Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Shu, Zhangkang; Zhang, Jianyun; Wang, Lin; Jin, Jian; Cui, Ningbo; Wang, Guoqing; Sun, Zhouliang; Li, Yanli; Bao, Zhenxin; Liu, Cuishan

doi:10.1016/j.eng.2022.06.007

Cited by 7 publications

(4 citation statements)

References 66 publications

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“…To simplify the notation of this example, all the following equations will assume that the input variables are uniform and that the support of the input set is 𝒟 = 0,1 . Thus, 𝑌 is defined by Equation (10). where all the sums in the expansion can be calculated by means of recursive integrals, the first term ℳ is constant and equal to the expected value (Equation 11):…”

Section: Metamodelingmentioning

confidence: 99%

“…Although hydrodynamic understanding in open environments, such as coastal zones, has been extensively studied, the investigation in closed enclosures, such as reservoirs, still faces challenges due to the scarcity of field data and in obtaining the database (e.g. winds and bathymetry) necessary for the calibration and validation of numerical modeling [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Deterministic Modeling and Uncertainty Quantification of Wind Waves in Ilha Solteira dam Reservoir, Brazil

Mattosinho,

Ferreira,

Maciel

2024

Preprint

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Deterministic simulations using the SWAN model were conducted along a stretch of the Tietê-Paraná waterway in the Ilha Solteira dam reservoir, São Paulo, Brazil, to assess the water waves generated by winds. Simultaneously employs a probabilistic metamodeling approach (PCE) and Sobol indices, to quantify uncertainty of the five involved physical parameters (wind velocity and direction, bottom friction (JONSWAP), whitecapping and depth-induced breaking index). In the deterministic part, Janssen’s formulation yielded the most successful and accurate estimations of wave heights. Probabilistic analysis underscored the importance of wind data and the whitecapping parameter in model calibration, with a significant influence of wind speed indicated by Sobol indices. The results not only refined the model calibration process, but also allowed the creation of contour maps for wave height and period that offer practical pre-project applications. This work advances understanding of wind wave hydrodynamics deterministically and probabilistically, with good concordance and performance indices compared to field data, despite being scarce.

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Section: Metamodelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deterministic Modeling and Uncertainty Quantification of Wind Waves in Ilha Solteira dam Reservoir, Brazil

Mattosinho,

Ferreira,

Maciel

2024

Preprint

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“…Among these methods, probabilistic ensemble forecasting is considered to overcome the limitations of a single model or a simple average with fixed model weights (Han and Coulibaly, 2017) and contains richer forecast information because it can consider the ensemble forecast results of multiple models to quantify and reduce integrated uncertainty that contains uncertainties in the inputs, model structure, and parameters (Li et al, 2017;Saleh et al, 2016). Bayesian model averaging (BMA), proposed by Raftery et al (2005), uses the Bayesian theory and a total probability formulation to transform ensemble forecasts into probabilistic forecasts and is one of the most representative and reliable methods that has been widely used to supplement uncertainty information beyond point estimates (Shu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Their results showed that the BMA method incorporating entropy could improve the probabilistic forecasting performance for high flows over the standard BMA method. In addition, some studies have developed various methods based on the BMA principle, such as the multi-model ensemble forecasting method based on Vine Copula (Zhang et al, 2022) and the combination of BMA and data assimilation techniques (Parrish et al, 2012). However, most studies ignore an essential issue: the BMA does not consider the constraint of initial conditions (i.e., observed flow at the start of the forecast).…”

Section: Introductionmentioning

confidence: 99%

Quantify and reduce flood forecast uncertainty by the CHUP-BMA method

Cui

Chen

Liu

et al. 2023

Preprint

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Abstract. The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study, for the first time, introduced a copula-based HUP in the framework of BMA and proposed the CHUP-BMA method to bypass the need for normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of 8 members (two forecast precipitation inputs, two advanced long short-term memory (LSTM) models, and two objective functions used to calibrate parameters) and is applied to the interval basin between Xiangjiaba and Three Gorges Reservoir (TGR) dam-site. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy, and the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width with the 90 % confidence level and continuous ranked probability score metrics of the CHUP-BMA method are highest reduced by 28.42 % and 17.86 %, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

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Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

Najafi,

Kuchak

2024

Intl Journal of Climatology

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Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

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Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Cited by 7 publications

References 66 publications

Deterministic Modeling and Uncertainty Quantification of Wind Waves in Ilha Solteira dam Reservoir, Brazil

Deterministic Modeling and Uncertainty Quantification of Wind Waves in Ilha Solteira dam Reservoir, Brazil

Quantify and reduce flood forecast uncertainty by the CHUP-BMA method

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

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