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.

scholarly journals Technical Note: Combining Quantile Forecasts and Predictive Distributions of Stream-flows

2017 ◽  
Author(s):  
Konrad Bogner ◽  
Katharina Liechti ◽  
Massimiliano Zappa
Keyword(s):  
Stream Flow ◽  
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 stream-flow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple stream-flow forecast information will be aggregated based on several different predictive distributions, resp. quantile forecasts. For this combination the Bayesian Model Averaging (BMA) approach, the Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble Model Output Statistic (EMOS) model 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 five years of forecast data will be compared and the differences between the raw and the 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 resources management.

Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

2021 ◽  
pp. 004728752110612
Author(s):  
Yuying Sun ◽  
Jian Zhang ◽  
Xin Li ◽  
Shouyang Wang
Keyword(s):  
Structural Changes ◽  
Model Averaging ◽  
Structural Instability ◽  
Time Varying ◽  
Forecast Combination ◽  
Tourism Demand ◽  
Single Model ◽  
Forecasting Accuracy ◽  
Combination Methods ◽  
Out Of Sample

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.

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 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.

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 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.

scholarly journals Bayesian Model Averaging’s Problematic Treatment of Extreme Weather and a Paradigm Shift That Fixes It

2008 ◽  
Vol 136 (12) ◽  
pp. 4641-4652 ◽  
Author(s):  
Craig H. Bishop ◽  
Kevin T. Shanley
Keyword(s):  
Paradigm Shift ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Extreme Weather ◽  
Ensemble Forecasts ◽  
Stochastic Perturbations ◽  
New Approach ◽  
Weather Forecasts ◽  
Probabilistic Forecasts

Abstract Methods of ensemble postprocessing in which continuous probability density functions are constructed from ensemble forecasts by centering functions around each of the ensemble members have come to be called Bayesian model averaging (BMA) or “dressing” methods. Here idealized ensemble forecasting experiments are used to show that these methods are liable to produce systematically unreliable probability forecasts of climatologically extreme weather. It is argued that the failure of these methods is linked to an assumption that the distribution of truth given the forecast can be sampled by adding stochastic perturbations to state estimates, even when these state estimates have a realistic climate. It is shown that this assumption is incorrect, and it is argued that such dressing techniques better describe the likelihood distribution of historical ensemble-mean forecasts given the truth for certain values of the truth. This paradigm shift leads to an approach that incorporates prior climatological information into BMA ensemble postprocessing through Bayes’s theorem. This new approach is shown to cure BMA’s ill treatment of extreme weather by providing a posterior BMA distribution whose probabilistic forecasts are reliable for both extreme and nonextreme weather forecasts.

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