Abstract
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 subject to systematic errors, statistical calibration is needed 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 predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts.
Highlights
As a variable measuring the evaporative demand of the atmosphere, reference crop evapotranspiration (ETo) has been widely used to estimate potential water loss from the land surface to the atmosphere (Hopson and Webster, 2009; Liu et al, 2019; Renard et al, 2010)
We use the ETo data derived from the Australian Water Availability Project (AWAP)’s gridded data of temperature, vapor pressure, and solar radiation (Jones et al, 2007, 2014), as well as wind speed data developed by Mcvicar et al (2008), as observations for ETo forecast calibration
The α index was only slightly different between calibrations 3 and 4 (Fig. S18). This additional comparison further confirms the general applicability of strategy ii for enhancing numerical weather prediction (NWP)-based ETo forecasting
Summary
As a variable measuring the evaporative demand of the atmosphere, reference crop evapotranspiration (ETo) has been widely used to estimate potential water loss from the land surface to the atmosphere (Hopson and Webster, 2009; Liu et al, 2019; Renard et al, 2010). ETo is affected jointly by temperature, vapor pressure, solar radiation, and wind speed (Bachour et al, 2016; Luo et al, 2014). Prediction models using these weather variables as inputs allow for representations of atmospheric dynamics and often produce reasonable ETo forecasts (Torres et al, 2011). Vapor pressure, solar radiation, and wind speed from numerical weather prediction (NWP) models/general circulation models (GCMs) could be translated into ETo forecasts using the Food and Agriculture Organization (FAO) ETo equation (Allen et al, 1998; Cai et al, 2007)
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