Meteorological Phenomena and Simulations

Numerical weather prediction turns physical equations governing the atmosphere into concrete forecasts by combining observations with computational models of processes ranging from turbulence in the lowest kilometer of air to the transfer of solar and infrared radiation through cloud layers. Getting those forecasts right depends on solving a fundamental tension: observations of the atmosphere are sparse and error-prone, while the models themselves carry structural uncertainties in how they represent phenomena too small to resolve directly, such as convective storms or ice crystal formation. Techniques like ensemble Kalman filtering address this by blending real-time measurements with model states in a statistically principled way, producing not a single forecast but a probability distribution over possible atmospheric futures. Active research is pushing toward higher-resolution simulations that explicitly resolve convection rather than approximating it, better coupling between atmospheric, land-surface, and hydrological components, and methods for rigorously quantifying forecast uncertainty at timescales from hours to weeks.

Works

131,369

Total citations

1,631,589

Keywords

Ensemble Kalman FilterData AssimilationConvective ParameterizationMesoscale ModelingProbabilistic ForecastingMicrophysics Scheme