Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Alishouse, J. C., S. A. Snyder, J. Vongsathorn, and R. R. Ferraro, 1990: Determination of oceanic total precipitable water from the SSM/I. IEEE Trans. Geosci. Remote Sens.,28, 811–816.

  • Baker, M. N., H. E. Fuelberg, and J. E. Ahlquist, 1993: Satellite-derived precipitable water over central Florida and its relation to thunderstorm development. Preprints, 17th Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 88–92.

  • Birkenheuer, D., 1991: An algorithm for operational water vapor analyses integrating GOES and dual-channel microwave radiometer data on the local scale. J. Appl. Meteor.,30, 834–843.

  • Chesters, D., L. W. Uccellini, and W. D. Robinson, 1983: Low-level water vapor fields from the VISSR Atmospheric Sounder (VAS) split window channels. J. Climate Appl. Meteor.,22, 725–743.

  • ——, W. D. Robinson, and L. W. Uccellini, 1987: Optimized retrievals of precipitable water from the VAS split window. J. Climate Appl. Meteor.,26, 1059–1066.

  • Fuelberg, H. E., and S. R. Olson, 1991: An assessment of VAS-derived retrievals and parameters used in thunderstorm forecasting. Mon. Wea. Rev.,119, 795–814.

  • Guillory, A. R., G. J. Jedlovec, and H. E. Fuelberg, 1993: A technique for deriving column-integrated water content using VAS split-window data. J. Appl. Meteor.,32, 1226–1241.

  • Hayden, C. M., 1988: GOES-VAS simultaneous temperature–moisture retrieval algorithm. J. Appl. Meteor.,27, 705–733.

  • ——, and T. J. Schmit, 1994: GOES-I temperature moisture retrievals and associated gradient wind estimates. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., 477–480.

  • Jedlovec, G. J., 1987: Determination of atmospheric moisture structure from high resolution MAMS radiance data. Ph.D. dissertation, University of Wisconsin–Madison, 187 pp.

  • ——, 1990: Precipitable water estimation from high-resolution split-window radiance measurements. J. Appl. Meteor.,29, 863–877.

  • ——, and G. S. Carlson, 1994: Guess dependence of the physical split window technique for the retrieval of integrated water content. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., 118–121.

  • ——, A. R. Guillory, and G. S. Carlson, 1994: The retrieval of integrated water content from GOES I. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., J3–J6.

  • Knabb, R. D., and H. E. Fuelberg, 1994: An evaluation of several techniques for computing precipitable water from GOES-VAS data. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., 122–123.

  • ——, and ——, 1996. The role of first-guess temperature and water vapor in three techniques for estimating precipitable water from GOES data. Preprints, Eighth Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., 19–23.

  • ——, ——, and G. J. Jedlovec, 1994: A sensitivity analysis of the split window variance ratio technique for estimating precipitable water. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., 86–89.

  • Le Marshall, J. F., 1988: An intercomparison of temperature and moisture fields derived from TIROS Operational Vertical Sounder data by different retrieval techniques. Part I: Basic statistics. J. Appl. Meteor.,27, 1282–1293.

  • McGuirk, J. P., M. Yin, and H. S. Chung, 1994: Statistical retrieval of precipitable water from TOVS and OLR. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., 94–95.

  • McMillin, L. M., H. E. Fleming, and M. L. Hill, 1979: Atmospheric transmittance of an absorbing gas. 3: A computationally fast and accurate transmittance model for absorbing gases with variable mixing ratios in inhomogeneous atmospheres. Appl. Opt.,18, 1600–1606.

  • Rao, P. A., H. E. Fuelberg, C. M. Hayden, and T. J. Schmit, 1996: An initial evaluation of GOES-8 retrievals. Preprints, Eighth Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., 498–502.

  • Reagan, J., K. Thome, B. Herman, R. Stone, J. DeLuisi, and J. Snider, 1995: A comparison of columnar water vapor retrievals obtained with near-IR solar radiometer and microwave radiometer measurements. J. Appl. Meteor.,34, 1384–1391.

  • Rogers, R. R., and A. P. Schwartz, 1991: Mesoscale fluctuations of columnar water vapor. J. Appl. Meteor.,30, 1305–1322.

  • Suggs, R. J., and G. J. Jedlovec, 1996: A comparison of total integrated water content retrieved from GOES-7 and GOES-8. Preprints, Eighth Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., 30–34.

