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AIRS/AMSU/HSB precipitation estimates

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2 Author(s)
F. W. Chen ; Res. Lab. of Electron., Massachusetts Inst. of Technol., Cambridge, MA, USA ; D. H. Staelin

Precipitation rates (mm per hour) with 15- and 50-km horizontal resolution are among the initial products of Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB). They will help identify the meteorological state of the atmosphere and any AIRS soundings potentially contaminated by precipitation. These retrieval methods can also be applied to the AMSU 23-191-GHz data from operational weather satellites such as NOAA-15, -16, and -17. The global extension and calibration of these methods are subjects for future research. The precipitation-rate estimation method presented is based on the opaque-channel approach described by Staelin and Chen (2000), but it utilizes more channels (17) and training data and infers 54-GHz band radiance perturbations at 15-km resolution. The dynamic range now reaches 100 mm/h. The method utilizes neural networks trained using the National Weather Service's Next Generation Weather Radar (NEXRAD) precipitation estimates for 38 coincident rainy orbits of NOAA-15 AMSU data obtained over the eastern United States and coastal waters during a full year. The rms discrepancies between AMSU and NEXRAD were evaluated for the following NEXRAD rain-rate categories: <0.5, 0.5-1, 1-2, 2-4, 4-8, 8-16, 16-32, and >32 mm/h. The rms discrepancies for the 3790 15-km pixels not used to train the estimator were 1.0, 2.0, 2.3, 2.7, 3.5, 6.9, 19.0, and 42.9 mm/h, respectively. The 50-km retrievals were computed by spatially filtering the 15-km retrievals. The rms discrepancies over the same categories for all 4709 50-km pixels flagged as potentially precipitating were 0.5, 0.9, 1.1, 1.8, 3.2, 6.6, 12.9, and 22.1 mm/h, respectively. Representative images of precipitation for tropical, mid-latitude, and snow conditions suggest the method's potential global applicability.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:41 ,  Issue: 2 )