Editorial Type:
Article
Article Type:
Research Article

Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events

Dimitrios Stampoulis Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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Emmanouil N. Anagnostou Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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Efthymios I. Nikolopoulos Department of Land and Agroforest Environment, University of Padua, Padua, Italy

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Print Publication:
01 Oct 2013
DOI:
https://doi.org/10.1175/JHM-D-12-0167.1
Page(s):
1500–1514
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Abstract

Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area.

Corresponding author address: Prof. Emmanouil Anagnostou, Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Unit 3037, Storrs, CT 06269. E-mail: manos@engr.uconn.edu

Abstract

Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area.

Corresponding author address: Prof. Emmanouil Anagnostou, Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Unit 3037, Storrs, CT 06269. E-mail: manos@engr.uconn.edu
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