大气模式

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一次850mbar处地势高度和温度的96小时预报

大气模式 atmospheric model是围绕控制大气运动的一整套原始的动力学方程所建立的数学模型。它可以通过湍流扩散、辐射、湿过程（云和降水）、热交换、土壤、植被、地表水、地形的动力学效应和对流等的参数化来补充这些方程。大多数大气模式是基于数值方法的，即将运动方程离散化。它们可以预测微尺度的现象，例如龙卷风、边界层的涡旋、流经建筑物上方的亚微尺度湍流，以及天气气流、全球气流。模式的水平区域全球性的，覆盖整个地球，也可以是区域性的（有限区域的），只覆盖部分地球。模式运行的不同类型包括热致的、正压的、流体静力学的和非流体静力学的。部分类型的模式对大气进行了一些假设，从而加长了时间步长并提高计算速度。


预报是使用大气物理和动力学方程计算得来的。这些方程是非线性的，无法获得准确解。因此只能使用数值方法获得近似解。不同的模式使用不同的求解方法。全球模式通常在水平维度上采用谱方法，而在垂直维度采用有限差分法；而区域模式通常在三个维度均使用有限差分法。对于特定的位置，模式的输出统计使用气候信息、数值天气预测结果以及当前地表天气观测数据来建立统计关系，以解释模式偏差和分辨率问题。


类型

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 模板:Convert and 模板:Convert geopotential height surfaces and the average thermal wind between them.


【终稿】由热致模式作出的主要假设是，热风的大小可以改变，但方向不随高度变化，因此大气的斜压性可以用位势高度面和它们之间的平均热风来模拟。[1][2]

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).[3] 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.[4] 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 模板:Convert 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 模板:Convert pressure surface.[5]

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 5.5 kilometres (3.4 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 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。

【终稿】正压模式假定大气接近正压，这意味着地转风的方向和速度与高度无关，即地转风无垂直切变。这也意味着温度的厚度等值线平行于上层高度等值线。在这种类型的大气中，高压区和低压区是冷暖温度异常的中心。暖心高压（如亚热带脊线和百慕大-亚速尔高压）和冷心低压具有随高度增强的风力，而冷心高压（北极浅层高压）和暖心低压（如热带气旋）则相反。正压模式试图基于大气处于地转平衡的假设（即空气中的罗斯比数小）来解决简化形式的大气动力学问题。如果假设大气无散度，则欧拉方程的旋度简化为正压涡度方程，后者可以在一层大气上求解。由于大气在大约5.5 千米（3.4 英里）处几乎无旋度，正压模式最接近大气在对应海拔处的位势高度时的状态，该海拔与大气压力面有关。

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.[6]

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移动声波的方程，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。

【终稿】流体静力学模式从垂直动量方恒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。流体静力学模式使用压力或sigma 压力作为垂直坐标。压力坐标与地形相交，而sigma 坐标随地形等高线变化。只要水平网格分辨率不小，该模式的流体静力学假设便是合理的。使用整个垂直动量方程的模式称为非流体静力学模式，它既可以滞弹性求解，这意味着它求解了不可压缩空气的完整的连续性方程；也可以弹性求解，这意味着它求解了完全可压缩空气的完整的连续性方程。非静力学假设使用海拔高度或sigma 高度作为其垂直坐标。海拔高度可以和地形相交，而sigma 高度坐标随地面等高线改变。

History 历史

The ENIAC main control panel at the Moore School of Electrical Engineering
Main article: 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.[7][8] 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,[5][9] and more powerful computers later increased the size of initial datasets and included more complicated versions of the equations of motion.[10] 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.[7][11] The development of global forecasting models led to the first climate models.[12][13] 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.[14][15]

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。

【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).[16][17] 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.[18][19] 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.[20][21][22]

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。

【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

模板:Transcluded section {{#section-h:Numerical weather prediction|Initialization}}

【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.

