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.

No abstract

No abstract

We examine the upper tail of flood peak distributions through analyses of annual peak observations from more than 8,000 U.S. Geological Survey (USGS) stream gaging stations and through hydrometeorological analyses of the storms that produce the most extreme floods. We focus on the distribution of the upper tail ratio, which is defined as the peak discharge for the flood of record at a stream gaging station divided by the sample 10‐year flood magnitude. The 14 June 1903 Heppner storm, which produced an upper tail ratio of 200, was the product of a hailstorm that formed along the Blue Mountains in eastern Oregon, a region dominated by snowmelt flooding. A striking contrast between record flood peaks and the larger distribution of annual flood peaks in the United States is in the seasonality of flood occurrence, with record floods reflecting a much stronger contribution from warm season thunderstorm systems. Mountainous terrain and intense convective rainfall are important elements of the geography and hydrometeorology of extreme upper tail ratio flood peaks. The distribution of upper tail ratio values for USGS stream gaging stations does not depend on basin area, a result which is consistent with scaling results based on extreme value theory. Downscaling simulations with the Weather Research and Forecasting model are used to examine the storm environment of the 1903 Heppner storm, along with two other record flood peaks near the Blue Mountains of eastern Oregon from the USGS miscellaneous flood record, the July 1956 Meyers Canyon flood and the July 1965 Lane Canyon flood.

Dead Run is a 14.3 km2 urban drainage basin, which is a tributary to the Gwynns Falls, the principal study watershed of the Baltimore Ecosystem Study. Hydrologic response in urban watersheds is examined through analyses of rainfall and discharge observations from the Dead Run watershed during a 6 month period beginning in June of 2003. Rainfall variability for flash flood–producing storms in Dead Run can be quite large when viewed from a Euclidean perspective. When viewed from the perspective of a distance metric imposed by the drainage network of Dead Run, however, the spatial variability of rainfall is small. The drainage network structure diminishes the effects of spatial rainfall variability for storm event hydrologic response, resulting in Dead Run exhibiting striking uniformity of response to storms with contrasting spatial distribution of rainfall. There is large storm‐to‐storm variation in the event water balance of Dead Run. Variation is linked to antecedent soil moisture (from the pervious portion of the watershed underlain by urban soils), rainfall variability, and spatial heterogeneity of runoff production.