• Bates, J. J., X. Wu, and D. L. Jackson, 1996: Interannual variability of upper-tropospheric water vapor brightness temperature. J. Climate,9, 427–438.

    • Crossref
    • Export Citation
  • Blackwell, K. G., and J. P. McGuirk, 1996: Tropical upper-tropospheric dry regions from TOVS and radiosondes. J. Appl. Meteor.,35, 464–481.

    • Crossref
    • Export Citation
  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • Dastoor, A. P., 1994: Cloudiness parameterization and verification in a large-scale atmospheric model. Tellus,46A, 615–634.

    • Crossref
    • Export Citation
  • Deblonde, G., L. Garand, P. Gauthier, and C. Grassotti, 1995: Assimilation of SSM/I and GOES humidity retrievals with a one-dimensional variational analysis scheme. J. Appl. Meteor.,34, 1536–1550.

    • Crossref
    • Export Citation
  • Garand, L., 1983: Some improvements and complements to the infrared emissivity algorithm including a parameterization of the absorption in the continuum region. J. Atmos. Sci.,40, 230–244.

    • Crossref
    • Export Citation
  • ——, 1993: A pattern recognition technique for retrieving humidity profiles from Meteosat or GOES imagery. J. Appl. Meteor.,32, 1592–1607.

    • Crossref
    • Export Citation
  • ——, 1994: Assimilation of satellite humidity retrievals at the CMC. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., 397–399.

  • Liou, K.-N., 1980: An Introduction to Atmospheric Radiation. Academic Press, 392 pp.

  • Macpherson, B., B. J. Wright, W. H. Hand, and A. J. Maycock, 1996: The impact of MOPS moisture data in the U. K. Meteorological Office mesoscale data assimilation scheme. Mon. Wea. Rev.,124, 1746–1766.

    • Crossref
    • Export Citation
  • McMillin, L. M., D. S. Crosby, and M. D. Goldberg, 1995: A water vapor index from satellite measurements. J. Appl. Meteor.,34, 1551–1558.

    • Crossref
    • Export Citation
  • McNally, A. P., and M. Vesperini, 1996: Variational analysis of humidity information from TOVS radiances at ECMWF. Quart. J. Roy. Meteor. Soc.,122, 1521–1544.

    • Crossref
    • Export Citation
  • Mitchell, H. L., C. Charette, C. Chouinard, and B. Brasnett, 1990: Revised interpolation statistics for the Canadian data assimilation procedure: Their derivation and application. Mon. Wea. Rev.,118, 1591–1614.

    • Crossref
    • Export Citation
  • Phalippou, L., 1996: Variational retrieval of humidity profile, wind speed and cloud liquid-water path with the SSM/I: Potential for numerical weather prediction. Quart. J. Roy. Meteor. Soc.,122B, 327–355.

    • Crossref
    • Export Citation
  • Rodgers, C. D., and C. D. Walshaw, 1966: The computation of infrared cooling rate in planetary atmospheres. Quart. J. Roy. Meteor. Soc.,92, 669–679.

    • Crossref
    • Export Citation
  • Rothman, L. S., and Coauthors, 1987: The HITRAN database: 1986 edition. Appl. Opt.,26, 4058–4097.

    • Crossref
    • Export Citation
  • Salathé, E. P., Jr., D. Chesters, and Y. C. Sud, 1995: Variability of moisture in the upper-troposphere as inferred from TOVS satellite observations and the ECMWF model analyses in 1989. J. Climate,8, 120–132.

  • Schmetz, J., and O. M. Turpeinen, 1988: Estimation of the upper tropospheric relative humidity field from Meteosat water vapor image data. J. Appl. Meteor.,27, 889–899.

    • Crossref
    • Export Citation
  • ——, C. Geijo, W. P. Menzel, K. Strabala, L. van De Berg, K. Holmlund, and S. Tjemkes, 1995: Satellite observations of upper tropospheric relative humidity, clouds and wind field divergence. Contrib. Atmos. Phys.,68, 345–357.

  • Soden, B. J., and F. P. Bretherton, 1993: Upper tropospheric relative humidity from GOES-7 channel: Method and climatology for July 1987. J. Geophys. Res.,98(D9), 16 669–16 688.

    • Crossref
    • Export Citation
  • ——, and ——, 1994: Evaluation of water vapour distribution in general circulation models using satellite observations. J. Geophys. Res.,99(D1), 1187–1210.

    • Crossref
    • Export Citation
  • ——, and ——, 1996: Interpretation of TOVS water vapor radiances in terms of layer-average relative humidities: Method and climatology for the upper, middle, and lower troposphere. J. Geophys. Res.,101(D5), 9333–9343.

    • Crossref
    • Export Citation
  • Stephens, G. L., D. L. Jackson, and I. Wittmeyer, 1996: Global observations of upper-tropospheric water vapor derived from TOVS radiance data. J. Climate,9, 305–326.

