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  • Author or Editor: Bisher Imam x
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Ali Behrangi
,
Koulin Hsu
,
Bisher Imam
, and
Soroosh Sorooshian

Abstract

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitude–longitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bispectral (visible and infrared) rain estimation scenarios were compared to investigate the technique’s ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude–longitude) scales. Overall, the results using daytime data during June–August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively.

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Liming Xu
,
Xiaogang Gao
,
Soroosh Sorooshian
,
Phillip A. Arkin
, and
Bisher Imam

Abstract

A method to improve the GOES Precipitation Index (GPI) technique by combining satellite microwave and infrared (IR) data is proposed and tested. Using microwave-based rainfall estimates, the method, termed the Universally Adjusted GPI (UAGPI), modifies both GPI parameters (i.e., the IR brightness temperature threshold and the mean rain rate) to minimize summation of estimation errors during the microwave sampling periods. With respect to each grid, monthly rainfall estimates are obtained in a manner identical to the GPI except for the use of the optimized parameters. The proposed method is compared with the Adjusted GPI (AGPI) method of , which adjusts the GPI monthly rainfall estimates directly using an adjustment ratio. The two methods are compared using the First Algorithm Intercomparison Project (AIP/1) dataset, which covers two month-long periods over the Japanese islands and surrounding oceanic regions. Two types of microwave-related errors are addressed during the comparison: (1) sampling error caused by insufficient sampling rate and (2) measurement error of instantaneous rain rate. Radar–gauge composite rainfall observations were used to simulate microwave rainfall estimates for studying the sampling error. The results of this comparison show that UAGPI is more capable of utilizing the limited information contained in sparse microwave observations to reduce sampling error and that UAGPI demonstrates stronger resistance to microwave measurement error. Comparison between the two methods using three different sizes of moving-average windows indicates that, while the smoothing operation is crucial to AGPI, it is not essential for UAGPI to consistently perform better than AGPI. This indicates that UAGPI provides stable estimates of monthly rainfall at various spatial scales.

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