A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

Yu, Linfei; Leng, Guoyong; Python, André; Peng, Jian

doi:10.3390/rs13061208

Cited by 31 publications

(22 citation statements)

References 60 publications

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“…For IMC the ability of the satellite product to detect and quantify rainfall was not much different for elevations (the highest elevation of rain gauge station was 1260 m) (Figure 10). consistent with previous studies which found similar results for stations below 2000 m [40,82,83].…”

Section: Discussionsupporting

confidence: 93%

“…Lower CC values in hills were followed by higher RMSE and RB values (Figure 10c), consistent with those found in Cyprus [82]. A decrease in the correlation of IMERG and rain gauge data with increasing elevation was also found across China [83]. Hence, daily rainfall observations using IMERG data in the hills still needs an improvement.…”

Section: Effect Of Topographysupporting

confidence: 81%

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Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

et al. 2022

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Accurate precipitation observations are crucial for water resources management and as inputs for a gamut of hydrometeorological applications. Precipitation data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) have recently been widely used to complement traditional rain gauge systems. However, the satellite precipitation data needs to be validated before being widely used in the applications and this is still missing over the Indonesian maritime continent (IMC). We conducted a validation of the IMERG product version 6 for this region. The evaluation was carried out using gauge data in the period from 2016 to 2020 for three types of IMERG: Early (E), Late (L), and Final (F) from annual, monthly, daily and hourly data. In general, the annual and monthly data from IMERG showed a good correlation with the rain gauge, with the mean correlation coefficient (CC) approximately 0.54–0.78 and 0.62–0.79, respectively. About 80% of stations in the IMC area showed a very good correlation between gauge data and IMERG-F estimates (CC = 0.7–0.9). For the daily assessment, the CC value was in the range of 0.39 to 0.44 and about 40% of stations had a correlation of 0.5–0.7. IMERG had a fairly good ability to detect daily rain in which the average probability of detection (POD) for all stations was above 0.8. However, the false alarm ratio (FAR) value is quite high (<0.5). For hourly data, IMERG’s performance was still poor with CC around 0.03–0.28. For all assessments, IMERG generally overestimated rainfall in comparison with rain gauge. The accuracy of the three types of IMERG in IMC was also influenced by season and topography. The highest and lowest CC values were observed for June–July–August and December–January–February, respectively. However, categorical statistics (POD, FAR and critical success index) did not show any clear seasonal variation. The CC value decreased with higher altitude, but with slight difference for each IMERG type. For all assessments conducted, IMERG-F generally showed the best rainfall observations in IMC, but with slightly difference from IMERG-E and IMERG-L. Thus, IMERG-E and IMERG-L data that had a faster latency than IMERG-F show potential to be used in rainfall observations in IMC.

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Section: Discussionsupporting

confidence: 93%

Section: Effect Of Topographysupporting

confidence: 81%

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

et al. 2022

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“…For precipitation, we use the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM IMERG) final product (Huffman et al, 2015). While we select the GPM IMERG final product which shows superior performance compared to other products (Beck et al, 2019) without shortening the study period, the results need to be considered in the context of performance and uncertainty of the data presented in existing evaluation studies (O et al, 2017;Yu et al, 2021). For precipitable water, we focus on the Atmospheric Infrared Sounder (AIRS; AIRS Project, 2019), utilizing four additional reanalyzes data sets to check consistency in the results, these being the second Modern-Era Retrospective Analysis for Research and Applications (MERRA2; Gelaro et al, 2017); European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5; Hersbach et al, 2020); the Japanese 55-year Reanalysis (JRA-55) Kobayashi et al (2015); and the National Centers for Environmental Prediction (NCEP; Saha et al, 2010Saha et al, , 2014.…”

Section: Datamentioning

confidence: 99%

Linking Total Precipitable Water to Precipitation Extremes Globally

et al. 2022

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The relationship between extreme precipitation (EP) and precipitable water (W) is useful to assess design extremes and speculate on their expected changes with rising global temperatures. This study investigates the relationship between daily and longer‐duration EP and corresponding W at a global scale by analyzing remote‐sensed and reanalysis data sets from 2003 to 2019. An assessment of the consistency in the temporal trend across various W data sets reveals a consistent statistically significant upward trend during the period. This upward trend, while predominant worldwide, is especially significant over tropical land regions. W is found to generally be positively correlated with surface (dew point) temperature, suggesting a rise in temperature will cause a greater W over time. To assess whether EPs occur coincident with extreme W, the Concurrent Extremes Index (CEI) is proposed, which compares the cumulative distribution functions between the two variables and assumes a value of unity if ranks of the EP series are identical to that of the coincident W series, and zero with no correspondence. For EP (defined as the five largest 1‐day events per year on average), a high CEI is pronounced across the tropics, except for rainforests. The W‐EP relationship is noticeably weakened in nontropics, except the inland regions of North America and East Asia. An assessment indicates that as the duration of the EP becomes longer, the influence of W on EP decreases. However, the contrast in the W‐EP relationship between the tropics and nontropics is found to become more pronounced as longer‐duration EPs are considered.

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“…(4) The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) Final Precipitation L3 Half Hourly 0.1-degree × 0.1-degree V06 (GPMI) [54]. The data set applies the unified algorithm that provides rainfall estimates by combining data from all of the passive-microwave instruments in the GPM Constellation, performs well in the lower reaches of the Yangtze River [55], and has a better capture of the actual variation tendency of hourly precipitation in extreme precipitation events [56].…”

Section: Observation Datamentioning

confidence: 99%

Impact of Interaction between Metropolitan Area and Shallow Lake on Daily Extreme Precipitation over Eastern China

Gao

2022

Atmosphere

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Both cities and lakes have significant impacts on regional precipitation. With global warming, extreme precipitation events in Eastern China have increased significantly, and the single/joint influences of metropolises and lakes on extreme precipitation still need to be quantitatively evaluated. To reveal the impact of the single/joint influences of metropolises and lakes on the shear line torrential rain process, the Suzhou-Wuxi-Changzhou Metropolitan Area (SXCMA) and Lake Taihu in Eastern China were selected as the study area. Utilizing a WRF model, comparative studies of sensitivity simulations were conducted for the two typical extreme precipitation events caused by the low-level shear line (LLSL) on 27 June 2015 (EP627) and 25 September 2017 (EP925). Both results show that the existence of Lake Taihu and SXCMA will increase precipitation in the study area. SXCMA has a more obvious effect on enhancing precipitation, which is about twice the effect of Lake Taihu. SXCMA mainly strengthens the intensity and movement of the surface convergence line (SCL) in the study area and indirectly affects the shift of the LLSL, which finally affects the intensity and location of precipitation. Lake Taihu affects the intensity and movement of SCL, triggering ground vertical convections due to lower surface roughness, and acts as a land-lake breeze and water vapor source, which will affect the distribution and intensity of precipitation.

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A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

Cited by 31 publications

References 60 publications

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

Linking Total Precipitable Water to Precipitation Extremes Globally

Impact of Interaction between Metropolitan Area and Shallow Lake on Daily Extreme Precipitation over Eastern China

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