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大气模式 (查看源代码)

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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。

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【终稿】一次850mbar处地势高度和温度的96小时预报|链接=Special:FilePath/GFS_850_MB.PNG]]

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.

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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。

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【终稿】数值天气预报的历史起于20世纪20年代，这得益于 Lewis Fry Richardson 使用了 Vihelm Bjerknes 开发的方法的成果。直到计算机和计算机模拟时代的到来，计算时间才降低到少于被预测时段。ENIAC 在1950年发明了第一台计算机预测系统，之后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的版本。1966年，西德和美国开始根据原始方程模式制作业务预测系统，1972年英国和1977年澳大利亚紧随其后。全球预报模式的发展导致了第一个气候模型的诞生。在20世纪70年代和20世纪80年代，有限区域（区域性）模型的发展推动了热带气旋轨道和空气质量预报的进步。

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【终稿】数值天气预报的历史起于20世纪20年代，这得益于 Lewis Fry Richardson 使用了 Vihelm Bjerknes 开发的方法的成果。直到计算机和计算机模拟时代的到来，计算时间才降低到少于被预测时段。ENIAC 在1950年发明了第一台计算机预测系统，之后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的版本。1966年，西德和美国开始根据原始方程模式制作业务预测系统，1972年英国和1977年澳大利亚紧随其后。全球预报模式的发展导致了第一个气候模式的诞生。在20世纪70年代和20世纪80年代，有限区域（区域性）模式的发展推动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>

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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。

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【终稿】由于基于大气动力学的预报模型的输出结果需要近地面处的修正，因此20世纪70年代和20世纪80年代开发了单个预报位点的模型输出统计（MOS）。尽管超级计算机的能力不断提升，数值天气模式的预报仅能延伸到未来两周左右，这是因为观测点的密度和质量以及被用来预测的偏微分方程的混沌本质都会引入每五天加倍的误差。自20世纪90年代以来，模式集合预报的使用帮助确定了不确定性，并且预测时段比其他可能的方式都要长。

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【终稿】由于基于大气动力学的预报模式的输出结果需要近地面处的修正，因此20世纪70年代和20世纪80年代开发了单个预报位点的模式输出统计（MOS）。尽管超级计算机的能力不断提升，数值天气模式的预报仅能延伸到未来两周左右，这是因为观测点的密度和质量以及被用来预测的偏微分方程的混沌本质都会引入每五天加倍的误差。自20世纪90年代以来，模式集合预报的使用帮助确定了不确定性，并且预测时段比其他可能的方式都要长。

==Initialization 初始化==

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【更正】The atmosphere is a fluid. As such, the idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future. The process of entering observation data into the model to generate initial conditions is called ''initialization''. On land, terrain maps available at resolutions down to 1 kilometer (0.6 mi) globally are used to help model atmospheric circulations within regions of rugged topography, in order to better depict features such as downslope winds, mountain waves and related cloudiness that affects incoming solar radiation. The main inputs from country-based weather services are observations from devices (called radiosondes) in weather balloons that measure various atmospheric parameters and transmits them to a fixed receiver, as well as from weather satellites. The World Meteorological Organization acts to standardize the instrumentation, observing practices and timing of these observations worldwide. Stations either report hourly in METAR reports, or every six hours in SYNOP reports. These observations are irregularly spaced, so they are processed by data assimilation and objective analysis methods, which perform quality control and obtain values at locations usable by the model's mathematical algorithms. The data are then used in the model as the starting point for a forecast.

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【终稿】

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【更正】A variety of methods are used to gather observational data for use in numerical models. Sites launch radiosondes in weather balloons which rise through the troposphere and well into the stratosphere. Information from weather satellites is used where traditional data sources are not available. Commerce provides pilot reports along aircraft routes and ship reports along shipping routes. Research projects use reconnaissance aircraft to fly in and around weather systems of interest, such as tropical cyclones. Reconnaissance aircraft are also flown over the open oceans during the cold season into systems which cause significant uncertainty in forecast guidance, or are expected to be of high impact from three to seven days into the future over the downstream continent. Sea ice began to be initialized in forecast models in 1971. Efforts to involve sea surface temperature in model initialization began in 1972 due to its role in modulating weather in higher latitudes of the Pacific.

