Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation

Abstract

Accurate rainfall detection and estimation are essential for many research and operational applications. Traditional rainfall detection and estimation techniques have achieved considerable success but with limitations. Thus, in this study, the relationships between the gauge (point measurement) and the microwave links (MWL) rainfall (line measurement), and the MWL to the satellite observations (area-wide measurement) are investigated for (area-wide) rainfall detection and rain rate retrieval. More precisely, we investigate if the combination of MWL with Meteosat Second Generation (MSG) satellite signals could improve rainfall detection and rainfall rate estimates. The investigated procedure includes an initial evaluation of the MWL rainfall estimates using gauge measurements, followed by a joint analysis of the rainfall estimates with the satellite signals by means of a conceptual model in which clouds with high cloud top optical thickness and large particle sizes have high rainfall probabilities and intensities. The analysis produced empirical thresholds that were used to test the capability of the MSG satellite data to detect rainfall on the MWL. The results from Kenya, during the “long rains” of 2013, 2014, and 2018 show convincing performance and reveal the potential of MWL and MSG data for area-wide rainfall detection.

Rain Area Detection in South-Western Kenya by Using Multispectral Satellite Data from Meteosat Second Generation

“…The method used for detecting raining clouds was based on investigating the relationship between the satellite data's spectral characteristics, cloud top properties (Table 1) and rain. The conceptual idea used was that clouds characterised by their top properties, such as high optical thickness and effective radius (consisting of either ice or water hydrometeors), have high rainfall probability and intensities than those with low optical thickness and effective radius [14,15]. The physical basis of this assumption is derived from the following characteristics of raining clouds: (i) the availability of adequate moisture, (ii) an effective mechanism for converting small cloud droplets that are suspended in the atmosphere into raining particles and (iii) existence of ice phase clouds to support rain generation by the Bergeron-Findeisen process [33].…”

“…To overcome these downsides, several authors suggest the use of multispectral satellite data [9,[13][14][15][16]. Multispectral satellites are passive remote sensing satellites that measure reflected energy within several specific regions (called spectral bands/channels) of the electromagnetic spectrum.…”

“…Likewise, [9] demonstrated rain detection by utilising spectral and textural cloud features inferred from several IR channels. Finally, [15] showed, for the first time, rainfall detection in gauge scarce areas by using commercial microwave links (CML-the infrastructure used for telecom data transmission) and visible and IR multispectral geostationary satellite data.…”

“…Multispectral satellite based rainfall detection often applies parametric based techniques that relate cloud top information (e.g., cloud top temperature and height) derived from the satellite data to rainfall occurrences and rain rates see, e.g., [9,15]. The application is straight forward and requires a definition of the underlying conceptual models and parametric test.…”

“…The measurement resolution of MSG allows for quasi-continuous observation of rainfall distribution in near-real-time. MSG's high spectral resolution offers the opportunity to infer cloud top properties such as optical thickness, effective radius size, phase, pressure and height, which can be used for successful rainfall detection and estimation [9,15,19,20].…”