Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data

Abstract

Based on the ensemble precipitation forecast data in the summers of 2014–2018 from the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), a comparative study of two multi-model ensemble methods, the Bayesian model average (BMA) and the logistic regression (LR), was conducted. Meanwhile, forecasts of heavy precipitation from the two models over the Wujiang River Basin in China for the summer of 2018 were compared to verify their performances. The training period sensitivity test results show that a training period of 2 years was the best for BMA probability forecast model. Compared with the BMA method, the LR model required more statistical samples and its optimal length of the training period was 5 years. According to the Brier score (BS), for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA outperformed the LR and the raw ensemble prediction system forecasts (RAW) except for forecasts with a lead time of 1 day. Furthermore, for heavy rainfall events exceeding 25 and 50 mm, the RAW and the BMA performed much the same in terms of prediction. The reliability diagram of the two multi-model ensembles (i.e., BMA and LR) was more reliable than the RAW for heavy and moderate rainfall forecasts, and the BMA model had the best performance. The BMA probabilistic forecast can produce a highly concentrated probability density function (PDF) curve and can also provide deterministic forecasts through analyzing percentile forecast results. With regard to the heavy rainfall forecast in mountainous areas, it is recommended to refer to the forecast with a larger percentile between the 75th and 90th percentiles. Nevertheless, extreme events with low probability forecasts may occur and cannot be ignored.

A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation

“…It combines the forecast distributions of different models and builds a weighted predictive distribution from them. Many empirical studies including those in [8,9,22,[41][42][43] have shown that various BMA approaches outperform other competitors, including a single best model and a simple averaging in prediction performance.…”

Evaluation of TIGGE Precipitation Forecast and Its Applicability in Streamflow Predictions over a Mountain River Basin, China

“…Although there are lots of studies concentrating on precipitation forecasts [26,27] and streamflow predictions [10,28] for TIGGE and the effectiveness of postprocessing methods [5,29,30], few studies explore both the performances of TIGGE ensemble precipitation forecasts and their applicability in streamflow predictions over mountain river basins. Qi, Zhi [27] investigated the performances of the raw and postprocessed ensemble forecasts of heavy precipitation in mountainous areas, while they did not investigate their performances in ensemble streamflow prediction. This study aims to evaluate raw and postprocessed EPFs and assess the degree of improvement with different postprocessing methods to verify the reliabilities of EPF and ESP in a western mountain basin of China.…”