Development of a Pressure–Precipitation Transmitter
A novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical...
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| Veröffentlicht in: | Journal of applied meteorology and climatology 2019-11, Vol.58 (11), p.2453-2468 |
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| container_title | Journal of applied meteorology and climatology |
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| creator | Inatsu, Masaru Suematsu, Tamaki Tamaki, Yuta Nakano, Naoto Mizushima, Kao Shinohara, Mizuki |
| description | A novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data. |
| doi_str_mv | 10.1175/jamc-d-19-0070.1 |
| format | Article |
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After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.</description><identifier>ISSN: 1558-8424</identifier><identifier>EISSN: 1558-8432</identifier><identifier>DOI: 10.1175/jamc-d-19-0070.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Analogs ; Annual precipitation ; Climate ; Daily precipitation ; Data ; Datasets ; Decomposition ; Disaster insurance ; Emulators ; Extreme values ; Extreme weather ; General circulation models ; Hydrologic data ; Learning ; Mathematical models ; Maximum precipitation ; Methods ; Oceanic analysis ; Precipitation ; Precipitation data ; Precipitation patterns ; Pressure ; Probability distribution ; Probability theory ; Rain ; Regression models ; Sea level ; Sea level pressure ; Singular value decomposition ; Statistical analysis ; Statistical methods ; Traffic control</subject><ispartof>Journal of applied meteorology and climatology, 2019-11, Vol.58 (11), p.2453-2468</ispartof><rights>2019 American Meteorological Society</rights><rights>Copyright American Meteorological Society Nov 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c401t-9dced5f3e1c06143bb02cc8d3461c8d6126e4254e3e5cc29316aa0981aa79ba13</citedby><cites>FETCH-LOGICAL-c401t-9dced5f3e1c06143bb02cc8d3461c8d6126e4254e3e5cc29316aa0981aa79ba13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26846366$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26846366$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,3668,27901,27902,57992,58225</link.rule.ids></links><search><creatorcontrib>Inatsu, Masaru</creatorcontrib><creatorcontrib>Suematsu, Tamaki</creatorcontrib><creatorcontrib>Tamaki, Yuta</creatorcontrib><creatorcontrib>Nakano, Naoto</creatorcontrib><creatorcontrib>Mizushima, Kao</creatorcontrib><creatorcontrib>Shinohara, Mizuki</creatorcontrib><title>Development of a Pressure–Precipitation Transmitter</title><title>Journal of applied meteorology and climatology</title><description>A novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.</description><subject>Analogs</subject><subject>Annual precipitation</subject><subject>Climate</subject><subject>Daily precipitation</subject><subject>Data</subject><subject>Datasets</subject><subject>Decomposition</subject><subject>Disaster insurance</subject><subject>Emulators</subject><subject>Extreme values</subject><subject>Extreme weather</subject><subject>General circulation models</subject><subject>Hydrologic data</subject><subject>Learning</subject><subject>Mathematical models</subject><subject>Maximum precipitation</subject><subject>Methods</subject><subject>Oceanic analysis</subject><subject>Precipitation</subject><subject>Precipitation data</subject><subject>Precipitation patterns</subject><subject>Pressure</subject><subject>Probability distribution</subject><subject>Probability theory</subject><subject>Rain</subject><subject>Regression models</subject><subject>Sea level</subject><subject>Sea level pressure</subject><subject>Singular value decomposition</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Traffic control</subject><issn>1558-8424</issn><issn>1558-8432</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNo9kEtLAzEUhYMoWKt7N8KA62huXjNZltYnFV3UdUgzd2CGzsMkFdz5H_yH_hKnVLo6h8s534VDyCWwG4Bc3Tau9bSkYChj-Xg7IhNQqqCFFPz44Lk8JWcxNoxJmedqQtQCP3HTDy12KeurzGVvAWPcBvz9_hmtr4c6uVT3XbYKrottnRKGc3JSuU3Ei3-dkvf7u9X8kS5fH57msyX1kkGipvRYqkogeKZBivWace-LUkgNo2jgGiVXEgUq77kRoJ1jpgDncrN2IKbkes8dQv-xxZhs029DN760XBjORM5H7JSwfcqHPsaAlR1C3brwZYHZ3Tj2efYytwsLxu7GsTvw1b7SxNSHQ57rQmqhtfgD8OZiUA</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Inatsu, 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data</topic><topic>Precipitation patterns</topic><topic>Pressure</topic><topic>Probability distribution</topic><topic>Probability theory</topic><topic>Rain</topic><topic>Regression models</topic><topic>Sea level</topic><topic>Sea level pressure</topic><topic>Singular value decomposition</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Traffic control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Inatsu, Masaru</creatorcontrib><creatorcontrib>Suematsu, Tamaki</creatorcontrib><creatorcontrib>Tamaki, Yuta</creatorcontrib><creatorcontrib>Nakano, Naoto</creatorcontrib><creatorcontrib>Mizushima, Kao</creatorcontrib><creatorcontrib>Shinohara, Mizuki</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Inatsu, Masaru</au><au>Suematsu, Tamaki</au><au>Tamaki, Yuta</au><au>Nakano, Naoto</au><au>Mizushima, Kao</au><au>Shinohara, Mizuki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of a Pressure–Precipitation Transmitter</atitle><jtitle>Journal of applied meteorology and climatology</jtitle><date>2019-11-01</date><risdate>2019</risdate><volume>58</volume><issue>11</issue><spage>2453</spage><epage>2468</epage><pages>2453-2468</pages><issn>1558-8424</issn><eissn>1558-8432</eissn><abstract>A novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/jamc-d-19-0070.1</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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| source | Jstor Complete Legacy; American Meteorological Society; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Analogs Annual precipitation Climate Daily precipitation Data Datasets Decomposition Disaster insurance Emulators Extreme values Extreme weather General circulation models Hydrologic data Learning Mathematical models Maximum precipitation Methods Oceanic analysis Precipitation Precipitation data Precipitation patterns Pressure Probability distribution Probability theory Rain Regression models Sea level Sea level pressure Singular value decomposition Statistical analysis Statistical methods Traffic control |
| title | Development of a Pressure–Precipitation Transmitter |
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