Articles | Volume 22, issue 3
https://doi.org/10.5194/hess-22-1957-2018
© Author(s) 2018. 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-22-1957-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Mar 2018
Research article |
| 23 Mar 2018
| 23 Mar 2018
Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment
Sanjeev K. Jha
CORRESPONDING AUTHOR
Department of Civil Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada
Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal, Madhya Pradesh, 462066, India
Durga L. Shrestha
Commonwealth Science and Industrial Research Organization, Clayton South Victoria, 3169, Australia
Tricia A. Stadnyk
Department of Civil Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada
Paulin Coulibaly
Department of Civil Engineering, McMaster University, Hamilton, L8S 4L7, Canada
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- Stream flow prediction using TIGGE ensemble precipitation forecast data for Sabarmati river basin A. Patel & S. Yadav 10.2166/ws.2022.362
- Evaluation of quantitative precipitation forecast in five Indian river basins A. Singh et al. 10.1080/02626667.2021.1982138
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- Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019 W. Ma et al. 10.1038/s41598-021-89522-8
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- Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts S. Han & P. Coulibaly 10.1175/JHM-D-18-0251.1
- Forecastability of a heavy precipitation event at different lead-times using WRF model: the case study in Karkheh River basin M. Maddah et al. 10.1007/s11600-021-00669-4
- Post-Processing Ensemble Precipitation Forecasts and Their Applications in Summer Streamflow Prediction over a Mountain River Basin Y. Xiang et al. 10.3390/atmos14111645
- Assessing Entropy-based Bayesian Model Averaging Method for Probabilistic Precipitation Forecasting 10.1175/JHM-D-21-0086.1
- Potential Use of Extreme Rainfall Forecast and Socio-Economic Data for Impact-Based Forecasting at the District Level in Northern India A. Singhal et al. 10.3389/feart.2022.846113
- Using a Bayesian joint probability approach to improve the skill of medium-range forecasts of the Indian summer monsoon rainfall N. Samal et al. 10.1016/j.ejrh.2022.101284
- Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model N. Samal et al. 10.1080/15715124.2023.2218094
- Development of a national 7-day ensemble streamflow forecasting service for Australia H. Hapuarachchi et al. 10.5194/hess-26-4801-2022
- Multi-Model Approaches for Improving Seasonal Ensemble Streamflow Prediction Scheme with Various Statistical Post-Processing Techniques in the Canadian Prairie Region A. Muhammad et al. 10.3390/w10111604
- Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam D. Ndione et al. 10.1007/s13201-020-01199-y
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- A New Hybrid Framework for Error Correction and Uncertainty Analysis of Precipitation Forecasts with Combined Postprocessors C. Cai et al. 10.3390/w14193072
- Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China J. Wen et al. 10.3390/su15043865
- Probabilistic post-processing of short to medium range temperature forecasts: Implications for heatwave prediction in India S. Saminathan & S. Mitra 10.1007/s10661-024-12418-3
- Can the approach of vulnerability assessment facilitate identification of suitable adaptation models for risk reduction? A. Singhal & S. Jha 10.1016/j.ijdrr.2021.102469
- Improving the reservoir inflow prediction using TIGGE ensemble data and hydrological model for Dharoi Dam, India A. Patel & S. Yadav 10.2166/ws.2023.274
- Machine Learning Approaches to Improve North American Precipitation Forecasts C. Sengoz et al. 10.1109/ACCESS.2023.3309054
- Postprocessing of hydrometeorological ensemble forecasts based on multisource precipitation in Ganjiang River basin, China X. Liu et al. 10.1016/j.jhydrol.2021.127323
- Relating forecast and satellite precipitation to generate future skillful ensemble forecasts over the northwest Himalayas at major avalanche and glacier sites A. Singhal et al. 10.1016/j.jhydrol.2022.128795
- Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. 10.1111/jfr3.12895
- An overview of river flood forecasting procedures in Canadian watersheds Z. Zahmatkesh et al. 10.1080/07011784.2019.1601598
- Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications Y. Tang et al. 10.1007/s11600-022-00877-6
- Stream flow prediction using TIGGE ensemble precipitation forecast data for Sabarmati river basin A. Patel & S. Yadav 10.2166/ws.2022.362
- Evaluation of quantitative precipitation forecast in five Indian river basins A. Singh et al. 10.1080/02626667.2021.1982138
- Probabilistic Forecast of Ecological Drought in Rivers Based on Numerical Weather Forecast from S2S Dataset C. Cai et al. 10.3390/w16040579
- Evaluation of TIGGE Precipitation Forecast and Its Applicability in Streamflow Predictions over a Mountain River Basin, China Y. Xiang et al. 10.3390/w14152432
- Assessment and modelling of uncertainty in precipitation forecasts from TIGGE using fuzzy probability and Bayesian theory C. Cai et al. 10.1016/j.jhydrol.2019.123995
- Performance of the WRF model in simulating intense precipitation events over the Hanjiang River Basin, China – A multi-physics ensemble approach Q. Yang et al. 10.1016/j.atmosres.2020.105206
- Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019 W. Ma et al. 10.1038/s41598-021-89522-8
- Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction A. Jabbari & D. Bae 10.3390/atmos11030300
- Towards the selection of a longer record of quantitative precipitation forecast dataset from TIGGE ensembles for India during the monsoon season A. Singh et al. 10.1080/15715124.2023.2270971
- Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts S. Han & P. Coulibaly 10.1175/JHM-D-18-0251.1
- Forecastability of a heavy precipitation event at different lead-times using WRF model: the case study in Karkheh River basin M. Maddah et al. 10.1007/s11600-021-00669-4
- Post-Processing Ensemble Precipitation Forecasts and Their Applications in Summer Streamflow Prediction over a Mountain River Basin Y. Xiang et al. 10.3390/atmos14111645
- Assessing Entropy-based Bayesian Model Averaging Method for Probabilistic Precipitation Forecasting 10.1175/JHM-D-21-0086.1
- Potential Use of Extreme Rainfall Forecast and Socio-Economic Data for Impact-Based Forecasting at the District Level in Northern India A. Singhal et al. 10.3389/feart.2022.846113
- Using a Bayesian joint probability approach to improve the skill of medium-range forecasts of the Indian summer monsoon rainfall N. Samal et al. 10.1016/j.ejrh.2022.101284
- Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model N. Samal et al. 10.1080/15715124.2023.2218094
- Development of a national 7-day ensemble streamflow forecasting service for Australia H. Hapuarachchi et al. 10.5194/hess-26-4801-2022
2 citations as recorded by crossref.
- Multi-Model Approaches for Improving Seasonal Ensemble Streamflow Prediction Scheme with Various Statistical Post-Processing Techniques in the Canadian Prairie Region A. Muhammad et al. 10.3390/w10111604
- Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam D. Ndione et al. 10.1007/s13201-020-01199-y
Saved (final revised paper)
Latest update: 20 Nov 2024
Short summary
The output from numerical weather prediction (NWP) models is known to have errors. River forecast centers in Canada mostly use precipitation forecasts directly obtained from American and Canadian NWP models. In this study, we evaluate the forecast performance of ensembles generated by a Bayesian post-processing approach in cold climates. We demonstrate that the post-processing approach generates bias-free forecasts and provides a better picture of uncertainty in the case of an extreme event.
The output from numerical weather prediction (NWP) models is known to have errors. River...
