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Proceedings Paper

Evaluating several satellite precipitation estimates and global ground-based dataset on Sicily (Italy)
Author(s): Francesco Lo Conti; Kuo-Lin Hsu; Leonardo V. Noto; Soroosh Sorooshian
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Paper Abstract

The developing of satellite-based precipitation retrieval systems, presents great potentialities for several ap­ plications ranging from weather and meteorological applications to hydrological modelling. Evaluating perfor­ mances for these estimates is essential in order to understand their real capabilities and suitability related to each application. In this study an evaluation analysis of satellite precipitation retrieval systems has been carried out for the area of Sicily (Italy). Sicily is an island in the Mediterranean sea with a particular climatology and morphology, which is considered as an interesting test site for satellite precipitation products on the European mid-latitude area. A high density rain-gauges network has been used to evaluate selected satellite precipitation products. Sicily has an area of 26,000 km2 and the gauge density of the network considered in this study is about 250 km2 /gauge. Four satellite products (CMORPH, PERSIANN, TMPA-RT, PERSIANN-CCS) along with two adjusted products (TMPA and PERSIANN Adjusted) have been selected for the evaluation. Evalua­ tion and comparisons among selected products is performed with reference to the data provided by the gauge network of Sicily and using statistical and visualization tools. Results show that bias is relevant for all satellite products and climatic considerations are reported to address this issue. Moreover bias errors are observed for the adjusted products even though they are reduced respect to only-satellite products. In order to analyze this result, the ground-based precipitation dataset used by adjusted products (GPCC dataset), has been examined and weaknesses arising from spatial sampling of precipitation process have been identified for the study area. Therefore possible issues deriving from using global ground-based datasets for local scales are pointed out from this application.

Paper Details

Date Published: 19 October 2012
PDF: 15 pages
Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 853106 (19 October 2012); doi: 10.1117/12.974669
Show Author Affiliations
Francesco Lo Conti, Univ. degli Studi di Palermo (Italy)
Kuo-Lin Hsu, Univ of California, Irvine (United States)
Leonardo V. Noto, Univ. degli Studi di Palermo (Italy)
Soroosh Sorooshian, Univ of California, Irvine (United States)

Published in SPIE Proceedings Vol. 8531:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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