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Object-Based Analysis of Satellite-Derived Precipitation Systems over the Low- and Midlatitude Pacific Ocean
The Method for Object-based Diagnostic Evaluation (MODE) developed by Davis et al. is implemented and extended to characterize the temporal behavior of objects and to perform a diagnostic analysis on the spatial distribution and properties of precipitation systems over the equatorial Pacific Ocean....
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Published in: | Monthly weather review 2009-10, Vol.137 (10), p.3196-3218 |
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description | The Method for Object-based Diagnostic Evaluation (MODE) developed by Davis et al. is implemented and extended to characterize the temporal behavior of objects and to perform a diagnostic analysis on the spatial distribution and properties of precipitation systems over the equatorial Pacific Ocean. The analysis is performed on two satellite-derived datasets [Tropical Rainfall Measuring Mission (TRMM) 3B42 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)]. A sensitivity analysis showed that temporal convolution produces an unwanted "spillover" effect and that a large spatial convolution radius produces too much smoothing, which results in unrealistically large objects. The analysis showed that the largest and most long-lived precipitation systems in the tropical Pacific are typically located in the western part. A good ability to track precipitation systems in the tropical Pacific was demonstrated: movement of precipitation systems in the ITCZ is both westward and eastward although westward movement is more frequent and in the eastern part of the Pacific ITCZ the westward movement is dominant. Movement of systems in the midlatitudes was predominantly eastward. These findings were common to both satellite products, despite the fact that the average rainfall accumulation can differ by 20%-30% and the occurrence of systems with long life spans can differ by 20%. [PUBLICATION ABSTRACT] |
doi_str_mv | 10.1175/2009MWR2900.1 |
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The analysis is performed on two satellite-derived datasets [Tropical Rainfall Measuring Mission (TRMM) 3B42 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)]. A sensitivity analysis showed that temporal convolution produces an unwanted "spillover" effect and that a large spatial convolution radius produces too much smoothing, which results in unrealistically large objects. The analysis showed that the largest and most long-lived precipitation systems in the tropical Pacific are typically located in the western part. A good ability to track precipitation systems in the tropical Pacific was demonstrated: movement of precipitation systems in the ITCZ is both westward and eastward although westward movement is more frequent and in the eastern part of the Pacific ITCZ the westward movement is dominant. Movement of systems in the midlatitudes was predominantly eastward. These findings were common to both satellite products, despite the fact that the average rainfall accumulation can differ by 20%-30% and the occurrence of systems with long life spans can differ by 20%. [PUBLICATION ABSTRACT]</description><identifier>ISSN: 0027-0644</identifier><identifier>EISSN: 1520-0493</identifier><identifier>DOI: 10.1175/2009MWR2900.1</identifier><identifier>CODEN: MWREAB</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Analysis ; Artificial neural networks ; Atmospheric models ; Climate ; Convolution ; Datasets ; Diagnostic systems ; Earth, ocean, space ; El Nino ; Exact sciences and technology ; External geophysics ; Marine ; Meteorology ; Missing data ; Movement ; Neural networks ; Oceans ; Pacific Ocean ; Precipitation ; Precipitation estimation ; Precipitation systems ; Rainfall ; Remote sensing ; Satellites ; Sensitivity analysis ; Spatial analysis ; Spatial distribution ; Studies ; Temporal logic ; Tropical rainfall ; Tropical Rainfall Measuring Mission (TRMM)</subject><ispartof>Monthly weather review, 2009-10, Vol.137 (10), p.3196-3218</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright American Meteorological Society Oct 2009</rights><rights>Copyright American Meteorological Society 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-b48889ddff88a137d0c13248bab8e49290d3d75cc3661c6a8c76c76718b01ab43</citedby><cites>FETCH-LOGICAL-c415t-b48889ddff88a137d0c13248bab8e49290d3d75cc3661c6a8c76c76718b01ab43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22081131$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>SKOK, Gregor</creatorcontrib><creatorcontrib>TRIBBIA, Joe</creatorcontrib><creatorcontrib>RAKOVEC, Jože</creatorcontrib><creatorcontrib>BROWN, Barbara</creatorcontrib><title>Object-Based Analysis of Satellite-Derived Precipitation Systems over the Low- and Midlatitude Pacific