Assessing forecasting performance of daily reference evapotranspiration using public weather forecast and numerical weather prediction

Abstract

Accurate estimation of reference evapotranspiration (ET0) is important for water resource management and irrigation scheduling design. Limited by data availability of the numerical weather prediction (NWP), the public weather forecasting (PWF) has been widely used to forecast daily ET0 in China. Recently, two Chinese independently developed NWP products have been open to the public, namely, the Global/Regional Assimilation and Prediction System (GRAPES) and the T639 Global Medium-term Numerical Forecast Model (T639L60). In this paper, the forecast of ET0 is evaluated based on the recently released NWP outputs and the PWF using the FAO-56 PM equation. The daily ET0 forecast of the two datasets was compared against the ET0 that was calculated using weather variables observed from 31 automatic weather stations across a wide range of climate zones in China, including the temperate continental zone (TC), temperate monsoon zone (TM), mountain plateau zone (MP) and subtropical monsoon zone (SM). The results showed that the forecast performance of NWP for maximum and minimum air temperature (Tmax, Tmin) was worse due to the large errors in the altitude estimation process. The forecast performance of NWP for net radiation (Rnet) forecasted was better than that of PWF in the MP, TC and TM, while slightly worse in the SM. The VPD from the NWP was more accurate in TC and TM, while showed little difference with that from PWF in MP and SM. For the wind speed (u2), the NWP showed better forecast accuracy than PWF in all climate zones. Furthermore, the better forecast performance of ET0 was obtained by the NWP in the MP and TC. In the TM and SM, the PWF can provide a more accurate ET0 forecast compared to the NWP. Overall, the NWP can serve as a promising data source to generate acceptable ET0 forecasts across China. The high spatial resolution of the NWP provides the ability to capture the high resolution of the spatial variability in ET0, especially in the MP and TC where weather stations are sparse.