Using Spectral Moment Data From Noaas 404-mhz Radar Wind Profilers To Observe Precipitation

Abstract

A brief description is given of NOAA's 404-MHz Wind Profiler Demonstration Network (WPDN), including the radar configuration, sampling strategy, site locations and characteristics, and a discussion of the Doppler power spectrum and its first three spectral moments: signal power (S), radial velocity (V-r), and velocity variance,(sigma(2)). Evidence is presented showing that g-min time resolution spectral moment data from the vertically pointing beam of a WPDN wind profiler can be used to identify when precipitation is present above the profiler. Signatures of snow, light and moderate stratiform rain, heavy convective rain, freezing rain, and snow within jet stream cirrus are illustrated and summarized. Although radar reflectivity factor (Z) cannot be determined from WPDN wind profilers, the precipitation rates and fall speeds shown to be observable in the cases documented here are roughly consistent with earlier studies suggesting that precipitation with Z > 0-15 dBZ should typically be observable at 404 MHz, and that precipitation or clouds with Z < 0 dBZ should not be readily distinguishable from clear-air echoes. General signatures common to most precipitation, and characteristics in the data that allow different types of precipitation to be distinguished from one another, are revealed from three case studies. The most useful indicators of stratiform rain are downward V-r > 3-5 m s(-1) and sigma(2) > 1.0 m(2) s(-2). Snow is indicated by 2 m s(-1) > V-r > 0.5-0.9 m s(-1) and sigma(2) < 1.0 m(2) s(-2). Evidence of a melting level in S, Vr, and sigma(2) is a very good indicator of stratiform precipitation, and when absent helps identify precipitation as convective when S and sigma(2) are large. Because the spectral moment data are regularly archived, this information can be examined in real time and compared with simultaneously measured wind profiles. Such information should be useful in both research and operational meteorology. The ability to infer relationships between precipitation and kinematic features evident in the observed winds is also illustrated.