistics}}
Because forecast models based upon the equations for atmospheric dynamics do not perfectly determine weather conditions near the ground, statistical corrections were developed to attempt to resolve this problem. Statistical models were created based upon the three-dimensional fields produced by numerical weather models, surface observations, and the climatological conditions for specific locations. These statistical models are collectively referred to as [[model output statistics]] (MOS),<ref>{{cite book|url=https://books.google.com/books?id=blEMoIKX_0IC&pg=PA188|page=189|title=When nature strikes: weather disasters and the law|author=Baum, Marsha L.|publisher=Greenwood Publishing Group|year=2007|isbn=978-0-275-22129-4}}</ref> and were developed by the [[National Weather Service]] for their suite of weather forecasting models.<ref name="MOS">{{cite book|title=Model output statistics forecast guidance|author=Harry Hughes|publisher=United States Air Force Environmental Technical Applications Center|year=1976|pages=1–16}}</ref> The [[United States Air Force]] developed its own set of MOS based upon their dynamical weather model by 1983.<ref name="L. Best, D. L. and S. P. Pryor 1983 1–90"/>
Because forecast models based upon the equations for atmospheric dynamics do not perfectly determine weather conditions near the ground, statistical corrections were developed to attempt to resolve this problem. Statistical models were created based upon the three-dimensional fields produced by numerical weather models, surface observations, and the climatological conditions for specific locations. These statistical models are collectively referred to as model output statistics (MOS), and were developed by the National Weather Service for their suite of weather forecasting models. The United States Air Force developed its own set of MOS based upon their dynamical weather model by 1983.
由于基于大气动力学方程式的预报模式不能完全确定近地天气状况，因此开发了统计修正以试图解决这一问题。基于数值天气模型、地面观测和特定地点的气候条件产生的三维场，建立了统计模型。这些统计模型统称为模型输出统计(MOS) ，由国家气象局为他们的一套天气预报模型开发。到1983年，美国空军根据其动态天气模型开发了自己的一套 MOS。
Model output statistics differ from the ''perfect prog'' technique, which assumes that the output of numerical weather prediction guidance is perfect.<ref>{{cite book|url=https://books.google.com/books?id=QwzHZ-wV-BAC&pg=PA1144|page=1144|title=Fog and boundary layer clouds: fog visibility and forecasting|author=Gultepe, Ismail|publisher=Springer|year=2007|isbn=978-3-7643-8418-0}}</ref> MOS can correct for local effects that cannot be resolved by the model due to insufficient grid resolution, as well as model biases. Forecast parameters within MOS include maximum and minimum temperatures, percentage chance of rain within a several hour period, precipitation amount expected, chance that the precipitation will be frozen in nature, chance for thunderstorms, cloudiness, and surface winds.<ref>{{cite book|url=https://books.google.com/books?id=Xs9LiGpNX-AC&pg=PA171|page=172|author1=Barry, Roger Graham |author2=Richard J. Chorley |name-list-style=amp |title=Atmosphere, weather, and climate|publisher=Psychology Press|year=2003|isbn=978-0-415-27171-4}}</ref>
Model output statistics differ from the perfect prog technique, which assumes that the output of numerical weather prediction guidance is perfect. MOS can correct for local effects that cannot be resolved by the model due to insufficient grid resolution, as well as model biases. Forecast parameters within MOS include maximum and minimum temperatures, percentage chance of rain within a several hour period, precipitation amount expected, chance that the precipitation will be frozen in nature, chance for thunderstorms, cloudiness, and surface winds.
模型输出统计不同于完美的前端技术，前端技术假设数值天气预报指导的输出是完美的。MOS 可以修正由于网格分辨率不足以及模型偏差而无法由模型解决的局部效应。MOS 内的预报参数包括最高和最低气温、几个小时内降雨的百分比、预期降水量、降水在自然界结冰的可能性、雷暴的可能性、云量和地面风。
== Applications ==
== Applications ==
= = 应用程序 = =
===Climate modeling===
{{Main|Climate model|General circulation model}}
In 1956, Norman Phillips developed a mathematical model that realistically depicted monthly and seasonal patterns in the troposphere. This was the first successful [[climate model]].<ref name="Phillips">{{cite journal | url=http://www.phy.pku.edu.cn/climate/class/cm2010/Phillips_QJRMS_1956.pdf | title=The general circulation of the atmosphere: a numerical experiment | journal=[[Quarterly Journal of the Royal Meteorological Society]] | author=Norman A. Phillips | date=April 1956 | volume=82 | issue=352 | pages=123–154 | doi=10.1002/qj.49708235202 | bibcode=1956QJRMS..82..123P}}</ref><ref name="Cox210">{{cite book | title=Storm Watchers | author=John D. Cox | publisher=John Wiley & Sons, Inc. | page=[https://archive.org/details/stormwatcherstur00cox_df1/page/210 210] | year=2002 | isbn=978-0-471-38108-2 | url=https://archive.org/details/stormwatcherstur00cox_df1/page/210 }}</ref> Several groups then began working to create [[general circulation model]]s.<ref name="Lynch Ch10"/> The first general circulation climate model combined oceanic and atmospheric processes and was developed in the late 1960s at the [[Geophysical Fluid Dynamics Laboratory]], a component of the U.S. [[National Oceanic and Atmospheric Administration]].<ref>{{cite web | url=http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html | title=The First Climate Model | author=National Oceanic and Atmospheric Administration | author-link=National Oceanic and Atmospheric Administration | date=22 May 2008 | access-date=8 January 2011}}</ref> By the early 1980s, the U.S. [[National Center for Atmospheric Research]] had developed the Community Atmosphere Model (CAM), which can be run by itself or as the atmospheric component of the [[Community Climate System Model]]. The latest update (version 3.1) of the standalone CAM was issued on 1 February 2006.<ref>{{Cite web|url=http://www.cesm.ucar.edu/models/atm-cam/download/|title=CAM 3.1 Download|website=www.cesm.ucar.edu|access-date=2019-06-25}}</ref><ref>{{cite web | url=http://www.cesm.ucar.edu/models/atm-cam/docs/description/description.pdf | title=Description of the NCAR Community Atmosphere Model (CAM 3.0) | author=William D. Collins | publisher=[[University Corporation for Atmospheric Research]] | date=June 2004 | access-date=3 January 2011 | display-authors=et al.}}</ref><ref>{{cite web | url=http://www.cesm.ucar.edu/models/atm-cam/ | title=CAM3.0 COMMUNITY ATMOSPHERE MODEL | publisher=[[University Corporation for Atmospheric Research]] | access-date=6 February 2018}}</ref> In 1986, efforts began to initialize and model soil and vegetation types, resulting in more realistic forecasts.<ref>{{cite journal | url=http://www.geog.ucla.edu/~yxue/pdf/1996jgr.pdf | title=Impact of vegetation properties on U. S. summer weather prediction | journal=[[Journal of Geophysical Research]] | author1=Yongkang Xue | author2=Michael J. Fennessey | name-list-style=amp | date=20 March 1996 | volume=101 | issue=D3 | page=7419 | access-date=6 January 2011 | bibcode=1996JGR...101.7419X | doi=10.1029/95JD02169 | url-status=dead | archive-url=https://web.archive.org/web/20100710080304/http://www.geog.ucla.edu/~yxue/pdf/1996jgr.pdf | archive-date=10 July 2010| citeseerx=10.1.1.453.551 }}</ref> Coupled ocean-atmosphere climate models, such as the [[Hadley Centre for Climate Prediction and Research]]'s [[HadCM3]] model, are being used as inputs for [[climate change]] studies.<ref name="Lynch Ch10">{{cite book | chapter-url=https://books.google.com/books?id=EV5bZqOO7kkC&pg=PA208 | title=The Emergence of Numerical Weather Prediction: Richardson's Dream | chapter=The ENIAC Integrations | author=Peter Lynch | publisher=[[Cambridge University Press]] | year=2006 | isbn=978-0-521-85729-1 | page=208 | access-date=6 February 2018}}</ref>
In 1956, Norman Phillips developed a mathematical model that realistically depicted monthly and seasonal patterns in the troposphere. This was the first successful climate model. Several groups then began working to create general circulation models. The first general circulation climate model combined oceanic and atmospheric processes and was developed in the late 1960s at the Geophysical Fluid Dynamics Laboratory, a component of the U.S. National Oceanic and Atmospheric Administration. By the early 1980s, the U.S. National Center for Atmospheric Research had developed the Community Atmosphere Model (CAM), which can be run by itself or as the atmospheric component of the Community Climate System Model. The latest update (version 3.1) of the standalone CAM was issued on 1 February 2006. In 1986, efforts began to initialize and model soil and vegetation types, resulting in more realistic forecasts. Coupled ocean-atmosphere climate models, such as the Hadley Centre for Climate Prediction and Research's HadCM3 model, are being used as inputs for climate change studies.
1956年，诺曼 · 菲利普斯开发了一个数学模型，这个模型真实地描述了对流层的每月和季节的模式。这是第一个成功的气候模型。几个小组随后开始建立大体循环模型。第一个大气环流气候模式结合了海洋和大气过程，于20世纪60年代末在美国地球物理流体动力学实验室气候研究中心发展起来，该中心是美国美国国家海洋和大气管理局气候研究中心的一个组成部分。到20世纪80年代早期，美国国家大气研究中心开发了社区大气模型(CAM) ，它可以自己运行，也可以作为社区气候系统模型的大气成分。最新更新(3.1版本)已于2006年2月1日发出。在1986年，开始努力初始化和模型的土壤和植被类型，导致更现实的预测。耦合的海洋-大气气候模型，如哈德利气候预测与研究中心的 hadcm3模型，正被用作气候变化研究的输入。
===Limited area modeling===
[[File:Ernesto2006modelspread.png|thumb|right|Model spread with [[Hurricane Ernesto (2006)]] within the National Hurricane Center limited area models]]
[[Air pollution forecasting|Air pollution forecasts]] depend on atmospheric models to provide [[fluid flow]] information for tracking the movement of pollutants.<ref>{{cite journal | author1=Alexander Baklanov | author2=Alix Rasmussen | author3=Barbara Fay | author4=Erik Berge | author5=Sandro Finardi | title=Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting | journal=Water, Air, & Soil Pollution: Focus | date=September 2002 | volume=2 | issue=5 | pages=43–60 | doi=10.1023/A:1021394126149| s2cid=94747027 }}</ref> In 1970, a private company in the U.S. developed the regional Urban Airshed Model (UAM), which was used to forecast the effects of air pollution and [[acid rain]]. In the mid- to late-1970s, the [[United States Environmental Protection Agency]] took over the development of the UAM and then used the results from a regional air pollution study to improve it. Although the UAM was developed for [[California]], it was during the 1980s used elsewhere in North America, Europe, and Asia.<ref name="Steyn, D. G. 1991 241–242"/>
Air pollution forecasts depend on atmospheric models to provide fluid flow information for tracking the movement of pollutants. In 1970, a private company in the U.S. developed the regional Urban Airshed Model (UAM), which was used to forecast the effects of air pollution and acid rain. In the mid- to late-1970s, the United States Environmental Protection Agency took over the development of the UAM and then used the results from a regional air pollution study to improve it. Although the UAM was developed for California, it was during the 1980s used elsewhere in North America, Europe, and Asia.
