scholarly journals Uncertainty analysis of hydrological multi-model ensembles based on CBP-BMA method

2018 ◽  
Vol 49 (5) ◽  
pp. 1636-1651 ◽  
Author(s):  
Shaokun He ◽  
Shenglian Guo ◽  
Zhangjun Liu ◽  
Jiabo Yin ◽  
Kebing Chen ◽  
...  
Keyword(s):  
Flood Control ◽  
Bayesian Model Averaging ◽  
Evaluation Criteria ◽  
Model Performance ◽  
Model Averaging ◽  
Probability Forecast ◽  
Average Deviation ◽  
Probabilistic Forecasts ◽  
Study Results ◽  
Model Ensembles

Abstract Quantification of the inherent uncertainty in hydrologic forecasting is essential for flood control and water resources management. The existing approaches, such as Bayesian model averaging (BMA), hydrologic uncertainty processor (HUP), copula-BMA (CBMA), aim at developing reliable probabilistic forecasts to characterize the uncertainty induced by model structures. In the probability forecast framework, these approaches either assume the probability density function (PDF) to follow a certain distribution, or are unable to reduce bias effectively for complex hydrological forecasts. To overcome these limitations, a copula Bayesian processor associated with BMA (CBP-BMA) method is proposed with ensemble lumped hydrological models. Comparing with the BMA and CBMA methods, the CBP-BMA method relaxes any assumption on the distribution of conditional PDFs. Several evaluation criteria, such as containing ratio, average bandwidth and average deviation amplitude of probabilistic application, are utilized to evaluate the model performance. The case study results demonstrate that the CBP-BMA method can improve hydrological forecasting precision with higher cover ratios more than 90%, which are increased by 4.4% and 3.2%, 2.2% and 1.7% over those of BMA and CBMA during the calibration and validation periods, respectively. The proposed CBP-BMA method provides an alternative approach for uncertainty estimation of hydrological multi-model forecasts.

Forecast and Uncertainty Analysis of Extreme Precipitation in China from Ensemble of Multiple Climate Models

2021 ◽  
Author(s):  
Peng Deng ◽  
Jianting Zhu
Keyword(s):  
Extreme Precipitation ◽  
Flood Control ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Model Averaging ◽  
Statistical Distribution ◽  
Global Climate Models ◽  
Effective Measure

Abstract Global climate change is expected to have major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various Global Climate Models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, Generalized Extreme Value (GEV) distribution and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian Model Averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

scholarly journals Designing Multimodel Applications with Surrogate Forecast Systems

2020 ◽  
Vol 148 (6) ◽  
pp. 2233-2249
Author(s):  
Leonard A. Smith ◽  
Hailiang Du ◽  
Sarah Higgins
Keyword(s):  
Bayesian Model Averaging ◽  
Weather Forecasting ◽  
Model Averaging ◽  
Probabilistic Forecasting ◽  
Single Model ◽  
Forecast System ◽  
Mathematical System ◽  
Probabilistic Forecasts ◽  
Perfect Model ◽  
Seasonal Weather

Abstract Probabilistic forecasting is common in a wide variety of fields including geoscience, social science, and finance. It is sometimes the case that one has multiple probability forecasts for the same target. How is the information in these multiple nonlinear forecast systems best “combined”? Assuming stationarity, in the limit of a very large forecast–outcome archive, each model-based probability density function can be weighted to form a “multimodel forecast” that will, in expectation, provide at least as much information as the most informative single model forecast system. If one of the forecast systems yields a probability distribution that reflects the distribution from which the outcome will be drawn, Bayesian model averaging will identify this forecast system as the preferred system in the limit as the number of forecast–outcome pairs goes to infinity. In many applications, like those of seasonal weather forecasting, data are precious; the archive is often limited to fewer than 26 entries. In addition, no perfect model is in hand. It is shown that in this case forming a single “multimodel probabilistic forecast” can be expected to prove misleading. These issues are investigated in the surrogate model (here a forecast system) regime, where using probabilistic forecasts of a simple mathematical system allows many limiting behaviors of forecast systems to be quantified and compared with those under more realistic conditions.

scholarly journals Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach

2019 ◽  
Vol 51 (02) ◽  
pp. 249-266
Author(s):  
Nicholas D. Payne ◽  
Berna Karali ◽  
Jeffrey H. Dorfman
Keyword(s):  
Bayesian Approach ◽  
Model Uncertainty ◽  
Bayesian Model Averaging ◽  
Model Performance ◽  
Model Averaging ◽  
Forecasting Model ◽  
Forecast Errors ◽  
Forecast Horizon ◽  
Combining Forecasts ◽  
The Bayesian Approach

AbstractBasis forecasting is important for producers and consumers of agricultural commodities in their risk management decisions. However, the best performing forecasting model found in previous studies varies substantially. Given this inconsistency, we take a Bayesian approach, which addresses model uncertainty by combining forecasts from different models. Results show model performance differs by location and forecast horizon, but the forecast from the Bayesian approach often performs favorably. In some cases, however, the simple moving averages have lower forecast errors. Besides the nearby basis, we also examine basis in a specific month and find that regression-based models outperform others in longer horizons.

scholarly journals Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

2011 ◽  
Vol 29 (7) ◽  
pp. 1295-1303 ◽  
Author(s):  
I. Soltanzadeh ◽  
M. Azadi ◽  
G. A. Vakili
Keyword(s):  
Boundary Conditions ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Training Sample ◽  
Limited Area ◽  
Probabilistic Forecasts ◽  
Rank Histogram ◽  
Initial And Boundary Conditions ◽  
Environmental Prediction

