Insights on Satellite-Based IMERG Precipitation Estimates at Multiple Space and Time Scales for a Developing Urban Region in India

IF 3.1 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Padmini Ponukumati, Azharuddin Mohammed, Satish Regonda
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引用次数: 0

Abstract

Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm. Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.
基于卫星的印度发展中城市地区多时空尺度IMERG降水估算的见解
基于卫星的降雨估计是数据稀缺地区(包括城市地区)的重要资源,因为它的分辨率更高。综合多卫星反演GPM (IMERG)是一种广泛使用的产品,在海德拉巴地区的城市尺度上使用两种不同的地面数据进行评估,即印度气象部门(IMD)网格降雨和泰伦甘纳邦发展规划协会(TSDPS)自动气象站(AWS)测量的降雨量。IMERG的降雨估计是在多个时空尺度以及降雨事件尺度上进行评估的。连续和分类验证指标都表明,IMERG在日常规模上表现良好;然而,在小时尺度上观察到相对下降的表现。IMD网格化降雨量和AWS测量降雨量的IMERG估计值分别被低估和高估,这表明性能取决于地面真实值的类型。与分类指标不同,RMSE和PBIAS的模式暗示了降雨量方面的系统误差。此外,发现样本量、日变化和季节对IMERG估计的性能有影响。小时到日时间尺度的时间聚合显示IMERG性能有所提高;然而,在区域和海得拉巴地区的降雨量估计之间没有观察到空间尺度的依赖性。原始和偏差校正后的IMERG降雨强度-持续时间-频率(IDF)曲线与相应的小时雨量计IDF曲线的比较显示了通过简单的偏差校正技术所增加的价值。总的来说,研究表明IMERG估计可以作为一种替代数据源,并且可以通过修改检索算法进一步改进。许多城市地区通常数据稀疏,这限制了对各种城市水文气象相关任务的科学理解和可靠的工程设计,包括气候和极端降雨特征、洪水危害评估和雨水管理系统。卫星降水估算是一个很好的资源,而多卫星综合反演GPM (IMERG)是一个最好的替代方案。印度第六大都会区海得拉巴地区被选中分析广泛使用的卫星估计值,即GPM的检索。该研究发现,IMERG估算的不准确性随着降雨量、空间和时间尺度的变化而变化;尽管如此,这些估计值还是可以作为决策的替代数据源,比如降雨是否超过某个阈值。
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来源期刊
Journal of Hydrometeorology
Journal of Hydrometeorology 地学-气象与大气科学
CiteScore
7.40
自引率
5.30%
发文量
116
审稿时长
4-8 weeks
期刊介绍: The Journal of Hydrometeorology (JHM) (ISSN: 1525-755X; eISSN: 1525-7541) publishes research on modeling, observing, and forecasting processes related to fluxes and storage of water and energy, including interactions with the boundary layer and lower atmosphere, and processes related to precipitation, radiation, and other meteorological inputs.
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