Regnmodeller

Regnmodeller, or rain models, are mathematical and computational tools used to simulate and forecast rainfall. They span a range from physical, physically based models that simulate the formation and evolution of raindrop events in the atmosphere to statistical and empirical models that describe rainfall using historical data. They are employed in meteorology, hydrology, water resources planning, agriculture, and climate research. Depending on the purpose, regnmodeller can produce deterministic forecasts or probabilistic projections that quantify uncertainty.

Data and inputs include atmospheric state from numerical weather prediction (NWP) models, radar-derived rainfall estimates, rain gauges, and satellite observations. Methods range from physically based rainfall microphysical schemes and distributed hydrological models that route rainfall across catchments to stochastic rainfall models such as Poisson-cluster processes or gamma-distributed rainfall intensities, and time-series or machine learning models (ARIMA, neural nets, random forests) that learn from past data. Models may operate at varying scales, from point rainfall to catchment-wide grids.

Outputs and applications cover short- to medium-range forecasts, design rainfall statistics such as intensity-duration-frequency curves, probabilistic rainfall forecasts and ensemble forecasts, flood forecasting, drought monitoring, rainfall-runoff simulations, and climate impact studies.

Evaluation and challenges include verification against observations using metrics like bias, RMSE, or probabilistic scores such as CRPS; calibration and uncertainty quantification; representation of extreme events and nonstationarity due to climate change; and data scarcity in some regions.