Rainfall Estimation by the WSR-88D for Heavy Rainfall Events

Rainfall Estimation with a Polarimetric Prototype of WSR-88D

As part of the Joint Polarization Experiment (JPOLE), the National Severe Storms Laboratory conducted an operational demonstration of the polarimetric utility of the Norman, Oklahoma (KOUN), Weather Surveillance Radar-1988 Doppler (WSR-88D). The capability of the KOUN radar to estimate rainfall is tested on a large dataset representing different seasons and different types of rain. A dense gaug...

Rainfall Measurements with the Polarimetric WSR-88D Radar

An Adaptive Neural Network Scheme for Radar Rainfall Estimation from WSR-88D Observations

Recent research has shown that neural network techniques can be used successfully for ground rainfall estimation from radar measurements. The neural network is a nonparametric method for representing the relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The effectiveness of...

3. Frequency Distributions of Heavy Rainfall Events

The data used in developing the frequency relations consisted of both daily and hourly precipitation records from Illinois and from nearby stations in surrounding states. The daily data, mostly from nonrecording raingages, spanned the 83-year period from 1901 through 1983. They included data from the 61 precipitation-reporting stations discussed previously (figure 71, whose records had been car...

3. Frequency Distributions of Heavy Rainfall Events

The data used in developing the frequency relations consisted of both daily and hourly precipitation records from Illinois and from nearby stations in surrounding states. The daily data, mostly from nonrecording raingages, spanned the 83-year period from 1901 through 1983. They included data from the 61 precipitation-reporting stations discussed previously (figure 71, whose records had been car...