Paper Info
Title
Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique
Abstract
Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001-2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.
Year
DOI
Venue
2015
10.3390/rs70201758
REMOTE SENSING
Keywords
Field
DocType
rain gauge,pairwise comparison,satellite,flood,rainfall
Meteorology,Satellite,Cyclone,Rain gauge,Remote sensing,Extreme weather,Wet season,Climatology,Geology,Early warning system,Flood myth,Precipitation
Journal
Volume
Issue
ISSN
7
2
2072-4292
Citations 
PageRank 
References 
17
2.07
1
Authors
6
Name
Order
Citations
PageRank
Carolien Tote1335.03
domingos patricio2172.07
H.L. Boogaard3182.80
raymond van der wijngaart4172.07
elena tarnavsky5216.24
Christopher C. Funk6518.86