    • Crossref
    • Export Citation
  • Sullivan, J., L. Gandin, A. Gruber, and W. Baker, 1993: Observation error statistics for NOAA-10 temperature and height retrievals. Mon. Wea. Rev.,121, 2578–2587.

  • Yu, W., L. Garand, and A. P. Dastoor, 1997: Evaluation of model clouds and radiation at 100 km scale using GOES data. Tellus,49A, 246–262.

    • Crossref
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 102 102 3
PDF Downloads 14 14 2

Assimilation of Clear- and Cloudy-Sky Upper-Tropospheric Humidity Estimates Using GOES-8 and GOES-9 Data

View More View Less
  • 1 Atmospheric Environment Service of Canada, Dorval, Quebec, Canada
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

A strong linearity exists between the 6.7-μm clear-sky outgoing brightness temperature (BT) and dewpoint depression (DPD) at upper-tropospheric levels. A similar relationship, using the logarithm of relative humidity instead of DPD, was developed by Soden and Bretherton. Here, however, the humidity at specific levels is derived as opposed to the humidity integrated over upper-tropospheric levels. Linear relationships are obtained between a 6-h model forecast of DPD and calculated BTs at different viewing angles. The data are further stratified in terms of 400-mb temperature as an indicator of airmass type. Applying these relationships using observed 6.7-μm BTs and a 6-h forecast of 400-mb temperature yields vertically correlated estimates of DPD between 200 and 500 mb, with DPD typically decreasing with height, and corresponding rms error estimates in the range 3–6 K. The retrieval technique is applied to GOES-8 and GOES-9 data, which cover about 40% of the globe. In cloudy regions, proxy humidity estimates based on cloud classification are used. These clear- and cloudy-sky DPD estimates are assimilated every 6 h in a global forecast model, taking into consideration the horizontal correlation of the error. The system is supplemented by quality-control procedures.

In parallel runs at the Canadian Meteorological Centre, the analyses and forecasts with satellite data (SAT) were found significantly improved with respect to those without satellite data (NOSAT). The system was therefore implemented. The superiority of the SAT forecasts in terms of 6.7-μm BT, 2-K versus 4-K rms at initial time, gradually decreases to the level of the NOSAT forecasts in 48 h. A slight improvement on geopotential, DPD, and temperature is observed in 48-h forecasts with respect to radiosondes over North America. The new upper-tropospheric DPD retrieval technique is robust and could easily be applied to other geostationary or polar-orbiting platforms providing 6.7-μm imagery.

Corresponding author address: Dr. Louis Garand, Atmospheric Environment Service, 2121 Trans-Canada Highway, Dorval, PQ H9P 1J3, Canada.

Email: louis.garand@ec.gc.ca

Abstract

A strong linearity exists between the 6.7-μm clear-sky outgoing brightness temperature (BT) and dewpoint depression (DPD) at upper-tropospheric levels. A similar relationship, using the logarithm of relative humidity instead of DPD, was developed by Soden and Bretherton. Here, however, the humidity at specific levels is derived as opposed to the humidity integrated over upper-tropospheric levels. Linear relationships are obtained between a 6-h model forecast of DPD and calculated BTs at different viewing angles. The data are further stratified in terms of 400-mb temperature as an indicator of airmass type. Applying these relationships using observed 6.7-μm BTs and a 6-h forecast of 400-mb temperature yields vertically correlated estimates of DPD between 200 and 500 mb, with DPD typically decreasing with height, and corresponding rms error estimates in the range 3–6 K. The retrieval technique is applied to GOES-8 and GOES-9 data, which cover about 40% of the globe. In cloudy regions, proxy humidity estimates based on cloud classification are used. These clear- and cloudy-sky DPD estimates are assimilated every 6 h in a global forecast model, taking into consideration the horizontal correlation of the error. The system is supplemented by quality-control procedures.

In parallel runs at the Canadian Meteorological Centre, the analyses and forecasts with satellite data (SAT) were found significantly improved with respect to those without satellite data (NOSAT). The system was therefore implemented. The superiority of the SAT forecasts in terms of 6.7-μm BT, 2-K versus 4-K rms at initial time, gradually decreases to the level of the NOSAT forecasts in 48 h. A slight improvement on geopotential, DPD, and temperature is observed in 48-h forecasts with respect to radiosondes over North America. The new upper-tropospheric DPD retrieval technique is robust and could easily be applied to other geostationary or polar-orbiting platforms providing 6.7-μm imagery.

Corresponding author address: Dr. Louis Garand, Atmospheric Environment Service, 2121 Trans-Canada Highway, Dorval, PQ H9P 1J3, Canada.

Email: louis.garand@ec.gc.ca

Save