Evaluation and Comparison of Daily Rainfall From Latest GPM and TRMM Products Over the Mekong River Basin

Wang, Wei; Lu, Hui; Zhao, Tianjie; Jiang, Linqin; Shi, Jiancheng

doi:10.1109/jstars.2017.2672786

Cited by 89 publications

(48 citation statements)

References 31 publications

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“…5, with a median of CCs equal to 0.6 (for early and late runs) and 0.7 (for final), we concluded that there is generally a moderate correlation [0.5 , CC , 0.7 was considered as a moderate correlation based on Tan and Santo (2018)] between IPPs and gauge observations. The same results have been reported for other regions, for example, Blue Nile basin (Sahlu et al 2016), Mekong River basin (Wang et al 2017), Malaysia (Tan and Santo 2018), and northern Pakistan (Anjum et al 2018). Therefore, following the Brown (2006) and Condom et al (2011) who suggested a correlation higher than 0.7 and RBias between 610% for a reliable precipitation dataset, none of the IPPs (without preprocessing) can be considered as an alternative for gauge observations over Iran (see Table 7).…”

Section: (Iv) Gpcc Full Data Reanalysis and Gpcc Monitoringsupporting

confidence: 71%

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

Hosseini‐Moghari

Tang

2020

Journal of Hydrometeorology

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This study attempts to assess the validity of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) products across Iran. Six IMERG precipitation products (IPPs) including early, late, and final runs for versions 05 and 06 were compared with precipitation data from 76 synoptic stations on a daily scale for the period from June 2014 to June 2018. According to the results, V05 performed better than V06, particularly in early and late runs. The IPPs overestimate precipitation ranging from 5% to 32%; however, IPPs tended to underestimate (overestimate) the amount of precipitation for wet (dry) areas and precipitation classes higher than 5 mm day 21 (less than 5 mm day 21 ). The probability of detection (POD) in IPPs was almost similar (with a median equal to 0.60), whereas other categorical validation metrics like false alarm ratio (FAR) improved in the final run. Our assessments revealed that the dependency of IPPs to the elevation was low, while the error characteristics of IPPs were strongly dependent on the climate and precipitation intensity. For instance, the systematic error varied between less than 12% in dry regions to more than 60% in wet regions. Also, according to modified Kling-Gupta efficiency (KGE) and relative bias (RBias), the performance of IPPs in winter with the highest KGE (ranging from 0.47 to 0.63) and lowest RBias (ranging from 0% to 16%) was better than other seasons. Further improvement is recommended in the satellite sensors and the precipitation retrieval algorithms to achieve a reliable precipitation source.

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Section: (Iv) Gpcc Full Data Reanalysis and Gpcc Monitoringsupporting

confidence: 71%

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

Hosseini‐Moghari

Tang

2020

Journal of Hydrometeorology

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“…The r was used to quantify the type of correlation and dependence between satellite products and gauge observations in fundamental statistics. The RB and RRMSE were used to describe the bias and error of satellite precipitation compared with gauge observations [33][34][35]. The equations of the indicators are as follows:…”

Section: Methodologiesmentioning

confidence: 99%

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

Chen

Duan

et al. 2018

Remote Sensing

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Abstract:The performance of the latest released Integrated Multi-satellitE Retrievals for GPM mission (IMERG) version 5 (IMERG v5) and the TRMM Multisatellite Precipitation Analysis 3B42 version 7 (3B42 v7) are evaluated and compared at multiple temporal scales over a semi-humid to humid climate transition area (Huaihe River basin) from 2015 to 2017. The impacts of rainfall rate, latitude and elevation on precipitation detection skills are also investigated. Results indicate that both satellite estimates showed a high Pearson correlation coefficient (r, above 0.89) with gauge observations, and an overestimation of precipitation at monthly and annual scales. Mean daily precipitation of IMERG v5 and 3B42 v7 display a consistent spatial pattern, and both characterize the observed precipitation distribution well, but 3B42 v7 tends to markedly overestimate precipitation over water bodies. Both satellite precipitation products overestimate rainfalls with intensity ranging from 0.5 to 25 mm/day, but tend to underestimate light (0-0.5 mm/day) and heavy (>25 mm/day) rainfalls, especially for torrential rains (above 100 mm/day). Regarding each gauge station, the IMERG v5 has larger mean r (0.36 for GPM, 0.33 for TRMM) and lower mean relative root mean square error (RRMSE, 1.73 for GPM, 1.88 for TRMM) than those of 3B42 v7. The higher probability of detection (POD), critical success index (CSI) and lower false alarm ratio (FAR) of IMERG v5 than those of 3B42 v7 at different rainfall rates indicates that IMERG v5 in general performs better in detecting the observed precipitations. This study provides a better understanding of the spatiotemporal distribution of accuracy of IMERG v5 and 3B42 v7 precipitation and the influencing factors, which is of great significance to hydrological applications.

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“…The IMERG combines passive microwave data from the GPM satellite, microwave‐calibrated infrared satellite data, and monthly data from the Global Precipitation Climatology Center to estimate precipitation (Huffman et al, ). This GPM product has been demonstrated to perform better than the TRMM product across the TP and its surrounding regions (Li et al, ; Wang, Lu, et al, ; Xu et al, ) and can describe the spatial variation of precipitation in detail (Ma et al, ) (albeit with some obvious regions with a wet bias over the TP; Chen & Li, ; Tang et al, ). One additional advantage of the IMERG product is its high temporal resolution (30 min), which can be used for resolving the full diurnal variation of precipitation and, therefore, for furthering our understanding of this important aspect of the regional weather processes.…”

Section: Study Area and Datamentioning

confidence: 99%

Ground‐Based Observations Reveal Unique Valley Precipitation Patterns in the Central Himalaya

Ouyang

Yang

et al. 2020

JGR Atmospheres

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While the Himalayas act as a natural barrier to water vapor transport to the Tibetan Plateau, many north‐south‐oriented valleys channel moisture onto the Plateau. However, owing to the lack of in situ data, the spatiotemporal characteristics of precipitation remain unclear along these valleys. In this study, a high‐altitude (2,800–4,500 m above mean sea level) rain‐gauge network was established in the Yadong Valley, one of the main valleys in the central Himalayan Region (CHR). New observations from this network are used to evaluate the Integrated Multi‐satellitE Retrieval for Global Precipitation Measurement (IMERG) precipitation product, with results demonstrating that the IMERG data can reproduce the seasonal and diurnal patterns of precipitation seen in the observational data. This combination of in situ and IMERG precipitation data reveals two unique characteristics in the CHR. First, precipitation during the premonsoon season (March‐May) contributes 20–40% of the annual total, from the high altitudes of the CHR to Southeast Tibetan Plateau, due to water vapor flux conveyed by southwesterlies from the Bay of Bengal, whereas this percentage is much lower in the northern Himalaya and South Asia. Second, diurnal variations in precipitation during the monsoon season (June‐September) vary between the low and high altitudes of the north‐south‐oriented valleys in the CHR: Precipitation at high altitudes has two peaks (one in the afternoon and the other at night), which is different from the single nighttime peak in low altitudes that is usually reported. These two notable spatiotemporal characteristics are indicative of the unique climate that exists at the high altitudes of the CHR.

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Evaluation and Comparison of Daily Rainfall From Latest GPM and TRMM Products Over the Mekong River Basin

Cited by 89 publications

References 31 publications

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

Ground‐Based Observations Reveal Unique Valley Precipitation Patterns in the Central Himalaya

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