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【终稿】

==Computation 计算==

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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。

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【终稿】模式指的是一种可以在给定的位置和海拔高度生成未来气象信息的一种计算机程序。任何模型中都有一套称为“原始方程组”的方程组，用于预测未来的大气状态。这些方程组依据分析数据初始化，并确定变化速率。这些变化速率可以预测未来一小段时间的大气状态，每一个时间增量被称为一个时间步长。然后这些方程组被用于新的大气状态，得到新的变化速率，新的变化速率接着被用于预测再往后的大气状态。不断推进时间步，直到方程组的解到达了想要的预测时间。模式内时间步长的选择与计算网格间距有关，需要确保数值稳定性。全球模式的时间步长约为数十分钟，而区域模式则为1到4分钟。全球模式预测时段各有不同。UKMET联合模式可预测未来6天，欧洲中心的中期天气预测模型（European Centre for Medium-Range Weather Forecasts model）可预测未来10天，而环境建模中心（Environmental Modeling Center）的全球预测系统模式（Global Forest System model）可以预测未来16天。

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【终稿】模式指的是一种可以在给定的位置和海拔高度生成未来气象信息的一种计算机程序。任何模型中都有一套称为“原始方程组”的方程组，用于预测未来的大气状态。这些方程组依据分析数据初始化，并确定变化速率。这些变化速率可以预测未来一小段时间的大气状态，每一个时间增量被称为一个时间步长。然后这些方程组被用于新的大气状态，得到新的变化速率，新的变化速率接着被用于预测再往后的大气状态。不断推进时间步，直到方程组的解到达了想要的预测时间。模式内时间步长的选择与计算网格间距有关，需要确保数值稳定性。全球模式的时间步长约为数十分钟，而区域模式则为1到4分钟。全球模式预测时段各有不同。UKMET联合模式可预测未来6天，欧洲中心的中程天气预测模式（European Centre for Medium-Range Weather Forecasts model）可预测未来10天，而环境建模中心（Environmental Modeling Center）的全球预测系统模式（Global Forest System model）可以预测未来16天。

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>

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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.

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【更正】Weather and climate model gridboxes have sides of between 5 kilometres (3.1 mi) and 300 kilometres (190 mi). A typical cumulus cloud has a scale of less than 1 kilometre (0.62 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 ''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 5 kilometres (3.1 mi) and 25 kilometres (16 mi) 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。

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~~【终稿】天气和气象模式~~

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【终稿】天气和气象模式网格具有5千米（3.1英里）到300千米（190英里）之间的边界。典型的积云尺度小于1千米（0.62英里），因此需要比这更精细的网格才能被流体运动方程表示。故而，这些云所代表的过程是通过各种复杂的处理来表示的。最早的模式中，如果模式中的空气柱是不稳定的（即底部比顶部热），那么它将被破坏，该垂直柱中的空气将被混合。更加复杂的模式中有增强功能，它们知道整个网格中只有一部分会发生对流、夹带或者一些其它过程。边界在5千米（3.1英里）到25千米（16英里）的气象模式可以明确地表示对流云，尽管它们仍然需要参数化云的微物理过程。大尺度（层云型）云的形成更加基于物理规律，它们在相对湿度达到某个规定值时形成。此时仍然有亚网格尺寸的过程也需要被考虑进来。层云形成的临界湿度被设定为70%而不是100%，相对湿度超过80%时认为形成的是积云，这反应了现实世界中可能发生的亚网格尺寸的变化。

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>

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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。

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~~【终稿】在因为变化的云量或崎岖的地形中达到地面的太阳辐射量被~~

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【终稿】在崎岖的地形中或云量多变地区达到地面的太阳辐射量也被参数化了，因为该过程发生在分子尺寸。并且，模型的网格尺寸相对于实际的云和地形的尺寸及粗糙度都要大得多。太阳角度以及其对多个云层的影响均被考虑在内。土壤类型、植被类型以及土壤湿度均决定了多少辐射参与邻近大气的加热以及湿度的增加。因此，它们也是重要的需要参数化的量。

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==Domains==

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==Domains 范围==

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>

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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。

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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>

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【终稿】一个模式的水平范围可以是全球性的，覆盖整个地球；也可以是区域性的，只覆盖地球的一部分。区域模式也被称为有限区域模式（LAMs）。区域模式使用更加精细的网格来明确地解决较小尺度的气象现象，因为它们更小的水平范围降低了计算量的要求。区域模式使用一个兼容的全球模式来获得区域模式边界处的初始条件。用以获得区域模式的边界条件的全球模式，以及区域模式本身创造的边界条件，共同引入区域模式的的不确定度和误差。

+

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.