空气污染预报依靠大气模型来提供流体流动信息，以跟踪污染物的运动。1970年，美国的一家私营公司开发了区域城市气流模型(UAM) ，用于预测空气污染和酸雨的影响。在1970年代中后期，美国环境保护局接管了 UAM 的开发工作，然后利用区域空气污染研究的结果来改进 UAM。虽然 UAM 是为加利福尼亚州开发的，但在20世纪80年代，它在北美、欧洲和亚洲的其他地方得到了应用。
The Movable Fine-Mesh model, which began operating in 1978, was the first [[tropical cyclone forecast model]] to be based on [[Atmospheric dynamics#Dynamic meteorology|atmospheric dynamics]].<ref name="Shuman W&F"/> Despite the constantly improving dynamical model guidance made possible by increasing computational power, it was not until the 1980s that numerical weather prediction (NWP) showed [[Forecast skill|skill]] in forecasting the track of tropical cyclones. And it was not until the 1990s that NWP consistently outperformed [[statistical model|statistical]] or simple dynamical models.<ref>{{cite web | url=http://www.nhc.noaa.gov/verification/verify6.shtml | publisher=[[National Hurricane Center]] | date=20 April 2010 | access-date=2 January 2011 | author=James Franklin | title=National Hurricane Center Forecast Verification | author-link=James Franklin (meteorologist) | archive-url=https://web.archive.org/web/20110102062753/http://www.nhc.noaa.gov/verification/verify6.shtml | archive-date=2 January 2011 | url-status=live}}</ref> Predicting the intensity of tropical cyclones using NWP has also been challenging. As of 2009, dynamical guidance remained less skillful than statistical methods.<ref>{{cite journal | author1=Edward N. Rappaport | author2=James L. Franklin | author3=Lixion A. Avila | author4=Stephen R. Baig | author5=John L. Beven II | author6=Eric S. Blake | author7=Christopher A. Burr | author8=Jiann-Gwo Jiing | author9=Christopher A. Juckins | author10=Richard D. Knabb | author11=Christopher W. Landsea | author12=Michelle Mainelli | author13=Max Mayfield | author14=Colin J. McAdie | author15=Richard J. Pasch | author16=Christopher Sisko | author17=Stacy R. Stewart | author18=Ahsha N. Tribble | title=Advances and Challenges at the National Hurricane Center | journal=[[Weather and Forecasting]] | date=April 2009 | volume=24 | issue=2 | pages=395–419 | doi=10.1175/2008WAF2222128.1 | bibcode=2009WtFor..24..395R| citeseerx=10.1.1.207.4667 }}</ref>
The Movable Fine-Mesh model, which began operating in 1978, was the first tropical cyclone forecast model to be based on atmospheric dynamics. Despite the constantly improving dynamical model guidance made possible by increasing computational power, it was not until the 1980s that numerical weather prediction (NWP) showed skill in forecasting the track of tropical cyclones. And it was not until the 1990s that NWP consistently outperformed statistical or simple dynamical models. Predicting the intensity of tropical cyclones using NWP has also been challenging. As of 2009, dynamical guidance remained less skillful than statistical methods.
可移动细网模型于1978年开始运行，是第一个基于大气动力学的热带气旋预报模型模型。尽管由于计算能力的提高，不断改进的动力学模型指南成为可能，但直到20世纪80年代，数值天气预报才显示出预报热带气旋路径的技术。直到20世纪90年代，数值天气预报才始终优于统计或简单的动力学模型。使用数值预报方法预报热带气旋的强度也是一个挑战。截至2009年，动态指导仍然不如统计方法熟练。
==See also==
* [[Atmospheric reanalysis]]
* [[Climate model]]
* [[Numerical weather prediction]]
* [[Upper-atmospheric models]]
* [[Static atmospheric model]]
* Atmospheric reanalysis
* Climate model
* Numerical weather prediction
* Upper-atmospheric models
* Static atmospheric model
= = = = =
* 大气重新分析
* 气候模式
* 数值天气预报
* 高层大气模式
* 静态大气模式
== References ==
{{Reflist|2}}
==Further reading==
{{Refbegin}}
*{{cite book |author1=Roulstone, Ian |author2=Norbury, John |title=Invisible in the Storm: the role of mathematics in understanding weather |location=Princeton |publisher=Princeton University Press |year=2013 |isbn=978-0-691-15272-1 }}
{{Refend}}
*
= = 进一步阅读 = = =
*
==External links==
* [https://web.archive.org/web/20110921153007/http://www.mmm.ucar.edu/wrf/users/download/get_source2.html WRF Source Codes and Graphics Software Download Page]
* [https://web.archive.org/web/20140712133145/http://bridge.atmet.org/users/software.php RAMS source code available under the GNU General Public License]
* [https://web.archive.org/web/20110928000959/http://www.mmm.ucar.edu/mm5/mm5v3/wherev3.html MM5 Source Code download]
* [http://www.caps.ou.edu/ARPS The source code of ARPS]
* [http://www.ventusky.com/ Model Visualisation]
* WRF Source Codes and Graphics Software Download Page
* RAMS source code available under the GNU General Public License
* MM5 Source Code download
* The source code of ARPS
* Model Visualisation
= = = 外部链接 = =
* WRF 源代码和绘图软件下载页
* RAMS 源代码可在 GNU通用公共许可协议
* mm5源代码下载
* 源代码 ARPS
* 模型可视化
{{Atmospheric, Oceanographic and Climate Models|state=expanded}}
{{Computer modeling}}
{{DEFAULTSORT:Atmospheric Model}}
[[Category:Numerical climate and weather models]]
[[Category:Articles containing video clips]]
Category:Numerical climate and weather models
Category:Articles containing video clips
类别: 数值气候和天气模型类别: 包含视频剪辑的文章
<noinclude>
<small>This page was moved from [[wikipedia:en:Atmospheric model]]. Its edit history can be viewed at [[大气模式/edithistory]]</small></noinclude>
[[Category:待整理页面]]
[[File:GFS 850 MB.PNG|right|250px|thumb|A 96-hour forecast of 850 [[millibar|mbar]] [[geopotential height]] and [[temperature]] from the [[Global Forecast System]]]]
An '''atmospheric model''' is a [[mathematical model]] constructed around the full set of [[primitive equations|primitive]] [[Dynamical systems theory|dynamical equations]] which govern atmospheric motions. It can supplement these equations with [[Parametrization (climate)|parameterizations]] for [[Turbulence|turbulent]] diffusion, [[radiation]], [[moist processes]] ([[clouds]] and [[precipitation (meteorology)|precipitation]]), [[heat transfer|heat exchange]], [[soil]], vegetation, surface water, the [[Kinematics|kinematic]] effects of [[terrain]], and convection. Most atmospheric models are numerical, i.e. they discretize equations of motion. They can predict microscale phenomena such as [[tornadoes]] and [[Eddy covariance|boundary layer eddies]], sub-microscale turbulent flow over buildings, as well as synoptic and global flows. The horizontal domain of a model is either ''global'', covering the entire [[Earth]], or ''regional'' (''limited-area''), covering only part of the Earth. The different types of models run are thermotropic, [[barotropic]], hydrostatic, and nonhydrostatic. Some of the model types make assumptions about the atmosphere which lengthens the time steps used and increases computational speed.
An atmospheric model is a mathematical model constructed around the full set of primitive dynamical equations which govern atmospheric motions. It can supplement these equations with parameterizations for turbulent diffusion, radiation, moist processes (clouds and precipitation), heat exchange, soil, vegetation, surface water, the kinematic effects of terrain, and convection. Most atmospheric models are numerical, i.e. they discretize equations of motion. They can predict microscale phenomena such as tornadoes and boundary layer eddies, sub-microscale turbulent flow over buildings, as well as synoptic and global flows. The horizontal domain of a model is either global, covering the entire Earth, or regional (limited-area), covering only part of the Earth. The different types of models run are thermotropic, barotropic, hydrostatic, and nonhydrostatic. Some of the model types make assumptions about the atmosphere which lengthens the time steps used and increases computational speed.
大气模式是围绕控制大气运动的一整套原始动力学方程建立的数学模式。它可以用湍流扩散、辐射、湿过程(云和降水)、热交换、土壤、植被、地表水、地形的运动学效应和对流等参数化来补充这些方程。大多数大气模型都是数字化的，例如。他们把运动方程分开。他们可以预测微尺度现象，例如龙卷风和边界层涡旋，建筑物上空的亚微尺度湍流，以及天气和全球气流。模型的水平域要么是全球性的，覆盖了整个地球; 要么是区域性的，只覆盖了地球的一部分。不同类型的模式运行是热力学，正压，流体静力学和非流体静力学。一些模型类型对大气层做出假设，从而延长了使用的时间步骤，提高了计算速度。
Forecasts are computed using mathematical equations for the physics and dynamics of the atmosphere. These equations are nonlinear and are impossible to solve exactly. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods. Global models often use [[spectral method]]s for the horizontal dimensions and [[Finite difference method|finite-difference methods]] for the vertical dimension, while regional models usually use finite-difference methods in all three dimensions. For specific locations, [[model output statistics]] use climate information, output from [[numerical weather prediction]], and current [[surface weather observation]]s to develop statistical relationships which account for model bias and resolution issues.
Forecasts are computed using mathematical equations for the physics and dynamics of the atmosphere. These equations are nonlinear and are impossible to solve exactly. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods. Global models often use spectral methods for the horizontal dimensions and finite-difference methods for the vertical dimension, while regional models usually use finite-difference methods in all three dimensions. For specific locations, model output statistics use climate information, output from numerical weather prediction, and current surface weather observations to develop statistical relationships which account for model bias and resolution issues.
预报是利用大气物理和动力学的数学方程式计算出来的。这些方程是非线性的，不可能精确求解。因此，数值方法可以得到近似解。不同的模型使用不同的求解方法。全球模型通常采用谱方法计算水平维数，有限差分方法计算垂直维数，而区域模型通常采用三维有限差分方法。对于特定的位置，模型输出统计使用气候信息、数值天气预报气象观测数据和当前地面天气观测数据来建立统计关系，以解释模型偏差和分辨率问题。
== Types ==
The main assumption made by the '''thermotropic''' model is that while the magnitude of the [[thermal wind]] may change, its direction does not change with respect to height, and thus the [[Baroclinity|baroclinicity]] in the atmosphere can be simulated using the {{convert|500|mb|inHg|adj=on |abbr=on |lk=on}} and {{convert|1000|mb|inHg|adj=on|abbr=on}} [[geopotential height]] surfaces and the average thermal wind between them.<ref>{{cite book|last= Gates|first=W. Lawrence|title=Results Of Numerical Forecasting With The Barotropic And Thermotropic Atmospheric Models|date=August 1955|publisher=Air Force Cambridge Research Laboratories|location=[[Hanscom Air Force Base]]|url=http://handle.dtic.mil/100.2/AD101943}}</ref><ref>{{cite journal |last=Thompson|first=P. D.|author2=W. Lawrence Gates|title=A Test of Numerical Prediction Methods Based on the Barotropic and Two-Parameter Baroclinic Models|journal=[[Journal of the Atmospheric Sciences|Journal of Meteorology]]| date=April 1956 |volume=13|issue=2|pages=127–141 |doi= 10.1175/1520-0469(1956)013<0127:ATONPM>2.0.CO;2 |issn=1520-0469|bibcode = 1956JAtS...13..127T |doi-access=free}}</ref>
The main assumption made by the thermotropic model is that while the magnitude of the thermal wind may change, its direction does not change with respect to height, and thus the baroclinicity in the atmosphere can be simulated using the and geopotential height surfaces and the average thermal wind between them.
