A Spatiotemporal Correlation Technique to Improve Satellite Rainfall Accumulation
Identifieur interne : 000933 ( Main/Exploration ); précédent : 000932; suivant : 000934A Spatiotemporal Correlation Technique to Improve Satellite Rainfall Accumulation
Auteurs : Veljko Petkovic [États-Unis] ; Christian D. Kummerow [États-Unis]Source :
- Journal of applied meteorology and climatology [ 1558-8424 ] ; 2012.
Descripteurs français
- Pascal (Inist)
English descriptors
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Abstract
A spatiotemporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of limited and infrequent measurements while accounting for the measurement accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. The technique is developed using synthetic radar data. Nine months of Operational Program for the Exchange of Weather Radar (OPERA) data are used on grid sizes of 100, 248, and 500 km with pixel resolutions of 8,12, and 24 km to simulate satellite fields of view and are then applied to the real satellite data over the Southwest to calculate 3-h rainfall accumulations. The results are compared with the simple averaging technique, which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. Using synthetic data, depending on the time separation of the measurements and their accuracy, a spatiotemporal correlation technique has shown the potential to yield improvements of up to 40% in absolute error and up to 25% in root-mean-square error when compared with the simple averaging technique. When applied to the real satellite data over the Southeast, the technique showed much less skill (general improvement of only 2%-6%).
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Kummerow</name><affiliation wicri:level="2"><inist:fA14 i1="01"><s1>Colorado State University</s1><s2>Fort Collins, Colorado</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><region type="state">Colorado</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">13-0198061</idno><date when="2012">2012</date><idno type="stanalyst">PASCAL 13-0198061 INIST</idno><idno type="RBID">Pascal:13-0198061</idno><idno type="wicri:Area/PascalFrancis/Corpus">000040</idno><idno type="wicri:Area/PascalFrancis/Curation">000491</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000087</idno><idno type="wicri:doubleKey">1558-8424:2012:Petkovic V:a:spatiotemporal:correlation</idno><idno type="wicri:Area/Main/Merge">000932</idno><idno type="wicri:Area/Main/Curation">000933</idno><idno type="wicri:Area/Main/Exploration">000933</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">A Spatiotemporal Correlation Technique to Improve Satellite Rainfall Accumulation</title><author><name sortKey="Petkovic, Veljko" sort="Petkovic, Veljko" uniqKey="Petkovic V" first="Veljko" last="Petkovic">Veljko Petkovic</name><affiliation wicri:level="2"><inist:fA14 i1="01"><s1>Colorado State University</s1><s2>Fort Collins, Colorado</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><region type="state">Colorado</region></placeName></affiliation></author><author><name sortKey="Kummerow, Christian D" sort="Kummerow, Christian D" uniqKey="Kummerow C" first="Christian D." last="Kummerow">Christian D. Kummerow</name><affiliation wicri:level="2"><inist:fA14 i1="01"><s1>Colorado State University</s1><s2>Fort Collins, Colorado</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><region type="state">Colorado</region></placeName></affiliation></author></analytic><series><title level="j" type="main">Journal of applied meteorology and climatology </title><title level="j" type="abbreviated">J. appl. meteorol. climatol. </title><idno type="ISSN">1558-8424</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Journal of applied meteorology and climatology </title><title level="j" type="abbreviated">J. appl. meteorol. climatol. </title><idno type="ISSN">1558-8424</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Measurement technique</term><term>Meteorological radar</term><term>Performance evaluation</term><term>Radar observation</term><term>Rainfall rate</term><term>Satellite observation</term><term>Space remote sensing</term><term>Space time correlation</term><term>atmospheric precipitation</term><term>rainfall</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Corrélation spatiotemporelle</term><term>Observation par satellite</term><term>Pluie</term><term>Technique mesure</term><term>Observation radar</term><term>Radar météorologique</term><term>Intensité pluie</term><term>Evaluation performance</term><term>Télédétection spatiale</term><term>Précipitation atmosphérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A spatiotemporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of limited and infrequent measurements while accounting for the measurement accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. The technique is developed using synthetic radar data. Nine months of Operational Program for the Exchange of Weather Radar (OPERA) data are used on grid sizes of 100, 248, and 500 km with pixel resolutions of 8,12, and 24 km to simulate satellite fields of view and are then applied to the real satellite data over the Southwest to calculate 3-h rainfall accumulations. The results are compared with the simple averaging technique, which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. Using synthetic data, depending on the time separation of the measurements and their accuracy, a spatiotemporal correlation technique has shown the potential to yield improvements of up to 40% in absolute error and up to 25% in root-mean-square error when compared with the simple averaging technique. When applied to the real satellite data over the Southeast, the technique showed much less skill (general improvement of only 2%-6%).</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Colorado</li></region></list><tree><country name="États-Unis"><region name="Colorado"><name sortKey="Petkovic, Veljko" sort="Petkovic, Veljko" uniqKey="Petkovic V" first="Veljko" last="Petkovic">Veljko Petkovic</name></region><name sortKey="Kummerow, Christian D" sort="Kummerow, Christian D" uniqKey="Kummerow C" first="Christian D." last="Kummerow">Christian D. Kummerow</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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