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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。

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~~【终稿】~~

+

【终稿】垂直坐标有多种方式处理。一些模式，如Richardson的1922模式，使用几何高度（z）作为垂直坐标。后来的模式使用压力坐标系代替了几何z坐标系，从而等压面的位势高度变成了因变量，极大地简化了原始方程组。这是因为地球大气层的压力随着高度增加而降低。第一个用于业务预报的模式，即单层正压模式，在水平面上使用一个简单的压力坐标，并因此基本上是二维的。高分辨率模式（也被称为中尺度模式），如WRF模式，则往往使用标准化压力坐标（sigma坐标）。

=== Global versions ===

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=== Global versions ===

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= = = 全球版本 = = =

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=== 全球版本 ===

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=== 【终稿】全球模式版本 ===

Some of the better known global numerical models are:

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一些比较著名的全球数值模型是:

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* '''GFS''' [[Global Forecast System]] (previously AVN) – developed by [[NOAA]]

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【终稿】一些比较著名的全球数值模式有：

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* '''NOGAPS''' – developed by the [[US Navy]] to compare with the GFS

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* '''GEM''' [[Global Environmental Multiscale Model]] – developed by the [[Meteorological Service of Canada]] (MSC)

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*'''GFS''' [[Global Forecast System]] (previously AVN) – developed by [[NOAA]]

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* '''IFS''' developed by the [[European Centre for Medium-Range Weather Forecasts]]

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*'''NOGAPS''' – developed by the [[US Navy]] to compare with the GFS

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* '''UM''' [[Unified Model]] developed by the [[United Kingdom|UK]] [[Met Office]]

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*'''GEM''' [[Global Environmental Multiscale Model]] – developed by the [[Meteorological Service of Canada]] (MSC)

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* '''ICON''' developed by the [[German Weather Service]], DWD, jointly with the Max-Planck-Institute (MPI) for Meteorology, Hamburg, NWP Global model of DWD

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*'''IFS''' developed by the [[European Centre for Medium-Range Weather Forecasts]]

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* '''ARPEGE''' developed by the French Weather Service, [[Météo-France]]

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*'''UM''' [[Unified Model]] developed by the [[United Kingdom|UK]] [[Met Office]]

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* '''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>

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*'''ICON''' developed by the [[German Weather Service]], DWD, jointly with the Max-Planck-Institute (MPI) for Meteorology, Hamburg, NWP Global model of DWD

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*'''ARPEGE''' developed by the French Weather Service, [[Météo-France]]

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*'''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

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* 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

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【终稿】

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* '''GFS''' 全球预测系统（Global Forecast System，前身为AVN）——由NOAA开发

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* '''NOGAPS''' ——由美国海军开发，用于和GFS比对

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* '''GEM''' 全球环境多尺度模式（Global Environmental Multiscale Model）——由加拿大气象局（MSC）开发

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* '''IFS''' 由欧洲中心的中程度天气预测部门（the European Centre for Medium-Range Weather Forecasts）开发

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* '''UM''' 统一模式（Unified Model），由英国气象办公室（the UK Met Office）

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* '''ICON''' 由德国天气局、DWD以及马普所（MPI）气象部门（汉堡）联合开发

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* '''ARPEGE''' 由法国天气局开发（the French Weather Service, Météo-France）

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* '''IGCM''' 中间大气环流模式（Intermediate General Circulation Model）

=== Regional versions ===

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=== Regional versions ===

−

= = = 区域版本 = = =

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=== 区域版本 ===

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=== 【终稿】区域模式版本 ===

Some of the better known regional numerical models are:

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一些比较著名的区域数值模型是:

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* '''WRF''' The [[Weather Research and Forecasting model]] was developed cooperatively by NCEP, NCAR, and the meteorological research community. WRF has several configurations, including:

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【终稿】一些比较著名的区域数值模式有：

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** '''WRF-NMM''' The WRF Nonhydrostatic Mesoscale Model is the primary short-term weather forecast model for the U.S., replacing the Eta model.

