Keywords

Maximum-likelihood, PR, Precipitation Radar, SeaWinds, TRMM, Tropical Rainfall Measuring Mission, scatterometer, simultaneous wind/rain retrieval, water

Abstract

The SeaWinds scatterometers onboard the QuikSCAT and the Advanced Earth Observing Satellite 2 measure ocean winds on a global scale via the relationship between the normalized radar backscattering cross section of the ocean and the vector wind. The current wind retrieval method ignores scattering and attenuation of ocean rain, which alter backscatter measurements and corrupt retrieved winds. Using a simple rain backscatter and attenuation model, two methods of improving wind estimation in the presence of rain are evaluated. First, if no suitable prior knowledge of the rain rate is available, a maximum-likelihood estimation technique is used to simultaneously retrieve the wind velocity and rain rate. Second, when a suitable outside estimate of the rain rate is available, wind retrieval is performed by correcting the wind geophysical model function for the known rain via the rain backscatter model. The new retrieval techniques are evaluated via simulation and validation with data from the National Centers for Environmental Prediction and the Tropical Rainfall Measuring Mission Precipitation Radar. The simultaneous wind/rain estimation method yields most accurate winds in the "sweet spot" of SeaWinds' swath. On the outer-beam edges of the swath, simultaneous wind/rain estimation is not usable. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased for all rain rates and wind speeds, while conventionally retrieved wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method is capable of reducing the rain-induced wind vector error while producing a consistent (yet noisy) estimate of the rain rate.

Original Publication Citation

Draper, D. W., and D. G. Long. "Simultaneous Wind and Rain Retrieval using SeaWinds Data." Geoscience and Remote Sensing, IEEE Transactions on 42.7 (24): 1411-23

Document Type

Peer-Reviewed Article

Publication Date

2004-07-01

Permanent URL

http://hdl.lib.byu.edu/1877/1107

Publisher

IEEE

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Electrical and Computer Engineering

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