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Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts

Author

Listed:
  • Tanvir Islam

    (California Institute of Technology
    NOAA/NESDIS Center for Satellite Applications and Research
    Colorado State University)

  • Prashant K. Srivastava

    (NASA Goddard Space Flight Center
    University of Maryland)

  • Dinesh Kumar

    (Central University of Jammu)

  • George P. Petropoulos

    (Aberystwyth University)

  • Qiang Dai

    (Nanjing Normal University)

  • Lu Zhuo

    (University of Bristol)

Abstract

In this article, we present an assimilation impact study for forecasting hurricane Sandy using a three‐dimensional variational data assimilation system (3DVAR). In particular, we employ the 3DVAR component of the Weather Research and Forecasting Model and conduct analysis/forecast cycling experiments for “control” and “radiance” assimilation cases for the hurricane Sandy period. In “control” assimilation experiment, only conventional air and surface observations data are assimilated, while, in “radiance” assimilation experiment, along with the conventional air and surface observations data, the satellite radiance data from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) sensors are also assimilated. For the radiance assimilation, we employ the community radiative transfer model as the forward operator and perform quality control and bias correction procedure before the radiance data are assimilated. In order to assess the impact of the assimilation experiments, we produce 132-h deterministic forecast starting on 00 UTC October 25, 2012. The results reveal that, in particular, the assimilation of AMSU-A satellite radiances helps to improve the short- to medium-range forecast (up to ~60-h lead time). The forecast skill is degraded in the long-range forecast (beyond 60 h) with the AMSU-A assimilation.

Suggested Citation

  • Tanvir Islam & Prashant K. Srivastava & Dinesh Kumar & George P. Petropoulos & Qiang Dai & Lu Zhuo, 2016. "Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 845-855, June.
    • Handle: RePEc:spr:nathaz:v:82:y:2016:i:2:d:10.1007_s11069-016-2221-4
    DOI: 10.1007/s11069-016-2221-4
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    References listed on IDEAS

    as
    1. Deepak Subramani & R. Chandrasekar & K. Ramanujam & C. Balaji, 2014. "A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(1), pages 659-682, March.
    2. Asnor Ishak & Renji Remesan & Prashant Srivastava & Tanvir Islam & Dawei Han, 2013. "Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 1-23, January.
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