  • Thome, K. J., B. M. Herman, and J. A. Reagan, 1992: Determination of precipitable water from solar transmission. J. Appl. Meteor.,31, 157–165.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 245 87 3
PDF Downloads 22 10 2
Editorial Type:
Article
Article Type:
Research Article

A Comparison of the First-Guess Dependence of Precipitable Water Estimates from Three Techniques Using GOES Data

Richard D. KnabbDepartment of Meteorology, The Florida State University, Tallahassee, Florida

Search for other papers by Richard D. Knabb in
Current site
Google Scholar
PubMedClose
and
Henry E. FuelbergDepartment of Meteorology, The Florida State University, Tallahassee, Florida

Search for other papers by Henry E. Fuelberg in
Current site
Google Scholar
PubMedClose
Print Publication:
01 May 1997
DOI:
https://doi.org/10.1175/1520-0450(1997)036<0417:ACOTFG>2.0.CO;2
Page(s):
417–427
Restricted access

Abstract

This paper evaluates and intercompares three existing algorithms for calculating precipitable water (PW) using infrared radiances from the GOES-7 VISSR (Visible and Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS). The study exclusively uses simulated, rather than observed, VAS radiances in all retrievals. The National Environmental Satellite, Data, and Information Service simultaneous physical algorithm utilizes data from all 12 VAS channels and produces a vertical profile of temperature and dewpoint from which PW can be calculated. The Chesters technique and Jedlovec’s physical split-window technique retrieve PW from radiances in the two split window channels without first computing a dewpoint profile. All three algorithms also can be used with GOES-8 and GOES-9 data.

These algorithms have not been intercompared previously. Each is applied on case days having wide variations in temperature and moisture. The algorithms are supplied with first-guess information of varying accuracy to assess their sensitivity to the guess data. The performance of the techniques relative to one another is described, including important similarities and differences among them.

Results show that all three algorithms perform well within most temperature and moisture regimes. Each retrieves PW that is generally an improvement upon the first guess and is more accurate than PW predicted by surface temperature alone. However, each algorithm is somewhat dependent upon the first guess. Warm-biased first-guess surface temperatures are generally associated with moist-biased PW retrievals, while cold-biased first-guess surface temperatures are generally associated with dry-biased retrievals. The first-guess surface temperature errors reflect the presence, in either the first-guess or observed temperature profiles, of low-level inversions that cause the PW retrieval errors. Retrievals made where the observed contribution of low-level moisture to total column PW is small are usually moist biased, while those where the low-level contribution is large are usually dry biased. Both of these relationships exist irrespective of the sign of the first-guess PW error.

Corresponding author address: Richard D. Knabb, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-3034.

knabb@met.fsu.edu

Abstract

This paper evaluates and intercompares three existing algorithms for calculating precipitable water (PW) using infrared radiances from the GOES-7 VISSR (Visible and Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS). The study exclusively uses simulated, rather than observed, VAS radiances in all retrievals. The National Environmental Satellite, Data, and Information Service simultaneous physical algorithm utilizes data from all 12 VAS channels and produces a vertical profile of temperature and dewpoint from which PW can be calculated. The Chesters technique and Jedlovec’s physical split-window technique retrieve PW from radiances in the two split window channels without first computing a dewpoint profile. All three algorithms also can be used with GOES-8 and GOES-9 data.

These algorithms have not been intercompared previously. Each is applied on case days having wide variations in temperature and moisture. The algorithms are supplied with first-guess information of varying accuracy to assess their sensitivity to the guess data. The performance of the techniques relative to one another is described, including important similarities and differences among them.

Results show that all three algorithms perform well within most temperature and moisture regimes. Each retrieves PW that is generally an improvement upon the first guess and is more accurate than PW predicted by surface temperature alone. However, each algorithm is somewhat dependent upon the first guess. Warm-biased first-guess surface temperatures are generally associated with moist-biased PW retrievals, while cold-biased first-guess surface temperatures are generally associated with dry-biased retrievals. The first-guess surface temperature errors reflect the presence, in either the first-guess or observed temperature profiles, of low-level inversions that cause the PW retrieval errors. Retrievals made where the observed contribution of low-level moisture to total column PW is small are usually moist biased, while those where the low-level contribution is large are usually dry biased. Both of these relationships exist irrespective of the sign of the first-guess PW error.

Corresponding author address: Richard D. Knabb, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-3034.

knabb@met.fsu.edu

Save