• DocumentCode
    1503446
  • Title

    Global Coverage of Total Precipitable Water Using a Microwave Variational Algorithm

  • Author

    Boukabara, Sid-Ahmed ; Garrett, Kevin ; Chen, Wanchun

  • Author_Institution
    NOAA Center for Satellite Applic. & Res., Camp Springs, MD, USA
  • Volume
    48
  • Issue
    10
  • fYear
    2010
  • Firstpage
    3608
  • Lastpage
    3621
  • Abstract
    This study introduces a variational approach to retrieve total precipitable water (TPW) over all surface backgrounds including ocean, land, snow, sea-ice, and coastal areas, from microwave sensors. The product has been used routinely by forecasters since its recent operational implementation. The emissivity is accounted for by including its spectrum within the retrieved state vector, which allows for a pixel-to-pixel variation of the emissivity, a factor usually preventing the TPW retrieval over land. The algorithm, implemented operationally at the National Atmospheric and Oceanic Administration (NOAA), is called the Microwave Integrated Retrieval System (MiRS). Its main characteristic, besides its applicability over all surfaces, is its validity under all weather conditions. With a generic design, the algorithm is being applied to the following microwave sensors: (1) AMSU and MHS onboard NOAA-18; (2) NOAA-19 and Metop-A; as well as (3) SSMI/S onboard DMSP-F16 platform. The assessment of the MiRS performances is done by undertaking extensive comparisons to the National Center for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, to a network of radiosondes and to existing well-established algorithms over ocean, encompassing a wide variety of meteorological situations. The performance of MiRS TPW is shown to depend on the sensor, the reference data source as well as on the surface background considered. It is shown to behave quite well over all surfaces and in all weather conditions, except when there is rain. Although this study focuses on the retrieval of TPW with an emphasis on non-oceanic surfaces, the underlying application of this study is the potential improvement in the variational data assimilation of Numerical Weather Prediction (NWP) models. Indeed, the same dynamic approach could be employed in order to assimilate more surface-sensitive microwave channels, over a multitude of surfaces.
  • Keywords
    data assimilation; radiosondes; rain; sea ice; weather forecasting; AMSU; Advanced Microwave Sounding Unit; DMSP-F16 platform; European Centre for Medium-Range Weather Forecasts analyses; MHS; Metop-A; MiRS TPW; Microwave Humidity Sounder; Microwave Integrated Retrieval System; NOAA-18; NOAA-19; National Atmospheric and Oceanic Administration; National Center for Environmental Prediction; SSMI-S; coastal areas; emissivity; microwave sensors; microwave variational algorithm; nonoceanic surfaces; numerical weather prediction models; pixel-to-pixel variation; radiosondes; rain; reference data source; sea-ice; snow; surface-sensitive microwave channels; total precipitable water retrieval; variational data assimilation; weather conditions; Algorithm design and analysis; Land surface; Microwave sensors; Oceans; Performance analysis; Sea ice; Sea measurements; Sea surface; Snow; Weather forecasting; Advanced Microwave Sounding Unit (AMSU); Microwave Humidity Sounder (MHS); SSMIS; data assimilation; microwave variational retrieval; radiosondes; total precipitable water (TPW);
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/TGRS.2010.2048035
  • Filename
    5473047