Editorial Type:
Article
Article Type:
Research Article

Spatial Variability of Surface Rainfall as Observed from TRMM Field Campaign Data

Saswati Datta Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland

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W. Linwood Jones School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida

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Biswadev Roy Science Systems and Applications, Inc., Seabrook, and Mesoscale Processes Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Ali Tokay Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland

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Print Publication:
01 May 2003
DOI:
https://doi.org/10.1175/1520-0450(2003)042<0598:SVOSRA>2.0.CO;2
Page(s):
598–610
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Abstract

The spatial variability of surface rainfall over 5- and 30-day time periods is observed, and it is found that the spatial decorrelation length of precipitation is comparable to the size of a single surface gauge network. The observed variability is found to affect radar-derived precipitation estimation, particularly if it is based on calibration using rain gauges. The radar subgrid-scale variability is also observed using some redundant and finer-scale gauge networks deployed during the Tropical Rainfall Measuring Mission (TRMM) ground-validation field campaigns. Based upon statistical analysis and a point-based decision-making system, a best-suited spatial–temporal filtering technique is suggested and, when applied to match radar data with any other surface observation, is found to reduce bias.

Corresponding author address: Dr. Saswati Datta, Joint Center for Earth Systems Technology (JCET), University of Maryland, Baltimore County, ACIV Wing A, Rm 114, 1000 Hilltop Circle, Baltimore, MD 21250. sdatta@umbc.edu

Abstract

The spatial variability of surface rainfall over 5- and 30-day time periods is observed, and it is found that the spatial decorrelation length of precipitation is comparable to the size of a single surface gauge network. The observed variability is found to affect radar-derived precipitation estimation, particularly if it is based on calibration using rain gauges. The radar subgrid-scale variability is also observed using some redundant and finer-scale gauge networks deployed during the Tropical Rainfall Measuring Mission (TRMM) ground-validation field campaigns. Based upon statistical analysis and a point-based decision-making system, a best-suited spatial–temporal filtering technique is suggested and, when applied to match radar data with any other surface observation, is found to reduce bias.

Corresponding author address: Dr. Saswati Datta, Joint Center for Earth Systems Technology (JCET), University of Maryland, Baltimore County, ACIV Wing A, Rm 114, 1000 Hilltop Circle, Baltimore, MD 21250. sdatta@umbc.edu

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Journal of Applied Meteorology and Climatology

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