= = = = = 由热带模式作出的主要假设是，虽然热风的大小可能改变，但其方向不随高度而改变，因此大气的斜压性可以利用位势高度表面和它们之间的平均热风来模拟。
'''Barotropic''' models assume the atmosphere is nearly [[barotropic]], which means that the direction and speed of the [[geostrophic wind]] are independent of height. In other words, no vertical [[wind shear]] of the geostrophic wind. It also implies that thickness contours (a proxy for temperature) are parallel to upper level height contours. In this type of atmosphere, [[high-pressure area|high]] and [[low-pressure area|low pressure area]]s are centers of warm and cold temperature anomalies. Warm-core highs (such as the [[subtropical ridge]] and Bermuda-Azores high) and [[cold-core low]]s have strengthening winds with height, with the reverse true for cold-core highs (shallow arctic highs) and warm-core lows (such as [[tropical cyclone]]s).<ref>{{cite book|title=Atmospheric Science: An Introductory Survey|author1=Wallace, John M. |author2=Peter V. Hobbs |name-list-style=amp |year=1977|isbn=978-0-12-732950-5|publisher=Academic Press, Inc.|pages=384–385}}</ref> A barotropic model tries to solve a simplified form of [[atmospheric dynamics]] based on the assumption that the atmosphere is in [[geostrophic balance]]; that is, that the [[Rossby number]] of the air in the atmosphere is small.<ref>{{cite book|last=Marshall|first=John|title=Atmosphere, ocean, and climate dynamics : an introductory text|year=2008|publisher=Elsevier Academic Press|location=Amsterdam|isbn=978-0-12-558691-7|author2=Plumb, R. Alan|pages=109–12|chapter=Balanced flow}}</ref> If the assumption is made that the atmosphere is [[divergence-free]], the [[curl (mathematics)|curl]] of the [[Euler equations (fluid dynamics)|Euler equations]] reduces into the [[barotropic vorticity equation]]. This latter equation can be solved over a single layer of the atmosphere. Since the atmosphere at a height of approximately {{convert|5.5|km|mi}} is mostly divergence-free, the barotropic model best approximates the state of the atmosphere at a [[geopotential height]] corresponding to that altitude, which corresponds to the atmosphere's {{convert|500|mb|inHg|abbr=on}} pressure surface.<ref name="Charney 1950"/><!-- also solved as a stream function, need to find a reference for that -->
Barotropic models assume the atmosphere is nearly barotropic, which means that the direction and speed of the geostrophic wind are independent of height. In other words, no vertical wind shear of the geostrophic wind. It also implies that thickness contours (a proxy for temperature) are parallel to upper level height contours. In this type of atmosphere, high and low pressure areas are centers of warm and cold temperature anomalies. Warm-core highs (such as the subtropical ridge and Bermuda-Azores high) and cold-core lows have strengthening winds with height, with the reverse true for cold-core highs (shallow arctic highs) and warm-core lows (such as tropical cyclones). A barotropic model tries to solve a simplified form of atmospheric dynamics based on the assumption that the atmosphere is in geostrophic balance; that is, that the Rossby number of the air in the atmosphere is small. If the assumption is made that the atmosphere is divergence-free, the curl of the Euler equations reduces into the barotropic vorticity equation. This latter equation can be solved over a single layer of the atmosphere. Since the atmosphere at a height of approximately is mostly divergence-free, the barotropic model best approximates the state of the atmosphere at a geopotential height corresponding to that altitude, which corresponds to the atmosphere's pressure surface.
正压模式假设大气接近正压，这意味着地转风的方向和速度与高度无关。换句话说，没有地转风的垂直风切变。这也意味着厚度等值线(代表温度)是平行于上层高度等值线。在这种类型的大气中，高压区和低压区是暖温和冷温异常的中心。温核高压(如副热带嵴线和百慕大-亚速尔群岛高压)和冷核低压具有随高度增强的风力，而冷核高压(北极浅层高压)和温核低压(如热带气旋)则相反。正压模式试图解决一个简化形式的大气动力学的基础上的假设，大气是地转平衡，即大气中的空气罗斯比数量很小。如果假设大气是无散度的，则欧拉方程的旋度降为正压涡度方程。后一个方程可以在一层大气上求解。由于大气在大约高度处基本上是无辐散的，正压模式最接近大气在相应于高度的位势高度处的状态，也就是相应于大气压力表面的状态。
[[Primitive equations#Vertical pressure.2C Cartesian tangential plane|'''Hydrostatic''' model]]s filter out vertically moving [[acoustic wave]]s from the vertical momentum equation, which significantly increases the time step used within the model's run. This is known as the [[hydrostatic equilibrium|hydrostatic approximation]]. Hydrostatic models use either pressure or [[sigma coordinate system|sigma-pressure]] vertical coordinates. Pressure coordinates intersect topography while sigma coordinates follow the contour of the land. Its hydrostatic assumption is reasonable as long as horizontal grid resolution is not small, which is a scale where the hydrostatic assumption fails. Models which use the entire vertical momentum equation are known as '''nonhydrostatic'''. A nonhydrostatic model can be solved anelastically, meaning it solves the complete [[continuity equation]] for air assuming it is incompressible, or elastically, meaning it solves the complete continuity equation for air and is fully compressible. Nonhydrostatic models use altitude or sigma altitude for their vertical coordinates. Altitude coordinates can intersect land while sigma-altitude coordinates follow the contours of the land.<ref>{{cite book|url=https://books.google.com/books?id=41qztAEACAAJ|pages=138–143|title=Fundamentals of atmospheric modeling|author=Jacobson, Mark Zachary|year=2005|publisher=Cambridge University Press|isbn=978-0-521-83970-9}}</ref>
Hydrostatic models filter out vertically moving acoustic waves from the vertical momentum equation, which significantly increases the time step used within the model's run. This is known as the hydrostatic approximation. Hydrostatic models use either pressure or sigma-pressure vertical coordinates. Pressure coordinates intersect topography while sigma coordinates follow the contour of the land. Its hydrostatic assumption is reasonable as long as horizontal grid resolution is not small, which is a scale where the hydrostatic assumption fails. Models which use the entire vertical momentum equation are known as nonhydrostatic. A nonhydrostatic model can be solved anelastically, meaning it solves the complete continuity equation for air assuming it is incompressible, or elastically, meaning it solves the complete continuity equation for air and is fully compressible. Nonhydrostatic models use altitude or sigma altitude for their vertical coordinates. Altitude coordinates can intersect land while sigma-altitude coordinates follow the contours of the land.
流体静力学模型从垂直动量方程中过滤出垂直运动的声波，这显著地增加了模型运行中使用的时间步长。这就是流体静力学近似。流体静力学模型使用压力或 sigma 压力的垂直坐标。压力坐标与地形相交，而 sigma 坐标跟随地形等高线。只要水平网格分辨率不小，其静力学假设是合理的，这是静力学假设失效的尺度。使用整个垂直动量方程的模型称为非静力学模型。非流体静力学模型可以用分弹性方法求解，这意味着它可以求解空气的完全连续性方程，前提是它是不可压缩的，或者是弹性的，这意味着它可以求解空气的完全连续性方程，并且是完全可压缩的。非静力学模型使用高度或西格玛高度作为其垂直坐标。高度坐标可以与地面相交，而 sigma 高度坐标则跟随地面的等高线。
== History ==
[[File:Two women operating ENIAC.gif|thumb|280px|The ENIAC main control panel at the [[Moore School of Electrical Engineering]]]]
{{Main|History of numerical weather prediction}}
The [[history of numerical weather prediction]] began in the 1920s through the efforts of [[Lewis Fry Richardson]] who utilized procedures developed by [[Vilhelm Bjerknes]].<ref name="Lynch JCP">{{cite journal|last=Lynch|author-link=Peter Lynch (meteorologist)|first=Peter|title=The origins of computer weather prediction and climate modeling|journal=[[Journal of Computational Physics]]|date=2008-03-20|volume=227|issue=7|pages=3431–44|doi= 10.1016/j.jcp.2007.02.034 |url=http://www.rsmas.miami.edu/personal/miskandarani/Courses/MPO662/Lynch,Peter/OriginsCompWF.JCP227.pdf|access-date= 2010-12-23 |bibcode=2008JCoPh.227.3431L|archive-url=https://web.archive.org/web/20100708191309/http://www.rsmas.miami.edu/personal/miskandarani/Courses/MPO662/Lynch,Peter/OriginsCompWF.JCP227.pdf|archive-date=2010-07-08|url-status=dead}}</ref><ref name="Lynch Ch1">{{cite book|last=Lynch |first= Peter |title=The Emergence of Numerical Weather Prediction|year=2006|publisher=[[Cambridge University Press]]|isbn=978-0-521-85729-1|pages=1–27 |chapter= Weather Prediction by Numerical Process}}</ref> It was not until the advent of the computer and [[computer simulation]] that computation time was reduced to less than the forecast period itself. [[ENIAC]] created the first computer forecasts in 1950,<ref name="Charney 1950">{{cite journal|last1= Charney|first1=Jule|last2=Fjörtoft|first2=Ragnar|last3=von Neumann|first3=John|title=Numerical Integration of the Barotropic Vorticity Equation|journal= Tellus|date=November 1950|volume=2|issue=4|doi=10.3402/tellusa.v2i4.8607|author-link1=Jule Charney|author-link3=John von Neumann|bibcode= 1950TellA...2..237C |pages=237–254|doi-access=free}}</ref><ref>{{cite book|title=Storm Watchers|page=[https://archive.org/details/stormwatcherstur00cox_df1/page/208 208]|year=2002|author=Cox, John D.|publisher=John Wiley & Sons, Inc.|isbn=978-0-471-38108-2|url=https://archive.org/details/stormwatcherstur00cox_df1/page/208}}</ref> and more powerful computers later increased the size of initial datasets and included more complicated versions of the equations of motion.<ref name="Harper BAMS">{{cite journal|last=Harper|first=Kristine|author2=Uccellini, Louis W.|author3= Kalnay, Eugenia|author4= Carey, Kenneth|author5= Morone, Lauren|title=2007: 50th Anniversary of Operational Numerical Weather Prediction|journal=[[Bulletin of the American Meteorological Society]]|date=May 2007|volume=88|issue=5|pages=639–650|doi=10.1175/BAMS-88-5-639 |bibcode=2007BAMS...88..639H |doi-access=free}}</ref> In 1966, [[West Germany]] and the United States began producing operational forecasts based on [[primitive equations|primitive-equation]] models, followed by the United Kingdom in 1972 and Australia in 1977.<ref name="Lynch JCP"/><ref name="Leslie BOM">{{cite journal|last=Leslie|first=L.M.|author2=Dietachmeyer, G.S.|title=Real-time limited area numerical weather prediction in Australia: a historical perspective|journal=Australian Meteorological Magazine|date=December 1992|volume=41|issue=SP|pages=61–77|url=http://www.bom.gov.au/amoj/docs/1992/leslie2.pdf|access-date=2011-01-03|publisher=[[Bureau of Meteorology]]}}</ref> The development of global [[Forecasting#Categories of forecasting methods|forecasting models]] led to the first climate models.<ref name="Phillips"/><ref name="Cox210"/> The development of limited area (regional) models facilitated advances in forecasting the tracks of [[tropical cyclone]] as well as [[air quality]] in the 1970s and 1980s.<ref name="Shuman W&F">{{cite journal|last=Shuman|first=Frederick G.|author-link=Frederick Gale Shuman|title=History of Numerical Weather Prediction at the National Meteorological Center|journal=[[Weather and Forecasting]]|date=September 1989|volume=4|issue=3|pages=286–296|doi= 10.1175/1520-0434(1989)004<0286:HONWPA>2.0.CO;2 |issn=1520-0434|bibcode=1989WtFor...4..286S|doi-access=free}}</ref><ref name="Steyn, D. G. 1991 241–242">{{cite book|title=Air pollution modeling and its application VIII, Volume 8|author=Steyn, D. G.|publisher=Birkhäuser|year=1991|pages=241–242|isbn= 978-0-306-43828-8}}</ref>
The history of numerical weather prediction began in the 1920s through the efforts of Lewis Fry Richardson who utilized procedures developed by Vilhelm Bjerknes. It was not until the advent of the computer and computer simulation that computation time was reduced to less than the forecast period itself. ENIAC created the first computer forecasts in 1950, and more powerful computers later increased the size of initial datasets and included more complicated versions of the equations of motion. In 1966, West Germany and the United States began producing operational forecasts based on primitive-equation models, followed by the United Kingdom in 1972 and Australia in 1977. The development of global forecasting models led to the first climate models. The development of limited area (regional) models facilitated advances in forecasting the tracks of tropical cyclone as well as air quality in the 1970s and 1980s.
