Observations of the Spatial-Statistical Structures of Precipitation

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Radar-derived estimates of rainfall intensity and accumulations offer unsurpassed spatial and temporal resolution, and are thus critical not only for issuance of flash-flood warnings but more broadly as input to agricultural and hydrological models for cropland and river/streamflow management. These measurements are thus a critically important product of the nationwide WSR-88D "NEXRAD" radar network. This research effort is focused on improved rainfall estimates through detection of departures from well-behaved "Rayleigh-type" radar signal behavior that may induce errors in deduced rainfall. The presumption that well-behaved Rayleigh-type statistics dominate observed storm properties is at the foundation of current radar-based precipitation estimation techniques. Though strong spatial gradients of rainfall intensity characteristic of thunderstorms are one potential source of non-Rayleigh signal behavior, research suggests that this complication may also arise in the context of more homogeneous precipitation for certain types of radars and radar scanning strategies (including measurements of differential radar reflectivity and derived hydrometeor type from polarized radars). In extreme cases--again generally associated with the heaviest areas of precipitation--induced errors may locally approach the magnitude of the derived rain rate itself. Within the context of this study, the existence of this statistical complication will be conveyed via computation of a "clustering index (CI)." With the advent of high-resolution weather forecast models, radar observations will likely soon be assimilated into these in real-time to help better guide their predictions. The ability to reduce radar errors (or even simply better quantify the degree of uncertainty inherent in these measurements) would be of particular value in the context of modern data assimilation schemes. This proposal seeks to process high-resolution radar data collected by the NSF-supported CHILL radar in eastern Colorado to better relate the volumetric structure and evolution of CI anomalies to storm morphology and underlying cloud microphysical processes, as well as to extend CI measurements to snow, graupel and hail events. Other potential contributors to radar reflectivity bias (including "Bragg scattering," generally regarded as arising from the turbulent mixing of media with differing indices of refraction, as may occur at cloud/plume edges) will also be examined. The availability of high resolution measurements from CHILL (viz. radial data spacing as fine as 30 m) will further facilitate this work. Subsidiary efforts will address independent data sources such as surface-based disdrometer raindrop size distributions to shed additional light on non-Rayleigh precipitation behavior. The intellectual merit of this effort thus focuses on improved radar signal processing and associated understanding of the structure and dynamics of a wide variety of precipitating clouds. As suggested above, Broader Impacts will include the potential for appreciably improved measurements and short-term forecasts of precipitation intensity and amount.