【终稿】大气是流动的。因此，数值天气预报的思想是对给定时间的流体状态进行采样，并使用流体动力学和热力学方程来估计未来某个时间的流体状态。将观测数据输入模型以生成初始条件的过程称为初始化。在陆地上，全球分辨率低至1公里（0.6英里）的地形图用于帮助模拟崎岖地形区域内的大气环流，以便更好地描述影响入射太阳辐射的下坡风、过山波和相关云量等特征。基于国家的气象服务的主要输入是来自气象气球上的设备（称为无线电探空仪）的观测，这些设备测量各种大气参数并将其传输到固定接收器，以及来自气象卫星的观测。世界气象组织（World Meteorological Organization）在全球范围内对仪器、观测实践和观测时间进行标准化。观测站在METAR报告中每小时报告一次，或者在SYNOP报告中每六小时报告一次。这些观测数据的间隔不规则，因此通过数据同化和客观分析方法进行处理，以实现质量控制并在模型数学算法可用的位置获取数值，使得最后在模式中被用作预测的起点。

【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.

【终稿】有许多方法可以收集数值模型的观测数据。这些站点通过从对流层上升到平流层的气象气球来发射无线电探空仪。在传统数据源不可用的情况下，则可以使用气象卫星提供的信息。商务部提供飞机航线上的飞行员报告和航运航线上的船舶报告。研究项目使用侦察机在关注的天气系统内和周围飞行，如热带气旋。在寒冷季节，侦察机也会飞越公海，进入一些对预报制导造成重大不确定性、预计在未来三到七天内对下游大陆产生重大影响的系统中。1971年，海冰开始在预报模型中得到初始化。1972年，由于海洋表面温度在调节太平洋高纬度地区天气方面的作用，将其纳入模型初始化的工作也开始进行。

Computation 计算

An example of 500 mbar geopotential height prediction from a numerical weather prediction model.

文件:Supercomputing the Climate.ogv


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。

【终稿】超级计算机能够运行高度复杂的模型，从而帮助科学家更好地理解地球的气候。

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.[23] 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.[24] Time steps for global models are on the order of tens of minutes,[25] while time steps for regional models are between one and four minutes.[26] The global models are run at varying times into the future. The UKMET Unified model is run six days into the future,[27] the European Centre for Medium-Range Weather Forecasts model is run out to 10 days into the future,[28] while the Global Forecast System model run by the Environmental Modeling Center is run 16 days into the future.[29]

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。

【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 partial differential equations which are impossible to solve exactly through analytical methods,[30] with the exception of a few idealized cases.[31] 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.[30] The visual output produced by a model solution is known as a prognostic chart, or prog.[32]

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。

【终稿】 由于使用的方程组是非线性的偏微分方程组，除了少数理想情况外无法用解析方法得到准确解，因此使用数值方法来获得近似解。不同的模式使用不同的求解方法：一些全球模式在水平维度使用谱方法求解，在垂直维度使用有限差分方法求解；而另一些全球模式以及区域模式则在三个维度都使用有限差分方法求解。模式的结果可视化通常称为预测图，或简称为“prog”。

Parameterization 参数化

Main article: Parametrization (climate)

Weather and climate model gridboxes have sides of between 模板:Convert and 模板:Convert. A typical cumulus cloud has a scale of less than 模板:Convert, 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 模板:Convert and 模板:Convert can explicitly represent convective clouds, although they still need to parameterize cloud microphysics.[33] 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,[34] 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.

【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。

【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.[35] 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.[36] 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.[37]

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。

【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。

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.[38]

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。

【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]\displaystyle{ z }[/math]) as the vertical coordinate. Later models substituted the geometric [math]\displaystyle{ z }[/math] coordinate with a pressure coordinate system, in which the geopotential heights of constant-pressure surfaces become dependent variables, greatly simplifying the primitive equations.[39] This follows since pressure decreases with height through the Earth's atmosphere.[40] The first model used for operational forecasts, the single-layer barotropic model, used a single pressure coordinate at the 模板:Convert level,[5] 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.[41]

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。

【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

Global versions

全球版本

【终稿】全球模式版本

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:

【终稿】一些比较著名的全球数值模式有：

  • 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

【终稿】

  • GFS 全球预测系统（Global Forecast System，前身为AVN）——由NOAA开发
  • NOGAPS ——由美国海军开发，用于和GFS比对
  • GEM 全球环境多尺度模式（Global Environmental Multiscale Model）——由加拿大气象局（MSC）开发
  • 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
  • UM 统一模式（Unified Model），由英国气象办公室（the UK Met Office）
  • ICON 由德国天气局、DWD以及马普所（MPI）气象部门（汉堡）联合开发
  • ARPEGE 由法国天气局开发（the French Weather Service, Météo-France）
  • IGCM 中间大气环流模式（Intermediate General Circulation Model）

Regional versions

Regional versions

区域版本

【终稿】区域模式版本

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:

【终稿】一些比较著名的区域数值模式有：

  • 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 2.5 km (1.6 mi) 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[27]
  • 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).[42]
  • Meso-NH The Meso-NH Model[43] 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.[44] 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。


【终稿】

  • 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：
    • 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
    • WRF-ARW 主要由NCAR开发的高级研究WRF（Advanced Research WRF）
  • 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。
  • 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
  • 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）
  • 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。
  • 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。
  • 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。
  • 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
  • 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。

Model output statistics 模式输出统计

Main article: 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),[45] and were developed by the National Weather Service for their suite of weather forecasting models.[16] The United States Air Force developed its own set of MOS based upon their dynamical weather model by 1983.[17]


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。

【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.[46] 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.[47]

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。

【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

Applications 应用

Climate modeling 气候模拟

Main articles: Climate model and 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.[12][13] Several groups then began working to create general circulation models.[48] 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.[49] 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.[50][51][52] In 1986, efforts began to initialize and model soil and vegetation types, resulting in more realistic forecasts.[53] 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.[48]

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。

【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。

Limited area modeling 有限区域模拟

Model spread with Hurricane Ernesto (2006) within the National Hurricane Center limited area models

Air pollution forecasts depend on atmospheric models to provide fluid flow information for tracking the movement of pollutants.[54] 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.[15]

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。

【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.[14] 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.[55] Predicting the intensity of tropical cyclones using NWP has also been challenging. As of 2009, dynamical guidance remained less skillful than statistical methods.[56]

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。

【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。

See also 另见

  • Atmospheric reanalysis
  • Climate model
  • Numerical weather prediction
  • Upper-atmospheric models
  • Static atmospheric model


