HIMAWARI 8、CHIRPS和GSMaP数据在印尼降雨探测中的比较分析

Rido Dwi Ismanto, Indah Prasasti, Hana Listi Fitriana
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引用次数: 0

摘要

对降雨数据的需求,特别是在观测站数量不是很接近的地区,对当地气候分析活动非常重要。这方面的数据需求是可以满足的,其中之一就是来自遥感数据,如Himawari 8。Himawari 8降水数据是基于Himawari 8卫星红外通道,采用INSAT多光谱降雨算法(IMSRA)方法获得的数据。然而,对IMSRA方法的研究是通过对印度一个地区的案例研究进行的。因此,需要进行验证,以确定Himawari 8降雨数据探测印度尼西亚降雨的能力。用于比较的数据是CHIRPS和GSMaP的降雨数据。另外,以BMKG降雨数据作为基准数据。用于验证的技术是使用列联表方法。验证结果表明,Himawari 8降雨数据的降雨检测能力较好,2019年为66%,2020年为85%。此外,与使用CHIRPS降雨数据和GSMaP降雨数据相比,使用Himawari 8降雨数据检测降雨的能力相当好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COMPARISON ANALYSIS OF HIMAWARI 8, CHIRPS AND GSMaP DATA TO DETECT RAIN IN INDONESIA
The need for rainfall data, especially for areas where the number of observation stations is not very close, is very important for local climate analysis activities. This data need can be met, one of which is from remote sensing data, such as Himawari 8. The Himawari 8 rainfall data are data derived using the INSAT Multi-Spectral Rainfall Algorithm (IMSRA) method based on the infrared channel on the Himawari 8 satellite. However, research on the IMSRA method was carried out using a case study of a region in India. Thus, validation is needed to determine the ability of Himawari 8 rainfall data to detect rain in Indonesia. The data used for comparison are CHIRPS and GSMaP rainfall data. In addition, BMKG rainfall data are used as benchmark data. The technique used for validation is using the Contingency Table method. The results of the validation show that the rain detection ability for Himawari 8 rainfall data is relatively good, namely 66% for 2019 and 85% for 2020. In addition, the ability to detect rain using Himawari 8 rainfall data is quite good compared to the ability to detect rain using CHIRPS rainfall data and GSMaP rainfall data.
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