Ocean</title><title>Monthly weather review</title><description>The Method for Object-based Diagnostic Evaluation (MODE) developed by Davis et al. is implemented and extended to characterize the temporal behavior of objects and to perform a diagnostic analysis on the spatial distribution and properties of precipitation systems over the equatorial Pacific Ocean. The analysis is performed on two satellite-derived datasets [Tropical Rainfall Measuring Mission (TRMM) 3B42 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)]. A sensitivity analysis showed that temporal convolution produces an unwanted "spillover" effect and that a large spatial convolution radius produces too much smoothing, which results in unrealistically large objects. The analysis showed that the largest and most long-lived precipitation systems in the tropical Pacific are typically located in the western part. A good ability to track precipitation systems in the tropical Pacific was demonstrated: movement of precipitation systems in the ITCZ is both westward and eastward although westward movement is more frequent and in the eastern part of the Pacific ITCZ the westward movement is dominant. Movement of systems in the midlatitudes was predominantly eastward. 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[PUBLICATION ABSTRACT]</description><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Atmospheric models</subject><subject>Climate</subject><subject>Convolution</subject><subject>Datasets</subject><subject>Diagnostic systems</subject><subject>Earth, ocean, space</subject><subject>El Nino</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Marine</subject><subject>Meteorology</subject><subject>Missing data</subject><subject>Movement</subject><subject>Neural networks</subject><subject>Oceans</subject><subject>Pacific Ocean</subject><subject>Precipitation</subject><subject>Precipitation estimation</subject><subject>Precipitation systems</subject><subject>Rainfall</subject><subject>Remote sensing</subject><subject>Satellites</subject><subject>Sensitivity analysis</subject><subject>Spatial analysis</subject><subject>Spatial distribution</subject><subject>Studies</subject><subject>Temporal 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Barbara</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Object-Based Analysis of Satellite-Derived Precipitation Systems over the Low- and Midlatitude Pacific Ocean</atitle><jtitle>Monthly weather review</jtitle><date>2009-10-01</date><risdate>2009</risdate><volume>137</volume><issue>10</issue><spage>3196</spage><epage>3218</epage><pages>3196-3218</pages><issn>0027-0644</issn><eissn>1520-0493</eissn><coden>MWREAB</coden><abstract>The Method for Object-based Diagnostic Evaluation (MODE) developed by Davis et al. is implemented and extended to characterize the temporal behavior of objects and to perform a diagnostic analysis on the spatial distribution and properties of precipitation systems over the equatorial Pacific Ocean. The analysis is performed on two satellite-derived datasets [Tropical Rainfall Measuring Mission (TRMM) 3B42 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)]. A sensitivity analysis showed that temporal convolution produces an unwanted "spillover" effect and that a large spatial convolution radius produces too much smoothing, which results in unrealistically large objects. The analysis showed that the largest and most long-lived precipitation systems in the tropical Pacific are typically located in the western part. A good ability to track precipitation systems in the tropical Pacific was demonstrated: movement of precipitation systems in the ITCZ is both westward and eastward although westward movement is more frequent and in the eastern part of the Pacific ITCZ the westward movement is dominant. Movement of systems in the midlatitudes was predominantly eastward. These findings were common to both satellite products, despite the fact that the average rainfall accumulation can differ by 20%-30% and the occurrence of systems with long life spans can differ by 20%. [PUBLICATION ABSTRACT]</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/2009MWR2900.1</doi><tpages>23</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Artificial neural networks Atmospheric models Climate Convolution Datasets Diagnostic systems Earth, ocean, space El Nino Exact sciences and technology External geophysics Marine Meteorology Missing data Movement Neural networks Oceans Pacific Ocean Precipitation Precipitation estimation Precipitation systems Rainfall Remote sensing Satellites Sensitivity analysis Spatial analysis Spatial distribution Studies Temporal logic Tropical rainfall Tropical Rainfall Measuring Mission (TRMM) |
title | Object-Based Analysis of Satellite-Derived Precipitation Systems over the Low- and Midlatitude Pacific Ocean |
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