Integration of max-stable processes and Bayesian model averaging to predict extreme climatic events in multi-model ensembles

Shin, Yonggwan; Lee, Youngsaeng; Choi, Jun-Tae; Park, Jeong‐Soo

doi:10.1007/s00477-018-1629-7

Cited by 13 publications

(9 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the many ensemble methods, the BMA combines the forecast distribution of different models and builds a weighted predictive distribution from them (e.g., Sloughter et al, 2007;Wang et al, 2012;Niu et al, 2018;Shin et al, 2019). Among the many ensemble methods, the BMA combines the forecast distribution of different models and builds a weighted predictive distribution from them (e.g., Sloughter et al, 2007;Wang et al, 2012;Niu et al, 2018;Shin et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Over the past few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to their ability to help reduce uncertainty in prediction. Among the many ensemble methods, the BMA combines the forecast distribution of different models and builds a weighted predictive distribution from them (e.g., Sloughter et al, 2007;Wang et al, 2012;Niu et al, 2018;Shin et al, 2019). In a climate change study of extreme rainfall, Zhu et al (2013) employed the GEV distribution in a BMA framework, where the weight of each forecast model was calculated by comparing reanalysis data and the historical data from a simulation model.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

Lee

Shin

Boo

et al. 2020

Atmospheric Science Letters

Self Cite

View full text Add to dashboard Cite

Projections of changes in extreme climate are sometimes predicted by multimodel ensemble methods that combine forecasts from individual simulation models using weighted averaging. One method to assign weight to each model is the Bayesian model averaging (BMA) in which posterior probability is used. For the cases of extreme climate, the generalized extreme value distribution (GEVD) is typically used. We applied the approach of GEV-embedded BMA to a series of 35 years of the annual maximum daily precipitation data (both historical data and data gathered from simulation experiments for future periods) over the Korean peninsula as simulated by the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulation data under two Representative Concentration Pathway (RCP) scenarios, namely RCP4.5 and RCP8.5, were used. Observed data and 17 CMIP5 models for 12 gird cells in Korea have been examined to predict future changes in precipitation extremes. A simple regional frequency analysis of pooling observations from three stations in each cell was employed to reduce the estimation variance and local fluctuations. A bias correction technique using the regression-type transfer function was applied to these simulation data. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years , were estimated for two future periods, namely Period 1 (2021-2050) and Period 2 (2066-2095). From these analyses, relative increase observed in the spatially averaged 20-year (50-year) return level was approximately 23% (16%) and 45% (36%) in the RCP4.5 and RCP8.5 experiments, respectively, by the end of the 21st century. We concluded that extreme rainfalls will likely occur two times and four times more frequently in the RCP4.5 and RCP8.5 scenarios, respectively, as compared to in the reference years by the end of the 21st century. K E Y W O R D Sclimatic change, global climate model, heavy rainfall, multimodel simulation, Taylor diagram, weighted averaging

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

Lee

Shin

Boo

et al. 2020

Atmospheric Science Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ensemble methods applied in this study are the simple (or equally weighted) average or the median of the statistics obtained from multimodels. Many studies including Suh et al (2012), Zhu et al (2013), Niu et al (2018), and Shin et al (2019b), however, applied the unequally weighted average methods. These weighted average methods, including the Bayesian model averaging, combine the forecast distribution of different models and build a weighted predictive distribution out of multiple models.…”

Section: Discussionmentioning

confidence: 99%

Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble

Lee

Paek

Park

et al. 2020

Theor Appl Climatol

Self Cite

View full text Add to dashboard Cite

We analyze annual extremes of daily maximum and minimum surface air temperature and of daily rainfall in East Asia and the Korean peninsula. This study made intensive use of the simulation data available from the CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodels in historical and future experiments up to year 2100, employing three different radiative forcings: RCP2.6, RCP4.5, and RCP8.5 (representative concentration pathways). Several reanalysis datasets are used to compare and evaluate the simulated climate extremes in the late 20th century. We estimate the future changes in precipitation and temperature extremes in East Asia and Korea, and compare them to the global result, for the reference period 1986-2005. The rising rate of future cold extremes over East Asia and Korea is faster than that of warm extremes. This phenomenon appears more distinctly in Korea as a local scale, indicating more sensitivity of the Korean penisula to global warming. The increase of the 20-year return level of maximum precipitation in the CMIP5 over East Asia by the end of 21st century are about 7% in the RCP2.6, 15% in the RCP4.5, and 35% in the RCP8.5 experiments, which exceed the corresponding global values. We also estimate the changes in precipitation extremes across East Asia as a function of the annual mean temperature variation at the same location. The CMIP5 sensitivity in maximum precipitation across East Asia is 5.5%/ • C, which is lower than the global figure (5.8%/ • C). The sensitivity for the Korean peninsula is 7.38%/ • C, indicating the strong impact of global warming to Korea. The results will be important in mitigating the detrimental effects of variations of climatic extremes and in improving the regional strategy for water resource and eco-environmental management, particulaly for such areas in East Asia under significant changes in temperature and rainfall extremes.

show abstract

“…Investigating further the behaviour of different approaches on the range / threshold of interest and eventually developing a postprocessing method with a focus on rare events would open exciting avenues of research. The work of Friederichs et al (2018) offers an introduction to ensemble postprocessing of extreme weather events, an exemplary application for extreme rainfall intensity can be found in Shin et al (2019). To avoid local fluctuations and reflect the spatial dependencies between neighbouring locations, Shin et al (2019) use a spatial extreme model, namely a maxstable process.…”

Section: Discussionmentioning

confidence: 99%

Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

Friedli

Ginsbourger

Bhend

2020

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation predictions. We rely on the ensemble model output statistics (EMOS) approach, which generates probabilistic forecasts with a parametric distribution whose parameters depend on (statistics of) the ensemble prediction. A case study with daily precipitation predictions across Switzerland highlights that postprocessing at observation locations indeed improves high-resolution ensemble forecasts, with $$4.5\%$$ 4.5 % CRPS reduction on average in the case of a lead time of 1 day. Our main aim is to achieve such an improvement without binding the model to stations, by leveraging topographical covariates. Specifically, regression coefficients are estimated by weighting the training data in relation to the topographical similarity between their station of origin and the prediction location. In our case study, this approach is found to reproduce the performance of the local model without using local historical data for calibration. We further identify that one key difficulty is that postprocessing often degrades the performance of the ensemble forecast during summer and early autumn. To mitigate, we additionally estimate on the training set whether postprocessing at a specific location is expected to improve the prediction. If not, the direct model output is used. This extension reduces the CRPS of the topographical model by up to another $$1.7 \%$$ 1.7 % on average at the price of a slight degradation in calibration. In this case, the highest improvement is achieved for a lead time of 4 days.

show abstract

Integration of max-stable processes and Bayesian model averaging to predict extreme climatic events in multi-model ensembles

Cited by 13 publications

References 47 publications

Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble

Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

Contact Info

Product

Resources

About