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The Global Positioning System for the Geosciences: Summary and Proceedings of a Workshop on Improving the GPS Reference Station Infrastructure for Earth, Oceanic, and Atmospheric Science Applications (1997)
Commission on Engineering and Technical Systems (CETS)

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The Global Positioning System for the Geosciences: Summary and Proceedings of a Workshop on Improving the GPS Reference Station Infrastructure for Earth, Oceanic, and Atmospheric Science Applications
Application of GPS Data in Automated Hazardous Weather Detection and Forecast Systems

Larry Cornman, Jothiram Vivekanandan, Richard Wagoner

Research Applications Program, National Center for Atmospheric Research

INTRODUCTION

In order to provide operationally useful hazardous weather information to both meteorologists and non-meteorologists, a robust method for synthesizing all available data is required. This methodology should include: data quality controls, algorithm modules, data and product integration, and concise, user-friendly outputs. In the following, a brief description of such a system is presented.

The strength of the system described below lies in the use of multiple data sources and multiple detection, diagnostic, and forecast algorithms. The synthesis of the disparate data and algorithm output is implemented via a fuzzy logic algorithm. As the use of fuzzy logic algorithms have been limited in the atmospheric sciences, a detailed introduction is given below.

As it is a novel source of data, a brief outline of possible applications of GPS data in a real-time weather system is presented.

THE USES OF GPS DATA

The following list indicates a few potential applications of GPS data in a hazardous weather detection and forecasting system.

  • Hydrology

    • Watershed management.

    • Flash-flood monitoring.

    • Calibration of radar-based rainfall estimation techniques (Z/R relations).

  • Meso- and Storm Scale

    • Convective initiation.

    • Hazardous convective weather.

  • Icing and Winter Storms

    • Cloud liquid water content.

    • Snowfall rate estimation (calibration of radar-based Z/S relations)

  • Aviation-Specific Applications

    • Flight-level temperature.

    • Tropopause-height.

OVERALL SYSTEM DESCRIPTION

Figure 1 below illustrates the logical structure of the real-time system. At the top level is the data ingest and quality control algorithms. The output of these modules then feed into a suite of detection, diagnostic, and model-based algorithms.

Data Quality Control

Each algorithm module will perform its own quality control functions. These tasks will include the detection of time gaps or intermittence in the data stream and outlier detection from a meteorological perspective. Pertinent information about the data quality from a given algorithm module will be transmitted to the Integration Module (discussed below) via a set of confidence values.

The Analysis Grid

In order to efficiently combine all the detection, diagnostic, and forecast information from the individual algorithm modules it is desirable to map all of this information onto a common spatial grid. The use of a common grid, the so-called analysis grid, will also facilitate the functions of the Alert Generation Module.

Confidence Values

Along with the gridded detection and diagnostic information, each algorithm will produce a confidence value at each grid point. The idea behind the confidence value concept is twofold: producing a real time quality control metric and enabling an adaptive weighting

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