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** '''WRF-ARW''' Advanced Research WRF developed primarily at the U.S. [[National Center for Atmospheric Research]] (NCAR)

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*'''WRF''' The [[Weather Research and Forecasting model]] was developed cooperatively by NCEP, NCAR, and the meteorological research community. WRF has several configurations, including:

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**'''WRF-NMM''' The WRF Nonhydrostatic Mesoscale Model is the primary short-term weather forecast model for the U.S., replacing the Eta model.

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**'''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.

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* '''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

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*'''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]]

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* '''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..

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* '''[[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.

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*'''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.

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* '''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)

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* '''[[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.

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* '''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"/>

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* '''GEM-LAM''' Global Environmental Multiscale Limited Area Model, the high resolution 2.5 km (1.6 mi) GEM by the Meteorological Service of Canada (MSC)

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* '''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>

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*'''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" />

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* '''[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.

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*'''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>

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*'''[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:

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* 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。

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==Model output statistics==

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【终稿】

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* '''WRF''' 天气研究与预测模式（the Weather Research and Forecasting model），由NCEP、NCAR以及气象研究社区共同开发。WRF有多种配置，比如：

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** '''WRF-NMM''' WRF非流体静力学中尺度模式（the WRF Nonhydrostatic Mesoscale Model），这是美国主要的短期天气预测模式，用于替代Eta模式

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** '''WRF-ARW''' 主要由NCAR开发的高级研究WRF（Advanced Research WRF）

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* '''NAM''' 北美中尺度模式（North American Mesoscale model），指的是 NCEP 在北美地区运作的任何区域模式。NCEP 于2005年1月开始使用这一称呼系统。2005年1月至2006年5月期间，Eta 模式使用了这一称号。从2006年5月开始，NCEP 开始使用 WRF-NMM 作为业务预报中的北美中尺度模式。

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* '''RAMS''' 区域大气模拟系统（the Regional Atmospheric Modeling System）由科罗拉多州立大学开发，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

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* '''MM5''' 第五代宾夕法尼亚州立大学/NCAR 中尺度模式（the Fifth Generation Penn State/NCAR Mesoscale Model）

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* '''ARPS''' 奥克拉荷马大学开发的高级区域预报系统（the Advanced Region Prediction System）是一个综合性的多尺度非流体静力学的模拟和预报系统，应用范围从区域尺度的天气预报，到龙卷尺度的模拟和预报。用于雷暴预报的高级雷达数据同化是该系统的关键部分。

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* '''HIRLAM''' 高分辨率有限区域模式（High Resolution Limited Area Model），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 模式被称为 HARMONIE，是 Meteo France 和 ALADIN 联盟合作开发的。

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* '''GEM-LAM''' 全球环境多尺度有限区域模式（Global Environmental Multiscale Limited Area Model），由加拿大气象局开发的高分辨率（2.5千米，约1.6英里）的 GEM。

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* '''ALADIN''' 高分辨率有限区域流体静力学和非流体静力学模式（the high-resolution limited-area hydrostatic and non-hydrostatic model），由欧洲和北非的一些国家在 Météo-France 领导下开发和运行

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* '''COSMO''' 模式，以前称为 LM，aLMo 或 LAMI，是在小尺度模式联合会(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流体静力学模式。

+

* '''Meso-NH''' Meso-NH 模式是有限区域非流体静力学模式，自1998年来由法国国家气象研究中心和航空实验室（法国，图卢兹）联合开发，其应用领域包括从中尺度到厘米尺度的天气模拟。

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==Model output statistics 模式输出统计==

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"/>

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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。

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【终稿】由于基于大气动力学方程的预报模式无法完美地决定近地天气状况，人们开发了统计修正的方法来尝试解决这个问题。统计模型基于数值天气模式、地面观测站、特定地点的气候条件产生的三维场。这些统计模型被统称为模式输出统计（MOS），并由美国国家气象局开发为一套完整的天气预报模式。美国空军在1983年根据其动态天气模式开发了自己的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>

