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

Theodor Bughici, Naftali Lazarovitch, Erick Fredj, Eran Tas

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 Ws, were significantly sensitive to seasonality and synoptic conditions, whereas forecasts of Ws consistently showed high bias associated with strong local effects, leading to high bias in the evaluated PM-ET0. Local Ws 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.

Original languageEnglish
Pages (from-to)965-983
Number of pages19
JournalJournal of Hydrometeorology
Volume20
Issue number5
DOIs
StatePublished - 1 May 2019

Keywords

  • Agriculture
  • Automatic weather stations
  • Bias
  • Evapotranspiration
  • Numerical weather prediction/forecasting
  • Seasonal variability

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