Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

cg.authorship.typesCGIAR and developing country instituteen
cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationUniversity of Coloradoen
cg.contributor.affiliationNational Oceanic and Atmospheric Administrationen
cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.affiliationBangladesh Meteorological Departmenten
cg.contributor.donorUnited States Agency for International Developmenten
cg.contributor.donorBill & Melinda Gates Foundationen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeTransforming Agrifood Systems in South Asia
cg.coverage.countryBangladesh
cg.coverage.iso3166-alpha2BD
cg.coverage.regionAsia
cg.coverage.regionSouthern Asia
cg.creator.identifierTimothy Joseph Krupnik: 0000-0001-6973-0106
cg.creator.identifierCarlo Montes: 0000-0003-4828-5589
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1002/joc.8246en
cg.isijournalISI Journalen
cg.issn0899-8418en
cg.issue15en
cg.journalInternational Journal of Climatologyen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.impactAreaNutrition, health and food security
cg.volume43en
dc.contributor.authorAcharya, Nachiketaen
dc.contributor.authorMontes, Carloen
dc.contributor.authorHassan, S.M.Q.en
dc.contributor.authorSultana, Raziaen
dc.contributor.authorRashid, Md. Bazluren
dc.contributor.authorMannan, Md. Abdulen
dc.contributor.authorKrupnik, Timothy J.en
dc.date.accessioned2023-10-12T19:00:28Zen
dc.date.available2023-10-12T19:00:28Zen
dc.identifier.urihttps://hdl.handle.net/10568/132226
dc.titleCalibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfallen
dcterms.abstractBangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.en
dcterms.accessRightsOpen Access
dcterms.audienceAcademicsen
dcterms.available2023-09-26
dcterms.bibliographicCitationAcharya, N., Montes, C., Hassan, S. M. Q., Sultana, R., Rashid, Md. B., Mannan, Md. A., & Krupnik, T. J. (2023). Calibrated multi‐model ensemble seasonal prediction of Bangladesh summer monsoon rainfall. International Journal of Climatology, joc.8246. https://doi.org/10.1002/joc.8246en
dcterms.extentpp. 6979-6992en
dcterms.issued2023
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherWileyen
dcterms.subjectclimate servicesen
dcterms.subjectforecastingen
dcterms.subjectmonsoonsen
dcterms.subjectmodellingen
dcterms.subjectrainfallen
dcterms.typeJournal Article

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