IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v82y2016i2d10.1007_s11069-016-2221-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2221-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-016-2221-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tanvir Islam & Prashant Srivastava & Miguel Rico-Ramirez & Qiang Dai & Manika Gupta & Sudhir Singh, 2015. "Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics," 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. 76(3), pages 1473-1495, April.
    2. Prashant Srivastava & Tanvir Islam & Manika Gupta & George Petropoulos & Qiang Dai, 2015. "WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2267-2284, May.
    3. Prashant Srivastava & Dawei Han & Miguel Rico-Ramirez & Deleen Al-Shrafany & Tanvir Islam, 2013. "Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5069-5087, December.
    4. Prashant Srivastava & Dawei Han & Miguel Ramirez & Tanvir Islam, 2013. "Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3127-3144, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:82:y:2016:i:2:d:10.1007_s11069-016-2221-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.