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Comparison of IMERG Level-3 and TMPA 3B42V7 in Estimating Typhoon-Related Heavy Rain

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Comparison of IMERG Level-3 and TMPA 3B42V7 in Estimating Typhoon-Related Heavy Rain water Article Comparison of IMERG Level-3 and TMPA 3B42V7 in Estimating Typhoon-Related Heavy Rain Ren Wang 1,2, Jianyao Chen 1,2,* and Xianwei Wang 2,* 1 Department of Water Resources and Environment, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; [email protected] 2 Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China * Correspondence: [email protected] (J.C.); [email protected] (X.W.); Tel.: +86-20-8411-5930 (J.C.) Academic Editor: Ataur Rahman Received: 17 January 2017; Accepted: 10 April 2017; Published: 22 April 2017 Abstract: Typhoon-related heavy rain has unique structures in both time and space, and use of satellite-retrieved products to delineate the structure of heavy rain is especially meaningful for early warning systems and disaster management. This study compares two newly-released satellite products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG final run) and the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA 3B42V7) with daily rainfall observed by ground rain gauges. The comparison is implemented for eight typhoons over the coastal region of China for a two-year period from 2014 to 2015. The results show that all correlation coefficients (CCs) of both IMERG and TMPA for the investigated typhoon events are significant at the 0.01 level, but they tend to underestimate the heavy rainfall, especially around the storm center. The IMERG final run exhibits an overall better performance than TMPA 3B42V7. It is also shown that both products have a better applicability (i.e., a smaller absolute relative bias) when rain intensities are within 20–40 and 80–100 mm/day than those of 40–80 mm/day and larger than 100 mm/day. In space, they generally have the best applicability within the range of 50–100 km away from typhoon tracks, and have the worst applicability beyond the 300-km range. The results are beneficial to understand the errors of satellite data in operational applications, such as storm monitoring and hydrological modeling. Keywords: IMERG final run; TMPA 3B42V7; typhoon; heavy rain; coastal region of China 1. Introduction Heavy rain events have profound impacts on human society, hydrological processes, and natural ecosystems [1,2]. They can adjust river regimes, flood peak, and waterlogging patterns rapidly, and even cause significant losses in human life and social economy [3–5]. A typhoon is a type of cyclone formed in the tropical ocean and often brings heavy rainfall to coastal territory. Typhoon-related heavy rain has unique patterns in both time and space, e.g., it can last from one day to several days, and is dominated by typhoon track, translation speed, atmospheric environment, etc. Therefore, reliable measurements of the heavy rainfall provide essential information to monitor and forecast its changing patterns, which are crucial for early warning systems and disaster management strategies [6–9]. Moreover, detailed regularity of heavy rainfall across different spatiotemporal scales leads to insights about the variability of runoff, which can further contribute to reduce inundation of urban regions [10]. However, rainfall is highly variable in both space and time during a typhoon event, creating significant challenges in its accurate monitoring. Water 2017, 9, 276; doi:10.3390/w9040276 www.mdpi.com/journal/water Water 2017, 9, 276 2 of 15 Radar and satellite precipitation measurements provide more homogeneous datasets than ground gauge observations [11,12]. Radar precipitation estimates are constrained by the monitoring scope of radar, while satellite precipitation products have advantages in global coverage and fine resolution [13,14]. There are currently various open access satellite-based precipitation products that could bring valuable scientific and societal benefits. Meanwhile, those products often contain large uncertainties and inevitable errors in different aspects, such as the variability of the precipitation fields and systematic errors [15–17]. These various errors and differential resolutions influence the accuracy of hydrological modeling [18,19]. Evaluation of these products is, therefore, necessary for further understanding of their error characteristics, and is vital to algorithm improvement and subsequent applications. The Global Precipitation Measurement (GPM) mission, which was launched on 27 February 2014, provides the next generation satellite-based global observations of rainfall and snow. GPM is built upon the success of the Tropical Rainfall Measuring Mission (TRMM). Currently, the accessible Integrated Multi-satellitE Retrievals for GPM (IMERG) Level-3 products have finer resolutions (0.1◦ × 0.1◦, 30 min) than the TRMM precipitation series (0.25◦ × 0.25◦, 3 h), and are valuable for applications over the band of 60◦ N to 60◦ S[20]. Meanwhile, the TRMM Multi-satellitE Precipitation Analysis (TMPA) products have provided abundant precipitation information since 1997 [12]. The recently-updated