An Intercomparison Study of NEXRAD Precipitation Estimates

Abstract

Systematic biases in WSR‐88D (Weather Surveillance Radar–1988 Doppler) hourly precipitation accumulation estimates are characterized from analyses of more than 1 year of WSR‐88D data and rain gage data from the southern plains. Biases are examined in three contexts: (1) biases that arise from the range‐dependent sampling of the WSR‐88D, (2) systematic differences in radar rainfall estimates from two radars observing the same area, and (3) systematic differences between radar and rain gage estimates of rainfall. Range‐dependent biases affect hourly rainfall accumulations products over much of the area covered by the WSR‐88D. Significant underestimation of rainfall occurs within 40 km range of the radar due to bias in reflectivity observations at the higher elevation angles used for rainfall estimation close to the radar. Bright band and anomalous propagation (AP) lead to systematic overestimation of rainfall at intermediate range. Beyond 150 km in spring‐summer and beyond 100 km in winter‐fall, underestimation of precipitation is pronounced due to incomplete beam filling and overshooting of precipitation. Radar‐radar intercomparison studies suggest that radar calibration is a significant problem at some sites. Anomalous propagation during clear‐air conditions, a major problem with previous National Weather Service network radars, has been largely eliminated by the WSR‐88D processing. AP remains a problem for cases in which AP returns are embedded in rain. Radar–rain gage intercomparison analyses indicate systematic underestimation by the WSR‐88D relative to rain gages for paired gage‐radar rainfall estimates. Analyses of spatial coverage of heavy rainfall, however, illustrate fundamental advantages of radar over rain gage networks for rainfall estimation.

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

“…Empirical spatial correlation of the random component with a two-parameter exponential model approximation for zone 2 at the hourly scale for the three seasons and the entire data set. the three seasons, different time averaging scales, and radar range effects [e.g., Fabry and Zawadzki, 1995;Smith et al, 1996]. The deterministic and random radar rainfall error components, as well as the spatial and temporal correlation functions of the random factor, can be approximated using simple parametric models.…”

Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty

“…In Stage I, a power law Z-R relationship is applied to estimate fields of precipitation rates, R, that are then integrated over time to produce hourly accumulations. The result of the Stage I process is radar-only products known as digital precipitation products (DPA) (Smith et al, 1996). In Stage II, gauge observations are used to construct the mean field bias for the radar estimates, which is then used to produce bias-adjusted radar precipitation estimates over the HRAP cells.…”

Stochastic simulation experiment to assess radar rainfall retrieval uncertainties associated with attenuation and its correction

“…Moreover, after adjustment of the radar data using rain gauge measurements (called "calibration" at the time (e.g., Collier, 1986)), several errors and inconsistencies remained which this approach was not able to resolve. As opposed to the largely statistical approach of the 1980s, the more physical approach to radar rainfall retrieval adopted since the 1990s considers the principle of radar measurements and the microstructure of rainfall in quite some detail (e.g., Smith et al, 1996;Andrieu et al, 1997;Creutin et al, 1997;Serrar et al, 2000;Sánchez-Diezma et al, 2000;Berne et al, 2005a,b;Delrieu et al, 2005;Berenguer et al, 2005). Another aspect is that rain gauges are no longer used to "calibrate" the radar images, but mainly for verification purposes.…”