• Andersson, E., J. Pailleux, J.-N. Thépaut, J. R. Eyre, A. P. McNally, G. A. Kelly, and P. Courtier, 1994: Use of cloud-cleared radiances in three/four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 120, 627653.

    • Search Google Scholar
    • Export Citation
  • Brill, K., and F. Mesinger, 2009: Applying a general analytic method for assessing bias sensitivity to bias-adjusted threat and equitable threat scores. Wea. Forecasting, 24, 17481754.

    • Search Google Scholar
    • Export Citation
  • Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 22872299.

    • Search Google Scholar
    • Export Citation
  • Eyre, J. R., G. Kelly, A. P. McNally, E. Andersson, and A. Persson, 1993: Assimilation of TOVS radiance information through one-dimensional variational analysis. Quart. J. Roy. Meteor. Soc., 119, 14271463.

    • Search Google Scholar
    • Export Citation
  • Goerss, J. S., C. S. Velden, and J. D. Hawkins, 1998: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone forecasts in 1995. Part II: NOGAPS forecasts. Mon. Wea. Rev., 126, 12191227.

    • Search Google Scholar
    • Export Citation
  • Han, Y., F. Weng, Q. Liu, and P. van Delst, 2007: A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J. Geophys. Res., 112, D11121, doi:10.1029/2006JD008208.

    • Search Google Scholar
    • Export Citation
  • Hong, S. Y., and J. Dudhia, 2003: Testing of a new non-local boundary layer vertical diffusion scheme in numerical weather prediction applications. Preprints, 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 20.3. [Available online at http://ams.confex.com/ams/pdfpapers/72744.pdf.]

    • Search Google Scholar
    • Export Citation
  • Hong, S. Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Kor. Meteor. Soc., 42, 129151.

  • Junker, N. W., J. E. Hoke, B. E. Sullivan, K. F. Brill, and F. J. Hughes, 1992: Seasonal and geographic variations in quantitative precipitation prediction by NMC’s nested-grid model and medium-range forecast model. Wea. Forecasting, 7, 410429.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181.

  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

    • Search Google Scholar
    • Export Citation
  • Köpken, C., G. Kelly, and J.-N. Thépaut, 2004: Assimilation of Meteosat radiance data within the 4D-Var system at ECMWF: Assimilation experiments and forecast impact. Quart. J. Roy. Meteor. Soc., 130, 22772292.

    • Search Google Scholar
    • Export Citation
  • McMillin, L., and H. Fleming, 1976: Atmospheric transmittance model of an absorbing gas: A computationally fast and accurate model for absorbing gases with constant mixing ratios in inhomogeneous atmospheres. Appl. Opt., 15, 358363.

    • Search Google Scholar
    • Export Citation
  • Nieman, S. J., J. Schmetz, and W. P. Menzel, 1993: A comparison of several techniques to assign heights to cloud tracers. J. Appl. Meteor., 32, 15591568.

    • Search Google Scholar
    • Export Citation
  • NOAA/NESDIS, 2010: The GOES-14 Science Test: Imager and sounder radiance and product validations. NOAA Tech. Rep. NESDIS 131, 120 pp.

  • Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts, 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 15241535.

    • Search Google Scholar
    • Export Citation
  • Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts, 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 15361548.

    • Search Google Scholar
    • Export Citation
  • Rao, P. A., C. S. Velden, and S. A. Braun, 2002: The vertical error characteristics of GOES-derived winds: Description and experiments with numerical weather prediction. J. Appl. Meteor., 41, 253271.

    • Search Google Scholar
    • Export Citation
  • Saunders, R. W., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 14071425.

    • Search Google Scholar
    • Export Citation
  • Saunders, R., and Coauthors, 2007: A comparison of radiative transfer models for simulating Atmospheric Infrared Sounder (AIRS) radiance. J. Geophys. Res., 112, D01S90, doi:10.1029/2006JD007088.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., C. S. Velden, and R. E. Tuleya, 2001: The impact of satellite winds on experimental GFDL hurricane model forecasts. Mon. Wea. Rev., 129, 835852.

    • Search Google Scholar
    • Export Citation
  • Stengel, M., P. Undén, M. Lindskog, P. Dahlgren, N. Gustafsson, and R. Bennartz, 2009: Assimilation of SEVIRI infrared radiances with HIRLAM 4D-Var. Quart. J. Roy. Meteor. Soc., 135, 21002109.

    • Search Google Scholar
    • Export Citation
  • Su, X., J. Derber, J. Jung, Y. Tahara, D. Keyser, and R. Treadon, 2003: The usage of GOES imager clear-sky radiance in the NCEP global data assimilation system. Preprints, 12th Conf. on Satellite Meteorological and Oceanography, Long Beach, CA, Amer. Meteor. Soc., 3.20. [Available online at http://ams.confex.com/ams/pdfpapers/56361.pdf.]

    • Search Google Scholar
    • Export Citation
  • Szyndel, M. D. E., J.-N. Thépaut, and G. Kelly, 2005: Evaluation of potential benefit of SEVIRI water vapour radiance data from Meteosat-8 into global numerical weather prediction analyses. Atmos. Sci. Lett., 6, 105111.

