A method to improve the GOES Precipitation Index (GPI) technique by combining satellite microwave and infrared (IR) data is proposed and rested. 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 Adler et al. (1993), 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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