A heuristic method for time disaggregation of seasonal climate forecasts
To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealisti...
Gespeichert in:
Veröffentlicht in: | Weather and forecasting 2005-04, Vol.20 (2), p.212-221 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 221 |
---|---|
container_issue | 2 |
container_start_page | 212 |
container_title | Weather and forecasting |
container_volume | 20 |
creator | SCHNEIDER, J. M GARBRECHT, J. D UNGER, D. A |
description | To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon. |
doi_str_mv | 10.1175/WAF839.1 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_17608960</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>17608960</sourcerecordid><originalsourceid>FETCH-LOGICAL-c349t-281308ddba19793ce6bcc2dff2128f129cfe0fdcdab0452996168beea226d5e53</originalsourceid><addsrcrecordid>eNpd0M1KAzEUBeAgCtYq-AiDoLiZmp9JJlkWsVYouFFcDpnkpk2ZmdQkXfj2TmlBcHU3H4dzD0K3BM8IqfnT13whmZqRMzQhnOISV6w6RxMsJS0l4eISXaW0xRhTTtUELefFBvbRp-xN0UPeBFu4EIvseyisT3q9jrDW2YehCK5IoFMYdFeYzvc6w8GC0Smna3ThdJfg5nSn6HPx8vG8LFfvr2_P81VpWKVySSVhWFrbaqJqxQyI1hhqnaOESkeoMg6ws8bqFldjQyWIkC2AplRYDpxN0cMxdxfD9x5SbnqfDHSdHiDsU0NqgaUSeIR3_-A27OPYfTRKMMZreUh7PCITQ0oRXLOL42PxpyG4OezZHPdsyEjvT3k6Gd25qAfj058XNasVF-wXY_F0tQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>196335785</pqid></control><display><type>article</type><title>A heuristic method for time disaggregation of seasonal climate forecasts</title><source>American Meteorological Society</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>SCHNEIDER, J. M ; GARBRECHT, J. D ; UNGER, D. A</creator><creatorcontrib>SCHNEIDER, J. M ; GARBRECHT, J. D ; UNGER, D. A</creatorcontrib><description>To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon.</description><identifier>ISSN: 0882-8156</identifier><identifier>EISSN: 1520-0434</identifier><identifier>DOI: 10.1175/WAF839.1</identifier><identifier>CODEN: WEFOE3</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage ; Agricultural and forest meteorology ; Agronomy. Soil science and plant productions ; Biological and medical sciences ; Climate ; Climate prediction ; Climatology, meteorology ; Earth, ocean, space ; Exact sciences and technology ; External geophysics ; Fundamental and applied biological sciences. Psychology ; General agronomy. Plant production ; Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production ; Heuristic ; Hydrologic data ; Meteorological applications ; Meteorology ; Seasonal variations ; Seasons ; Weather forecasting</subject><ispartof>Weather and forecasting, 2005-04, Vol.20 (2), p.212-221</ispartof><rights>2005 INIST-CNRS</rights><rights>Copyright American Meteorological Society Apr 2005</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-281308ddba19793ce6bcc2dff2128f129cfe0fdcdab0452996168beea226d5e53</citedby><cites>FETCH-LOGICAL-c349t-281308ddba19793ce6bcc2dff2128f129cfe0fdcdab0452996168beea226d5e53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,3681,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16737956$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>SCHNEIDER, J. M</creatorcontrib><creatorcontrib>GARBRECHT, J. D</creatorcontrib><creatorcontrib>UNGER, D. A</creatorcontrib><title>A heuristic method for time disaggregation of seasonal climate forecasts</title><title>Weather and forecasting</title><description>To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon.</description><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage</subject><subject>Agricultural and forest meteorology</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>Climate</subject><subject>Climate prediction</subject><subject>Climatology, meteorology</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agronomy. Plant production</subject><subject>Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production</subject><subject>Heuristic</subject><subject>Hydrologic data</subject><subject>Meteorological applications</subject><subject>Meteorology</subject><subject>Seasonal variations</subject><subject>Seasons</subject><subject>Weather