U.S. Department of Commerce
Date of this Version
2012
Citation
Published in Remote Sensing of Drought: Innovative Monitoring Approaches, edited by Brian D. Wardlow, Martha C. Anderson, & James P. Verdin (CRC Press/Taylor & Francis, 2012).
Abstract
Since its deployment, the precipitation estimates from the network of National Weather Service (NWS) Weather Surveillance Radars-1988 Doppler (WSR-88D) have become widely used. These precipitation estimates are used for the flash flood warning program at NWS Weather Forecast Offices (WFOs) and the hydrologic program at NWS River Forecast Centers (RFCs), and they also show potential as an input data set for drought monitoring. However, radar-based precipitation estimates can contain considerable error because of radar limitations such as range degradation and radar beam blockage or false precipitation estimates from anomalous propagation (AP) of the radar beam itself. Because of these errors, for operational applications, the RFCs adjust the WSR-88D precipitation estimates using a multisensor approach. The primary goal of this approach is to reduce both areal-mean and local bias errors in radar-derived precipitation by using rain gauge data so that the final estimate of rainfall is better than an estimate from a single sensor.
This chapter briefly discusses the past efforts for estimating mean areal precipitation (MAP). Although there are currently several radar and rain gauge estimation techniques, such as Process 3, Mountain Mapper, and Daily Quality Control (QC), this chapter will emphasize the Multisensor Precipitation Estimator (MPE) Precipitation Processing System (PPS). The challenges faced by the Hydrometeorological Analysis and Support (HAS) forecasters at RFCs to quality control all sources of precipitation data in the MPE program, including the WSR-88D estimates, will be discussed. The HAS forecaster must determine in real time if a particular radar is correctly estimating, overestimating, or underestimating precipitation and make adjustments within the MPE program so the proper amount of precipitation is determined. In this chapter, we discuss procedures used by the HAS forecasters to improve initial best estimates of precipitation using 24 h rain gauge data, achieving correlation coefficients greater than 0.85. Finally, since several organizations are now using the output of MPE for deriving short- and long-term Standardized Precipitation Indices (SPIs), this chapter will discuss how spatially distributed estimates of precipitation can be used for drought monitoring.
Comments
U.S. Government Work