A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System

Li Bi Cooperative Institute for Meteorological Satellite Studies, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

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James A. Jung Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin, and Joint Center for Satellite Data Assimilation, Camp Springs, Maryland

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Michael C. Morgan Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

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John F. Le Marshall Centre for Australian Weather and Climate Research, Melbourne, Victoria, Australia

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Abstract

A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments.

A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed.

The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

Corresponding author address: Li Bi, Dept. of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. Email: li.bi@ssec.wisc.edu

Abstract

A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments.

A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed.

The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

Corresponding author address: Li Bi, Dept. of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. Email: li.bi@ssec.wisc.edu

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  • Alishouse, J. C., Snyder S. , Vongsathorn J. , and Ferraro R. R. , 1990: Determination of oceanic total precipitable water from the SSM/I. IEEE Trans. Geosci. Remote Sens., 28 , 811816.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bettenhausen, M. H., Smith C. K. , Bevilacqua R. M. , Wang N. , Gaiser P. W. , and Cox S. , 2006: A non-linear optimization algorithm for wind sat wind vector retrievals. IEEE Trans. Geosci. Remote Sens., 44 , 597610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caplan, P., Derber J. C. , Gemmill W. , Hong S. , Pan H-L. , and Parrish D. F. , 1997: Changes to the 1995 NCEP operational medium-range forecast model analysis–forecast system. Wea. Forecasting, 12 , 581594.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derber, J. C., Parrish D. F. , and Lord S. J. , 1991: The new global operational analysis system at the National Meteorological Center. Wea. Forecasting, 6 , 538547.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derber, J. C., Van Delst P. , Su X. J. , Li X. , Okamoto K. , and Treadon R. , 2003: Enhanced use of radiance data in the NCEP data assimilation system. Proc. 13th Int. TOVS Study Conf., Sainte Adele, QC, Canada, Int. TOVS Working Group, 1.8. [Available online at http://cimss.ssec.wisc.edu/itwg/itsc/itsc13/proceedings/session1/1_8_derber.pdf].

    • Search Google Scholar
    • Export Citation
  • Gaiser, P. W., and Coauthors, 2004: The WindSat spaceborne polarimetric microwave radiometer: Sensor description and early orbit performance. IEEE Trans. Geosci. Remote Sens., 42 , 23472361.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, J. A., Zapotocny T. H. , Le Marshall J. F. , and Treadon R. E. , 2008: A two-season impact study of NOAA polar orbiting satellites in the NCEP Global Data Assimilation System. Wea. Forecasting, 23 , 854877.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., Kanamitsu M. , and Baker W. E. , 1990: Global numerical weather prediction at the National Meteorological Center. Bull. Amer. Meteor. Soc., 71 , 14101428.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., 1989: Description of the NMC Global Data Assimilation and Forecast System. Wea. Forecasting, 4 , 335342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., and Coauthors, 1991: Recent changes implemented into the Global Forecast System at NMC. Wea. Forecasting, 6 , 425435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keyser, D., cited. 2001a: Code table for PREPBUFR report types used by the Global GFS and GDAS GSI analyses. [Available online at http://www.emc.ncep.noaa.gov/mmb/data_processing/prepbufr.doc/table_2.htm].

    • Search Google Scholar
    • Export Citation
  • Keyser, D., cited. 2001b: Summary of the current NCEP analysis system usage of data types that do not pass through PREPBUFR processing. [Available online at http://www.emc.ncep.noaa.gov/mmb/data_processing/prepbufr.doc/table_18.htm].

    • Search Google Scholar
    • Export Citation
  • Keyser, D., cited. 2003: Observational data processing at NCEP. [Available online at http://www.emc.ncep.noaa.gov/mmb/data_processing/data_processing/].

    • Search Google Scholar
    • Export Citation
  • Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82 , 247267.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lahoz, W. A., 1999: Predictive skill of the UKMO Unified Model in the lower stratosphere. Quart. J. Roy. Meteor. Soc., 125 , 22052238.

  • Le Marshall, J., Bi L. , Jung J. , Zapotocny T. , and Morgan M. , 2007: WindSat polarimetric microwave observations improve Southern Hemisphere numerical weather prediction. Aust. Meteor. Mag., 56 , 3540.

    • Search Google Scholar
    • Export Citation
  • Le Marshall, J., Jung J. , Zapotocny T. , Redder C. , Dunn M. , Daniels J. , and Riishojgaard L. P. , 2008: Impacts of MODIS atmospheric motion vectors on a global NWP system. Aust. Meteor. Mag., 57 , 4551.

    • Search Google Scholar
    • Export Citation
  • Menzel, W. P., Holt F. C. , Schmit T. J. , Aune R. M. , Schreiner A. J. , Wade G. S. , and Gray D. G. , 1998: Application of GOES-8/9 soundings to weather forecasting and nowcasting. Bull. Amer. Meteor. Soc., 79 , 20592077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, A. J., and Coauthors, 1997: Information content of Umkehr and SBUV(2) satellite data for ozone trends and solar responses in the stratosphere. J. Geophys. Res., 102 , 1925719263.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA, cited. 2000: NOAA KLM users’ guide, September 2000 revision. [Available online at http://www2.ncdc.noaa.gov/docs/klm/cover.htm].

    • Search Google Scholar
    • Export Citation
  • NOAA, cited. 2005: NOAA Polar Orbiter Data (POD) user’s guide, November 1998 revision. [Available online at http://www2.ncdc.noaa.gov/docs/klm/html/c3/sec3-3.htm].

    • Search Google Scholar
    • Export Citation
  • NWS, cited. 2006: NCEP anomaly correlations. [Available online at http://wwwt.emc.ncep.noaa.gov/gmb/STATS/STATS.html].

  • Smith, W. L., Woolf H. M. , Hayden C. M. , Wark D. Q. , and McMillin L. M. , 1979: The TIROS-N Operational Vertical Sounder. Bull. Amer. Meteor. Soc., 60 , 11771187.

    • Search Google Scholar
    • Export Citation
  • Spencer, R. W., Christy J. R. , and Grody N. C. , 1990: Global atmospheric temperature monitoring with satellite microwave measurements: Method and results. J. Climate, 3 , 11111128.

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

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, T-W., and McPherson R. D. , 1984: Global data assimilation experiments with scatterometer winds from Seasat-A. Mon. Wea. Rev., 112 , 368376.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zapotocny, T., Menzel W. P. , Jung J. A. , and Nelson J. P. III, 2005: A four-season impact study of rawinsonde, GOES, and POES data in the Eta Data Assimilation System. Part I: The total contribution. Wea. Forecasting, 20 , 161177.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zapotocny, T., Jung J. A. , Le Marshall J. F. , and Treadon R. , 2007: A two-season impact study of satellite and in situ data in the NCEP Global Data Assimilation System. Wea. Forecasting, 22 , 887909.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zapotocny, T., Jung J. A. , Le Marshall J. F. , and Treadon R. , 2008: A two-season impact study of four satellite data types and rawinsonde data in the NCEP Global Data Assimilation System. Wea. Forecasting, 23 , 80100.

    • Crossref
    • Search Google Scholar
    • Export Citation
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