Deterministic and probabilistic evaluation of raw and post processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes

Abstract

Precipitation is an important and difficult climate variable to predict. Skillful sub-seasonal precipitation forecast can provide useful information for agriculture and water resources management communities. Nevertheless, sub-seasonal forecasts have been given less attention compared with forecasts of shorter/longer time horizons. Recently, the S2S database has made sub-seasonal to seasonal forecasts/reforecasts from 11 operational centers available to researchers. In this work, reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) spanning over a 20-year period were evaluated. Raw and post-processed precipitation forecasts were put against observed precipitation series of a number of synoptic stations with different precipitation regimes for all months of the year. By comparison, precipitation forecasts were more skillful in wet months. Raw forecasts in December and February were better than that in other months, while the weakest results were detected in August. There was no significant relationship between precipitation regime and prediction skill. Furthermore, quantile mapping (QM), Bayesian model averaging (BMA), and QM_BMA combination were adopted for post-processing. BMA outperformed QM and QM_BMA through improving the correlation coefficient between observations and average of ensemble forecasts; however, BMA performed weaker in other evaluation criteria, in particular in humid regions. It is concluded that the application of post-processing techniques greatly improved the results of ensemble precipitation forecasts. However, in a number of stations/months, the forecast results were not acceptable even after post-processing.