Satellite-based high-resolution mapping of rainfall over southern Africa

Hanna Meyer,Johannes Drönner,Thomas Nauss

doi:10.5194/amt-10-2009-2017

Abstract

Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

Highlights

This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI)
The dynamics of rainfall play an important role in southern Africa, especially in the arid and semi-arid areas where farming is the main source of income and the quality of the pastures mainly depends on water availability (Fynn and O’Connor, 2000)
The rainfall retrieval technique developed in this study provides hourly rainfall estimates at high spatial resolution based on the spectral properties of MSG SEVIRI data and neural networks

Summary

Introduction

The dynamics of rainfall play an important role in southern Africa, especially in the arid and semi-arid areas where farming is the main source of income and the quality of the pastures mainly depends on water availability (Fynn and O’Connor, 2000). Accurate nowcasting of rainfall at high temporal and spatial resolutions is of interest for the farmers in southern Africa and would help them to assess the carrying capacity of their land. It is of further importance as a baseline product for a variety of environmental research studies as rainfall is a key variable for many ecological and hydrological processes. Rain gauges are still considered the most accurate way to measure rainfall. Southern Africa features a network of rain gauges operated by the weather services of the individual countries as well as by a variety of research projects.

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