Estimates of the Rainrate Over the Oceans Using SeaWinds Radar Cross Section Measurements

The unanticipated, relatively strong response of the SeaWinds scatterometer to moderate rainrates was initially viewed as a detrimental situation, because it produces erroneous wind estimates. But it can also be exploited for precipitation detection and measurement on many different time and spatial scales. This program seeks to improve the ability of SeaWinds to estimate sea surface winds in the presence of rain, and it is developing the ability to estimate average rainrate within a scatterometer resolution cell on the surface from the Level 2A standard data. Methods based on the Z-R (reflectivity-rainrate) relationships are being evaluated. Our approach to addressing these new problems and pursuing our objectives is to collect the SeaWinds radar cross section data for both polarizations (Level 2A data product) and the wind vectors (Level 2B) derived from them, with simultaneous, collocated and accurate rain measurements. We are using two sources of rainrate measurements to calibrate our SeaWinds data: NEXRAD radars situated at coastal sites that provide observations every 6 minutes, having a spatial resolution of 4km, and with a useful range of 250 km. Also utilized are hourly TRMM observations of rainrate, using the TMI and PR, that are collocated with QuikSCAT and gridded for ease of use. Measurements indicate that the radar backscatter from the rain volume in the atmosphere can exceed the sea surface radar cross section in many common situations. Their relative dominance depends on the rainrate, the rain column and surface wind speed. Errors in the SeaWind wind speed and direction are being studied using collocated NDBC buoy observation. The in-situ rainrate measurements from NEXRAD and TRMM are being used to calibrate the absolute SeaWinds radar cross section, for both vertical and horizontal polarizations. Also being calibrated is the differential reflectivity between the two polarizations since heavy rain causes this polarization ratio to invert from its value under no-rain conditions. Initial results indicate that a "rain signature" can be developed for the SeaWinds RCS data which will enable the rainrate to be estimated with an accuracy comparable to or better than other satellite-based sensors.