Evaluation and bias correction in WRF model forecasting of precipitation and potential evapotranspiration

A reliable forecast of potential evapotranspiration (ET₀) is key to precise irrigation scheduling toward reducing water and agrochemical use while optimizing crop yield. In this study, we examine the benefits of using the Weather Research and Forecasting (WRF) Model for ET₀ and precipitation forecasts with simulations at a 3-km grid spatial resolution and an hourly temporal resolution output over Israel. The simulated parameters needed to calculate ET₀ using the Penman-Monteith (PM) approach, as well as calculated ET₀ and precipitation, were compared to observations from a network of meteorological stations. WRF forecasts of all PM meteorological parameters, except wind speed W_s, were significantly sensitive to seasonality and synoptic conditions, whereas forecasts of W_s consistently showed high bias associated with strong local effects, leading to high bias in the evaluated PM-ET₀. Local W_s bias correction using observations on days preceding the forecast and interpolation of the resulting PM-ET₀ to other locations led to significant improvement in ET₀ forecasts over the investigated area. By using this hybrid forecast approach (WRFBC) that combines WRF numerical simulations with statistical bias corrections, daily ET₀ forecast bias was reduced from an annual mean of 13% with WRF to 3% with WRFBC, while maintaining a high model-observation correlation. WRF was successful in predicting precipitation events on a daily event basis for all four forecast lead days. Considering the benefit of the hybrid approach for forecasting ET₀, the WRF Model was found to be a high-potential tool for improving crop irrigation management.