Bias-correcting individual inputs prior to combined calibration leads to more skillful forecasts 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 forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subjective to systematic errors, calibration is necessary for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the potential predictability of ETo may not be fully explored by this approach, which ignores the non-linear interactions of input variables in constructing ETo forecasts. We hypothesize that reducing errors in individual inputs as a precursor to ETo forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies, including (i) calibration directly applied to raw ETo forecasts constructed with raw forecasts of input variables, and (ii) bias-correcting input variables first, and then calibrating the ETo forecasts constructed with bias-corrected input variables. We calibrate ETo forecasts based on weather forecasts of the Australian Community Climate and Earth System Simulator G2 version (ACCESS-G2). Calibrated ETo forecasts with bias-corrected input variables (strategy ii) demonstrate lower bias, higher correlation coefficient, and higher skills than the calibration based on raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could enhance ETo forecast calibration and produce more skillful ETo forecasts. This calibration strategy is expected to enhance future NWP-based ETo forecasting.