Articles | Volume 25, issue 1
https://doi.org/10.5194/hess-25-359-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-359-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jan 2021
Research article |
| 21 Jan 2021
| 21 Jan 2021
A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau
Yingzhao Ma
Colorado State University, Fort Collins, CO 80523, USA
Key Laboratory of Geographic Information Science (Ministry of
Education), East China Normal University, Shanghai, 200241, China
Columbia Water Center, Earth Institute, Columbia University, New York, NY 10027, USA
Haonan Chen
Colorado State University, Fort Collins, CO 80523, USA
NOAA/Physical Sciences Laboratory, Boulder, CO 80305, USA
Yang Hong
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA
Yinsheng Zhang
Key Laboratory of Tibetan Environment Changes and Land Surface
Processes, Institute of Tibetan Plateau Research, Chinese Academy of
Sciences, Beijing, 100101, China
CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, 100101, China
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- Quantifying the Reliability and Uncertainty of Satellite, Reanalysis, and Merged Precipitation Products in Hydrological Simulations over the Topographically Diverse Basin in Southwest China H. Lei et al. 10.3390/rs15010213
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27 citations as recorded by crossref.
- The Uncertainty of IMERG Over the Western Edge of the Eastern Pacific Fresh Pool: An Error Model Based on SPURS-2 Field Campaign Observations Z. Li et al. 10.1109/TGRS.2023.3306795
- Analysis of the IMERG-GPM Precipitation Product Analysis in Brazilian Midwestern Basins Considering Different Time and Spatial Scales L. Duarte et al. 10.3390/w14162472
- Deep learning-based multi-source precipitation merging for the Tibetan Plateau T. Nan et al. 10.1007/s11430-022-1050-2
- Statistical blending of global-gridded climatological products: an approach to inverse hydrological model R. Mousavi et al. 10.2166/hydro.2023.141
- A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information K. He et al. 10.1016/j.scitotenv.2023.162378
- A new approach to ensemble precipitation Estimation: Coupling satellite hydrological products with backward water balance models in Large-Scale P. Dastjerdi et al. 10.1016/j.jhydrol.2023.130564
- Mapping Australia’s precipitation: harnessing the synergies of multi-satellite remote sensing and gauge network data B. Hines et al. 10.1080/15481603.2022.2143807
- A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and regression over China H. Lei et al. 10.5194/hess-26-2969-2022
- Sequential Data Processing for IMERG Satellite Rainfall Comparison and Improvement Using LSTM and ADAM Optimizer S. Toh et al. 10.3390/app13127237
- Improving spatio-temporal precipitation estimates in data scarce river basins: an application of machine learning-based multi-source data merging J. Mohammed et al. 10.1007/s00477-022-02346-4
- Reconstructing high-resolution gridded precipitation data in the southwest China highland canyon area using an improved (MGWR) downscaling method L. Wang et al. 10.1016/j.scitotenv.2024.174866
- Cross Validation of GOES-16 and NOAA Multi-Radar Multi-Sensor (MRMS) QPE over the Continental United States L. Sun et al. 10.3390/rs13204030
- Comparison of Methods for Filling Daily and Monthly Rainfall Missing Data: Statistical Models or Imputation of Satellite Retrievals? L. Duarte et al. 10.3390/w14193144
- Assessment and Data Fusion of Satellite-Based Precipitation Estimation Products over Ungauged Areas Based on Triple Collocation without In Situ Observations X. Wu et al. 10.3390/rs15174210
- Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan K. Rahman et al. 10.3390/rs13091662
- Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework Y. Zhou et al. 10.3390/rs13061057
- SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation K. He et al. 10.5194/hess-27-169-2023
- A framework for merging precipitation retrievals and gauge-based observations based on a novel concept namely virtual gauges Y. Dou et al. 10.1016/j.jhydrol.2023.129506
- Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach W. Asfaw et al. 10.1016/j.ejrh.2022.101287
- Assessment and Hydrological Validation of Merged Near-Real-Time Satellite Precipitation Estimates Based on the Gauge-Free Triple Collocation Approach D. Cao et al. 10.3390/rs14153835
- Triple collocation-based error estimation and data fusion of global gridded precipitation products over the Yangtze River basin C. Chen et al. 10.1016/j.jhydrol.2021.127307
- A Data- and Knowledge-Driven Method for Fusing Satellite-Derived and Ground-Based Precipitation Observations F. Chen et al. 10.1109/TGRS.2024.3385647
- Quantifying the Reliability and Uncertainty of Satellite, Reanalysis, and Merged Precipitation Products in Hydrological Simulations over the Topographically Diverse Basin in Southwest China H. Lei et al. 10.3390/rs15010213
- Effective multi-satellite precipitation fusion procedure conditioned by gauge background fields over the Chinese mainland W. Li et al. 10.1016/j.jhydrol.2022.127783
- Spatial Downscaling of IMERG Considering Vegetation Index Based on Adaptive Lag Phase Z. Zeng et al. 10.1109/TGRS.2021.3070417
- An Assimilating Model Using Broad Learning System for Incorporating Multi‐Source Precipitation Data With Environmental Factors Over Southeast China Y. Zhou et al. 10.1029/2021EA002043
- 基于深度学习的青藏高原多源降水融合 天. 南 et al. 10.1360/SSTe-2022-0077
Latest update: 22 Nov 2024
Short summary
A two-stage blending approach is proposed for the data fusion of multiple satellite precipitation estimates (SPEs), which firstly reduces the systematic errors of original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. The model is evaluated in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared with the original SPEs, even in heavy rainfall events.
A two-stage blending approach is proposed for the data fusion of multiple satellite...
