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dc.contributor.authorJangid, Buddhi Prakash
dc.contributor.authorKumar, Prashant
dc.contributor.authorAttada, Raju
dc.contributor.authorKumar, Raj
dc.date.accessioned2017-02-28T12:11:06Z
dc.date.available2017-02-28T12:11:06Z
dc.date.issued2017-02-24
dc.identifier.citationJangid BP, Kumar P, Raju A, Kumar R (2017) Significance of High Resolution GHRSST on prediction of Indian Summer Monsoon. Advances in Space Research. Available: http://dx.doi.org/10.1016/j.asr.2017.02.025.
dc.identifier.issn0273-1177
dc.identifier.doi10.1016/j.asr.2017.02.025
dc.identifier.urihttp://hdl.handle.net/10754/622950
dc.description.abstractIn this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4 K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.
dc.description.sponsorshipThe authors would like to thank Director, SAC. Authors are also thankful to Dr. Neeraj Agarwal for his motivation to use GHRSST in WRF model. Authors are thankful to NCAR for WRF model. The NCEP global model analyses are obtained from Data Support Section of the Computational and Information Systems Laboratory (CISL) at NCAR. TRMM data are obtained from http://daac.gsfc.nasa.gov/data/datasets. Authors are also thankful to Tropical Atmosphere Ocean project office of NOAA/PMEL to provide RAMA (Research Moored Array for African-Asian-Australian Monsson Analysis and Prediction) Buoy observations. www.pmel.noaa.gov/tao/data_deliv/deliv-nojava-rama.html.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0273117717301229
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Advances in Space Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advances in Space Research, [, , (2017-02-24)] DOI: 10.1016/j.asr.2017.02.025 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIndian Summer Monsoon
dc.subjectSea Surface Temperature
dc.subjectWRF model
dc.subjectSeasonal Prediction
dc.titleSignificance of High Resolution GHRSST on prediction of Indian Summer Monsoon
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.identifier.journalAdvances in Space Research
dc.eprint.versionPost-print
dc.contributor.institutionAtmospheric and Oceanic Sciences Group, EPSA, Space Applications Centre (ISRO), Ahmedabad, India
kaust.personAttada, Raju
dc.date.published-online2017-02-24
dc.date.published-print2017-05


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