Bias-correcting input variables enhances forecasting of reference crop evapotranspiration

Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when of these variables from numerical weather prediction (NWP) models are available. As raw often subject to systematic errors, statistical calibration needed for improving forecast quality. The most straightforward widely used approach directly calibrate constructed the However, predictable signal in may not fully implemented by this approach, which does deal error propagation forecasts. We hypothesize that correcting errors precursor will lead more skillful To test hypothesis, we evaluate two strategies construct (strategy i) or bias-corrected ii) across Australia. Calibrated based on demonstrate lower biases, higher correlation coefficients, skills than i). This investigation indicates could effectively reduce enhance calibration. anticipate future NWP-based forecasting benefit adopting strategy developed study produce