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James Foster, Michael Bevis, and Steven Businger

Abstract

The sliding-window technique uses a moving time window to select GPS data for processing. This makes it possible to routinely incorporate the most recently collected data and generate estimates for atmospheric delay or precipitable water in (near) real time. As a consequence of the technique several estimates may be generated for each time epoch, and these multiple estimates can be used to explore and analyze the characteristics of the atmospheric estimates and the effect of the processing model and parameters. Examples of some of the analyses that can be undertaken are presented. Insights into the phenomenology of the atmospheric estimates provided by sliding-window analysis permit the fine-tuning of the GPS processing as well as the possibility of both improving the accuracy of the near-real-time estimates themselves and constraining the errors associated with them. The overlapping data windows and the multiple estimates that characterize the sliding-window method can lead to ambiguity in the meaning of many terms and expressions commonly used in GPS meteorology. In order to prevent confusion in discussions of sliding-window processing, a nomenclature is proposed that formalizes the meaning of the primary terms and defines the geometric and physical relationships between them.

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Steven R. Chiswell, Steven Businger, Michael Bevis, Fredrick Solheim, Christian Rocken, and Randolph Ware

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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