Real-time Nationwide Precipitation Gauge Measurement Acquisition, Quality Control, and Integration into Hydrometeorological Applications
Katie L. Laro, Brad Wells, Tye W. Parzybok, MetStat, Inc.
Chris Galli, Joe Young, John Horel, Synoptic Data Corp.
Aggregating and integrating a multi-sensor quality control (QC) to a plethora of individual rain gauge networks across the country has historically been difficult to achieve in a real-time setting. However, advances in computing resources and radar, satellite, and rain gauge technology have provided the opportunity for this to become a reality. Partners Synoptic Data Corp and MetStat, Inc. have aggregated data from over 20,000 precipitation stations across the United States and adjacent parts of Canada and Mexico. Reliable real-time acquisition protocols coupled with a multi-sensor QC algorithm produces a high-quality dataset of 1-hour rain gauge data. This multi-sensor QC algorithm is based on surrounding gauges, radar reflectivity data, National Weather Service Stage IV gauge-adjusted radar-estimated rain data, and satellite-estimated rain data.
It is known that gauges can suffer from a number of different shortcomings, including freezing rain, windy conditions, gauge siting (e.g., obstructions around the gauge), under-measurement by tipping bucket gauges in high intensity rainfall, and gauge maintenance, therefore making QC an imperative initial step before utilizing the data. The QC system processes data multiple times for each hour in order to capture all available gauge data and leverage increasingly rich independent datasets that have varying reporting latencies. The complexities of gauge QC often prevent a binary (correct or incorrect) decision to be made, therefore a QC confidence flag is computed for each measured value. Ranging from 0 to 1, the QC confidence flag provides an objective measure of how well the value compares to independent, multi-sensor datasets.
The quality controlled gauge data and QC statistics provide the foundation for various hydrometeorological applications, including gauge-adjusted radar-estimated rain algorithms, identification of gauge malfunctions, alert notifications of rainfall intensities that meet thresholds, detailed real-time and post-event analyses, as well as climatological studies. A recent application of this dataset has been its integration into MetStorm®Live, a near real-time multi-sensor quantitative precipitation estimate system. MetStorm®Live, developed by MetStat, Inc. and Weather Decision Technologies, Inc., creates gridded precipitation which provides the necessary spatial and temporal data used in hydrologic model calibration and validation. Therefore, with enhanced QC of ground truth measurements comes improved models which enable better forecasts and warning systems.
This presentation will include an overview of the processes involved in the real-time rain gauge acquisition and quality control as well as examples of its applications and future improvements involving cloud computing and disaggregation of the Global Historical Climatological Network Daily dataset into an hourly time series.