Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Ma, Zhanshan; He, Kang; Tan, Xiao; Xu, Jintao; Fang, Wenxia; He, Yu; Hong, Yang

doi:10.3390/rs10121883

Cited by 30 publications

(35 citation statements)

References 47 publications

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“…Nonetheless, users should still pay great attention to the not so satisfying performance of IMERG at hourly and diurnal scales. Furthermore, some algorithms and methods, such as downscaling and retrospective studies, could be applied to yield long-term precipitation estimates with finer spatiotemporal resolutions in the future [36][37][38][39].…”

Section: The Advantages and Disadvantages Of Fy-2e Qpe Fy-2g Qpe Anmentioning

confidence: 99%

Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018

Tang

et al. 2019

Remote Sensing

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Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.Collecting precipitation information from ground rain gauge stations is the traditional and common method of measurement. However, the limitations are obvious due to the uneven spatial distribution of the stations. The measurements of ground stations are usually very sparse over some regions of the earth (e.g., the Tibetan plateau), which are meteorologically important [6,7]. As for ground-based weather radars, they have certain superiorities when observing precipitation in local areas. Nevertheless, due to the limitations of the scope of observation and the huge cost of equipment acquisition and maintenance, ground-based weather radars are not the first choice for large-scale precipitation observations. However, precipitation information obtained from satellites does not meet such limitations. Satellite-based precipitation datasets can depict the spatial and temporal variability of precipitation with a considerable accuracy over regions that have few ground stations [5,8]. Over the last four decades, the progress of meteorological satellites has made it possible for scientists to acquire reliable and cost-effective precipitation datasets through a variety of sensors and inversion algorithms [9][10][11][12][13]. Therefore, obtaining high-resolution and accurate precipitation estimates derived from sensors on satellites at a regional or global scale has become a highly-efficient research method at present [4,14,15].Satellite-based precipitation products provided by several institutions and organizations from all over the world are different in terms o...

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Section: The Advantages and Disadvantages Of Fy-2e Qpe Fy-2g Qpe Anmentioning

confidence: 99%

Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018

Tang

et al. 2019

Remote Sensing

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“…Precipitation is a critical linkage in the global water, energy and carbon cycles (Ruhi et al, ). And precipitation estimates with fine spatiotemporal resolutions and quality are of great significance for water cycle‐related fields, such as hydrology, meteorology, climatology, and ecology (Hegerl et al, ; Ma et al, ). Various manners have been developed for monitoring the precipitation, for example, ground‐based weather radars and satellite‐based sensors (Jongjin et al, ; Kidd & Huffman, ; Ma et al, ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Assessments on the Satellite‐Based Precipitation Products From Fengyun and GPM Over the Yunnan‐Kweichow Plateau, China

Ding

et al. 2020

Earth and Space Science

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High-quality precipitation data are vital for hydrological applications and climate change research. In this study, we evaluated two satellite-based precipitation products from Chinese Fengyun (FY) 2G (G is one of the third batch of operational geostationary satellites in the Chinese FY-2 series) and the Global Precipitation Measurement (GPM) against the rain gauge-based precipitation data, respectively, over the Yunnan-Kweichow Plateau, China, at different temporal scales (e.g., monthly, daily, and hourly). And the main findings of this study were as follows: (1) FY2G Quantitative Precipitation Estimation (QPE) and Integrated Multisatellite Retrievals for GPM (IMERG) shared similar spatial precipitation patterns with those of rain gauge data, while the FY2G QPE general underestimated the precipitation (bias, −30%-0%), and IMERG obviously overestimated the precipitation (bias, 0%-60%) at monthly scale over the Yunnan-Kweichow Plateau; (2) the FY2G QPE correlated significantly better (Correlation coefficient, CC, 0.80) than IMERG (CC~0.20) against rain gauge data at daily scale, meanwhile, the average daily bias value of IMERG (~30%) was around 6 times larger than that of FY2G QPE (~5%); (3) The FY2G QPE (CC 0.7, bias~− 8%, root-mean-square error~2.0 mm/hr, mean absolute error~0.6 mm/hr) also generally outperformed the IMERG (CC~0.1, bias~13%, root-mean-square error~3.0 mm/hr, mean absolute error1.0 mm/hr), at hourly scale; (4) The FY2G QPE significantly underestimated the precipitation in the period between 16:00 and 20:00 with the probability of detection (POD) decreased from 0.8 to 0.5, while the IMERG significantly overestimated the precipitation in the period between 4:00 and 12:00, at diurnal scale; and (5) one of the reasons resulting the significant overestimations of IMERG might due to its weak abilities in detecting the precipitation events with POD~0.2 and false alarm ratio (FAR)~0.7 at diurnal scale, which performed worse than those of the FY2G QPE (POD~0.8 and FAR~0.4). These findings would provide valuable recommendations for using these satellite-based precipitation products in various application fields, such as hydrology, agriculture, and meteorology over the YKP, and also for improving the algorithms of GPM. . (2020). Spatiotemporal assessments on the satellite-based precipitation products from Fengyun and GPM over the Yunnan-Kweichow Plateau, China. Earth and Space Science, 7, e2019EA000857. https://doi.

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“…Thereafter, Lloyd et al [10], Duan et al [11], Park et al [12], and Fang et al [13] established a linear or exponential regression model between precipitation, NDVI, and digital elevation model (DEM) to achieve the spatial downscaling of TRMM. Later, the wavelet [14], multifractal [15], Bayesian model [16], area-to-point kriging (ATPK) [17][18][19][20][21], geographic weight regression methods (GWR) [18,[22][23][24][25][26], random forests (RF) method [5,27,28], support vector machine (SVM) [29], and artificial neural network method [30] were also introduced into the spatial downscaling of TRMM data by establishing a statistical relationship between TRMM data and environmental parameters, such as NDVI, DEM, latitude, longitude, slope, aspect, land surface temperature, and so on [31][32][33][34]. However, these spatial downscaling methods are only available on an annual scale, because environmental variables, such as vegetation and DEM, usually show a long-term distribution of precipitation.…”

Section: Introductionmentioning

confidence: 99%

“…2019, 11, 2962 3 of 19 which cannot reflect the amount of precipitation in the short term. In order to improve the temporal resolution of satellite precipitation observation data, Wang et al [9], Duan et al [11], Zhang et al [21], and Ma et al [28] used the monthly ratio multiplied by the corresponding spatial downscaled annual datasets to obtain monthly downscaled data. However, because precipitation changes dramatically over time, it is difficult to use these precipitation downscaling methods at higher temporal resolutions.…”

Section: Introductionmentioning

confidence: 99%

A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

Fan

Dong³

et al. 2019

Remote Sensing

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Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the "hysteresis phenomenon" of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.

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Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Cited by 30 publications

References 47 publications

Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018

Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018

Spatiotemporal Assessments on the Satellite‐Based Precipitation Products From Fengyun and GPM Over the Yunnan‐Kweichow Plateau, China

A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

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