Abstract. Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

Evaluation and application of Bayesian multi-model estimation in temperature simulations

2013 ◽  
Vol 37 (6) ◽  
pp. 727-744 ◽  
Author(s):  
Chiyuan Miao ◽  
Qingyun Duan ◽  
Qiaohong Sun ◽  
Jianduo Li
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Global Climate Models ◽  
Ensemble Prediction ◽  
Future Climate Change ◽  
Northern Asia ◽  
Decadal Scale ◽  
Model Ensembles

Use of multi-model ensembles from global climate models to simulate the current and future climate change has flourished as a research topic during recent decades. This paper assesses the performance of multi-model ensembles in simulating global land temperature from 1960 to 1999, using Nash-Sutcliffe model efficiency and Taylor diagrams. The future trends of temperature for different scales and emission scenarios are projected based on the posterior model probabilities estimated by Bayesian methods. The results show that ensemble prediction can improve the accuracy of simulations of the spatiotemporal distribution of global temperature. The performance of Bayesian model averaging (BMA) at simulating the annual temperature dynamic is significantly better than single climate models and their simple model averaging (SMA). However, BMA simulation can demonstrate the temperature trend on the decadal scale, but its annual assessment of accuracy is relatively weak. The ensemble prediction presents dissimilarly accurate descriptions in different regions, and the best performance appears in Australia. The results also indicate that future temperatures in northern Asia rise with the greatest speed in some scenarios, and Australia is the most sensitive region for the effects of greenhouse gas emissions. In addition to the uncertainty of ensemble prediction, the impacts of climate change on agriculture production and water resources are discussed as an extension of this research.

Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging

2013 ◽  
Vol 141 (6) ◽  
pp. 2107-2119 ◽  
Author(s):  
J. McLean Sloughter ◽  
Tilmann Gneiting ◽  
Adrian E. Raftery
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Prediction Models ◽  
Weather Prediction ◽  
Model Averaging ◽  
Wind Vector ◽  
Bivariate Distributions ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Wide Range

Abstract Probabilistic forecasts of wind vectors are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Unlike other common forecasting problems, which deal with univariate quantities, statistical approaches to wind vector forecasting must be based on bivariate distributions. The prevailing paradigm in weather forecasting is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty can then be assessed through ensemble forecasts, where multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic predictions. Ensemble forecasts are often uncalibrated, however, and Bayesian model averaging (BMA) is a statistical way of postprocessing these forecast ensembles to create calibrated predictive probability density functions (PDFs). It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights reflect the forecasts’ relative contributions to predictive skill over a training period. In this paper the authors extend the BMA methodology to use bivariate distributions, enabling them to provide probabilistic forecasts of wind vectors. The BMA method is applied to 48-h-ahead forecasts of wind vectors over the North American Pacific Northwest in 2003 using the University of Washington mesoscale ensemble and is shown to provide better-calibrated probabilistic forecasts than the raw ensemble, which are also sharper than probabilistic forecasts derived from climatology.

scholarly journals Probabilistic Forecast of the Extended Range Heatwave Over Eastern China

2022 ◽  
Vol 9 ◽  
Author(s):  
Wei Zhang ◽  
Jianyun Gao ◽  
Qiaozhen Lai ◽  
Yanzhen Chi ◽  
Tonghua Su
Keyword(s):  
Bayesian Model Averaging ◽  
Grid Point ◽  
Eastern China ◽  
Model Averaging ◽  
Probabilistic Forecast ◽  
Brier Score ◽  
Verification Method ◽  
Pattern Correlation ◽  
Extended Range ◽  
Model Ensembles

Several probabilistic forecast methods for heatwave (HW) in extended-range scales over China are constructed using four models (ECMWF, CMA, UKMO, and NCEP) from the Subseasonal-to-Seasonal (S2S) database. The methods include four single-model ensembles (SME; ECMWF, CMA, UKMO, and NCEP), multi-model ensemble (MME), and Bayesian model averaging (BMA). The construction and verification of reforecasts are implemented by a defined heat wave index (HWI) which is not only able to reflect the actual occurrence of heatwaves, but also to facilitate forecast and verification. The performance is measured by traditional verification method at each grid point of the 105°E to 132°E; 20°N to 45°N domain for the July, August, and September (JAS) of 1999–2010. For deterministic evaluations of HWI forecast, BMA shows a better pattern correlation coefficient than SME and MME and comparable equitable threat score (ETS) with ECMWF and MME. The good performance of ECMWF and MME take advantage of setting the percentile thresholds for forecasting HW. For the probabilistic forecast, the Brier score of BMA is comparable (superior) to that of MME and ECMWF at short (long) lead-time. BMA also demonstrates an improvement on the reliability of probabilistic forecast, indicating that BMA method is a useful tool for an extended-range forecast of HW. Meanwhile, in the real-time extended-range probabilistic forecast, the beginning date, end date, and probability of HW event can be predicted by the HWI probabilistic forecast of BMA.

scholarly journals Technical note: Combining quantile forecasts and predictive distributions of streamflows

2017 ◽  
Vol 21 (11) ◽  
pp. 5493-5502 ◽  
Author(s):  
Konrad Bogner ◽  
Katharina Liechti ◽  
Massimiliano Zappa
Keyword(s):  
Water Resource Management ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Relevant Information ◽  
Technical Note ◽  
Decision Makers ◽  
Forecast Combination ◽  
Predictive Distributions ◽  
Combination Methods ◽  
Probabilistic Forecasts

Abstract. The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

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