Revealing the SeaWinds ocean vector winds under the rain using AMSR. Part I: The physical approach

Rain contamination is one of the most vexing problems for Ku-band scatterometer ocean wind data. With scatterometer data alone, correcting the effects of rain contamination is an exceedingly difficult if not insurmountable problem. Even flagging rain contaminated data can be problematic, as one often has to choose between flagging large amounts of good data or leaving significant amounts of contaminated data unflagged [1]. Wind vectors around storms are important for global vorticity analysis. Flagging all rainy areas as contaminated removes some of the most variable and interesting portions of the wind field from analysis. Fortunately, for the six months of the MIDORI II mission, one need not depend on scatterometer measurements alone. Coincident SeaWinds scatterometer and AMSR radiometer measurements were obtained. We used these measurements as inputs to a three-step rain correction strategy. 1) We retrieved the geophysical quantities sea surface temperature (SST), columnar water vapor, and total liquid, from the multi-channel AMSR brightness temperatures using a physical model. Wind speed was also retrieved but not used in rain correction. 2) We developed two complementary methods (physical and empirical) to quantify the impact of rain on scatterometer measurements as a function of the retrieved geophysical parameters and the SeaWinds antenna beam. 3) We corrected scatterometer measurements using each of the rain impact models and retrieved new wind fields from the corrected measurements.

This presentation will have two objectives: i)the first objective is to describe the basic features of the AMSR retrieval algorithms for sea surface temperature, wind speed, columnar vapor and columnar liquid. The performance of the algorithms will be evaluated through comparisons to retrievals based on other instruments. ii)the second objective is to discuss the physical approach to estimating AMSR-based atmospheric corrections of the scatterometer measurements. The scatterometer signal that propagates through rain is impacted in three ways: the signal is attenuated by the rain, clouds and vapor in the atmosphere; the signal is augmented by the backscatter from rain droplets; finally, the signal is augmented by the rain-induced roughening of the ocean surface ("splash"). Estimation of the near-surface wind velocity from scatterometer measurements is based on the assumption that variations in the measured power are solely due to variations in the normalized radar cross-section (sigma0) of the ocean surface that result from variations in the wind. It is, thus, very important to properly account for the three rain effects and to correct the sigma0 measurements before they are used to estimate wind vectors. The physical approach to evaluating the atmospheric attenuation, rain backscatter and rain-induced surface roughening (the splash) using the AMSR geophysical retrievals will be discussed. The results will be related to the retrieved precipitation, cloud and vapor. Comparisons to estimates from other instruments will be presented.

A related presentation [2] will evaluate the quality of the rain-corrected scatterometer winds. It will compare and contrast the performance of the physical approach, described here, to the performance of the empirical approach to rain correction of the scatterometer winds , described in Part II [2].

[1] Huddleston, J. N., and B. W. Stiles, "A Multi-dimensional Histogram Rain Flagging Technique for SeaWinds on QuikSCAT." Proc. of IGARSS Conference, Vol. 3, pp 1232-1234, Honolulu, 2000.

[2] Stiles, B. W. et al, "Revealing the SeaWinds ocean vector winds under the rain using AMSR . Part II: The empirical approach", 14th Conf. on satellite meteorology and oceanography., Atlanta, GA, 2006