TITLE:
Bayesian Processor of Output for Probabilistic Quantitative Precipitation Forecast over Central and West Africa
AUTHORS:
Romeo S. Tanessong, Derbetini A. Vondou, P. Moudi Igri, F. Mkankam Kamga
KEYWORDS:
Probabilistic Quantitative Precipitation Forecast, BPO, WRF, Weibull Distribution
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.7 No.3,
July
4,
2017
ABSTRACT: The main goal of this work is a feasibility study for the Bayesian Processor of Output (BPO) method applied to tropical convective precipitation regimes over Central and West Africa. The study uses outputs from the Weather Research and Forecasting (WRF) model to develop and test the BPO technique. The model ran from June 01 to September 30 of 2010 and 2011. The BPO method is applied in each grid point and then in each climatic zone. Prior (climatic) distribution function is estimated from the Tropical Rainfall Measuring Mission (TRMM) data for the period 2002-2011. Many distribution functions have been tested for the fitting. Weibull distribution is found to be a suitable fitting function as shown by goodness of fit (gof) test in both cases. The rain pattern increases with the value of the probability p. BPO method noticeably improves the distribution of precipitation as shown by the spatial correlation coefficients. It better detects certain observed maxima compared to the raw WRF outputs. Posterior distribution (forecasting) functions allow for a simulated rainfall amount, to deduce the probability that observed rainfall falls above a given threshold. The probability of observing rainfall above a given threshold increases with simulated rainfall amounts.