数值天气预报的历史始于20世纪20年代，这得益于 Lewis Fry Richardson 的努力，他运用了威廉·皮耶克尼斯的方法。直到计算机和计算机模拟时代的到来，计算时间才减少到低于预测期本身。1950年 ENIAC 发明了第一台计算机预测系统，后来功能更强大的计算机增加了初始数据集的规模，并包含了更复杂的运动方程预测系统。1966年，西德和美国开始根据原始方程模型制作业务预报，1972年联合王国和1977年澳大利亚紧随其后。全球预报模型的发展导致了第一个气候模型的诞生。在20世纪70年代和80年代，有限区域(区域)模型的发展促进了热带气旋轨道和空气质量预报的进步。
Because the output of forecast models based on [[atmospheric dynamics]] requires corrections near ground level, [[model output statistics]] (MOS) were developed in the 1970s and 1980s for individual ''forecast points'' (locations).<ref name="MOS"/><ref name="L. Best, D. L. and S. P. Pryor 1983 1–90">{{cite book|title=Air Weather Service Model Output Statistics Systems|author1=L. Best, D. L. |author2=S. P. Pryor |name-list-style=amp |year=1983|pages=1–90|publisher=Air Force Global Weather Central}}</ref> Even with the increasing power of supercomputers, the [[forecast skill]] of numerical weather models only extends to about two weeks into the future, since the density and quality of observations—together with the [[Chaos theory|chaotic]] nature of the [[partial differential equation]]s used to calculate the forecast—introduce errors which double every five days.<ref name="Cox">{{cite book|title=Storm Watchers|pages=[https://archive.org/details/stormwatcherstur00cox_df1/page/222 222–224]|year=2002|author=Cox, John D.|publisher=John Wiley & Sons, Inc.|isbn=978-0-471-38108-2|url=https://archive.org/details/stormwatcherstur00cox_df1/page/222}}</ref><ref name="Klaus">Weickmann, Klaus, Jeff Whitaker, Andres Roubicek and Catherine Smith (2001-12-01). [http://www.cdc.noaa.gov/spotlight/12012001/ The Use of Ensemble Forecasts to Produce Improved Medium Range (3–15 days) Weather Forecasts.] [[Climate Diagnostics Center]]. Retrieved 2007-02-16.</ref> The use of model ensemble forecasts since the 1990s helps to define the forecast uncertainty and extend [[weather forecasting]] farther into the future than otherwise possible.<ref name="Toth">{{cite journal|last=Toth|first=Zoltan|author2=Kalnay, Eugenia|title=Ensemble Forecasting at NCEP and the Breeding Method |journal=[[Monthly Weather Review]]|date=December 1997|volume=125|issue=12|pages=3297–3319|doi=10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2|issn=1520-0493|bibcode=1997MWRv..125.3297T|author-link2=Eugenia Kalnay|citeseerx=10.1.1.324.3941}}</ref><ref name="ECens">{{cite web|url=http://ecmwf.int/products/forecasts/guide/The_Ensemble_Prediction_System_EPS_1.html |title=The Ensemble Prediction System (EPS) |publisher=[[ECMWF]] |access-date=2011-01-05 |archive-url=https://web.archive.org/web/20110125125209/http://ecmwf.int/products/forecasts/guide/The_Ensemble_Prediction_System_EPS_1.html |archive-date=25 January 2011 |url-status=dead }}</ref><ref name="RMS">{{cite journal|title=The ECMWF Ensemble Prediction System: Methodology and validation|journal=Quarterly Journal of the Royal Meteorological Society|date=January 1996|volume=122|issue=529|pages=73–119|author1=Molteni, F. |author2=Buizza, R. |author3=Palmer, T.N. |author4=Petroliagis, T. |doi=10.1002/qj.49712252905|bibcode=1996QJRMS.122...73M}}</ref>
Because the output of forecast models based on atmospheric dynamics requires corrections near ground level, model output statistics (MOS) were developed in the 1970s and 1980s for individual forecast points (locations). Even with the increasing power of supercomputers, the forecast skill of numerical weather models only extends to about two weeks into the future, since the density and quality of observations—together with the chaotic nature of the partial differential equations used to calculate the forecast—introduce errors which double every five days.Weickmann, Klaus, Jeff Whitaker, Andres Roubicek and Catherine Smith (2001-12-01). The Use of Ensemble Forecasts to Produce Improved Medium Range (3–15 days) Weather Forecasts. Climate Diagnostics Center. Retrieved 2007-02-16. The use of model ensemble forecasts since the 1990s helps to define the forecast uncertainty and extend weather forecasting farther into the future than otherwise possible.
由于基于大气动力学的预报模式的输出需要近地面水平的修正，因此在20世纪70年代和80年代发展了单个预报点(位置)的模式输出统计学(MOS)。即使超级计算机的能力越来越强，数值天气模式的预报技巧也只能延伸到未来两周左右，因为观测的密度和质量ーー以及用于计算预报的偏微分方程的混沌性ーー带来了每五天翻一番的误差。Weickmann，Klaus，Jeff Whitaker，Andres Roubicek 和 Catherine Smith (2001-12-01)。利用集合天气预报制作经改进的中期(3-15天)天气预报。气候诊断中心。检索2007-02-16。自20世纪90年代以来，模式集合预报的使用有助于确定预报的不确定性，并且比其他方式可能延长未来一个天气预报。
==Initialization==
{{transcluded section|source=Numerical weather prediction}}
{{#section-h:Numerical weather prediction|Initialization}}
==Computation==
[[File:NAM 500 MB.PNG|thumb|An example of 500 [[millibar|mbar]] [[geopotential height]] prediction from a numerical weather prediction model.]]
[[File:Supercomputing the Climate.ogv|thumb|Supercomputers are capable of running highly complex models to help scientists better understand Earth's climate.]]
thumb|Supercomputers are capable of running highly complex models to help scientists better understand Earth's climate.
超级计算机能够运行高度复杂的模型，以帮助科学家更好地了解地球的气候。
A model is a computer program that produces [[meteorological]] information for future times at given locations and altitudes. Within any model is a set of equations, known as the [[primitive equations]], used to predict the future state of the atmosphere.<ref>{{cite book|last=Pielke|first=Roger A.|title=Mesoscale Meteorological Modeling|year=2002|publisher=[[Academic Press]]|isbn=978-0-12-554766-6|pages=48–49}}</ref> These equations are initialized from the analysis data and rates of change are determined. These rates of change predict the state of the atmosphere a short time into the future, with each time increment known as a time step. The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time into the future. ''Time stepping'' is repeated until the solution reaches the desired forecast time. The length of the time step chosen within the model is related to the distance between the points on the computational grid, and is chosen to maintain [[numerical stability]].<ref>{{cite book|last=Pielke|first=Roger A.|title=Mesoscale Meteorological Modeling|year=2002|publisher=[[Academic Press]]|isbn=978-0-12-554766-6|pages=285–287}}</ref> Time steps for global models are on the order of tens of minutes,<ref>{{cite book|url=https://books.google.com/books?id=JZikIbXzipwC&pg=PA131|page=132|title=Computational Science – ICCS 2005: 5th International Conference, Atlanta, GA, USA, May 22–25, 2005, Proceedings, Part 1|author=Sunderam, V. S. |author2=G. Dick van Albada |author3=Peter M. A. Sloot |author4=J. J. Dongarra|year=2005|publisher=Springer|isbn=978-3-540-26032-5}}</ref> while time steps for regional models are between one and four minutes.<ref>{{cite book|url=https://books.google.com/books?id=UV6PnF2z5_wC&pg=PA276|page=276|title=Developments in teracomputing: proceedings of the ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology|author=Zwieflhofer, Walter |author2=Norbert Kreitz |author3=European Centre for Medium Range Weather Forecasts|year=2001|publisher=World Scientific|isbn=978-981-02-4761-4}}</ref> The global models are run at varying times into the future. The [[UKMET]] [[Unified model]] is run six days into the future,<ref name="models"/> the [[European Centre for Medium-Range Weather Forecasts]] model is run out to 10 days into the future,<ref>{{cite book|url=https://books.google.com/books?id=fhW5oDv3EPsC&pg=PA474|page=480|author=Holton, James R.|title=An introduction to dynamic meteorology, Volume 1|year=2004|publisher=Academic Press|isbn=978-0-12-354015-7}}</ref> while the [[Global Forecast System]] model run by the [[Environmental Modeling Center]] is run 16 days into the future.<ref>{{cite book|url=https://books.google.com/books?id=mTZvR3R6YdkC&pg=PA121|page=121|title=Famine early warning systems and remote sensing data|author=Brown, Molly E.|publisher=Springer|year=2008|isbn=978-3-540-75367-4}}</ref>
A model is a computer program that produces meteorological information for future times at given locations and altitudes. Within any model is a set of equations, known as the primitive equations, used to predict the future state of the atmosphere. These equations are initialized from the analysis data and rates of change are determined. These rates of change predict the state of the atmosphere a short time into the future, with each time increment known as a time step. The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time into the future. Time stepping is repeated until the solution reaches the desired forecast time. The length of the time step chosen within the model is related to the distance between the points on the computational grid, and is chosen to maintain numerical stability. Time steps for global models are on the order of tens of minutes, while time steps for regional models are between one and four minutes. The global models are run at varying times into the future. The UKMET Unified model is run six days into the future, the European Centre for Medium-Range Weather Forecasts model is run out to 10 days into the future, while the Global Forecast System model run by the Environmental Modeling Center is run 16 days into the future.
