Editing Numerical weather prediction (section)

{{short description|Weather prediction using mathematical models of the atmosphere and oceans}}
{{broader|Atmospheric model}}

[[File:AtmosphericModelSchematic.png|thumb|300px|right|Weather models use systems of [[differential equations]] based on the laws of [[physics]], which are in detail [[Fluid dynamics|fluid motion]], [[thermodynamics]], [[radiative transfer]], and [[chemistry]], and use a coordinate system which divides the planet into a 3D grid. [[Winds]], [[heat transfer]], [[solar radiation]], [[relative humidity]], [[Phase transition|phase changes of water]] and surface [[hydrology]] are calculated within each grid cell, and the interactions with neighboring cells are used to calculate atmospheric properties in the future.|alt=A grid for a numerical weather model is shown. The grid divides the surface of the Earth along meridians and parallels, and simulates the thickness of the atmosphere by stacking grid cells away from the Earth's center. An inset shows the different physical processes analyzed in each grid cell, such as advection, precipitation, solar radiation, and terrestrial radiative cooling.]]

'''Numerical weather prediction''' ('''NWP''') uses [[mathematical model]]s of the atmosphere and oceans to [[weather forecasting|predict the weather]] based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of [[computer simulation]] in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from [[radiosonde]]s, [[weather satellites]] and other observing systems as inputs.

Mathematical models based on the same physical principles can be used to generate either short-term weather forecasts or longer-term climate predictions; the latter are widely applied for understanding and projecting [[climate change]]. The improvements made to regional models have allowed significant improvements in [[Tropical cyclone track forecasting|tropical cyclone track]] and [[air quality]] forecasts; however, atmospheric models perform poorly at handling processes that occur in a relatively constricted area, such as [[wildfire]]s.

Manipulating the vast datasets and performing the complex calculations necessary to modern numerical weather prediction requires some of the most powerful [[supercomputer]]s in the world. Even with the increasing power of supercomputers, the [[forecast skill]] of numerical weather models extends to only about six days. Factors affecting the accuracy of numerical predictions include the density and quality of observations used as input to the forecasts, along with deficiencies in the numerical models themselves. Post-processing techniques such as [[model output statistics]] (MOS) have been developed to improve the handling of errors in numerical predictions.

A more fundamental problem lies in the [[Chaos theory|chaotic]] nature of the [[partial differential equation]]s that describe the atmosphere. It is impossible to solve these equations exactly, and small errors grow with time (doubling about every five days). Present understanding is that this chaotic behavior limits accurate forecasts to about 14 days even with accurate input data and a flawless model. In addition, the partial differential equations used in the model need to be supplemented with [[Parametrization (climate)|parameterizations]] for [[solar radiation]], [[moist processes]] (clouds and [[precipitation (meteorology)|precipitation]]), [[heat transfer|heat exchange]], soil, vegetation, surface water, and the effects of terrain. In an effort to quantify the large amount of inherent uncertainty remaining in numerical predictions, [[ensemble forecasting|ensemble forecasts]] have been used since the 1990s to help gauge the confidence in the forecast, and to obtain useful results farther into the future than otherwise possible. This approach analyzes multiple forecasts created with an individual forecast model or multiple models.