Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America

Abstract

In this work, the quality of several probabilistic quantitative precipitation forecasts (PQPFs) is examined. The analysis is focused over South America during a 2-month period in the warm season. Several ways of generating and calibrating the PQPFs have been tested, using different ensemble systems and single-model runs. Two alternative calibration techniques (static and dynamic) have been tested. To take into account different precipitation regimes, PQPF performance has been evaluated over two regions: the northern part of South America, characterized by a tropical regime, and the southern part, where synoptic-scale forcing is stronger. The results support the adoption of such area separation, since differences in the precipitation regimes produce significant differences in PQPF performance. The more skillful PQPFs are the ones obtained after calibration. PQPFs derived from the ensemble mean also show higher skill and better reliability than those derived from the single ensemble members. The performance of the PQPFs derived from both ensemble systems is similar over the southern part of the region; however, over the northern part the superensemble approach seems to achieve better results in both reliability and skill. Finally, the impact of using Climate Prediction Center morphing technique (CMORPH) estimates to calibrate the precipitation forecast has been explored since the more extensive coverage of this dataset would allow its use over areas where the rain gauge coverage is insufficient. Results suggest that systematic biases present in the CMORPH estimates produce only a slight degradation of the resulting PQPF.