Application of SEM-based contours for OPC model weighting and sample plan reduction

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

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Mediterranean climate change projections: an update from CMIP5 and CMIP6.

10.5194/egusphere-egu21-12803 ◽

2021 ◽

Author(s):

Josep Cos ◽

Francisco J Doblas-Reyes ◽

Martin Jury

Keyword(s):

Climate Change ◽

21St Century ◽

Hot Spot ◽

Weighting Scheme ◽

Temperature And Precipitation ◽

High Emission ◽

The Mediterranean ◽

Model Ensembles ◽

Projected Changes ◽

Model Weighting

The Mediterranean has been identified as a climate change hot-spot due to increased warming trends and precipitation decline. Recently, CMIP6 was found to show a higher climate sensitivity than its predecessor CMIP5, potentially further exacerbating related impacts on the Mediterranean region.To estimate the impacts of the ongoing climate change on the region, we compare projections of various CMIP5 and CMIP6 experiments and scenarios. In particular, we focus on summer and winter changes in temperature and precipitation for the 21st century under RCP2.6/SSP1-2.6, RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5 as well as the high resolution HighResMIP experiments. Additionally, to give robust estimates of projected changes we apply a novel model weighting scheme, accounting for historical performance and inter-independence of the multi-member multi-model ensembles, using ERA5, JRA55 and WFDE5 as observational reference.&#160;Our results indicate a significant and robust warming over the Mediterranean during the 21st century irrespective of the used ensemble and experiments. Nevertheless, the often attested amplified Mediterranean warming is only found for summer. The projected changes vary between the CMIP5 and CMIP6, with the latter projecting a stronger warming. For the high emission scenarios and without weighting, CMIP5 indicates a warming between 4 and 7.7&#186;C in summer and 2.7 and 5&#186;C in winter, while CMIP6 projects temperature increases between 5.6 and 9.2&#186;C in summer and 3.2 to 6.8&#186;C in winter until 2081-2100 in respect to 1985-2005. In contrast to temperature, precipitation changes show a higher level of uncertainty and spatial heterogeneity. However, for the high emission scenario, a robust decline in precipitation is projected for large parts of the Mediterranean during summer. First results applying the model weighting scheme indicate reductions in CMIP6 and increases in CMIP5 warming trends, thereby reducing differences between the two ensembles.

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A Systematic Planning Tool for Environmental Characterisation

ASME 2009 12th International Conference on Environmental Remediation and Radioactive Waste Management, Volume 2 ◽

10.1115/icem2009-16010 ◽

2009 ◽

Author(s):

Steven Wilcox ◽

Richard Wilkins ◽

Martin Lyons

Keyword(s):

Best Practice ◽

Planning Process ◽

Cost Effective ◽

Decision Makers ◽

Performance Criteria ◽

Final Decision ◽

Cost Effective Approach ◽

Systematic Planning ◽

Sample Plan

Many organisations are currently dealing with long standing legacy issues in clean up, decommissioning and demolition projects. Industry is required to ensure that all bulk articles, substances and waste arisings are adequately characterised and assigned to the correct disposal routes in compliance with UK legislation and best practice. It is essential that data used to support waste sentencing is of the correct type, quality and quantity, and that it is appropriately assessed in order to support defensible, confident decisions that account for inherent uncertainties. AMEC has adopted the Data Quality Objectives (DQO) based methodology and the software package Visual Sample Plan (VSP) to provide a better, faster, and more cost effective approach to meeting regulatory and client requirements, whilst minimising the time spent gathering data and assessing the information. The DQO methodology is based on a scientific approach that requires clear objectives to be established from the outset of a project and that there is a demonstration of acceptability of the results. Through systematic planning, the team develops acceptance or performance criteria for the quality of the data collected and for the confidence in the final decision. The systematic planning process promotes communication between all departments and individuals involved in the decision-making process thus the planning phase gives an open and unambiguous method to support the decisions and enables the decision-makers (technical authorities on the materials of concern) to document all assumptions. The DQO process allows better planning, control and understanding of all the issues. All types of waste can be sentenced under one controllable system providing a more defensible position. This paper will explain that the DQO process consists of seven main steps that lead to a detailed Sampling and Analysis Plan (SAP). The process gives transparency to any assumptions made about the site or material being characterised and identifies individuals involved. The associated calculation effort is reduced using the statistically based sampling models produced with VSP. The first part of this paper explains the DQO based methodology and Visual Sample Plan and the second part shows how the DQO process has been applied in practice.