【终稿】

  • 大气再分析
  • 气候模式
  • 数值天气预报
  • 高层大气模式
  • 稳定大气模式

= = =

  • 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

  1. ↑ Gates, W. Lawrence (August 1955). Results Of Numerical Forecasting With The Barotropic And Thermotropic Atmospheric Models. Hanscom Air Force Base: Air Force Cambridge Research Laboratories. http://handle.dtic.mil/100.2/AD101943. 
  2. ↑ Thompson, P. D.; W. Lawrence Gates (April 1956). "A Test of Numerical Prediction Methods Based on the Barotropic and Two-Parameter Baroclinic Models". Journal of Meteorology. 13 (2): 127–141. Bibcode:1956JAtS...13..127T. doi:10.1175/1520-0469(1956)013<0127:ATONPM>2.0.CO;2. ISSN 1520-0469.
  3. ↑ Wallace, John M.; Peter V. Hobbs (1977). Atmospheric Science: An Introductory Survey. Academic Press, Inc.. pp. 384–385. ISBN 978-0-12-732950-5. 
  4. ↑ Marshall, John; Plumb, R. Alan (2008). "Balanced flow". Atmosphere, ocean, and climate dynamics : an introductory text. Amsterdam: Elsevier Academic Press. pp. 109–12. ISBN 978-0-12-558691-7. 
  5. ↑ 5.0 5.1 5.2 Charney, Jule; Fjörtoft, Ragnar; von Neumann, John (November 1950). "Numerical Integration of the Barotropic Vorticity Equation". Tellus. 2 (4): 237–254. Bibcode:1950TellA...2..237C. doi:10.3402/tellusa.v2i4.8607.
  6. ↑ Jacobson, Mark Zachary (2005). Fundamentals of atmospheric modeling. Cambridge University Press. pp. 138–143. ISBN 978-0-521-83970-9. https://books.google.com/books?id=41qztAEACAAJ. 
  7. ↑ 7.0 7.1 Lynch, Peter (2008-03-20). "The origins of computer weather prediction and climate modeling" (PDF). Journal of Computational Physics. 227 (7): 3431–44. Bibcode:2008JCoPh.227.3431L. doi:10.1016/j.jcp.2007.02.034. Archived from the original (PDF) on 2010-07-08. Retrieved 2010-12-23.
  8. ↑ Lynch, Peter (2006). "Weather Prediction by Numerical Process". The Emergence of Numerical Weather Prediction. Cambridge University Press. pp. 1–27. ISBN 978-0-521-85729-1. 
  9. ↑ Cox, John D. (2002). Storm Watchers. John Wiley & Sons, Inc.. p. 208. ISBN 978-0-471-38108-2. https://archive.org/details/stormwatcherstur00cox_df1/page/208. 
  10. ↑ Harper, Kristine; Uccellini, Louis W.; Kalnay, Eugenia; Carey, Kenneth; Morone, Lauren (May 2007). "2007: 50th Anniversary of Operational Numerical Weather Prediction". Bulletin of the American Meteorological Society. 88 (5): 639–650. Bibcode:2007BAMS...88..639H. doi:10.1175/BAMS-88-5-639.
  11. ↑ Leslie, L.M.; Dietachmeyer, G.S. (December 1992). "Real-time limited area numerical weather prediction in Australia: a historical perspective" (PDF). Australian Meteorological Magazine. Bureau of Meteorology. 41 (SP): 61–77. Retrieved 2011-01-03.
  12. ↑ 12.0 12.1 Norman A. Phillips (April 1956). "The general circulation of the atmosphere: a numerical experiment" (PDF). Quarterly Journal of the Royal Meteorological Society. 82 (352): 123–154. Bibcode:1956QJRMS..82..123P. doi:10.1002/qj.49708235202.
  13. ↑ 13.0 13.1 John D. Cox (2002). Storm Watchers. John Wiley & Sons, Inc.. p. 210. ISBN 978-0-471-38108-2. https://archive.org/details/stormwatcherstur00cox_df1/page/210. 
  14. ↑ 14.0 14.1 Shuman, Frederick G. (September 1989). "History of Numerical Weather Prediction at the National Meteorological Center". Weather and Forecasting. 4 (3): 286–296. Bibcode:1989WtFor...4..286S. doi:10.1175/1520-0434(1989)004<0286:HONWPA>2.0.CO;2. ISSN 1520-0434.
  15. ↑ 15.0 15.1 Steyn, D. G. (1991). Air pollution modeling and its application VIII, Volume 8. Birkhäuser. pp. 241–242. ISBN 978-0-306-43828-8. 
  16. ↑ 16.0 16.1 Harry Hughes (1976). Model output statistics forecast guidance. United States Air Force Environmental Technical Applications Center. pp. 1–16. 
