Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China

Fang, Jian; Yang, Wentao; Luan, Yibo; Du, Jinzhou; Lin, Aiming; Zhao, Lin

doi:10.1016/j.atmosres.2019.03.001

Cited by 201 publications

(123 citation statements)

References 35 publications

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“…They also found that the latest version of IMERG-V06A performed better than the other products (except MSWEP-2.2), which was consistent with the study results by Beck et al [22]. With respect to extreme precipitation events, Fang et al indicated that although IMERG well captured the spatial pattern of extreme precipitation over China, the topography and climate condition had a significant influence on its performance [24]. In another study, Sunikumar et al demonstrated the ability of IMERG to follow the intraseasonal variability with minor differences observed in the maximum values of precipitation during the rainy season over Japan, Philippines, and Nepal [25].…”

Section: Introductionsupporting

confidence: 76%

“…Then the interpolated precipitation at each station was In this study, the evaluations were conducted in three steps. In the first step, the performance of the three NWP models and SPEs in terms of capturing the spatial distribution of precipitation for the three flood events (17)(18)(19)(20)(21)(22)(24)(25)(26) March, and 31 March to 2 April 2019, respectively) were compared. The numerical forecast and SPE data have a 50 × 50 km and~10 × 10 km spatial resolution, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

2019

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Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March-April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models. disturbing the stratospheric polar vortex, which can lead to a breakdown of this circulation with the potential to also significantly impact the troposphere in mid-to late-winter and early spring [6,7].Iran's climate is generally semi-arid and is subject to frequent flooding, causing major damage to people and society. In spring 2019, major floods occurred almost concurrently in different parts of the country. The first flood event occurred in late winter to early spring 2019 in the northeastern provinces due to heavy precipitation over the March 17-22 period. In at least one station, over 280 mm of precipitation was recorded over the six-day period. The subsequent second and third flood events occurred in the March 24-26 and March 31-April 2 periods, respectively, where most of the precipitation fell in the southwest, causing widespread damage to the people and infrastructure while filling/causing an overflow of most reservoirs. The total economic cost of these floods is estimated to be $3.5 billion U.S. dollars. Studies on the causes of the March-April 2019 severe floods are still ongoing, although exceptional precipitation and climate change attribution are on the minds of most experts.Given the heavy negative impacts imposed by the 2019 flood events, the monitoring and forecas...

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Section: Introductionsupporting

confidence: 76%

Section: Methodsmentioning

confidence: 99%

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

2019

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“…However, Beria et al (2017) reported an inverse relationship between IMERG skill and the elevation over India. Fang et al (2019) also found this relationship, but generally weak, over China. Here, we point out some factors that might be generally changed by altitude but not necessarily, which may not be noticed:…”

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

confidence: 87%

“…GPM is responsible for extending the TRMM mission to create the next generation of Earth's precipitation. In addition to one core observatory, GPM also consists of approximately 10 constellation satellites (Fang et al 2019). The GPM Microwave Imager (GMI) and the Dual-Frequency Precipitation Radar (DPR) are the two primary sensors of GPM satellite that are used to estimate precipitation type and intensity, as well as to detect the internal structure of storms under and within clouds, respectively (Tan and Santo 2018).…”

Section: B Gpm Imerg Precipitation Productsmentioning

confidence: 99%

“…(iii) Precipitation processes are usually complex in high-elevation regions. Therefore, its estimation will be more complicated for satellite sensors (Fang et al 2019). (iv) When the surface is covered by snow or ice (which is common in high mountainous regions), due to some difficulty in retrievals algorithms, IMERG uses only infrared-based estimations that are much less certain than the microwave estimations (Zhou et al 2019).…”

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

confidence: 99%

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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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Growing‐season climate as an explanation of spatial variations in temperature sensitivity of green‐up on Tibetan Plateau

Yang,

Jiang,

Huang

et al. 2024

Ecosphere

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Temperature sensitivity (TS) of the green‐up date (GUD) of plants is crucial for the prediction of grassland phenology that is important for animal husbandry and pasture management. Spatial variations in the TS are known to reflect interannual temperature variability and/or accumulated precipitation preceding the GUD (pre‐GUD). However, whether spatial TS variations are related to the interaction between pre‐GUD temperature variability and precipitation, which is a potential indicator of frost risk, remains unclear. Furthermore, because the interaction between interannual temperature variability and accumulated precipitation following the GUD (post‐GUD) can exert selection pressure on the plant life cycle, it may also be involved in shaping the spatial TS pattern. Using long‐term ground observations of GUD on the Tibetan Plateau, we show that TS is more negative (greater GUD advance per unit temperature increase) in areas with more pre‐GUD precipitation and low pre‐GUD interannual temperature variability, but less negative in areas with more pre‐GUD precipitation and high pre‐GUD interannual temperature variability. This result is likely because more pre‐GUD precipitation facilitates sprout and leaf development under stable temperature conditions, whereas it increases frost risk when the temperature variability is high. In contrast, TS magnitude decreases with increases in post‐GUD precipitation in areas where post‐GUD interannual temperature variability is low, but increases with post‐GUD precipitation in areas where post‐GUD interannual temperature variability is high. We speculate that because hydrothermal demands for leaf growth from the onset of green‐up to maturity are more easily fulfilled when interannual temperature variability is lower and precipitation is higher, green‐up need not be sensitive to pre‐GUD temperature. In contrast, high post‐GUD precipitation likely aggravates low‐temperature constraints on leaf growth when temperature variability is high, resulting in greater TS to maximize growing season length. These results suggest that spatial TS variations on the Tibetan Plateau are likely associated with adaptations of leaf‐out phenology to background pre‐GUD climatic conditions together with selection pressure from post‐GUD conditions.

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Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China

Cited by 201 publications

References 35 publications

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

Growing‐season climate as an explanation of spatial variations in temperature sensitivity of green‐up on Tibetan Plateau

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