    • Search Google Scholar
    • Export Citation
  • Tomassini, M., G. Kelly, and R. Saunders, 1999: Use and impact of satellite atmospheric motion winds on ECMWF analyses and forecasts. Mon. Wea. Rev., 127, 971986.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., 1996: Winds derived from geostationary satellite moisture channel observations: Applications and impact on numerical weather prediction. Meteor. Atmos. Phys., 60, 3746.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., C. M. Hayden, S. J. Nieman, W. P. Menzel, S. Wazong, and J. S. Goerss, 1997: Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bull. Amer. Meteor. Soc., 78, 173195.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., T. L. Olander, and S. Wazong, 1998: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset methodology, description, and case analysis. Mon. Wea. Rev., 126, 12021218.

    • Search Google Scholar
    • Export Citation
  • Weng, F., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64, 37993807.

  • Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 29052916.

    • Search Google Scholar
    • Export Citation
  • Zou, X., and Y.-H. Kuo, 1996: Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model. Mon. Wea. Rev., 124, 28592882.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 59 59 59
PDF Downloads 8 8 8

Improved Coastal Precipitation Forecasts with Direct Assimilation of GOES-11/12 Imager Radiances

View More View Less
  • 1 Department of Earth, Ocean, and Atmospheric Sciences, The Florida State University, Tallahassee, Florida
  • | 2 Department of Earth, Ocean, and Atmospheric Sciences, The Florida State University, Tallahassee, Florida, and Center of Data Assimilation for Research and Application, Nanjing University of Information and Science and Technology, Nanjing, China
  • | 3 National Environmental Satellite, Data, and Information Service, National Oceanic and Atmospheric Administration, Washington, D.C.
Restricted access

Abstract

The Geostationary Operational Environmental Satellite (GOES) imager provides observations that are of high spatial and temporal resolution and can be applied for effectively monitoring and nowcasting severe weather events. In this study, improved quantitative precipitation forecasts (QPFs) for three coastal storms over the northern Gulf of Mexico and the East Coast is demonstrated by assimilating GOES-11 and GOES-12 imager radiances into the Weather Research and Forecasting (WRF) model. Both the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to ingest GOES IR clear-sky data. Assimilation of GOES imager radiances during a 6–12-h time window prior to convective initiation and/or development could significantly improve the precipitation forecasts near the coast of the northern Gulf of Mexico. The 3-h accumulative precipitation threat scores are increased by about 20% after 6 h of model forecasts and more than 50% after 18–24 h of model forecasts. A detailed diagnosis of analysis fields and model forecast fields is carried out for one of the three convective precipitation events included in this study. It is shown that the assimilation of GOES data in regions of no or little clouds improved the model description of an upstream midlatitude trough and a subtropical high located in the south of the convection. The GOES observations located in the western part of land region covered by GOES within the latitude zone of 18°–37°N near 100°W contributed to a better forecast of the position of the eastward-propagating trough, while GOES observations over the Gulf of Mexico increased the amount of water vapor advection from the south into the convective region by the wind associated with the subtropical high. In the past, GOES imager radiances were not directly used in the GSI system. This study highlights the importance of satellite imagery information observed in the preconvective environment for improved cloud and precipitation forecasts. The developed data assimilation technique will prepare the NWP user community for accelerated use of advanced satellite data from the GOES-R series.

Corresponding author address: Xiaolei Zou, Dept. of Earth, Ocean, and Atmospheric Sciences, The Florida State University, 404 Love Bldg., Tallahassee, FL 32306-4520. E-mail: xzou@fsu.edu

Abstract

The Geostationary Operational Environmental Satellite (GOES) imager provides observations that are of high spatial and temporal resolution and can be applied for effectively monitoring and nowcasting severe weather events. In this study, improved quantitative precipitation forecasts (QPFs) for three coastal storms over the northern Gulf of Mexico and the East Coast is demonstrated by assimilating GOES-11 and GOES-12 imager radiances into the Weather Research and Forecasting (WRF) model. Both the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to ingest GOES IR clear-sky data. Assimilation of GOES imager radiances during a 6–12-h time window prior to convective initiation and/or development could significantly improve the precipitation forecasts near the coast of the northern Gulf of Mexico. The 3-h accumulative precipitation threat scores are increased by about 20% after 6 h of model forecasts and more than 50% after 18–24 h of model forecasts. A detailed diagnosis of analysis fields and model forecast fields is carried out for one of the three convective precipitation events included in this study. It is shown that the assimilation of GOES data in regions of no or little clouds improved the model description of an upstream midlatitude trough and a subtropical high located in the south of the convection. The GOES observations located in the western part of land region covered by GOES within the latitude zone of 18°–37°N near 100°W contributed to a better forecast of the position of the eastward-propagating trough, while GOES observations over the Gulf of Mexico increased the amount of water vapor advection from the south into the convective region by the wind associated with the subtropical high. In the past, GOES imager radiances were not directly used in the GSI system. This study highlights the importance of satellite imagery information observed in the preconvective environment for improved cloud and precipitation forecasts. The developed data assimilation technique will prepare the NWP user community for accelerated use of advanced satellite data from the GOES-R series.

Corresponding author address: Xiaolei Zou, Dept. of Earth, Ocean, and Atmospheric Sciences, The Florida State University, 404 Love Bldg., Tallahassee, FL 32306-4520. E-mail: xzou@fsu.edu
Save