A microwave infrared threshold technique to improve the GOES precipitationindex

A method to improve the GOES Precipitation Index (GPI) technique by combini ng satellite microwave and infrared (IR) data is proposed and rested. Using microwave-based rainfall estimates, the method, termed the Universally Adj usted GPI (UAGPI), modifies both GPI parameters (i.e., the IR brightness te mperature threshold and the mean rain rate) to minimize summation of estima tion errors during the microwave sampling periods. With respect to each gri d, monthly rainfall estimates are obtained in a manner identical to the GPI except for the use of the optimized parameters. The proposed method is com pared with the Adjusted GPI (AGPI) method of Adler et al. (1993), which adj usts the GPI monthly rainfall estimates directly using an adjustment ratio. The two methods are compared using the First Algorithm Intercomparison Pro ject (AIP/1) dataset, which covers two month-long periods over the Japanese islands and surrounding oceanic regions. Two types of microwave-related er rors are addressed during the comparison: (1) sampling error caused by insu fficient sampling rate and (2) measurement error of instantaneous rain rate . Radar-gauge composite rainfall observations were used to simulate microwa ve rainfall estimates for studying the sampling error. The results of this comparison show that UAGPI is more capable of utilizing the limited informa tion contained in sparse microwave observations to reduce sampling error an d that UAGPI demonstrates stronger resistance to microwave measurement erro r 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 AG PI. This indicates that UAGPI provides stable estimates of monthly rainfall at various spatial scales.