Articles | Volume 18, issue 12
https://doi.org/10.5194/hess-18-5077-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-18-5077-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Dec 2014
Research article |
| 11 Dec 2014
Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications
H. Seyyedi
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA
E. N. Anagnostou
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA
E. Beighley
Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA
J. McCollum
Center for Property Risk Solutions, FM Global, Research Division, Norwood, MA 02062-9102, USA
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- Similarity and difference of global reanalysis datasets (WFD and APHRODITE) in driving lumped and distributed hydrological models in a humid region of China H. Xu et al. 10.1016/j.jhydrol.2016.09.011
- Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region G. Xu et al. 10.3390/app9112294
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- Multi-Scale Hydrologic Sensitivity to Climatic and Anthropogenic Changes in Northern Morocco A. Milewski et al. 10.3390/geosciences10010013
- Development and Assessment of the Sand Dust Prediction Model by Utilizing Microwave-Based Satellite Soil Moisture and Reanalysis Datasets in East Asian Desert Areas H. Kim et al. 10.1155/2017/1917372
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- An ERA‐5 Derived CONUS‐Wide High‐Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework S. Emmanouil et al. 10.1029/2020WR029548
- Assessment and Comparison of Satellite-Based Rainfall Products: Validation by Hydrological Modeling Using ANN in a Semi-Arid Zone S. Rachidi et al. 10.3390/w15111997
- Enhancing the TRMM precipitation product in diverse regions of Iran through an intelligent-based post-processing approach R. Shahbazdashti et al. 10.1007/s11600-023-01280-5
- Quantifying Precipitation Uncertainty for Land Data Assimilation Applications S. Alemohammad et al. 10.1175/MWR-D-14-00337.1
- Evaluation of the performance of TRMM Multi-satellite Precipitation Analysis (TMPA) estimation over Iran M. Darand et al. 10.1016/j.atmosres.2017.02.011
- Evaluation of various meteorological datasets in estimation yield and actual evapotranspiration of wheat and maize (case study: Qazvin plain) H. Ramezani Etedali & M. Ahmadi 10.1016/j.agwat.2021.107080
- Multi-Scenario Integration Comparison of CMADS and TMPA Datasets for Hydro-Climatic Simulation over Ganjiang River Basin, China Q. Wang et al. 10.3390/w12113243
- Similarity and difference of global reanalysis datasets (WFD and APHRODITE) in driving lumped and distributed hydrological models in a humid region of China H. Xu et al. 10.1016/j.jhydrol.2016.09.011
- Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region G. Xu et al. 10.3390/app9112294
- Spatio-temporal evaluation of remote sensing rainfall data of TRMM satellite over the Kingdom of Saudi Arabia S. Hussain et al. 10.1007/s00704-022-04148-8
- Hydrological Responses of Headwater Basins to Monthly Perturbed Climate in the North American Cordillera K. Rasouli et al. 10.1175/JHM-D-18-0166.1
- Customized deep learning for precipitation bias correction and downscaling F. Wang et al. 10.5194/gmd-16-535-2023
- Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data - a machine learning approach S. Eltazarov et al. 10.1080/20964471.2023.2196830
- Precision of raw and bias-adjusted satellite precipitation estimations (TRMM, IMERG, CMORPH, and PERSIANN) over extreme flood events: case study in Langat river basin, Malaysia E. Soo et al. 10.2166/wcc.2020.180
- Temporal disaggregation of daily rainfall measurements using regional reanalysis for hydrological applications S. Acharya et al. 10.1016/j.jhydrol.2022.127867
- A Satellite-Based Approach for Quantifying Terrestrial Water Cycle Intensity F. Zowam et al. 10.3390/rs15143632
- Assessment and Comparison of TMPA Satellite Precipitation Products in Varying Climatic and Topographic Regimes in Morocco A. Milewski et al. 10.3390/rs70505697
- Using high‐resolution satellite precipitation for flood frequency analysis: case study over the Connecticut River Basin M. Dis et al. 10.1111/jfr3.12250
- Multi-Scale Hydrologic Sensitivity to Climatic and Anthropogenic Changes in Northern Morocco A. Milewski et al. 10.3390/geosciences10010013
- Development and Assessment of the Sand Dust Prediction Model by Utilizing Microwave-Based Satellite Soil Moisture and Reanalysis Datasets in East Asian Desert Areas H. Kim et al. 10.1155/2017/1917372
- Blue and green water re-distribution dependency on precipitation datasets for a tropical Indian River basin A. Dey et al. 10.1016/j.ejrh.2023.101361
- In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics Y. Yang et al. 10.1029/2018WR024178
- Inter-comparison of remotely sensed precipitation datasets over Kenya during 1998–2016 B. Ayugi et al. 10.1016/j.atmosres.2019.03.032
- Evaluation of Precipitation Datasets from TRMM Satellite and Down-scaled Reanalysis Products with Bias-correction in Middle Qilian Mountain, China L. Zhang et al. 10.1007/s11769-021-1205-9
3 citations as recorded by crossref.
- Integrating Runoff Generation and Flow Routing in Susquehanna River Basin to Characterize Key Hydrologic Processes Contributing to Maximum Annual Flood Events R. Ray et al. 10.1061/(ASCE)HE.1943-5584.0001389
- A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa R. Maidment et al. 10.1038/sdata.2017.63
- Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study T. Petty et al. 10.4236/jgis.2016.86055
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Short summary
The paper presents a methodology for using global precipitation products from satellite remote sensing to error-correct and downscale global atmospheric reanalysis precipitation data sets. It is shown that streamflow simulations from the satellite-adjusted precipitation reanalysis give similar statistics to the ones derived by high-resolution ground-based radar rainfall data sets. This approach can be applied globally to derive improved flood frequency maps over data-poor areas.
The paper presents a methodology for using global precipitation products from satellite remote...
