"Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions."
The study of mathematical models used to predict past, present, and future weather conditions, including data assimilation, forecasting techniques, and model evaluation.
Atmospheric physics: The study of the physical properties and behavior of the Earth's atmosphere, including thermodynamics, fluid mechanics, and radiative transfer.
Meteorological observations: The collection and analysis of data on atmospheric variables, such as temperature, humidity, wind speed, and direction, precipitation, and atmospheric pressure.
Numerical Weather Prediction (NWP): The use of computers and mathematical models to simulate and forecast weather patterns based on atmospheric observations and physical principles.
Atmospheric dynamics: The study of the motion and behavior of the atmosphere, including large-scale circulations, atmospheric waves, turbulence, and storms.
Atmospheric chemistry: The study of the chemical composition and reactions of the Earth's atmosphere, including the sources and fate of atmospheric pollutants.
Climate modeling: The use of computer models to simulate and predict climate patterns and trends, based on atmospheric observations and physical principles.
Weather forecasting: The process of using meteorological observations and models to predict short-term weather conditions, such as temperature, precipitation, and wind, for a given location.
Weather analysis: The interpretation and synthesis of meteorological data to understand current weather conditions and predict future patterns.
Meteorological instrumentation: The design, construction, and operation of instruments used to measure atmospheric variables, such as weather balloons, radiosondes, and remote sensing devices.
Data analysis and visualization: The use of statistical and graphical methods to analyze and present meteorological data, making it easier to understand and interpret.
Global models: These models simulate the Earth's atmosphere on a global scale, providing a forecast of weather patterns across the entire planet.
Regional models: These models focus on a specific region or area, providing a more detailed forecast of weather patterns and conditions.
Mesoscale models: These models provide even more detailed forecasts for smaller areas, such as cities or small regions.
Numerical weather prediction models: These models use complex mathematical equations to calculate potential weather patterns and make forecasts.
Data assimilation models: These models combine data from multiple sources, such as satellites and weather stations, to create a more accurate forecast.
Ensemble models: These models run multiple simulations of potential weather patterns and provide a range of possible outcomes.
Climate models: These models simulate long-term climate patterns and predict how they might change over time due to factors such as greenhouse gas emissions.
Air quality models: These models predict the concentrations of pollutants in the air, as well as the likelihood of specific air quality events, such as smog.
Ocean models: These models simulate ocean currents and temperature patterns, which can help predict the behavior of hurricanes and other weather events.
Space weather models: These models predict the behavior and effects of solar activity on Earth's atmosphere, including phenomena such as auroras and solar storms.
"It was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results."
"...using current weather observations relayed from radiosondes, 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 improvements made to regional models have allowed significant improvements in tropical cyclone track and air quality forecasts."
"Atmospheric models perform poorly at handling processes that occur in a relatively constricted area, such as wildfires."
"Manipulating the vast datasets and performing the complex calculations necessary to modern numerical weather prediction requires some of the most powerful supercomputers in the world."
"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 chaotic nature of the partial differential equations that describe the atmosphere. It is impossible to solve these equations exactly."
"Present understanding is that this chaotic behavior limits accurate forecasts to about 14 days even with accurate input data and a flawless model."
"The partial differential equations used in the model need to be supplemented with parameterizations for solar radiation, moist processes (clouds and precipitation), 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 forecasts have been used... This approach analyzes multiple forecasts created with an individual forecast model or multiple models."
"This approach analyzes multiple forecasts created with an individual forecast model or multiple models."
"This approach... helps gauge the confidence in the forecast, and to obtain useful results farther into the future than otherwise possible."
"Small errors grow with time (doubling about every five days)."
"The improvements made to regional models have allowed significant improvements in tropical cyclone track and air quality forecasts."
"Using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs."
"Post-processing techniques such as model output statistics (MOS) have been developed to improve the handling of errors in numerical predictions."