Oceanic rain rate estimates from the QuikSCAT Radiometer: A Global Precipitation Mission pathfinder

Abstract

[1] The SeaWinds scatterometer, launched onboard the QuikSCAT satellite in 1999, measures global ocean vector winds. In addition to measuring radar backscatter, SeaWinds simultaneously measures the microwave brightness temperature of the atmosphere/surface, and this passive microwave measurement capability is known as the QuikSCAT Radiometer (QRad). This paper presents a QRad retrieval algorithm used to infer instantaneous oceanic rain rates. This statistical algorithm is trained using near-simultaneous observations of major rain events by QRad and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). Rain rate retrieval algorithm validation is presented through comparisons with independent rain measurements from the TMI 2A12 surface rain rates and the TRMM 3B42RT composite microwave and visible and infrared near-real time data product. Results demonstrate that QRad rain rate measurements are in good agreement with these independent microwave rain observations and superior to the visible/infrared rain estimates. Thus the QRad rain measurement time series is a valuable addition to the oceanic precipitation climatology that can be used to improve the diurnal estimation of the global rainfall, which is a goal for the future Global Precipitation Mission program. Moreover, the availability of QRad data will provide GPM users early access to learn to use less-precise rain measurements that will occur in the GPM era with the use of less-capable constellation satellites. Finally, these QRad rain estimates will be available in the planned data reprocessing (FY 2006) of QuikSCAT winds to improve the rain flagging of rain-contaminated oceanic wind vector retrievals.

Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

“…The radiometer, thus produced, is referred to as QRad. In addition to its brightness temperature measurement, a QRad rain retrieval algorithm (Ahmad et al 2006) has been implemented in the JPL L2B data product to infer instantaneous and collocated oceanintegrated rain-rate measurements with wind retrievals. This statistical algorithm was trained using near-simultaneous Tb observations by QRad and the TMI 2A12 surface rain rates (Ahmad et al 2005).…”

Sea Surface Winds over the Mediterranean Basin from Satellite Data (2000–04): Meso- and Local-Scale Features on Annual and Seasonal Time Scales

“…QuikSCAT data can be seriously contaminated by rain (Ahmad et al 2005;Milliff et al 2004;Portabella and Stoffelen 2001;Stiles and Yueh 2002), and different rain flags are available in JPL's dataset (Huddleston and Stiles 2000;Mears et al 2000). In this work we have used the Huddleston and Stiles (2000) rain flag: data associated with a probability that the columnar rain rate exceeds 2 mm km h Ϫ1 is greater than 10% have been discarded (the 10% of available data).…”

Calibration/Validation of the SeaWinds Radiometer Rain Rate Algorithm

“…This rain algorithm is an experimental algorithm developed for the SeaWinds Radiometer on QuikSCAT (QRad) [1,2]. In the presence of rain, the observed brightness temperature is due to three components, namely; ocean/atmosphere background emission, ocean surface wind speed emission and rain emission.…”

“…In the presence of rain, the measurement of ocean backscatter, σ 0 , may be contaminated by rain volume backscattering and associated atmospheric attenuation of the surface echo. The AMSR provides independent estimates of rain rate that can be used to train the SeaWinds rain estimation algorithm developed for use on the QuikSCAT satellite [1,2]. This paper describes the calibration of the rain rate algorithm using the SeaWinds instrument on ADEOS-II and AMSR.…”