Downscaling of Seasonal Rainfall over the Philippines: Dynamical versus Statistical Approaches
The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling, is compared over the Philippines for the April–June monsoon transition season. Spatial interpolation of RCM and GCM...
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| Veröffentlicht in: | Monthly weather review 2012-04, Vol.140 (4), p.1204-1218 |
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| creator | ROBERTSON, Andrew W QIAN, Jian-Hua TIPPETT, Michael K MORON, Vincent LUCERO, Anthony |
| description | The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling, is compared over the Philippines for the April–June monsoon transition season. Spatial interpolation of RCM and GCM gridbox values to station locations is compared with model output statistics (MOS) correction. The anomaly correlation coefficient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally—and rather equally—superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case. Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-yr training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines. |
| doi_str_mv | 10.1175/mwr-d-11-00177.1 |
| format | Article |
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Spatial interpolation of RCM and GCM gridbox values to station locations is compared with model output statistics (MOS) correction. The anomaly correlation coefficient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally—and rather equally—superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case. Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-yr training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines.</description><identifier>ISSN: 0027-0644</identifier><identifier>EISSN: 1520-0493</identifier><identifier>DOI: 10.1175/mwr-d-11-00177.1</identifier><identifier>CODEN: MWREAB</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Climate change ; Climate models ; Climatology ; Correlation coefficient ; Correlation coefficients ; Daily rainfall ; Datasets ; Deforestation ; Earth Sciences ; Earth, ocean, space ; El Nino ; El Nino phenomena ; El Nino-Southern Oscillation event ; Exact sciences and technology ; External geophysics ; General circulation models ; Interpolation ; Metal oxide semiconductors ; Meteorology ; Monsoon onset ; Monsoons ; Mountains ; Ocean, Atmosphere ; Philippines ; Precipitation ; Probability theory ; Rain ; Rainfall ; Rainfall frequency ; RCM ; Regional climate models ; Regional climates ; Sciences of the Universe ; Seasonal rainfall ; Seasons ; Simulation ; Skills ; Southern Oscillation ; Stations ; Statistical analysis ; Statistical methods ; Statistics ; Studies ; Wind ; Wind fields</subject><ispartof>Monthly weather review, 2012-04, Vol.140 (4), p.1204-1218</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society Apr 2012</rights><rights>Copyright American Meteorological Society 2012</rights><rights>Attribution</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c537t-eb2dede60a715641b1bf5eaf1745adc81a8e7f6b402377e03069cba2c0ef15493</citedby><cites>FETCH-LOGICAL-c537t-eb2dede60a715641b1bf5eaf1745adc81a8e7f6b402377e03069cba2c0ef15493</cites><orcidid>0000-0002-0086-0524 ; 0000-0002-4981-9530</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,3681,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25755621$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-02895230$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>ROBERTSON, Andrew W</creatorcontrib><creatorcontrib>QIAN, Jian-Hua</creatorcontrib><creatorcontrib>TIPPETT, Michael K</creatorcontrib><creatorcontrib>MORON, Vincent</creatorcontrib><creatorcontrib>LUCERO, Anthony</creatorcontrib><title>Downscaling of Seasonal Rainfall over the Philippines: Dynamical versus Statistical Approaches</title><title>Monthly weather review</title><description>The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling, is compared over the Philippines for the April–June monsoon transition season. Spatial interpolation of RCM and GCM gridbox values to station locations is compared with model output statistics (MOS) correction. The anomaly correlation coefficient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally—and rather equally—superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case. Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-yr training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines.</description><subject>Climate change</subject><subject>Climate models</subject><subject>Climatology</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Daily rainfall</subject><subject>Datasets</subject><subject>Deforestation</subject><subject>Earth Sciences</subject><subject>Earth, ocean, space</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>El Nino-Southern Oscillation event</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>General circulation models</subject><subject>Interpolation</subject><subject>Metal oxide semiconductors</subject><subject>Meteorology</subject><subject>Monsoon 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review</jtitle><date>2012-04-01</date><risdate>2012</risdate><volume>140</volume><issue>4</issue><spage>1204</spage><epage>1218</epage><pages>1204-1218</pages><issn>0027-0644</issn><eissn>1520-0493</eissn><coden>MWREAB</coden><abstract>The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling, is compared over the Philippines for the April–June monsoon transition season. Spatial interpolation of RCM and GCM gridbox values to station locations is compared with model output statistics (MOS) correction. The anomaly correlation coefficient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally—and rather equally—superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case. Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-yr training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/mwr-d-11-00177.1</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-0086-0524</orcidid><orcidid>https://orcid.org/0000-0002-4981-9530</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Climate change Climate models Climatology Correlation coefficient Correlation coefficients Daily rainfall Datasets Deforestation Earth Sciences Earth, ocean, space El Nino El Nino phenomena El Nino-Southern Oscillation event Exact sciences and technology External geophysics General circulation models Interpolation Metal oxide semiconductors Meteorology Monsoon onset Monsoons Mountains Ocean, Atmosphere Philippines Precipitation Probability theory Rain Rainfall Rainfall frequency RCM Regional climate models Regional climates Sciences of the Universe Seasonal rainfall Seasons Simulation Skills Southern Oscillation Stations Statistical analysis Statistical methods Statistics Studies Wind Wind fields |
| title | Downscaling of Seasonal Rainfall over the Philippines: Dynamical versus Statistical Approaches |
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