模型是一种计算机程序，可以在给定的地点和高度为未来时间生成气象信息。在任何模型中都有一组方程，被称为原始方程组方程，用来预测未来的大气状态。这些方程由分析数据初始化，并确定变化率。这些变化率可以预测未来一小段时间内大气层的状态，每一个时间增量称为时间步长。然后将这些方程式应用到这种新的大气状态，以发现新的变化速率，而这些新的变化速率预测了未来更远时间的大气层。重复时间步进，直到解到达预期的预测时间。在模型中选择的时间步长与计算网格中点之间的距离有关，选择这个时间步长是为了保持数值稳定性。全球模型的时间步长约为数十分钟，而区域模型的时间步长约为1至4分钟。全球模型在未来的不同时间运行。统一模型在未来6天内运行，欧洲中期天气预报中心模型在未来10天内运行，而由环境建模中心运行的全球预报系统模型在未来16天内运行。
The equations used are [[nonlinear system|nonlinear]] partial differential equations which are impossible to solve exactly through analytical methods,<ref name="finite">{{cite book|url=https://books.google.com/books?id=SH8R_flZBGIC&pg=PA165|title=Finite difference schemes and partial differential equations|author=Strikwerda, John C.|pages=165–170|year=2004|publisher=SIAM|isbn=978-0-89871-567-5}}</ref> with the exception of a few idealized cases.<ref>{{cite book|last=Pielke|first=Roger A.|title=Mesoscale Meteorological Modeling|year=2002|publisher=[[Academic Press]]|isbn=978-0-12-554766-6|pages=65}}</ref> Therefore, numerical methods obtain approximate solutions. Different models use different solution methods: some global models use [[spectral method]]s for the horizontal dimensions and [[finite difference method]]s for the vertical dimension, while regional models and other global models usually use finite-difference methods in all three dimensions.<ref name="finite"/> The visual output produced by a model solution is known as a [[prognostic chart]], or ''prog''.<ref>{{cite book|author=Ahrens, C. Donald|page=244|isbn=978-0-495-11558-8|year=2008|publisher=Cengage Learning|title=Essentials of meteorology: an invitation to the atmosphere|url=https://books.google.com/books?id=2Yn29IFukbgC&pg=PA244}}</ref>
The equations used are nonlinear partial differential equations which are impossible to solve exactly through analytical methods, with the exception of a few idealized cases. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods: some global models use spectral methods for the horizontal dimensions and finite difference methods for the vertical dimension, while regional models and other global models usually use finite-difference methods in all three dimensions. The visual output produced by a model solution is known as a prognostic chart, or prog.
所用的方程是非线性偏微分方程，除了少数理想化的情况外，不可能用解析方法精确求解。因此，数值方法可以得到近似解。不同的模型使用不同的求解方法: 一些全球模型使用谱方法求解水平维度，而差分方法求解垂直维度，而区域模型和其他全球模型通常使用三维有限差分方法。模型解决方案产生的可视化输出被称为预测图，或 prog。
== Parameterization ==
{{main|Parametrization (climate)}}
Weather and climate model gridboxes have sides of between {{convert|5|km|mi}} and {{convert|300|km|mi}}. A typical [[cumulus cloud]] has a scale of less than {{convert|1|km|mi}}, and would require a grid even finer than this to be represented physically by the equations of fluid motion. Therefore, the processes that such [[clouds]] represent are ''[[Parametrization (atmospheric modeling)|parameterized]]'', by processes of various sophistication. In the earliest models, if a column of air in a model gridbox was unstable (i.e., the bottom warmer than the top) then it would be overturned, and the air in that vertical column mixed. More sophisticated schemes add enhancements, recognizing that only some portions of the box might [[convection|convect]] and that entrainment and other processes occur. Weather models that have gridboxes with sides between {{convert|5|km|mi}} and {{convert|25|km|mi}} can explicitly represent convective clouds, although they still need to parameterize [[cloud microphysics]].<ref>{{cite journal|url=http://ams.confex.com/ams/pdfpapers/126017.pdf|title=3.7 Improving Precipitation Forecasts by the Operational Nonhydrostatic Mesoscale Model with the Kain-Fritsch Convective Parameterization and Cloud Microphysics|author1=Narita, Masami |author2=Shiro Ohmori |name-list-style=amp |date=2007-08-06|access-date=2011-02-15|publisher=[[American Meteorological Society]]|journal=12th Conference on Mesoscale Processes}}</ref> The formation of large-scale ([[stratus cloud|stratus]]-type) clouds is more physically based, they form when the [[relative humidity]] reaches some prescribed value. Still, sub grid scale processes need to be taken into account. Rather than assuming that clouds form at 100% relative humidity, the [[cloud fraction]] can be related to a critical relative humidity of 70% for stratus-type clouds, and at or above 80% for cumuliform clouds,<ref>{{cite web|url=http://www.atmos.washington.edu/~dargan/591/diag_cloud.tech.pdf |pages=4–5 |title=The Diagnostic Cloud Parameterization Scheme |author=Frierson, Dargan |publisher=[[University of Washington]] |date=2000-09-14 |access-date=2011-02-15 |archive-url=https://web.archive.org/web/20110401013742/http://www.atmos.washington.edu/~dargan/591/diag_cloud.tech.pdf |archive-date=1 April 2011 |url-status=dead }}</ref> reflecting the sub grid scale variation that would occur in the real world.
Weather and climate model gridboxes have sides of between and . A typical cumulus cloud has a scale of less than , and would require a grid even finer than this to be represented physically by the equations of fluid motion. Therefore, the processes that such clouds represent are parameterized, by processes of various sophistication. In the earliest models, if a column of air in a model gridbox was unstable (i.e., the bottom warmer than the top) then it would be overturned, and the air in that vertical column mixed. More sophisticated schemes add enhancements, recognizing that only some portions of the box might convect and that entrainment and other processes occur. Weather models that have gridboxes with sides between and can explicitly represent convective clouds, although they still need to parameterize cloud microphysics. The formation of large-scale (stratus-type) clouds is more physically based, they form when the relative humidity reaches some prescribed value. Still, sub grid scale processes need to be taken into account. Rather than assuming that clouds form at 100% relative humidity, the cloud fraction can be related to a critical relative humidity of 70% for stratus-type clouds, and at or above 80% for cumuliform clouds, reflecting the sub grid scale variation that would occur in the real world.
= = = 天气和气候模型网格参量化的边界在到之间。一个典型的积云的尺度小于，并且需要一个比这更精细的网格才能用流体运动方程来表示。因此，这些云所代表的过程是通过各种复杂的过程来参数化的。在最早的模型中，如果模型网格盒中的空气柱是不稳定的(即，底部比顶部暖) ，那么它将被推翻，并且垂直柱中的空气将混合。更复杂的方案增加了增强功能，认识到只有盒子的一部分可能会突起，并且夹带和其他过程会发生。具有边界在到之间的网格框的天气模型可以明确地表示对流云，尽管它们仍然需要将云的微物理参数化。大尺度云(层云类型)的形成更多的是基于物理上的，它们是在相对湿度达到某个规定值时形成的。不过，次级电网规模的过程仍然需要考虑。与其假设云的形成相对湿度是100% ，不如假设层云型云的形成临界相对湿度是70% ，而积云型云的形成率是80% 或以上，这反映了现实世界中亚网格尺度的变化。
The amount of solar radiation reaching ground level in rugged terrain, or due to variable cloudiness, is parameterized as this process occurs on the molecular scale.<ref>{{cite book|url=https://books.google.com/books?id=lMXSpRwKNO8C&pg=PA56|title=Parameterization schemes: keys to understanding numerical weather prediction models|author=Stensrud, David J.|page=6|year=2007|publisher=Cambridge University Press|isbn=978-0-521-86540-1}}</ref> Also, the grid size of the models is large when compared to the actual size and roughness of clouds and topography. Sun angle as well as the impact of multiple cloud layers is taken into account.<ref>{{cite book|url=https://books.google.com/books?id=vdg5BgBmMkQC&pg=PA226|author1=Melʹnikova, Irina N.|author2=Alexander V. Vasilyev |name-list-style=amp |pages=226–228|title=Short-wave solar radiation in the earth's atmosphere: calculation, oberservation, interpretation|year=2005|publisher=Springer|isbn=978-3-540-21452-6}}</ref> Soil type, vegetation type, and soil moisture all determine how much radiation goes into warming and how much moisture is drawn up into the adjacent atmosphere. Thus, they are important to parameterize.<ref>{{cite book|url=https://books.google.com/books?id=lMXSpRwKNO8C&pg=PA56|title=Parameterization schemes: keys to understanding numerical weather prediction models|author=Stensrud, David J.|pages=12–14|year=2007|publisher=Cambridge University Press|isbn=978-0-521-86540-1}}</ref>
The amount of solar radiation reaching ground level in rugged terrain, or due to variable cloudiness, is parameterized as this process occurs on the molecular scale. Also, the grid size of the models is large when compared to the actual size and roughness of clouds and topography. Sun angle as well as the impact of multiple cloud layers is taken into account. Soil type, vegetation type, and soil moisture all determine how much radiation goes into warming and how much moisture is drawn up into the adjacent atmosphere. Thus, they are important to parameterize.
当这个过程在分子尺度上发生时，在崎岖地形中到达地面的太阳辐射量，或者由于变化的云量，被参数化了。此外，网格大小的模型是大相比，实际大小和粗糙的云和地形。太阳的角度以及多个云层的影响被考虑在内。土壤类型、植被类型和土壤湿度都决定有多少辐射进入气候变暖，有多少湿气进入邻近的大气层。因此，将它们参数化很重要。
==Domains==
The horizontal domain of a model is either ''global'', covering the entire Earth, or ''regional'', covering only part of the Earth. Regional models also are known as ''limited-area'' models, or LAMs. Regional models use finer grid spacing to resolve explicitly smaller-scale meteorological phenomena, since their smaller domain decreases computational demands. Regional models use a compatible global model for initial conditions of the edge of their domain. Uncertainty and errors within LAMs are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as within the creation of the boundary conditions for the LAMs itself.<ref>{{cite book|url=https://books.google.com/books?id=6RQ3dnjE8lgC&pg=PA261|title=Numerical Weather and Climate Prediction|author=Warner, Thomas Tomkins |publisher=[[Cambridge University Press]]|year=2010|isbn=978-0-521-51389-0|page=259}}</ref>
The horizontal domain of a model is either global, covering the entire Earth, or regional, covering only part of the Earth. Regional models also are known as limited-area models, or LAMs. Regional models use finer grid spacing to resolve explicitly smaller-scale meteorological phenomena, since their smaller domain decreases computational demands. Regional models use a compatible global model for initial conditions of the edge of their domain. Uncertainty and errors within LAMs are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as within the creation of the boundary conditions for the LAMs itself.
