Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models

Zhao, Pengcheng; Wang, QJ; Wu, Wenyan; Yang, Qiang

doi:10.1016/j.jhydrol.2021.127285

Cited by 13 publications

(8 citation statements)

References 71 publications

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“…For given ensemble outputs, the CRPS corresponds to the integrated quadratic distance between the cumulative distribution function (CDF) of the ensemble outputs and the observation. According to previous studies (Gneiting & Ranjan, 2011; P. Zhao et al., 2022), we also use twCPRS to assess the model performance on predicting heavy precipitation events. CRPS and twCRPS can be expressed as:

\text{CRPS}={\int }_{-\infty }^{\infty }{\left(F\left({P}_{t}\right)-O\left({P}_{t}\right)\right)}^{2}d{P}_{t}

tw\text{CRPS}={\int }_{-\infty }^{\infty }{\left(F\left({P}_{t}\right)-O\left({P}_{t}\right)\right)}^{2}\omega (P)d{P}_{t}

where ω ( P ) is a weight function that equals 1 (or 0) if P ≥ q (or P < q ); and q (95% in this study) is a given threshold.…”

Section: Methodsmentioning

confidence: 99%

“…For given ensemble outputs, the CRPS corresponds to the integrated quadratic distance between the cumulative distribution function (CDF) of the ensemble outputs and the observation. According to previous studies (Gneiting & Ranjan, 2011;P. Zhao et al, 2022), we also use twCPRS to assess the model performance on predicting heavy precipitation events.…”

Section: Continuous Rank Probability Score (Crps)mentioning

confidence: 99%

See 1 more Smart Citation

QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests

Zhang

Nguyen

et al. 2022

Water Resources Research

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\text{CRPS}={\int }_{-\infty }^{\infty }{\left(F\left({P}_{t}\right)-O\left({P}_{t}\right)\right)}^{2}d{P}_{t}

tw\text{CRPS}={\int }_{-\infty }^{\infty }{\left(F\left({P}_{t}\right)-O\left({P}_{t}\right)\right)}^{2}\omega (P)d{P}_{t}

where ω ( P ) is a weight function that equals 1 (or 0) if P ≥ q (or P < q ); and q (95% in this study) is a given threshold.…”

Section: Methodsmentioning

confidence: 99%

“…For given ensemble outputs, the CRPS corresponds to the integrated quadratic distance between the cumulative distribution function (CDF) of the ensemble outputs and the observation. According to previous studies (Gneiting & Ranjan, 2011;P. Zhao et al, 2022), we also use twCPRS to assess the model performance on predicting heavy precipitation events.…”

Section: Continuous Rank Probability Score (Crps)mentioning

confidence: 99%

QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests

Zhang

Nguyen

et al. 2022

Water Resources Research

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show abstract

“…Uncertainty can also be directly accommodated within the objective function(s), for example, having objective function values averaged across the range of inflows used (Saadatpour et al 2020). This is an intuitive approach and has been used in other applications, for example, post-processing ensemble climate forecasts (Zhao et al 2022). Yet another approach is to have the objective function calculated as the total of a criterion over the range of inflows used, for example, total hydro-electric power generated (Ghimire and Reddy 2014), total demand deficit (Saadatpour et al 2020) or total environmental stress (Mortazavi et al 2012).…”

Section: Incorporating Uncertainty In Reservoir Operation Optimisatio...mentioning

confidence: 99%

Evolutionary algorithm-based multiobjective reservoir operation policy optimisation under uncertainty

Zhou²,

Leonard³

2022

Environ. Res. Commun.

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Reservoir operation optimisation is a decision support tool to assist reservoir operators with water release decisions to achieve management objectives, such as maximising water supply security, mitigating flood risk, and maximising hydroelectric power generation. The effectiveness of reservoir operation decisions is subject to uncertainty in system inputs, such as inflow and therefore, methods such as stochastic dynamic programming (SDP) have been traditionally used. However, these methods suffer from the three curses of dimensionality, modelling, and multiple objectives. Evolutionary algorithm (EA)-based simulation-optimisation frameworks such as the Evolutionary Multi-Objective Direct Policy Search offer a new paradigm for multiobjective reservoir optimisation under uncertainty, directly addressing the shortcomings of SDP-based methods. They also enable the consideration of input uncertainty represented using ensemble forecasts that have become more accessible recently. However, there is no universally agreed approach to incorporate uncertainty into EA-based multiobjective reservoir operation policy optimisation and it is not clear which approach is more effective. Therefore, this study conducts a comparative analysis to demonstrate the advantages and limitations of different approaches to account for uncertainty in multiobjective reservoir operation policy optimisation via a real-world case study; and provide guidance on the selection of appropriate approaches. Based on the results obtained, it is evident that each approach has both advantages and limitations. A suitable approach needs to be carefully selected based on the needs of the study, e.g., whether a hard constraint is required, or a well-established decision-making process exists. In addition, potential gaps for future research are identified.

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“…Over the past decades, ensemble forecast products have been a major contributor to improving weather forecasts (Yang et al, 2015;Zhao et al, 2022). These products take into account the uncertainty of initial states and process description in numerical weather forecasting models.…”

Section: Introductionmentioning

confidence: 99%

Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas

Liu

Xiaoqin²,

Chen³

et al. 2023

Front. Earth Sci.

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Accurate long-term forecasts of PM2.5 pollution are essential to mitigating health risks and formulating pollutant control strategies for decision-makers in China. In this study, an objective identification and forecast method for PM2.5 pollution (OIF-PM2.5) is developed based on medium- and long-term ensemble forecasts of PM2.5 in Beijing-Tianjin-Hebei region and its surrounding areas. The results show that the observed PM2.5 pollution ratio increases with the aggravating PM2.5 pollution. For example, the ratio of meteorological stations with heavy pollution is 4.4 times that of light pollution and 3.9 times that of moderate pollution. In addition, the correlation coefficients between observations and forecasts are above 0.60 for all forecast leading times. Statistical results show that the average accuracy for forecasts with the leading times of 1–3 days, 4–7 days, and 8–15 days are 74.1%, 81.3%, and 72.9% respectively, indicating that the OIF-PM2.5 method has a high reliability in forecasts with the leading times of 1–15 days. The OIF-PM2.5 method is further applied in a severe PM2.5 pollution episode in the December of 2021, and the average forecast precision in forecasts with the leading times of 6–8 days reaches as high as 100%, showing a certain reference value for PM2.5 forecasts.

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Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models

Cited by 13 publications

References 71 publications

QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests

QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests

Evolutionary algorithm-based multiobjective reservoir operation policy optimisation under uncertainty

Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas

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