Prediction of tropical cyclone over North Indian Ocean using WRF model: sensitivity to scatterometer winds, ATOVS and ATMS radiances

Handle URI:
http://hdl.handle.net/10754/621986
Title:
Prediction of tropical cyclone over North Indian Ocean using WRF model: sensitivity to scatterometer winds, ATOVS and ATMS radiances
Authors:
Dodla, Venkata B.; Srinivas, Desamsetti; Dasari, Hari Prasad; Gubbala, Chinna Satyanarayana
Abstract:
Tropical cyclone prediction, in terms of intensification and movement, is important for disaster management and mitigation. Hitherto, research studies were focused on this issue that lead to improvement in numerical models, initial data with data assimilation, physical parameterizations and application of ensemble prediction. Weather Research and Forecasting (WRF) model is the state-of-art model for cyclone prediction. In the present study, prediction of tropical cyclone (Phailin, 2013) that formed in the North Indian Ocean (NIO) with and without data assimilation using WRF model has been made to assess impacts of data assimilation. WRF model was designed to have nested two domains of 15 and 5 km resolutions. In the present study, numerical experiments are made without and with the assimilation of scatterometer winds, and radiances from ATOVS and ATMS. The model performance was assessed in respect to the movement and intensification of cyclone. ATOVS data assimilation experiment had produced the best prediction with least errors less than 100 km up to 60 hours and producing pre-deepening and deepening periods accurately. The Control and SCAT wind assimilation experiments have shown good track but the errors were 150-200 km and gradual deepening from the beginning itself instead of sudden deepening.
KAUST Department:
King Abdullah University of Science and Technology, Saudi Arabia, Saudi Arabia
Citation:
Dodla VB, Srinivas D, Dasari HP, Gubbala CS (2016) Prediction of tropical cyclone over North Indian Ocean using WRF model: sensitivity to scatterometer winds, ATOVS and ATMS radiances. Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI. Available: http://dx.doi.org/10.1117/12.2223615.
Publisher:
SPIE-Intl Soc Optical Eng
Journal:
Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI
Conference/Event name:
Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI
Issue Date:
3-May-2016
DOI:
10.1117/12.2223615
Type:
Conference Paper
Sponsors:
The authors thank Dr. Zhang and Dr. Vijay Tallapragada for providing the observations and satellite data for the assimilation experiments in the study and acknowledge the data source from Environmental Modeling Center, NOAA, USA. The authors also thankful to Dr.V.S.Prasad, NCMRWF for providing the scatterometer wind observations during the study period.
Additional Links:
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2520764
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorDodla, Venkata B.en
dc.contributor.authorSrinivas, Desamsettien
dc.contributor.authorDasari, Hari Prasaden
dc.contributor.authorGubbala, Chinna Satyanarayanaen
dc.date.accessioned2016-12-08T13:54:25Z-
dc.date.available2016-12-08T13:54:25Z-
dc.date.issued2016-05-03en
dc.identifier.citationDodla VB, Srinivas D, Dasari HP, Gubbala CS (2016) Prediction of tropical cyclone over North Indian Ocean using WRF model: sensitivity to scatterometer winds, ATOVS and ATMS radiances. Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI. Available: http://dx.doi.org/10.1117/12.2223615.en
dc.identifier.doi10.1117/12.2223615en
dc.identifier.urihttp://hdl.handle.net/10754/621986-
dc.description.abstractTropical cyclone prediction, in terms of intensification and movement, is important for disaster management and mitigation. Hitherto, research studies were focused on this issue that lead to improvement in numerical models, initial data with data assimilation, physical parameterizations and application of ensemble prediction. Weather Research and Forecasting (WRF) model is the state-of-art model for cyclone prediction. In the present study, prediction of tropical cyclone (Phailin, 2013) that formed in the North Indian Ocean (NIO) with and without data assimilation using WRF model has been made to assess impacts of data assimilation. WRF model was designed to have nested two domains of 15 and 5 km resolutions. In the present study, numerical experiments are made without and with the assimilation of scatterometer winds, and radiances from ATOVS and ATMS. The model performance was assessed in respect to the movement and intensification of cyclone. ATOVS data assimilation experiment had produced the best prediction with least errors less than 100 km up to 60 hours and producing pre-deepening and deepening periods accurately. The Control and SCAT wind assimilation experiments have shown good track but the errors were 150-200 km and gradual deepening from the beginning itself instead of sudden deepening.en
dc.description.sponsorshipThe authors thank Dr. Zhang and Dr. Vijay Tallapragada for providing the observations and satellite data for the assimilation experiments in the study and acknowledge the data source from Environmental Modeling Center, NOAA, USA. The authors also thankful to Dr.V.S.Prasad, NCMRWF for providing the scatterometer wind observations during the study period.en
dc.publisherSPIE-Intl Soc Optical Engen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2520764en
dc.rightsCopyright 2016 (year) Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en
dc.titlePrediction of tropical cyclone over North Indian Ocean using WRF model: sensitivity to scatterometer winds, ATOVS and ATMS radiancesen
dc.typeConference Paperen
dc.contributor.departmentKing Abdullah University of Science and Technology, Saudi Arabia, Saudi Arabiaen
dc.identifier.journalRemote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VIen
dc.conference.date4 April 2016 through 7 April 2016en
dc.conference.nameRemote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VIen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionKL University, Indiaen
kaust.authorSrinivas, Desamsettien
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