Retrieval of Water Vapor Profiles from GPS/MET Radio Occultations

Retrieval of Water Vapor Profiles from GPS/MET Radio Occultations Present Global Positioning System Meteorology (GPS/MET) refractivity profiles cannot distinguish between refractivity effects due to water vapor and those due to air density. Current methods of resolving the ambiguity rely heavily on ancillary upper-air data, such as National Centers for Environmental Prediction and European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. However, the accuracy of these ancillary sources suffers in regions where upper-air data are sparse. A method of separating the water vapor and temperature effects in GPS/MET-derived refractivity profiles with the addition of only ancillary surface pressure and temperature data and the hydrostatic assumption is discussed. Water vapor and temperature data derived from this method are presented and compared with accepted values. This method allows for the construction of temperature profiles with a mean bias of 0.33 K and a mean standard deviation of 1.86 K when compared with ECMWF data from 30 to 1000 mb. Height fields can also be corrected to within an average bias of 6 m and a standard deviation of 31 m. These corrected profiles result in retrieved water vapor pressure profiles with an average bias of 0.19 mb and a standard deviation of 0.53 mb. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

Retrieval of Water Vapor Profiles from GPS/MET Radio Occultations

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
D.O.I.
10.1175/1520-0477(2000)081<1031:ROWVPF>2.3.CO;2
Publisher site
See Article on Publisher Site

Abstract

Present Global Positioning System Meteorology (GPS/MET) refractivity profiles cannot distinguish between refractivity effects due to water vapor and those due to air density. Current methods of resolving the ambiguity rely heavily on ancillary upper-air data, such as National Centers for Environmental Prediction and European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. However, the accuracy of these ancillary sources suffers in regions where upper-air data are sparse. A method of separating the water vapor and temperature effects in GPS/MET-derived refractivity profiles with the addition of only ancillary surface pressure and temperature data and the hydrostatic assumption is discussed. Water vapor and temperature data derived from this method are presented and compared with accepted values. This method allows for the construction of temperature profiles with a mean bias of 0.33 K and a mean standard deviation of 1.86 K when compared with ECMWF data from 30 to 1000 mb. Height fields can also be corrected to within an average bias of 6 m and a standard deviation of 31 m. These corrected profiles result in retrieved water vapor pressure profiles with an average bias of 0.19 mb and a standard deviation of 0.53 mb.

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: May 8, 2000

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