  17. ↑ 17.0 17.1 L. Best, D. L.; S. P. Pryor (1983). Air Weather Service Model Output Statistics Systems. Air Force Global Weather Central. pp. 1–90. 
  18. ↑ Cox, John D. (2002). Storm Watchers. John Wiley & Sons, Inc.. pp. 222–224. ISBN 978-0-471-38108-2. https://archive.org/details/stormwatcherstur00cox_df1/page/222. 
  19. ↑ 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.
  20. ↑ Toth, Zoltan; Kalnay, Eugenia (December 1997). "Ensemble Forecasting at NCEP and the Breeding Method". Monthly Weather Review. 125 (12): 3297–3319. Bibcode:1997MWRv..125.3297T. CiteSeerX 10.1.1.324.3941. doi:10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2. ISSN 1520-0493.
  21. ↑ "The Ensemble Prediction System (EPS)". ECMWF. Archived from the original on 25 January 2011. Retrieved 2011-01-05.
  22. ↑ Molteni, F.; Buizza, R.; Palmer, T.N.; Petroliagis, T. (January 1996). "The ECMWF Ensemble Prediction System: Methodology and validation". Quarterly Journal of the Royal Meteorological Society. 122 (529): 73–119. Bibcode:1996QJRMS.122...73M. doi:10.1002/qj.49712252905.
  23. ↑ Pielke, Roger A. (2002). Mesoscale Meteorological Modeling. Academic Press. pp. 48–49. ISBN 978-0-12-554766-6. 
  24. ↑ Pielke, Roger A. (2002). Mesoscale Meteorological Modeling. Academic Press. pp. 285–287. ISBN 978-0-12-554766-6. 
  25. ↑ Sunderam, V. S.; G. Dick van Albada; Peter M. A. Sloot; J. J. Dongarra (2005). Computational Science – ICCS 2005: 5th International Conference, Atlanta, GA, USA, May 22–25, 2005, Proceedings, Part 1. Springer. p. 132. ISBN 978-3-540-26032-5. https://books.google.com/books?id=JZikIbXzipwC&pg=PA131. 
  26. ↑ Zwieflhofer, Walter; Norbert Kreitz; European Centre for Medium Range Weather Forecasts (2001). Developments in teracomputing: proceedings of the ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology. World Scientific. p. 276. ISBN 978-981-02-4761-4. https://books.google.com/books?id=UV6PnF2z5_wC&pg=PA276. 
  27. ↑ 27.0 27.1 27.2 Chan, Johnny C. L.; Jeffrey D. Kepert (2010). Global Perspectives on Tropical Cyclones: From Science to Mitigation. World Scientific. pp. 295–301. ISBN 978-981-4293-47-1. https://books.google.com/books?id=6gFiunmKWWAC&pg=PA297. 
  28. ↑ Holton, James R. (2004). An introduction to dynamic meteorology, Volume 1. Academic Press. p. 480. ISBN 978-0-12-354015-7. https://books.google.com/books?id=fhW5oDv3EPsC&pg=PA474. 
  29. ↑ Brown, Molly E. (2008). Famine early warning systems and remote sensing data. Springer. p. 121. ISBN 978-3-540-75367-4. https://books.google.com/books?id=mTZvR3R6YdkC&pg=PA121. 
  30. ↑ 30.0 30.1 Strikwerda, John C. (2004). Finite difference schemes and partial differential equations. SIAM. pp. 165–170. ISBN 978-0-89871-567-5. https://books.google.com/books?id=SH8R_flZBGIC&pg=PA165. 
  31. ↑ Pielke, Roger A. (2002). Mesoscale Meteorological Modeling. Academic Press. pp. 65. ISBN 978-0-12-554766-6. 
  32. ↑ Ahrens, C. Donald (2008). Essentials of meteorology: an invitation to the atmosphere. Cengage Learning. p. 244. ISBN 978-0-495-11558-8. https://books.google.com/books?id=2Yn29IFukbgC&pg=PA244. 
  33. ↑ Narita, Masami & Shiro Ohmori (2007-08-06). "3.7 Improving Precipitation Forecasts by the Operational Nonhydrostatic Mesoscale Model with the Kain-Fritsch Convective Parameterization and Cloud Microphysics" (PDF). 12th Conference on Mesoscale Processes. American Meteorological Society. Retrieved 2011-02-15.
  34. ↑ Frierson, Dargan (2000-09-14). "The Diagnostic Cloud Parameterization Scheme" (PDF). University of Washington. pp. 4–5. Archived from the original (PDF) on 1 April 2011. Retrieved 2011-02-15.
  35. ↑ Stensrud, David J. (2007). Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge University Press. p. 6. ISBN 978-0-521-86540-1. https://books.google.com/books?id=lMXSpRwKNO8C&pg=PA56. 
