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dc.contributor.authorTiwari, P. R.
dc.contributor.authorS. C. Kar
dc.contributor.authorMohanty, U. C.
dc.contributor.authorSagnik Dey
dc.contributor.authorP. Sinha
dc.contributor.authorM. S. Shekhar
dc.contributor.authorSokhi, Ranjeet
dc.date.accessioned2018-12-21T15:03:41Z
dc.date.available2018-12-21T15:03:41Z
dc.date.issued2018-11-12
dc.identifier.citationTiwari , P R , S. C. Kar , Mohanty , U C , Sagnik Dey , P. Sinha , M. S. Shekhar & Sokhi , R 2018 , ' Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India ' , International Journal of Climatology . https://doi.org/10.1002/joc.5897
dc.identifier.issn1097-0088
dc.identifier.otherPURE: 15708701
dc.identifier.otherPURE UUID: 30c3a5b8-3b28-4043-b17a-badf3c5cd6e2
dc.identifier.otherScopus: 85056283195
dc.identifier.otherORCID: /0000-0002-7580-0446/work/62752078
dc.identifier.urihttp://hdl.handle.net/2299/20899
dc.description.abstractThe main aim of the present study is to analyse the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over north India. For this purpose, a canonical correlation analysis (CCA) based statistical downscaling approach and dynamical downscaling approach (at 30 km) with an optimized configuration of the regional climate model (RegCM) nested in coarse resolution global spectral model have been used for a period of 28 years (1982–2009). For CCA, nine predictors (precipitation, zonal and meridional winds at 850 and 200 hPa, temperature at 200 hPa and sea surface temperatures) over three different domains were selected. The predictors were chosen based on the statistically significant teleconnection maps and physically based relationships between precipitation over the study region and meteorological variables. The validation revealed that both the downscaling approaches provided improved precipitation forecasts compared to the global model. Reasons for improved prediction by downscaling techniques have been examined. The improvement mainly comes due to better representation of orography, westerly moisture transport and vertical pressure velocity in the regional climate model. Furthermore, two bias correction methods namely quantile mapping (QM) and mean bias-remove (MBR) have been applied on downscaled RegCM, statistically downscaled CCA as well as the global model products. It was found that when the QM-based bias correction is applied on dynamically downscaled RegCM products, it has better skill in predicting wintertime precipitation over the study region compared to the CCA-based statistical downscaling. Overall, the results indicate that the QM-based bias-corrected downscaled RegCM model is a useful tool for wintertime seasonal-scale precipitation prediction over north India.en
dc.language.isoeng
dc.relation.ispartofInternational Journal of Climatology
dc.rightsEmbargoed
dc.subjectbias correction
dc.subjectCCA
dc.subjectdownscaling
dc.subjectnorth India
dc.subjectRegCM
dc.subjectwinter precipitation
dc.subjectAtmospheric Science
dc.titleComparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north Indiaen
dc.contributor.institutionCentre for Atmospheric and Climate Physics Research
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.contributor.institutionAtmospheric Dynamics & Air Quality
dc.description.statusPeer reviewed
dc.date.embargoedUntil2019-10-19
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85056283195&partnerID=8YFLogxK
dc.relation.schoolSchool of Physics, Astronomy and Mathematics
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2018-11-12
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1002/joc.5897
rioxxterms.licenseref.startdate2019-10-19
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.date.embargo2019-10-19
herts.rights.accesstypeEmbargoed


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