IMERG-Like Precipitation Retrieval From Geo-Kompsat-2A Observations Using Conditional Generative Adversarial Networks

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-06-02 DOI:10.1109/JSTARS.2025.3575763

Kyung-Hoon Han;Jaehoon Jeong;Sungwook Hong

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引用次数: 0

Abstract

This study proposes an infrared-to-rain (IR2Rain) model to enhance the accuracy of the geostationary (GEO) weather satellite Geo-Kompsat-2A (GK-2A) rain rate (RR) product. The IR2Rain model is built upon a conditional generative adversarial network, taking GK-2A brightness temperatures as inputs and Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) Final RRs as target values. To address the distinct physical characteristics and ranges of the input and target datasets, IR2Rain employs preprocessing for normalization and postprocessing for denormalization. The IR2Rain model is developed and validated using the paired input and output datasets collected between September 2019 and December 2022, encompassing a broad region across Asia and Oceania. This study compares the performance of IR2Rain-derived RRs against IMERG RR, GK-2A RR, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network dynamic infrared (IR) rain rate-now products. The results demonstrated a probability of detection of 0.607, a critical success index of 0.482, a root-mean-square error of 0.759 mm/h, and a correlation coefficient of 0.671. By combining the high temporal resolution of GEO satellite observations with the reliability of IMERG Final data, the IR2Rain model produces a robust near-real-time IMERG-like precipitation product. Despite smoothing effects and the tendency to underestimate intense rainfall, IR2Rain improves the performance relative to RR products based on the same GK-2A IR data, mitigates the latency encountered in IMERG data generation, and provides timely and accurate precipitation information on intensity and distribution. These products are particularly valuable for operational weather forecasting and public end users in Asia and Oceania, supporting disaster preparedness and hydrological applications.

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利用条件生成对抗网络从Geo-Kompsat-2A观测数据中检索imerga样降水

为了提高地球同步气象卫星GEO - kompsat - 2a （GK-2A）雨率（RR）产品的精度，本文提出了一种红外降雨（IR2Rain）模型。IR2Rain模型建立在条件生成对抗网络的基础上，以GK-2A亮度温度作为输入，以IMERG （Integrated MultisatellitE retrials for Global Precipitation Measurement）最终rr作为目制值。为了解决输入和目标数据集的不同物理特性和范围，IR2Rain采用预处理进行规范化，后处理进行非规范化。IR2Rain模型是利用2019年9月至2022年12月期间收集的成对输入和输出数据集开发和验证的，涵盖了亚洲和大洋洲的广泛地区。本研究比较了ir2rain衍生的RR与IMERG RR、GK-2A RR以及基于遥感信息的人工神经网络动态红外（IR）降雨率产品的降水估计的性能。结果表明，检测概率为0.607，临界成功指数为0.482，均方根误差为0.759 mm/h，相关系数为0.671。IR2Rain模式将GEO卫星观测的高时间分辨率与IMERG最终数据的可靠性相结合，产生了鲁棒的接近实时的类似IMERG的降水产品。尽管有平滑效应和低估强降雨的倾向，IR2Rain相对于基于相同的GK-2A红外数据的RR产品提高了性能，减轻了IMERG数据生成中遇到的延迟，并提供了及时准确的降水强度和分布信息。这些产品对亚洲和大洋洲的业务天气预报和公共终端用户特别有价值，支持备灾和水文应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.