Evaluation of TMPA 3B42V7, GPM IMERG and CMPA precipitation estimates in Guangdong Province, China

Wang, Dashan; Wang, Xianwei; Liu, Lin; Huang, Huabing; Pan, Chaobin

doi:10.1002/joc.5839

Cited by 42 publications

(24 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 8 displays the error characteristics of satellite precipitation estimates with rain rate. Obviously, all precipitation products showed a similar variation of error, with overestimations for light rain and underestimations for heavy rain (Figure 8a), which is a common error feature of satellite-based retrievals, as documented in previous studies [73][74][75]. This error feature of rain-rate dependency is important for meteorological and hydrological applications, especially for typhoon monitoring and flood forecast, which are sensitive to higher rain rates [76,77].…”

Section: Statistical Performance Of Satellite Precipitation Estimatessupporting

confidence: 75%

Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau

2018

View full text Add to dashboard Cite

Satellite precipitation products provide alternative precipitation data in mountain areas. This study aimed to assess the performance of the latest Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) version 5 (IMERG V5) and Global Satellite Mapping of Precipitation version 7 (GSMaP V7) products and their hydrological utilities over the Tibetan Plateau (TP). Here, two IMERG Final Run products (uncalibrated IMERG (IMERG-UC) and gauge-calibrated IMERG (IMEEG-C)) and two GSMaP products (GSMaP Moving Vector with Kalman Filter (GSMaP-MVK) and gauge-adjusted GSMaP (GSMaP-Gauge)) were evaluated from April 2014 to March 2017. Results show that all four satellite precipitation products could generally capture the spatial patterns of precipitation over the TP. The two gauge-adjusted products were more consistent with the ground measurements than the satellite-only products in terms of statistical assessment. For hydrological simulation, IMERG-UC and GSMaP-MVK showed unsatisfactory performance for hydrological utility, while GSMaP-Gauge demonstrated comparable performance with gauge reference data, suggesting that GSMaP-Gauge can be selected for hydrological application in the TP. Our study also indicates that accurately measuring light rainfall and winter snow is still a challenging task for the current satellite precipitation retrievals.

show abstract

Section: Statistical Performance Of Satellite Precipitation Estimatessupporting

confidence: 75%

Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau

2018

View full text Add to dashboard Cite

show abstract

“…However, the performance of IMERG products in China was not completely similar due to various climates and elevation bands. For instance, the IMERG product only showed a moderate correlation with ground-based data in Guangdong Province [46], Tianshan Mountain Area [38], and Tibetan Plateau [31,69]. In contrast, IMERG did not show good consistency in Pakistan [13], Malaysia [17], Singapore [25], Northwestern South America [35], Korea, and Japan [68].…”

Section: Discussionmentioning

confidence: 94%

“…As a successor of TRMM, the GPM mission was launched in February 2014 [46]. The GPM Core Observatory carries two major sensors: the GPM Microwave Imager (GMI) and the Dual-frequency Precipitation Radar (DPR) [33].…”

Section: Satellite Precipitation Productsmentioning

confidence: 99%

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Guo

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

show abstract

“…Considering the short duration of the records, they were just the typical mean states for this area. The precipitation extremes were also found to be positively associated with the urban extent in the Pearl River Delta [39]. Besides the annual scale, meteorological factors also demonstrated different patterns for urban and suburban stations at the seasonal and diurnal scales ( Figure 3).…”

Section: Characteristics Of Meteorological Factorsmentioning

confidence: 90%

“…The China Hourly Merged Precipitation Analysis (CMPA) data merged the hourly precipitation products with 0.1 • × 0.1 • spatial resolution [38] (http://cdc.nmic.cn/home.do), and are available from 2008 to present. They show a much better performance in quantifying the extreme rainfall than the other satellite and reanalysis precipitation data in China [11,38,39]. The CMPA data are used to illustrate the spatial distributions of five typical storms for the peak intensity and event total precipitation.…”

Section: China Hourly Merged Precipitation Analysis (Cmpa)mentioning

confidence: 99%

Characteristics of Heavy Storms and the Scaling Relation with Air Temperature by Event Process-Based Analysis in South China

Pan

Wang

Liu

et al. 2019

Water

Self Cite

View full text Add to dashboard Cite

The negative scaling rate between precipitation extremes and the air temperature in tropic and subtropic regions is still a puzzling issue. This study investigates the scaling rate from two aspects, storm characteristics (types) and event process-based temperature variations. Heavy storms in South China are developed by different weather systems with unique meteorological characteristics each season, such as the warm-front storms (January), cold-front storms (April to mid-May), monsoon storms (late May to June), convective storms, and typhoon storms (July to September). This study analyzes the storm characteristics using the hourly rainfall data from 1990 to 2017; compares the storm hyetographs derived from the one-minute rainfall data during 2008–2017; and investigates the interactions between heavy storms and meteorological factors including air temperature, relative humidity, surface pressure, and wind speed at 42 weather stations in Guangzhou during 2015–2017. Most storms, except for typhoon and warm-front storms, had a short duration (3 h) and intense rates (~13 mm/h) in Guangzhou, South China. Convective storms were dominant (50%) in occurrence and had the strongest intensity (15.8 mm/h). Storms in urban areas had stronger interactions with meteorological factors and showed different hyetographs from suburban areas. Meteorological factors had larger variations with the storms that occurred in the day time than at night. The air temperature could rise 6 °C and drop 4 °C prior to and post-summer storms against the diurnal mean state. The 24-hour mean air temperature prior to the storms produced more reliable scaling rates than the naturally daily mean air temperature. The precipitation extremes showed a peak-like scaling relation with the 24-hour mean air temperature and had a break temperature of 28 °C. Below 28 °C, the relative humidity was 80%–100%, and it showed a positive scaling rate. Above 28 °C, the negative scaling relation was likely caused by a lack of moisture in the atmosphere, where the relative humidity decreased with the air temperature increase.

show abstract

Evaluation of TMPA 3B42V7, GPM IMERG and CMPA precipitation estimates in Guangdong Province, China

Cited by 42 publications

References 53 publications

Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau

Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Characteristics of Heavy Storms and the Scaling Relation with Air Temperature by Event Process-Based Analysis in South China

Contact Info

Product

Resources

About