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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 (T mr), which is utilized in these algorithms to relate WVR measurements to integrated water vapor. Current methods for specifying T mr rely on the climatology of the WVR site-for example, a seasonal average-or information from nearby soundings to specify T mr. However, values of T mr, 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, T mr 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 T mr are reviewed. Values of T mr obtained from current methods and this new approach are compared to those obtained from in situ radiosonde soundings. The improvement of the T mr 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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Michael Bevis, Steven Businger, Steven Chiswell, Thomas A. Herring, Richard A. Anthes, Christian Rocken, and Randolph H. Ware

Abstract

Emerging networks of Global Positioning System (GPS) receivers can be used in the remote sensing of atmospheric water vapor. The time-varying zenith wet delay observed at each GPS receiver in a network can be transformed into an estimate of the precipitable water overlying that receiver. This transformation is achieved by multiplying the zenith wet delay by a factor whose magnitude is a function of certain constants related to the refractivity of moist air and of the weighted mean temperature of the atmosphere. The mean temperature varies in space and time and must be estimated a priori in order to transform an observed zenith wet delay into an estimate of precipitable water. We show that the relative error introduced during this transformation closely approximates the relative error in the predicted mean temperature. Numerical weather models can be used to predict the mean temperature with an rms relative error of less than 1%.

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Jingping Duan, Michael Bevis, Peng Fang, Yehuda Bock, Steven Chiswell, Steven Businger, Christian Rocken, Frederick Solheim, Terasa van Hove, Randolph Ware, Simon McClusky, Thomas A. Herring, and Robert W. King

Abstract

A simple approach to estimating vertically integrated atmospheric water vapor, or precipitable water, from Global Positioning System (GPS) radio signals collected by a regional network of ground-based geodetic GPS receiver is illustrated and validated. Standard space geodetic methods are used to estimate the zenith delay caused by the neutral atmosphere, and surface pressure measurements are used to compute the hydrostatic (or “dry”) component of this delay. The zenith hydrostatic delay is subtracted from the zenith neutral delay to determine the zenith wet delay, which is then transformed into an estimate of precipitable water. By incorporating a few remote global tracking stations (and thus long baselines) into the geodetic analysis of a regional GPS network, it is possible to resolve the absolute (not merely the relative) value of the zenith neutral delay at each station in the augmented network. This approach eliminates any need for external comparisons with water vapor radiometer observations and delivers a pure GPS solution for precipitable water. Since the neutral delay is decomposed into its hydrostatic and wet components after the geodetic inversion, the geodetic analysis is not complicated by the fact that some GPS stations are equipped with barometers and some are not. This approach is taken to reduce observations collected in the field experiment GPS/STORM and recover precipitable water with an rms error of 1.0–1.5 mm.

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