一个模型的水平域要么是全球性的，覆盖整个地球; 要么是区域性的，只覆盖地球的一部分。区域模型也称为有限区域模型(LAMs)。区域模型使用更精细的网格间距来明确地解决较小尺度的气象现象，因为它们较小的区域减少了计算需求。区域模型使用一个兼容的全局模型来处理区域边缘的初始条件。区域模型边界条件的全局模型以及区域模型本身边界条件的创建都引入了区域模型内部的不确定性和误差。
The vertical coordinate is handled in various ways. Some models, such as Richardson's 1922 model, use geometric height (<math>z</math>) as the vertical coordinate. Later models substituted the geometric <math>z</math> coordinate with a pressure coordinate system, in which the [[geopotential height]]s of constant-pressure surfaces become [[dependent variable]]s, greatly simplifying the primitive equations.<ref name="Lynch Ch2">{{cite book|last=Lynch|first=Peter|title=The Emergence of Numerical Weather Prediction|year=2006|publisher=[[Cambridge University Press]]|isbn=978-0-521-85729-1|pages=45–46|chapter=The Fundamental Equations}}</ref> This follows since pressure decreases with height through the [[Earth's atmosphere]].<ref>{{cite book|author=Ahrens, C. Donald|page=10|isbn=978-0-495-11558-8|year=2008|publisher=Cengage Learning|title=Essentials of meteorology: an invitation to the atmosphere|url=https://books.google.com/books?id=2Yn29IFukbgC&pg=PA244}}</ref> The first model used for operational forecasts, the single-layer barotropic model, used a single pressure coordinate at the {{convert|500|mbar|inHg|adj=on}} level,<ref name="Charney 1950"/> and thus was essentially two-dimensional. High-resolution models—also called ''mesoscale models''—such as the [[Weather Research and Forecasting model]] tend to use normalized pressure coordinates referred to as ''[[sigma coordinates]]''.<ref>{{cite web|last=Janjic |first=Zavisa |title=Scientific Documentation for the NMM Solver |url=http://nldr.library.ucar.edu/collections/technotes/asset-000-000-000-845.pdf |publisher=[[National Center for Atmospheric Research]] |access-date=2011-01-03 |author2=Gall, Robert |author3=Pyle, Matthew E. |pages=12–13 |date=February 2010 |url-status=dead |archive-url=https://web.archive.org/web/20110823082059/http://nldr.library.ucar.edu/collections/technotes/asset-000-000-000-845.pdf |archive-date=2011-08-23 }}</ref>
The vertical coordinate is handled in various ways. Some models, such as Richardson's 1922 model, use geometric height (z) as the vertical coordinate. Later models substituted the geometric z coordinate with a pressure coordinate system, in which the geopotential heights of constant-pressure surfaces become dependent variables, greatly simplifying the primitive equations. This follows since pressure decreases with height through the Earth's atmosphere. The first model used for operational forecasts, the single-layer barotropic model, used a single pressure coordinate at the level, and thus was essentially two-dimensional. High-resolution models—also called mesoscale models—such as the Weather Research and Forecasting model tend to use normalized pressure coordinates referred to as sigma coordinates.
垂直坐标以各种方式处理。一些模型，如 Richardson 的1922模型，使用几何高度(z)作为垂直坐标。后来的模型用压力坐标系代替了几何 z 坐标，在压力坐标系中，等压面的位势高度变成了相关变量，极大地简化了原始方程组。这是因为通过地球大气层的压力随着高度的降低而降低。第一个用于业务预报的模式，即单层正压模式，在水平上使用单一的气压坐标，因此基本上是二维的。高分辨率模式ーー也称为中尺度模式ーー例如天气研究和预报模式往往使用称为 sigma 坐标的归一化气压坐标。
=== Global versions ===
=== Global versions ===
= = = 全球版本 = = =
Some of the better known global numerical models are:
Some of the better known global numerical models are:
一些比较著名的全球数值模型是:
* '''GFS''' [[Global Forecast System]] (previously AVN) – developed by [[NOAA]]
* '''NOGAPS''' – developed by the [[US Navy]] to compare with the GFS
* '''GEM''' [[Global Environmental Multiscale Model]] – developed by the [[Meteorological Service of Canada]] (MSC)
* '''IFS''' developed by the [[European Centre for Medium-Range Weather Forecasts]]
* '''UM''' [[Unified Model]] developed by the [[United Kingdom|UK]] [[Met Office]]
* '''ICON''' developed by the [[German Weather Service]], DWD, jointly with the Max-Planck-Institute (MPI) for Meteorology, Hamburg, NWP Global model of DWD
* '''ARPEGE''' developed by the French Weather Service, [[Météo-France]]
* '''IGCM''' [[Intermediate General Circulation Model]]<ref name="models">{{cite book|pages=295–301|url=https://books.google.com/books?id=6gFiunmKWWAC&pg=PA297|title=Global Perspectives on Tropical Cyclones: From Science to Mitigation|author1=Chan, Johnny C. L.|author2=Jeffrey D. Kepert |name-list-style=amp |year=2010|publisher=World Scientific|isbn=978-981-4293-47-1}}</ref>
* GFS Global Forecast System (previously AVN) – developed by NOAA
* NOGAPS – developed by the US Navy to compare with the GFS
* GEM Global Environmental Multiscale Model – developed by the Meteorological Service of Canada (MSC)
* IFS developed by the European Centre for Medium-Range Weather Forecasts
* UM Unified Model developed by the UK Met Office
* ICON developed by the German Weather Service, DWD, jointly with the Max-Planck-Institute (MPI) for Meteorology, Hamburg, NWP Global model of DWD
* ARPEGE developed by the French Weather Service, Météo-France
* IGCM Intermediate General Circulation Model
* GFS 全球预报系统(前身为 AVN) -- 由 NOAA 开发
* NOAA gap -- 由美国海军开发，用于与 GFS
* GEM 全球环境多尺度模式进行比较 -- 由加拿大气象服务中心开发
* GEM 全球环境多尺度模式——由英国气象服务中心开发
* UM 统一模式
* ICON，由德国气象局开发
* ICON，与德国汉堡马克斯-普朗克气象研究所(MPI)联合开发
* ARPEGE 全球模式，由法国气象服务中心开发，m é o-france
* IGCM 中级环流模式
=== Regional versions ===
=== Regional versions ===
= = = 区域版本 = = =
Some of the better known regional numerical models are:
Some of the better known regional numerical models are:
一些比较著名的区域数值模型是:
* '''WRF''' The [[Weather Research and Forecasting model]] was developed cooperatively by NCEP, NCAR, and the meteorological research community. WRF has several configurations, including:
** '''WRF-NMM''' The WRF Nonhydrostatic Mesoscale Model is the primary short-term weather forecast model for the U.S., replacing the Eta model.
** '''WRF-ARW''' Advanced Research WRF developed primarily at the U.S. [[National Center for Atmospheric Research]] (NCAR)
* '''[[North American Mesoscale Model|NAM]]''' The term North American Mesoscale model refers to whatever regional model [[National Centers for Environmental Prediction|NCEP]] operates over the North American domain. NCEP began using this designation system in January 2005. Between January 2005 and May 2006 the Eta model used this designation. Beginning in May 2006, NCEP began to use the WRF-NMM as the operational NAM.
* '''RAMS''' the [[Regional Atmospheric Modeling System]] developed at [[Colorado State University]] for numerical simulations of atmospheric meteorology and other environmental phenomena on scales from meters to hundreds of kilometers – now supported in the public domain
* '''MM5''' The [[MM5 (weather model)|Fifth Generation Penn State/NCAR Mesoscale Model]]
* '''ARPS''' the Advanced Region Prediction System developed at the [[University of Oklahoma]] is a comprehensive multi-scale nonhydrostatic simulation and prediction system that can be used for regional-scale weather prediction up to the tornado-scale simulation and prediction. Advanced radar data assimilation for thunderstorm prediction is a key part of the system..
* '''[[HIRLAM]]''' High Resolution Limited Area Model, is developed by the European NWP research consortia [http://hirlam.org/ HIRLAM ] co-funded by 10 European weather services. The meso-scale HIRLAM model is known as HARMONIE and developed in collaboration with Meteo France and ALADIN consortia.
* '''GEM-LAM''' Global Environmental Multiscale Limited Area Model, the high resolution {{convert|2.5|km|abbr=on}} GEM by the [[Meteorological Service of Canada]] (MSC)
* '''ALADIN''' The high-resolution limited-area hydrostatic and non-hydrostatic model developed and operated by several European and North African countries under the leadership of Météo-France<ref name="models"/>
* '''COSMO''' The COSMO Model, formerly known as LM, aLMo or LAMI, is a limited-area non-hydrostatic model developed within the framework of the Consortium for Small-Scale Modelling (Germany, Switzerland, Italy, Greece, Poland, Romania, and Russia).<ref>Consortium on Small Scale Modelling. [http://cosmo-model.cscs.ch/ Consortium for Small-scale Modeling.] Retrieved on 2008-01-13.</ref>
* '''[http://mesonh.aero.obs-mip.fr/ Meso-NH]''' The Meso-NH Model<ref>Lac, C., Chaboureau, P., Masson, V., Pinty, P., Tulet, P., Escobar, J., ... & Aumond, P. (2018). Overview of the Meso-NH model version 5.4 and its applications. Geoscientific Model Development, 11, 1929-1969.</ref> is a limited-area non-hydrostatic model developed jointly by the Centre National de Recherches Météorologiques and the Laboratoire d'Aérologie (France, Toulouse) since 1998.<ref>Lafore, Jean Philippe, et al. "The Meso-NH atmospheric simulation system. Part I: Adiabatic formulation and control simulations." Annales geophysicae. Vol. 16. No. 1. Copernicus GmbH, 1998.</ref> Its application is from mesoscale to centimetric scales weather simulations.
* WRF The Weather Research and Forecasting model was developed cooperatively by NCEP, NCAR, and the meteorological research community. WRF has several configurations, including:
** WRF-NMM The WRF Nonhydrostatic Mesoscale Model is the primary short-term weather forecast model for the U.S., replacing the Eta model.
** WRF-ARW Advanced Research WRF developed primarily at the U.S. National Center for Atmospheric Research (NCAR)
* NAM The term North American Mesoscale model refers to whatever regional model NCEP operates over the North American domain. NCEP began using this designation system in January 2005. Between January 2005 and May 2006 the Eta model used this designation. Beginning in May 2006, NCEP began to use the WRF-NMM as the operational NAM.