forecasting</subject><issn>0882-8156</issn><issn>1520-0434</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpd0M1KAzEUBeAgCtYq-AiDoLiZmp9JJlkWsVYouFFcDpnkpk2ZmdQkXfj2TmlBcHU3H4dzD0K3BM8IqfnT13whmZqRMzQhnOISV6w6RxMsJS0l4eISXaW0xRhTTtUELefFBvbRp-xN0UPeBFu4EIvseyisT3q9jrDW2YehCK5IoFMYdFeYzvc6w8GC0Smna3ThdJfg5nSn6HPx8vG8LFfvr2_P81VpWKVySSVhWFrbaqJqxQyI1hhqnaOESkeoMg6ws8bqFldjQyWIkC2AplRYDpxN0cMxdxfD9x5SbnqfDHSdHiDsU0NqgaUSeIR3_-A27OPYfTRKMMZreUh7PCITQ0oRXLOL42PxpyG4OezZHPdsyEjvT3k6Gd25qAfj058XNasVF-wXY_F0tQ</recordid><startdate>20050401</startdate><enddate>20050401</enddate><creator>SCHNEIDER, J. M</creator><creator>GARBRECHT, J. D</creator><creator>UNGER, D. A</creator><general>American Meteorological Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7RQ</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88F</scope><scope>8AF</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M1Q</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>U9A</scope></search><sort><creationdate>20050401</creationdate><title>A heuristic method for time disaggregation of seasonal climate forecasts</title><author>SCHNEIDER, J. M ; GARBRECHT, J. D ; UNGER, D. A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-281308ddba19793ce6bcc2dff2128f129cfe0fdcdab0452996168beea226d5e53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Agricultural and forest climatology and meteorology. Irrigation. Drainage</topic><topic>Agricultural and forest meteorology</topic><topic>Agronomy. Soil science and plant productions</topic><topic>Biological and medical sciences</topic><topic>Climate</topic><topic>Climate prediction</topic><topic>Climatology, meteorology</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agronomy. Plant production</topic><topic>Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production</topic><topic>Heuristic</topic><topic>Hydrologic data</topic><topic>Meteorological applications</topic><topic>Meteorology</topic><topic>Seasonal variations</topic><topic>Seasons</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>SCHNEIDER, J. M</creatorcontrib><creatorcontrib>GARBRECHT, J. D</creatorcontrib><creatorcontrib>UNGER, D. A</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Career & Technical Education Database</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Military Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Weather and forecasting</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>SCHNEIDER, J. M</au><au>GARBRECHT, J. D</au><au>UNGER, D. A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A heuristic method for time disaggregation of seasonal climate forecasts</atitle><jtitle>Weather and forecasting</jtitle><date>2005-04-01</date><risdate>2005</risdate><volume>20</volume><issue>2</issue><spage>212</spage><epage>221</epage><pages>212-221</pages><issn>0882-8156</issn><eissn>1520-0434</eissn><coden>WEFOE3</coden><abstract>To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/WAF839.1</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0882-8156 |
ispartof | Weather and forecasting, 2005-04, Vol.20 (2), p.212-221 |
issn | 0882-8156 1520-0434 |
language | eng |
recordid | cdi_proquest_miscellaneous_17608960 |
source | American Meteorological Society; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
subjects | Agricultural and forest climatology and meteorology. Irrigation. Drainage Agricultural and forest meteorology Agronomy. Soil science and plant productions Biological and medical sciences Climate Climate prediction Climatology, meteorology Earth, ocean, space Exact sciences and technology External geophysics Fundamental and applied biological sciences. Psychology General agronomy. Plant production Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production Heuristic Hydrologic data Meteorological applications Meteorology Seasonal variations Seasons Weather forecasting |
title | A heuristic method for time disaggregation of seasonal climate forecasts |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T23%3A05%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20heuristic%20method%20for%20time%20disaggregation%20of%20seasonal%20climate%20forecasts&rft.jtitle=Weather%20and%20forecasting&rft.au=SCHNEIDER,%20J.%20M&rft.date=2005-04-01&rft.volume=20&rft.issue=2&rft.spage=212&rft.epage=221&rft.pages=212-221&rft.issn=0882-8156&rft.eissn=1520-0434&rft.coden=WEFOE3&rft_id=info:doi/10.1175/WAF839.1&rft_dat=%3Cproquest_cross%3E17608960%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=196335785&rft_id=info:pmid/&rfr_iscdi=true |