Improved Retrieval of Integrated Water Vapor from Water Vapor Radiometer Measurements Using Numerical Weather Prediction Models

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  • 1 Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina
  • | 2 University Navstar Consortium (UNAVCO), Boulder, Colorado
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Abstract

Water vapor radiometer (WVR) retrieval algorithms require a priori information on atmospheric conditions along the line of sight of the radiometer in order to derive opacities from observed brightness temperatures. This paper's focus is the mean radiating temperature of the atmosphere (Tmr), which is utilized in these algorithms to relate WVR measurements to integrated water vapor. Current methods for specifying Tmr rely on the climatology of the WVR site-for example, a seasonal average-or information from nearby soundings to specify Tmr. However, values of Tmr, calculated from radiosonde data, not only vary according to site and season but also exhibit large fluctuations in response to local weather conditions. By utilizing output from numerical weather prediction (NWP) models, Tmr can be accurately prescribed for an arbitrary WVR site at a specific time. Temporal variations in local weather conditions can he resolved by NWP models on timescales shorter than standard radiosonde soundings.

Currently used methods for obtaining Tmr are reviewed. Values of Tmr obtained from current methods and this new approach are compared to those obtained from in situ radiosonde soundings. The improvement of the Tmr calculation using available model forecast data rather than climatological values yields a corresponding improvement of comparable magnitude in the retrieval of atmospheric opacity. Use of forecast model data relieves a WVR site of its dependency on local climatology or the necessity of a nearby sounding, allowing more accurate retrieval of observed conditions and increased flexibility in choosing site location. Furthermore, it is found that the calculation of precipitable water by means of atmospheric opacities does not require time-dependent tuning parameters when model data are used. These results were obtained using an archived subset of the full nested grid model output. The added horizontal and vertical resolution of operational data should further improve this approach.

Abstract

Water vapor radiometer (WVR) retrieval algorithms require a priori information on atmospheric conditions along the line of sight of the radiometer in order to derive opacities from observed brightness temperatures. This paper's focus is the mean radiating temperature of the atmosphere (Tmr), which is utilized in these algorithms to relate WVR measurements to integrated water vapor. Current methods for specifying Tmr rely on the climatology of the WVR site-for example, a seasonal average-or information from nearby soundings to specify Tmr. However, values of Tmr, calculated from radiosonde data, not only vary according to site and season but also exhibit large fluctuations in response to local weather conditions. By utilizing output from numerical weather prediction (NWP) models, Tmr can be accurately prescribed for an arbitrary WVR site at a specific time. Temporal variations in local weather conditions can he resolved by NWP models on timescales shorter than standard radiosonde soundings.

Currently used methods for obtaining Tmr are reviewed. Values of Tmr obtained from current methods and this new approach are compared to those obtained from in situ radiosonde soundings. The improvement of the Tmr calculation using available model forecast data rather than climatological values yields a corresponding improvement of comparable magnitude in the retrieval of atmospheric opacity. Use of forecast model data relieves a WVR site of its dependency on local climatology or the necessity of a nearby sounding, allowing more accurate retrieval of observed conditions and increased flexibility in choosing site location. Furthermore, it is found that the calculation of precipitable water by means of atmospheric opacities does not require time-dependent tuning parameters when model data are used. These results were obtained using an archived subset of the full nested grid model output. The added horizontal and vertical resolution of operational data should further improve this approach.

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