Comparison of probabilistic post-processing approaches for improving numerical weather prediction-based daily and weekly reference evapotranspiration forecasts

Abstract

Abstract. Reference evapotranspiration (ET0) forecasts play an important role in agricultural, environmental, and water management. This study evaluated probabilistic post-processing approaches, including the nonhomogeneous Gaussian regression (NGR), affine kernel dressing (AKD), and Bayesian model averaging (BMA) techniques, for improving daily and weekly ET0 forecasting based on single or multiple numerical weather predictions (NWPs) from the THORPEX Interactive Grand Global Ensemble (TIGGE), which includes the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), and the United Kingdom Meteorological Office (UKMO) forecasts. The approaches were examined for the forecasting of summer ET0 at 101 US Regional Climate Reference Network stations distributed all over the contiguous United States (CONUS). We found that the NGR, AKD, and BMA methods greatly improved the skill and reliability of the ET0 forecasts compared with a linear regression bias correction method, due to the considerable adjustments in the spread of ensemble forecasts. The methods were especially effective when applied over the raw NCEP forecasts, followed by the raw UKMO forecasts, because of their low skill compared with that of the raw ECMWF forecasts. The post-processed weekly forecasts had much lower rRMSE values (between 8 % and 11 %) than the persistence-based weekly forecasts (22 %) and the post-processed daily forecasts (between 13 % and 20 %). Compared with the single-model ensemble, ET0 forecasts based on ECMWF multi-model ensemble ET0 forecasts showed higher skill at shorter lead times (1 or 2 d) and over the southern and western regions of the US. The improvement was higher at a daily timescale than at a weekly timescale. The NGR and AKD methods showed the best performance; however, unlike the AKD method, the NGR method can post-process multi-model forecasts and is easier to interpret than the other methods. In summary, this study demonstrated that the three probabilistic approaches generally outperform conventional procedures based on the simple bias correction of single-model forecasts, with the NGR post-processing of the ECMWF and ECMWF–UKMO forecasts providing the most cost-effective ET0 forecasting.