A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data

Chen, Yuanyuan; Huang, Jingfeng; Sheng, Shaoxue; Mansaray, Lamin R.; Liu, Zhixiong; Wu, Hongyan; Wang, Xiuzhen

doi:10.1016/j.rse.2018.05.021

Cited by 83 publications

(43 citation statements)

References 65 publications

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“…Pan et al [165] found that the marginal effect of merging gauge rainfall with radar rainfall is more significant than that of CMORPH, and the latter only led to improvements in sparse gauge areas not covered by radar. Chen et al [169] stated that for the merging of gauge rainfall with TRMM 3B43V7, remarkable improvement occurred when TRMM 3B43V7 data downscaled from 0.25 • × 0.25 • to 1 km × 1 km. Of course, the rainfall merging effect is also affected by algorithms.…”

Section: Evaluation Of the Merging Effectmentioning

confidence: 99%

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Wang

et al. 2019

Water

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Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data.

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Section: Evaluation Of the Merging Effectmentioning

confidence: 99%

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Wang

et al. 2019

Water

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“…It is widely acknowledged that the response of vegetation to precipitation and temperature usually lags by about two or three months in different regions and at high elevations in mountainous areas [52,53]. Therefore, the response lags result in unreliable precipitation-PNDVI relationships at monthly scales, and the precipitation-NDVI relationships may be better than precipitation-PNDVI relationships [14,15,18,54]. Consequently, PNDVI is not suitable for downscaling studies at monthly scales because of the differences in plant-growth lag in different regions and different elevations.…”

Section: The Advantages and Disadvantages Of The Modelmentioning

confidence: 99%

“…Zhan et al [15] compared the accuracy of monthly downscaling results of multiple regression and geographically-weighted regression based on GPM, and the results show that geographically-weighted regression outperformed multiple regression models in the Hengduan Mountains. Recently, researchers have tried to construct the precipitation-variables relationships with different land surface characteristics as explanatory variables, examples are land surface temperature, longitude, and latitude [16][17][18]. Generally, the precipitation is affected by land surface characteristics, geographical factors, and sources of water vapor.…”

Section: Introductionmentioning

confidence: 99%

“…Research showed that in a large study area, the impact of water vapor sources on precipitation needed to be considered [19]. The latitude and longitude were introduced into the statistical model to indicate the distance from the study area to a source of vapor, which is more accurate than the model that only considers geographical factors [18,20].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Spatial–Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China’s Qilian Mountains

Wang

Chen

et al. 2019

Remote Sensing

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Remote sensing techniques provide data on the spatial–temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrological and meteorological research at basin scales. Downscaling research using coarse remote sensing data to obtain high-resolution precipitation data is significant for the development of basin-scale research. Here, we propose improvements to a spatial–temporal method for downscaling satellite precipitation. The improved method uses a nonlinear regression model and introduces longitude and latitude based on processed normalized difference vegetation index (NDVI) and a digital elevation model (DEM) to stimulate precipitation in the Qilian Mountains during 2006–2015. The final downscaled annual precipitation (FDAP) results are corrected by observed data to obtain corrected final downscaled annual precipitation (CFDAP) datasets. For temporal downscaling, monthly downscaled data are the corrected monthly ratio multiplied by the corresponding downscaled annual datasets. The results indicated that processed NDVI (PNDVI) reflected spatial precipitation patterns more accurately than the original NDVI. The accuracy was significantly improved when the final downscaled annual precipitation data were corrected by observed data. The average annual root mean square error (RMSE) from 2006 to 2015 of CFDAP was 66.48 and 83.07 mm less than that of FDAP and original Tropical Rainfall Measuring Mission (TRMM) data, respectively. Compared with previous methods, which use NDVI and/or DEM to downscale TRMM, the accuracy of FDAP and CFDAP from the improved method was higher, and the RMSE decreased on average by 13.63 and 80.11 mm. The RMSE of monthly data from corrected monthly ratio (CMR) decreased on average by 4.93 mm over monthly data from previous monthly ratio (PMR). In addition, the accuracy of the original satellite data affected the initial downscaling results but had no significant effects on the corrected downscaling results.

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“…As a joint project of the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA), the Tropical Rainfall Measuring Mission (TRMM) was successfully launched in 1997, carrying with it the first satellite-borne precipitation radar, which could provide new insights into tropical storm structures and intensification, water vapor, cloud water, and rainfall intensity in the atmosphere [6]. TRMM precipitation data have been widely used in recent years and have afforded researchers a large amount of surface precipitation data [7][8][9], which compensate for the current insufficiency of ground rainfall measurement stations.…”

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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A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data

Cited by 83 publications

References 65 publications

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

An Improved Spatial–Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China’s Qilian Mountains

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

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