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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。

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~~== Applications ==~~

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【终稿】模型输出统计不同于完美的预测图（prog），后者假定数值天气预报指导的输出是完美的。而MOS可以修正因网格分辨率不足以及模型偏差等模式无法解决的局地效应。MOS中的预报参数包括最高和最低温度、未来数小时降雨可能性、预期降水量、降水在自然界中结冰的可能性、雷暴可能性、云量和地面风。

== Applications ==

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= = ~~应用程序~~ = =

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== Applications 应用 ==

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===Climate modeling 气候模拟===

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===Climate modeling===

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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。

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===Limited area modeling===

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【终稿】1956年，诺曼·菲利普斯（Norman Phillips）开发了一个真实描述对流层逐月和逐季节模式的数学模型。这是第一个成功的气候模式。几个小组随后开始开创大气循环模式。20世纪60年代，第一个耦合海洋和大气过程的循环气候模式在美国地球物理流体动力学实验室气候研究中心被开发出来，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分部门。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式(CAM) ，既可以单独运行，也可以作为社区气候系统模型的大气模块部分运行。最新的独立CAM(3.1版本)已于2006年2月1日发布。在1986年，人们开始投入初始化和模拟的土壤、植被类型，以实现更真实的预测。耦合的海洋-大气气候模式，如哈德利气候预测与研究中心的 HadCM3模式（the Hadley Centre for Climate Prediction and Research's HadCM3 model），正被用作气候变化研究的输入。

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===Limited area modeling 有限区域模拟===

[[File:Ernesto2006modelspread.png|thumb|right|Model spread with [[Hurricane Ernesto (2006)]] within the National Hurricane Center limited area models|链接=Special:FilePath/Ernesto2006modelspread.png]]

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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。

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【终稿】空气污染预报依靠大气模式来提供流体流动信息，从而跟踪污染物运动。1970年，美国的一家私营公司开发了区域城市气流模式（the regional Urban Airshed Model，UAM），用于预报空气污染及酸雨的影响。在20世纪70年代年代中后期，美国环境保护局接管了UAM的开发工作，并利用区域空气污染研究的结果对其改进。尽管UAM是为加利福利亚州开发的，但到了20世纪80年代，它在北美、欧洲和亚洲的部分地区投入应用。

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>

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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。

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==See also==

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【终稿】可移动细网格模式（the Movable Fine-Mesh model）在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式。尽管由于不断增强的计算机算力，持续改进的动力学模式指导成为可能，但是直到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能力；直到20世纪90年代才持续地好于统计模型或简单的动力学模型。使用数值预报方法预测热带气旋强调也始终难度较高。直到2009年，动力学控制的方法仍不如统计方法效果好。

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==See also 另见==

* [[Atmospheric reanalysis]]

* [[Climate model]]

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* Upper-atmospheric models

* Static atmospheric model

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【终稿】

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* 大气再分析

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* 气候模式

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* 数值天气预报

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* 高层大气模式

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* 稳定大气模式

= = = = =

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==Further reading==

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==Further reading 进一步阅读==

*{{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 }}

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*

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~~= = 进一步阅读 = = =~~

*

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* Model Visualisation

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~~= =~~ = 外部链接 = =

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= 外部链接 =

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* WRF ~~源代码和绘图软件下载页~~

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*[https://web.archive.org/web/20110921153007/http://www.mmm.ucar.edu/wrf/users/download/get_source2.html WRF 源码和绘图软件下载页]

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* RAMS ~~源代码可在 GNU通用公共许可协议~~

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*[https://web.archive.org/web/20140712133145/http://bridge.atmet.org/users/software.php RAMS 源码（ GNU通用公共许可协议下可用）]

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* ~~mm5源代码下载~~

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*[https://web.archive.org/web/20110928000959/http://www.mmm.ucar.edu/mm5/mm5v3/wherev3.html MM5 源码下载]

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* ~~源代码~~ ARPS

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*[http://www.caps.ou.edu/ARPS ARPS源码]

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* 模型可视化

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*[http://www.ventusky.com/ 模型可视化]

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Categories: Numerical climate and weather models

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【终稿】数值气候与天气模式

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