* RAMS the Regional Atmospheric Modeling System developed at Colorado State University for numerical simulations of atmospheric meteorology and other environmental phenomena on scales from meters to hundreds of kilometers – now supported in the public domain
* MM5 The Fifth Generation Penn State/NCAR Mesoscale Model
* ARPS the Advanced Region Prediction System developed at the University of Oklahoma is a comprehensive multi-scale nonhydrostatic simulation and prediction system that can be used for regional-scale weather prediction up to the tornado-scale simulation and prediction. Advanced radar data assimilation for thunderstorm prediction is a key part of the system..
* HIRLAM High Resolution Limited Area Model, is developed by the European NWP research consortia HIRLAM co-funded by 10 European weather services. The meso-scale HIRLAM model is known as HARMONIE and developed in collaboration with Meteo France and ALADIN consortia.
* GEM-LAM Global Environmental Multiscale Limited Area Model, the high resolution GEM by the Meteorological Service of Canada (MSC)
* ALADIN The high-resolution limited-area hydrostatic and non-hydrostatic model developed and operated by several European and North African countries under the leadership of Météo-France
* COSMO The COSMO Model, formerly known as LM, aLMo or LAMI, is a limited-area non-hydrostatic model developed within the framework of the Consortium for Small-Scale Modelling (Germany, Switzerland, Italy, Greece, Poland, Romania, and Russia).Consortium on Small Scale Modelling. Consortium for Small-scale Modeling. Retrieved on 2008-01-13.
* Meso-NH The Meso-NH ModelLac, C., Chaboureau, P., Masson, V., Pinty, P., Tulet, P., Escobar, J., ... & Aumond, P. (2018). Overview of the Meso-NH model version 5.4 and its applications. Geoscientific Model Development, 11, 1929-1969. is a limited-area non-hydrostatic model developed jointly by the Centre National de Recherches Météorologiques and the Laboratoire d'Aérologie (France, Toulouse) since 1998.Lafore, Jean Philippe, et al. "The Meso-NH atmospheric simulation system. Part I: Adiabatic formulation and control simulations." Annales geophysicae. Vol. 16. No. 1. Copernicus GmbH, 1998. Its application is from mesoscale to centimetric scales weather simulations.
* WRF 天气研究和预报模型是由 NCEP、 NCAR 和气象研究团体共同开发的。WRF 有几种配置，包括:
*
* WRF-nmm WRF 非静力中尺度模式是美国主要的短期天气预报模式，取代了 Eta 模式。
*
* WRF-ARW 高级研究 WRF 主要由美国国家大气研究中心(NCAR)
* NAM 术语“北美中尺度模式”指的是 NCEP 在北美地区运作的任何区域模式。NCEP 于2005年1月开始使用这一指定系统。2005年1月至2006年5月期间，埃塔模式使用了这一称号。从2006年5月开始，NCEP 开始使用 WRF-NMM 作为业务不结盟运动。
* RAMS 区域大气模拟系统是科罗拉多州立大学开发的区域大气模拟系统，用于从数米到数百公里范围内的大气气象和其他环境现象的数值模拟，现已得到公共领域的支持
* mm5第五代宾夕法尼亚州立大学/NCAR 中尺度模式
* ARPS 奥克拉荷马大学开发的高级区域预报系统是一个综合性的多尺度非静力模拟和预报系统，可用于区域尺度的天气预报，直至龙卷尺度的模拟和预报。用于雷暴预报的先进雷达数据同化是该系统的关键部分。.
* HIRLAM 高分辨率有限区域模型，由欧洲数值天气预报研究联盟 HIRLAM 开发，由10个欧洲气象部门共同资助。中尺度的 HIRLAM 模型被称为 HARMONIE，是与 Meteo France 和 ALADIN 联盟合作开发的。
* GEM-LAM 全球环境多尺度有限区域模型，由加拿大气象局提供的高分辨率 GEM
* ALADIN 高分辨率有限区域静水压和非静水压模型，由几个欧洲和北非国家在 Météo-France 领导下开发和运行
* COSMO 模型，以前称为 COSMO，aLMo 或 LAMI，是在小尺度模型联合会(德国、瑞士、意大利、希腊、波兰、罗马尼亚和俄罗斯)框架内开发的有限区域非静水压模型。小尺度模型联合会。小规模建模联合会。2008-01-13.
* Meso-NH The Meso-NH ModelLac，c. ，Chaboureau，p. ，Masson，v. ，Pinty，p. ，Tulet，p. ，Escobar，j. ，... & Aumond，p. (2018).Meso-NH 模型版本5.4及其应用的概述。Geoscientific Model Development, 11, 1929-1969. is a limited-area non-hydrostatic model developed jointly by the Centre National de Recherches Météorologiques and the Laboratoire d'Aérologie (France, Toulouse) since 1998.Lafore, Jean Philippe, et al.”Meso-NH 大气模拟系统。第一部分: 绝热制定和控制模拟地球物理年鉴。第一卷。16.没有。1.Copernicus GmbH, 1998.它的应用是从中尺度到厘米尺度的天气模拟。
==Model output statistics==
{{Main|Model output statistics}}
Because forecast models based upon the equations for atmospheric dynamics do not perfectly determine weather conditions near the ground, statistical corrections were developed to attempt to resolve this problem. Statistical models were created based upon the three-dimensional fields produced by numerical weather models, surface observations, and the climatological conditions for specific locations. These statistical models are collectively referred to as [[model output statistics]] (MOS),<ref>{{cite book|url=https://books.google.com/books?id=blEMoIKX_0IC&pg=PA188|page=189|title=When nature strikes: weather disasters and the law|author=Baum, Marsha L.|publisher=Greenwood Publishing Group|year=2007|isbn=978-0-275-22129-4}}</ref> and were developed by the [[National Weather Service]] for their suite of weather forecasting models.<ref name="MOS">{{cite book|title=Model output statistics forecast guidance|author=Harry Hughes|publisher=United States Air Force Environmental Technical Applications Center|year=1976|pages=1–16}}</ref> The [[United States Air Force]] developed its own set of MOS based upon their dynamical weather model by 1983.<ref name="L. Best, D. L. and S. P. Pryor 1983 1–90"/>
Because forecast models based upon the equations for atmospheric dynamics do not perfectly determine weather conditions near the ground, statistical corrections were developed to attempt to resolve this problem. Statistical models were created based upon the three-dimensional fields produced by numerical weather models, surface observations, and the climatological conditions for specific locations. These statistical models are collectively referred to as model output statistics (MOS), and were developed by the National Weather Service for their suite of weather forecasting models. The United States Air Force developed its own set of MOS based upon their dynamical weather model by 1983.
由于基于大气动力学方程式的预报模式不能完全确定近地天气状况，因此开发了统计修正以试图解决这一问题。基于数值天气模型、地面观测和特定地点的气候条件产生的三维场，建立了统计模型。这些统计模型统称为模型输出统计(MOS) ，由国家气象局为他们的一套天气预报模型开发。到1983年，美国空军根据其动态天气模型开发了自己的一套 MOS。
Model output statistics differ from the ''perfect prog'' technique, which assumes that the output of numerical weather prediction guidance is perfect.<ref>{{cite book|url=https://books.google.com/books?id=QwzHZ-wV-BAC&pg=PA1144|page=1144|title=Fog and boundary layer clouds: fog visibility and forecasting|author=Gultepe, Ismail|publisher=Springer|year=2007|isbn=978-3-7643-8418-0}}</ref> MOS can correct for local effects that cannot be resolved by the model due to insufficient grid resolution, as well as model biases. Forecast parameters within MOS include maximum and minimum temperatures, percentage chance of rain within a several hour period, precipitation amount expected, chance that the precipitation will be frozen in nature, chance for thunderstorms, cloudiness, and surface winds.<ref>{{cite book|url=https://books.google.com/books?id=Xs9LiGpNX-AC&pg=PA171|page=172|author1=Barry, Roger Graham |author2=Richard J. Chorley |name-list-style=amp |title=Atmosphere, weather, and climate|publisher=Psychology Press|year=2003|isbn=978-0-415-27171-4}}</ref>
Model output statistics differ from the perfect prog technique, which assumes that the output of numerical weather prediction guidance is perfect. MOS can correct for local effects that cannot be resolved by the model due to insufficient grid resolution, as well as model biases. Forecast parameters within MOS include maximum and minimum temperatures, percentage chance of rain within a several hour period, precipitation amount expected, chance that the precipitation will be frozen in nature, chance for thunderstorms, cloudiness, and surface winds.
