Articles | Volume 24, issue 2
https://doi.org/10.5194/hess-24-1011-2020
© Author(s) 2020. 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-24-1011-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Mar 2020
Research article |
| 03 Mar 2020
| 03 Mar 2020
Comparison of probabilistic post-processing approaches for improving numerical weather prediction-based daily and weekly reference evapotranspiration forecasts
Hanoi Medina
Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL 36849, USA
Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL 36849, USA
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Cited
17 citations as recorded by crossref.
- Multimodel comparison and assessment of short to medium range precipitation and temperature forecasts over India: Implications towards forecasting of meteorological indices in India S. Saminathan et al. 10.1002/joc.8409
- Quantification of the effect of hydrological drivers on actual evapotranspiration using the Bayesian model averaging approach for various landscapes over Northeast Asia Y. Hao et al. 10.1016/j.jhydrol.2022.127543
- Evaluation of Empirical Equations and Machine Learning Models for Daily Reference Evapotranspiration Prediction Using Public Weather Forecasts Y. Liang et al. 10.3390/w15223954
- Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations J. Xu et al. 10.3390/rs13214464
- Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products G. Papacharalampous et al. 10.1016/j.mlwa.2024.100615
- Assessment of surface downward longwave radiation in CMIP6 with comparison to observations and CMIP5 J. Xu et al. 10.1016/j.atmosres.2022.106056
- Calibrating anomalies improves forecasting of daily reference crop evapotranspiration Q. Yang et al. 10.1016/j.jhydrol.2022.128009
- Bias-correcting input variables enhances forecasting of reference crop evapotranspiration Q. Yang et al. 10.5194/hess-25-4773-2021
- Classification of Weather Conditions Based on Supervised Learning for Swedish Cities M. Safia et al. 10.3390/atmos14071174
- 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
- Enhancing NWP-Based Reference Evapotranspiration Forecasts: Role of ETo Approaches and Temperature Postprocessing S. Saminathan & S. Mitra 10.1061/JHYEFF.HEENG-6315
- Multi-model Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Statistical Postprocessing Y. Li et al. 10.1175/JHM-D-21-0029.1
- Post-processing of the UKMO ensemble precipitation product over various regions of Iran: integration of long short-term memory model with principal component analysis S. Alizadeh et al. 10.1007/s00704-022-04170-w
- A Two‐Stage Framework for Bias and Reliability Tests of Ensemble Hydroclimatic Forecasts T. Zhao et al. 10.1029/2022WR032568
- Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models M. Achite et al. 10.1007/s10668-022-02835-0
- Medium-range forecasting of daily reference evapotranspiration across China using numerical weather prediction outputs downscaled by extreme gradient boosting J. Fan et al. 10.1016/j.jhydrol.2021.126664
- Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting Q. Yang et al. 10.5194/hess-26-941-2022
17 citations as recorded by crossref.
- Multimodel comparison and assessment of short to medium range precipitation and temperature forecasts over India: Implications towards forecasting of meteorological indices in India S. Saminathan et al. 10.1002/joc.8409
- Quantification of the effect of hydrological drivers on actual evapotranspiration using the Bayesian model averaging approach for various landscapes over Northeast Asia Y. Hao et al. 10.1016/j.jhydrol.2022.127543
- Evaluation of Empirical Equations and Machine Learning Models for Daily Reference Evapotranspiration Prediction Using Public Weather Forecasts Y. Liang et al. 10.3390/w15223954
- Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations J. Xu et al. 10.3390/rs13214464
- Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products G. Papacharalampous et al. 10.1016/j.mlwa.2024.100615
- Assessment of surface downward longwave radiation in CMIP6 with comparison to observations and CMIP5 J. Xu et al. 10.1016/j.atmosres.2022.106056
- Calibrating anomalies improves forecasting of daily reference crop evapotranspiration Q. Yang et al. 10.1016/j.jhydrol.2022.128009
- Bias-correcting input variables enhances forecasting of reference crop evapotranspiration Q. Yang et al. 10.5194/hess-25-4773-2021
- Classification of Weather Conditions Based on Supervised Learning for Swedish Cities M. Safia et al. 10.3390/atmos14071174
- 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
- Enhancing NWP-Based Reference Evapotranspiration Forecasts: Role of ETo Approaches and Temperature Postprocessing S. Saminathan & S. Mitra 10.1061/JHYEFF.HEENG-6315
- Multi-model Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Statistical Postprocessing Y. Li et al. 10.1175/JHM-D-21-0029.1
- Post-processing of the UKMO ensemble precipitation product over various regions of Iran: integration of long short-term memory model with principal component analysis S. Alizadeh et al. 10.1007/s00704-022-04170-w
- A Two‐Stage Framework for Bias and Reliability Tests of Ensemble Hydroclimatic Forecasts T. Zhao et al. 10.1029/2022WR032568
- Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models M. Achite et al. 10.1007/s10668-022-02835-0
- Medium-range forecasting of daily reference evapotranspiration across China using numerical weather prediction outputs downscaled by extreme gradient boosting J. Fan et al. 10.1016/j.jhydrol.2021.126664
- Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting Q. Yang et al. 10.5194/hess-26-941-2022
Latest update: 08 Mar 2025
Short summary
Reference evapotranspiration (ET0) forecasts play an important role in agricultural, environmental, and water management. This study evaluated probabilistic post-processing approaches for improving daily and weekly ensemble ET0 forecasting based on single or multiple numerical weather predictions. The three approaches used consistently improved the skill and reliability of the ET0 forecasts compared with the conventional method, due to the adjustment in the spread of the ensemble forecast.
Reference evapotranspiration (ET0) forecasts play an important role in agricultural,...