  36. ↑ Melʹnikova, Irina N.; Alexander V. Vasilyev (2005). Short-wave solar radiation in the earth's atmosphere: calculation, oberservation, interpretation. Springer. pp. 226–228. ISBN 978-3-540-21452-6. https://books.google.com/books?id=vdg5BgBmMkQC&pg=PA226. 
  37. ↑ Stensrud, David J. (2007). Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge University Press. pp. 12–14. ISBN 978-0-521-86540-1. https://books.google.com/books?id=lMXSpRwKNO8C&pg=PA56. 
  38. ↑ Warner, Thomas Tomkins (2010). Numerical Weather and Climate Prediction. Cambridge University Press. p. 259. ISBN 978-0-521-51389-0. https://books.google.com/books?id=6RQ3dnjE8lgC&pg=PA261. 
  39. ↑ Lynch, Peter (2006). "The Fundamental Equations". The Emergence of Numerical Weather Prediction. Cambridge University Press. pp. 45–46. ISBN 978-0-521-85729-1. 
  40. ↑ Ahrens, C. Donald (2008). Essentials of meteorology: an invitation to the atmosphere. Cengage Learning. p. 10. ISBN 978-0-495-11558-8. https://books.google.com/books?id=2Yn29IFukbgC&pg=PA244. 
  41. ↑ Janjic, Zavisa; Gall, Robert; Pyle, Matthew E. (February 2010). "Scientific Documentation for the NMM Solver" (PDF). National Center for Atmospheric Research. pp. 12–13. Archived from the original (PDF) on 2011-08-23. Retrieved 2011-01-03.
  42. ↑ Consortium on Small Scale Modelling. Consortium for Small-scale Modeling. Retrieved on 2008-01-13.
  43. ↑ 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.
  44. ↑ 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.
  45. ↑ Baum, Marsha L. (2007). When nature strikes: weather disasters and the law. Greenwood Publishing Group. p. 189. ISBN 978-0-275-22129-4. https://books.google.com/books?id=blEMoIKX_0IC&pg=PA188. 
  46. ↑ Gultepe, Ismail (2007). Fog and boundary layer clouds: fog visibility and forecasting. Springer. p. 1144. ISBN 978-3-7643-8418-0. https://books.google.com/books?id=QwzHZ-wV-BAC&pg=PA1144. 
  47. ↑ Barry, Roger Graham; Richard J. Chorley (2003). Atmosphere, weather, and climate. Psychology Press. p. 172. ISBN 978-0-415-27171-4. https://books.google.com/books?id=Xs9LiGpNX-AC&pg=PA171. 
  48. ↑ 48.0 48.1 Peter Lynch (2006). "The ENIAC Integrations". The Emergence of Numerical Weather Prediction: Richardson's Dream. Cambridge University Press. p. 208. ISBN 978-0-521-85729-1. https://books.google.com/books?id=EV5bZqOO7kkC&pg=PA208. 
  49. ↑ National Oceanic and Atmospheric Administration (22 May 2008). "The First Climate Model". Retrieved 8 January 2011.
  50. ↑ "CAM 3.1 Download". www.cesm.ucar.edu. Retrieved 2019-06-25.
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  52. ↑ "CAM3.0 COMMUNITY ATMOSPHERE MODEL". University Corporation for Atmospheric Research. Retrieved 6 February 2018.
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  56. ↑ Edward N. Rappaport; James L. Franklin; Lixion A. Avila; Stephen R. Baig; John L. Beven II; Eric S. Blake; Christopher A. Burr; Jiann-Gwo Jiing; Christopher A. Juckins; Richard D. Knabb; Christopher W. Landsea; Michelle Mainelli; Max Mayfield; Colin J. McAdie; Richard J. Pasch; Christopher Sisko; Stacy R. Stewart; Ahsha N. Tribble (April 2009). "Advances and Challenges at the National Hurricane Center". Weather and Forecasting. 24 (2): 395–419. Bibcode:2009WtFor..24..395R. CiteSeerX 10.1.1.207.4667. doi:10.1175/2008WAF2222128.1.

Further reading 进一步阅读

  • Roulstone, Ian; Norbury, John (2013). Invisible in the Storm: the role of mathematics in understanding weather. Princeton: Princeton University Press. ISBN 978-0-691-15272-1. 

External links

  • 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

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