模型输出统计不同于完美的前端技术，前端技术假设数值天气预报指导的输出是完美的。MOS 可以修正由于网格分辨率不足以及模型偏差而无法由模型解决的局部效应。MOS 内的预报参数包括最高和最低气温、几个小时内降雨的百分比、预期降水量、降水在自然界结冰的可能性、雷暴的可能性、云量和地面风。
== Applications ==
== Applications ==
= = 应用程序 = =
===Climate modeling===
{{Main|Climate model|General circulation model}}
In 1956, Norman Phillips developed a mathematical model that realistically depicted monthly and seasonal patterns in the troposphere. This was the first successful [[climate model]].<ref name="Phillips">{{cite journal | url=http://www.phy.pku.edu.cn/climate/class/cm2010/Phillips_QJRMS_1956.pdf | title=The general circulation of the atmosphere: a numerical experiment | journal=[[Quarterly Journal of the Royal Meteorological Society]] | author=Norman A. Phillips | date=April 1956 | volume=82 | issue=352 | pages=123–154 | doi=10.1002/qj.49708235202 | bibcode=1956QJRMS..82..123P}}</ref><ref name="Cox210">{{cite book | title=Storm Watchers | author=John D. Cox | publisher=John Wiley & Sons, Inc. | page=[https://archive.org/details/stormwatcherstur00cox_df1/page/210 210] | year=2002 | isbn=978-0-471-38108-2 | url=https://archive.org/details/stormwatcherstur00cox_df1/page/210 }}</ref> Several groups then began working to create [[general circulation model]]s.<ref name="Lynch Ch10"/> The first general circulation climate model combined oceanic and atmospheric processes and was developed in the late 1960s at the [[Geophysical Fluid Dynamics Laboratory]], a component of the U.S. [[National Oceanic and Atmospheric Administration]].<ref>{{cite web | url=http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html | title=The First Climate Model | author=National Oceanic and Atmospheric Administration | author-link=National Oceanic and Atmospheric Administration | date=22 May 2008 | access-date=8 January 2011}}</ref> By the early 1980s, the U.S. [[National Center for Atmospheric Research]] had developed the Community Atmosphere Model (CAM), which can be run by itself or as the atmospheric component of the [[Community Climate System Model]]. The latest update (version 3.1) of the standalone CAM was issued on 1 February 2006.<ref>{{Cite web|url=http://www.cesm.ucar.edu/models/atm-cam/download/|title=CAM 3.1 Download|website=www.cesm.ucar.edu|access-date=2019-06-25}}</ref><ref>{{cite web | url=http://www.cesm.ucar.edu/models/atm-cam/docs/description/description.pdf | title=Description of the NCAR Community Atmosphere Model (CAM 3.0) | author=William D. Collins | publisher=[[University Corporation for Atmospheric Research]] | date=June 2004 | access-date=3 January 2011 | display-authors=et al.}}</ref><ref>{{cite web | url=http://www.cesm.ucar.edu/models/atm-cam/ | title=CAM3.0 COMMUNITY ATMOSPHERE MODEL | publisher=[[University Corporation for Atmospheric Research]] | access-date=6 February 2018}}</ref> In 1986, efforts began to initialize and model soil and vegetation types, resulting in more realistic forecasts.<ref>{{cite journal | url=http://www.geog.ucla.edu/~yxue/pdf/1996jgr.pdf | title=Impact of vegetation properties on U. S. summer weather prediction | journal=[[Journal of Geophysical Research]] | author1=Yongkang Xue | author2=Michael J. Fennessey | name-list-style=amp | date=20 March 1996 | volume=101 | issue=D3 | page=7419 | access-date=6 January 2011 | bibcode=1996JGR...101.7419X | doi=10.1029/95JD02169 | url-status=dead | archive-url=https://web.archive.org/web/20100710080304/http://www.geog.ucla.edu/~yxue/pdf/1996jgr.pdf | archive-date=10 July 2010| citeseerx=10.1.1.453.551 }}</ref> Coupled ocean-atmosphere climate models, such as the [[Hadley Centre for Climate Prediction and Research]]'s [[HadCM3]] model, are being used as inputs for [[climate change]] studies.<ref name="Lynch Ch10">{{cite book | chapter-url=https://books.google.com/books?id=EV5bZqOO7kkC&pg=PA208 | title=The Emergence of Numerical Weather Prediction: Richardson's Dream | chapter=The ENIAC Integrations | author=Peter Lynch | publisher=[[Cambridge University Press]] | year=2006 | isbn=978-0-521-85729-1 | page=208 | access-date=6 February 2018}}</ref>
In 1956, Norman Phillips developed a mathematical model that realistically depicted monthly and seasonal patterns in the troposphere. This was the first successful climate model. Several groups then began working to create general circulation models. The first general circulation climate model combined oceanic and atmospheric processes and was developed in the late 1960s at the Geophysical Fluid Dynamics Laboratory, a component of the U.S. National Oceanic and Atmospheric Administration. By the early 1980s, the U.S. National Center for Atmospheric Research had developed the Community Atmosphere Model (CAM), which can be run by itself or as the atmospheric component of the Community Climate System Model. The latest update (version 3.1) of the standalone CAM was issued on 1 February 2006. In 1986, efforts began to initialize and model soil and vegetation types, resulting in more realistic forecasts. Coupled ocean-atmosphere climate models, such as the Hadley Centre for Climate Prediction and Research's HadCM3 model, are being used as inputs for climate change studies.
1956年，诺曼 · 菲利普斯开发了一个数学模型，这个模型真实地描述了对流层的每月和季节的模式。这是第一个成功的气候模型。几个小组随后开始建立大体循环模型。第一个大气环流气候模式结合了海洋和大气过程，于20世纪60年代末在美国地球物理流体动力学实验室气候研究中心发展起来，该中心是美国美国国家海洋和大气管理局气候研究中心的一个组成部分。到20世纪80年代早期，美国国家大气研究中心开发了社区大气模型(CAM) ，它可以自己运行，也可以作为社区气候系统模型的大气成分。最新更新(3.1版本)已于2006年2月1日发出。在1986年，开始努力初始化和模型的土壤和植被类型，导致更现实的预测。耦合的海洋-大气气候模型，如哈德利气候预测与研究中心的 hadcm3模型，正被用作气候变化研究的输入。
===Limited area modeling===
[[File:Ernesto2006modelspread.png|thumb|right|Model spread with [[Hurricane Ernesto (2006)]] within the National Hurricane Center limited area models]]
[[Air pollution forecasting|Air pollution forecasts]] depend on atmospheric models to provide [[fluid flow]] information for tracking the movement of pollutants.<ref>{{cite journal | author1=Alexander Baklanov | author2=Alix Rasmussen | author3=Barbara Fay | author4=Erik Berge | author5=Sandro Finardi | title=Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting | journal=Water, Air, & Soil Pollution: Focus | date=September 2002 | volume=2 | issue=5 | pages=43–60 | doi=10.1023/A:1021394126149| s2cid=94747027 }}</ref> In 1970, a private company in the U.S. developed the regional Urban Airshed Model (UAM), which was used to forecast the effects of air pollution and [[acid rain]]. In the mid- to late-1970s, the [[United States Environmental Protection Agency]] took over the development of the UAM and then used the results from a regional air pollution study to improve it. Although the UAM was developed for [[California]], it was during the 1980s used elsewhere in North America, Europe, and Asia.<ref name="Steyn, D. G. 1991 241–242"/>
Air pollution forecasts depend on atmospheric models to provide fluid flow information for tracking the movement of pollutants. In 1970, a private company in the U.S. developed the regional Urban Airshed Model (UAM), which was used to forecast the effects of air pollution and acid rain. In the mid- to late-1970s, the United States Environmental Protection Agency took over the development of the UAM and then used the results from a regional air pollution study to improve it. Although the UAM was developed for California, it was during the 1980s used elsewhere in North America, Europe, and Asia.
空气污染预报依靠大气模型来提供流体流动信息，以跟踪污染物的运动。1970年，美国的一家私营公司开发了区域城市气流模型(UAM) ，用于预测空气污染和酸雨的影响。在1970年代中后期，美国环境保护局接管了 UAM 的开发工作，然后利用区域空气污染研究的结果来改进 UAM。虽然 UAM 是为加利福尼亚州开发的，但在20世纪80年代，它在北美、欧洲和亚洲的其他地方得到了应用。
The Movable Fine-Mesh model, which began operating in 1978, was the first [[tropical cyclone forecast model]] to be based on [[Atmospheric dynamics#Dynamic meteorology|atmospheric dynamics]].<ref name="Shuman W&F"/> Despite the constantly improving dynamical model guidance made possible by increasing computational power, it was not until the 1980s that numerical weather prediction (NWP) showed [[Forecast skill|skill]] in forecasting the track of tropical cyclones. And it was not until the 1990s that NWP consistently outperformed [[statistical model|statistical]] or simple dynamical models.<ref>{{cite web | url=http://www.nhc.noaa.gov/verification/verify6.shtml | publisher=[[National Hurricane Center]] | date=20 April 2010 | access-date=2 January 2011 | author=James Franklin | title=National Hurricane Center Forecast Verification | author-link=James Franklin (meteorologist) | archive-url=https://web.archive.org/web/20110102062753/http://www.nhc.noaa.gov/verification/verify6.shtml | archive-date=2 January 2011 | url-status=live}}</ref> Predicting the intensity of tropical cyclones using NWP has also been challenging. As of 2009, dynamical guidance remained less skillful than statistical methods.<ref>{{cite journal | author1=Edward N. Rappaport | author2=James L. Franklin | author3=Lixion A. Avila | author4=Stephen R. Baig | author5=John L. Beven II | author6=Eric S. Blake | author7=Christopher A. Burr | author8=Jiann-Gwo Jiing | author9=Christopher A. Juckins | author10=Richard D. Knabb | author11=Christopher W. Landsea | author12=Michelle Mainelli | author13=Max Mayfield | author14=Colin J. McAdie | author15=Richard J. Pasch | author16=Christopher Sisko | author17=Stacy R. Stewart | author18=Ahsha N. Tribble | title=Advances and Challenges at the National Hurricane Center | journal=[[Weather and Forecasting]] | date=April 2009 | volume=24 | issue=2 | pages=395–419 | doi=10.1175/2008WAF2222128.1 | bibcode=2009WtFor..24..395R| citeseerx=10.1.1.207.4667 }}</ref>
The Movable Fine-Mesh model, which began operating in 1978, was the first tropical cyclone forecast model to be based on atmospheric dynamics. Despite the constantly improving dynamical model guidance made possible by increasing computational power, it was not until the 1980s that numerical weather prediction (NWP) showed skill in forecasting the track of tropical cyclones. And it was not until the 1990s that NWP consistently outperformed statistical or simple dynamical models. Predicting the intensity of tropical cyclones using NWP has also been challenging. As of 2009, dynamical guidance remained less skillful than statistical methods.
可移动细网模型于1978年开始运行，是第一个基于大气动力学的热带气旋预报模型模型。尽管由于计算能力的提高，不断改进的动力学模型指南成为可能，但直到20世纪80年代，数值天气预报才显示出预报热带气旋路径的技术。直到20世纪90年代，数值天气预报才始终优于统计或简单的动力学模型。使用数值预报方法预报热带气旋的强度也是一个挑战。截至2009年，动态指导仍然不如统计方法熟练。
==See also==
* [[Atmospheric reanalysis]]
* [[Climate model]]
* [[Numerical weather prediction]]
* [[Upper-atmospheric models]]
* [[Static atmospheric model]]
* Atmospheric reanalysis
* Climate model
* Numerical weather prediction
* Upper-atmospheric models
* Static atmospheric model
= = = = =
* 大气重新分析
* 气候模式
* 数值天气预报
* 高层大气模式
* 静态大气模式
== References ==
{{Reflist|2}}
==Further reading==
{{Refbegin}}
*{{cite book |author1=Roulstone, Ian |author2=Norbury, John |title=Invisible in the Storm: the role of mathematics in understanding weather |location=Princeton |publisher=Princeton University Press |year=2013 |isbn=978-0-691-15272-1 }}
{{Refend}}
*
= = 进一步阅读 = = =
*
==External links==
* [https://web.archive.org/web/20110921153007/http://www.mmm.ucar.edu/wrf/users/download/get_source2.html WRF Source Codes and Graphics Software Download Page]
* [https://web.archive.org/web/20140712133145/http://bridge.atmet.org/users/software.php RAMS source code available under the GNU General Public License]
* [https://web.archive.org/web/20110928000959/http://www.mmm.ucar.edu/mm5/mm5v3/wherev3.html MM5 Source Code download]
* [http://www.caps.ou.edu/ARPS The source code of ARPS]
* [http://www.ventusky.com/ Model Visualisation]
* WRF Source Codes and Graphics Software Download Page
* RAMS source code available under the GNU General Public License
* MM5 Source Code download
* The source code of ARPS
* Model Visualisation
= = = 外部链接 = =
* WRF 源代码和绘图软件下载页
* RAMS 源代码可在 GNU通用公共许可协议
* mm5源代码下载
* 源代码 ARPS
* 模型可视化
{{Atmospheric, Oceanographic and Climate Models|state=expanded}}
{{Computer modeling}}
{{DEFAULTSORT:Atmospheric Model}}
[[Category:Numerical climate and weather models]]
[[Category:Articles containing video clips]]
Category:Numerical climate and weather models
Category:Articles containing video clips
类别: 数值气候和天气模型类别: 包含视频剪辑的文章
<noinclude>
<small>This page was moved from [[wikipedia:en:Atmospheric model]]. Its edit history can be viewed at [[大气模式/edithistory]]</small></noinclude>
[[Category:待整理页面]]