Impact of satellite data assimilation on the predictability of monsoon intraseasonal oscillations in a regional model

Type
Article

Authors
Parekh, Anant
Attada, Raju
Chowdary, J. S.
Gnanaseelan, C.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2017-04-07

Print Publication Date
2017-07-03

Date
2017-04-07

Abstract
This study reports the improvement in the predictability of circulation and precipitation associated with monsoon intraseasonal oscillations (MISO) when the initial state is produced by assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and water vapour profiles in Weather Research Forecast (WRF) model. Two separate simulations are carried out for nine years (2003 to 2011) . In the first simulation, forcing is from National Centers for Environmental Prediction (NCEP, CTRL) and in the second, apart from NCEP forcing, AIRS temperature and moisture profiles are assimilated (ASSIM). Ten active and break cases are identified from each simulation. Three dimensional temperature states of identified active and break cases are perturbed using twin perturbation method and carried out predictability tests. Analysis reveals that the limit of predictability of low level zonal wind is improved by four (three) days during active (break) phase. Similarly the predictability of upper level zonal wind (precipitation) is enhanced by four (two) and two (four) days respectively during active and break phases. This suggests that the initial state using AIRS observations could enhance predictability limit of MISOs in WRF. More realistic baroclinic response and better representation of vertical state of atmosphere associated with monsoon enhance the predictability of circulation and rainfall.

Citation
Parekh A, Raju A, Chowdary JS, Gnanaseelan C (2017) Impact of satellite data assimilation on the predictability of monsoon intraseasonal oscillations in a regional model. Remote Sensing Letters 8: 686–695. Available: http://dx.doi.org/10.1080/2150704x.2017.1312614.

Acknowledgements
Authors acknowledge Director, ESSO-IITM for support and NCAR, Boulder, Colorado, USA for making the WRF-ARW model available. Authors acknowledge AIRS and NCEP for data. Figures are prepared in GrADS. We sincerely thank the anonymous reviewers for their valuable comments that helped us to improve the manuscript.

Publisher
Informa UK Limited

Journal
Remote Sensing Letters

DOI
10.1080/2150704x.2017.1312614

Additional Links
http://www.tandfonline.com/doi/full/10.1080/2150704X.2017.1312614

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