version, 3B42 version 7 (3B42V7), comprises near-real-time and research-grade products with a resolution of 0.25◦ × 0.25◦ in space and 3 h in time [21,22]. Scientists, worldwide, have been investigating the error characteristics of the satellite precipitation series at different spatial and temporal scales. Some studies [6,17,23] demonstrated that TMPA 3B42V7 performs better than TMPA 3B42V6, while both products have larger errors in mountainous regions [6,8]. Some other studies [24–30] made comparisons between IMERG and TMPA products, and reported that IMERG generally exhibits an overall better performance than TMPA, especially for estimating heavy and light precipitation. However, IMERG still has room to improve, such as in arid and high-latitude zones [26,29], and mountainous areas [28,31]. These studies provide a large amount of information to understand the applicability of IMERG and TMPA, but tend to focus on annual, seasonal, and monthly scales, not sufficient for short-term heavy rainfall events, especially when associated with the tropical cyclone rainfall system [4,32]. Evaluation of the products for heavy rainfall is a high standard to verify their performance, and is important in practical applications, such as flood forecasting and urban stormwater collection. The coastal region of China has experienced frequent typhoons and encountered severe socio-economic losses. In general, typhoons strike the coastal region frequently during the period of July to September every year and generate a large number of rainstorm events. There were more than $5.3 billion economic losses per year since 2001, and approximately 34 typhoons landed in this region, which caused 422 losses to life during 2011–2015 [33]. It is of great significance to focus on typhoon heavy rains over the coastal region of China. Therefore, our motivations are: (1) to evaluate the performance of two recently-released products, i.e., the IMERG final run and TMPA 3B42V7, in estimating typhoon-related heavy rain over the coastal region of China; and (2) to analyze their applicability with respect to different rainfall intensities and ranges away from the typhoon track. This paper is organized as follows: Section2 describes the study area and datasets; Section3 describes the statistical methods used in this study; Section4 presents and analyzes the results; Section5 discusses the causes of the error characteristics and the comparative results; and Section6 provides a summary and some concluding remarks. 2. Study Area and Datasets 2.1. Study Area The coastal region of China (18◦–38.5◦ N; 104◦50–123◦ E), which is located at the leading edge of Eurasia and Pacific Ocean, comprises seven provinces and one city, i.e., Shandong, Jiangsu, Zhejiang, Water 2017, 9, x FOR PEER REVIEW 3 of 15 2. Study Area and Datasets 2.1. Study Area Water 2017The, coastal9, 276 region of China (18°–38.5° N; 104°5′–123° E), which is located at the leading edge3 of of 15 Eurasia and Pacific Ocean, comprises seven provinces and one city, i.e., Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan, and Guangxi provinces and Shanghai city (Figure 1). The Fujian,region is Guangdong, a typical monsoon Hainan, climate and Guangxi zone, with provinces annual and average Shanghai precipitation city (Figure ranging1). The from region 550 is to a typical2600 mm, monsoon and a climate decreasing zone, pattern with annual from averagethe sout precipitationh to the north. ranging The fromtemporal 550 to distribution 2600 mm, and of aprecipitation decreasing patternis also uneven, from the with south more to thethan north. 70% of The the temporalannual rainfall distribution concentrating of precipitation in summer is alsoand uneven,autumn, with when more rainfall than is 70% primarily of the annual controlled rainfall by summer concentrating wet monsoons in summer and and typhoons. autumn, when Owing rainfall to its israpid primarily urbanization controlled and by topographic summer wet feature, monsoons i.e., and the typhoons. eastern areas Owing are to itsmainly rapid plains urbanization and rivers and topographicdownstream feature,where i.e.,most the of easternthe large areas cities are are mainly locate plainsd, therefore, and rivers the downstream risk of flooding where is most very of high the largeover the cities developed are located, areas. therefore, In addition, the risk the of flooding coastal isregion very highof China over thehas developed a well-developed areas. In economy addition, theand coastal dense regiondistribution of China of cities has a well-developedand people. Acco economyrding to and the dense 2015 distributionChina Statistical of cities Yearbook, and people. the Accordingregion possessed to the 2015 a total China gross Statistical domestic Yearbook, product the(GDP) region of possessed$4.48 trillion, a total accounting gross domestic for 48% product of the (GDP)national of $4.48GDP, trillion,and had accounting a total resident for 48% ofpopulati the nationalon of GDP, 459 andmillion, had aaccounting total resident for population 34% of the of 459country’s million, total accounting population. for 34% of the country’s total population.
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