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.

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.

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 Calibration Of Rainfall Ensemble Prediction Of ECMWF System 4 Using Bayesian Model Averaging

Agromet ◽  
2020 ◽  
Vol 34 (1) ◽  
pp. 20-33
Author(s):  
Robi Muharsyah ◽  
Tri Wahyu Hadi ◽  
Sapto Wahyu Indratno
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Probabilistic Forecast ◽  
Ensemble Prediction ◽  
Operating Characteristics ◽  
Ensemble Prediction System ◽  
Medium Range

Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles in the form of predictive PDFs. It is known that BMA is able to improve the reliability of probabilistic forecast of short range and medium range rainfall forecast. This study aims to develop the application of BMA to calibrate seasonal forecast (long range) in order to improved quality of seasonal forecast in Indonesia. The seasonal forecast used is monthly rainfall from the output of the ensemble prediction system European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4) and it is calibrated against observational data at 26 stations of the Agency for Meteorology Climatology and Geophysics of Republic of Indonesia (BMKG) in Java Island in 1981 – 2018. BMA predictive PDFs is generated with Gamma distribution approach which is obtained based on sequential training windows (JTS) and conditionals training windows (JTC). BMA-JTS approach is done by selecting the width of the 30-month training window as the optimal training period while the BMA-JTC is carried out with a cross-validation scheme for each month. In general, Both of BMA-JTS and BMA-JTC better than RAW models. BMA-JTC calibration results are varying according to spatial and temporal, but in general the result is better in the dry season and during the El Nino phase. BMA is able to improve the distribution characteristics of the RAW model ECS4 prediction which is shown by: a smaller value of Continuous Rank Probability Score (CRPS), a larger value of the Continuous Rank Probability Skill Score (CRPSS) and more flat form of the Verification Rank Histogram (VRH) than the RAW model. BMA also increases the skill, esolution and reliability of prediction of probability Below Normal (BN) and Above Normal (AN), which is known from the increasing Brier Skill Score (BSS), and the increasing area under curve of Relative Operating Characteristics (ROC) compared to the RAW model. Furthermore, the reliability of BN and AN of BMA results also has the category of “still very useful” and “perfect” compared to RAW models that are in the “dangerous”, “not useful” and “marginally useful” categories. The reliability of BMA results with the category “still very useful” and “perfect” show that the probabilistic forecast of BN and AN events can be used in making decisions related to seasonal forecast.

scholarly journals A Probabilistic Forecast of Wind Speed using Bayesian Model Averaging

2016 ◽  
Vol 11 (3) ◽  
pp. 221-235
Author(s):  
Keunhee Han ◽  
◽  
Chansik Kim ◽  
Chansoo Kim
Keyword(s):  
Wind Speed ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Probabilistic Forecast

scholarly journals Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging

2011 ◽  
Vol 139 (8) ◽  
pp. 2630-2649 ◽  
Author(s):  
William Kleiber ◽  
Adrian E. Raftery ◽  
Jeffrey Baars ◽  
Tilmann Gneiting ◽  
Clifford F. Mass ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Grid Point ◽  
Model Averaging ◽  
Training Data ◽  
Geostatistical Model ◽  
Local Method ◽  
Probabilistic Forecasts ◽  
Temperature Forecasting ◽  
Network Experiment

AbstractThe authors introduce two ways to produce locally calibrated grid-based probabilistic forecasts of temperature. Both start from the Global Bayesian model averaging (Global BMA) statistical postprocessing method, which has constant predictive bias and variance across the domain, and modify it to make it local. The first local method, geostatistical model averaging (GMA), computes the predictive bias and variance at observation stations and interpolates them using a geostatistical model. The second approach, Local BMA, estimates the parameters of BMA at a grid point from stations that are close to the grid point and similar to it in elevation and land use. The results of these two methods applied to the eight-member University of Washington Mesoscale Ensemble (UWME) are given for the 2006 calendar year. GMA was calibrated and sharper than Global BMA, with prediction intervals that were 8% narrower than Global BMA on average. Examples using sparse and dense training networks of stations are shown. The sparse network experiment illustrates the ability of GMA to draw information from the entire training network. The performance of Local BMA was not statistically different from Global BMA in the dense network experiment, and was superior to both GMA and Global BMA in areas with sufficient nearby training data.

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