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Effect of diversified performance metrics and climate model weighting on global and regional trend patterns of precipitation and temperature

Erdkunde ◽

10.3112/erdkunde.2019.04.04 ◽

2019 ◽

Vol 73 (4) ◽

pp. 303-322

Author(s):

Christoph Ring ◽

Felix Pollinger ◽

Luzia Keupp ◽

Irena Kaspar-Ott ◽

Elke Hertig ◽

...

Keyword(s):

Performance Metrics ◽

Climate Model ◽

Regional Trend ◽

Model Weighting ◽

Precipitation And Temperature

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Taiwan-Specific Model for VS30 Prediction Considering Between-Proxy Correlations

Earthquake Spectra ◽

10.1193/061217eqs113m ◽

2018 ◽

Vol 34 (4) ◽

pp. 1973-1993 ◽

Cited By ~ 12

Author(s):

On Lei Annie Kwok ◽

Jonathan P. Stewart ◽

Dong Youp Kwak ◽

Pang-Li Sun

Keyword(s):

Ground Motion ◽

Prediction Models ◽

Site Response ◽

Specific Model ◽

Weighted Mean ◽

Response Models ◽

Database File ◽

A Site ◽

Gradient Effects ◽

Model Weighting

Ergodic site response models are generally conditional on the time-averaged shear wave velocity in the upper 30 m ( V S30). Ground motion databases contain many recordings from Taiwan, and because of site characterization efforts, 56% of recording sites have V S30 derived from measurements. We develop proxy-based V S30 prediction models, one application of which is for the remaining 44% of Taiwan sites. Our approach, which can be suitable for other regions, differs from previous studies in which proxies are based on detailed geologic categories and possible within-category topographic gradient effects. Instead, we use three broad, age-based geologic categories, and for the youngest category of Holocene and Quaternary undivided sediments, we propose models conditioned on gradient and elevation. We also adapt a geomorphic terrain-based method, thus providing two V S30-prediction models. We describe a model weighting scheme that combines the models in consideration of their relative dispersions and correlation, producing a weighted mean and standard deviation natural-log V S30. Included as an electronic supplement is a profile database file and a site database with site parameters for Taiwan ground motion stations.

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Automated sample plan selection for OPC modeling

10.1117/12.2045461 ◽

2014 ◽

Cited By ~ 2

Author(s):

Nathalie Casati ◽

Maria Gabrani ◽

Ramya Viswanathan ◽

Zikri Bayraktar ◽

Om Jaiswal ◽

...

Keyword(s):

Plan Selection ◽

Selection For ◽

Sample Plan

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Deterministic adaptive control of SISO processes using model weighting adaptation

International Journal of Control ◽

10.1080/00207179308923045 ◽

1993 ◽

Vol 58 (5) ◽

pp. 1105-1123 ◽

Cited By ~ 12

Author(s):

S. GENDRON ◽

M. PERRIER ◽

J. BARRETTER ◽

M. AMJAD ◽

A. HOLKO ◽

...

Keyword(s):

Adaptive Control ◽

Model Weighting

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Sampling for IPM Decision Making: Where Should We Invest Time and Resources?

Phytopathology ◽

10.1094/phyto.1999.89.11.1104 ◽

1999 ◽

Vol 89 (11) ◽

pp. 1104-1111 ◽

Cited By ~ 19

Author(s):

Jan P. Nyrop ◽

Michael R. Binns ◽

Wopke van der Werf

Keyword(s):

Decision Making ◽

Sample Size ◽

Operating Characteristic ◽

Crop Protection ◽

Critical Density ◽

Sampling Distribution ◽

Fine Tuning ◽

Sampling Distributions ◽

Sample Plan

Guides for making crop protection decisions based on assessments of pest abundance or incidence are cornerstones of many integrated pest management systems. Much research has been devoted to developing sample plans for use in these guides. The development of sampling plans has usually focused on collecting information on the sampling distribution of the pest, describing this sampling distribution with a mathematical model, formulating a sample plan, and sometimes, but not always, evaluating the performance of the proposed sample plan. For crop protection decision making, classification of density or incidence is usually more appropriate than estimation. When classification is done, the average outcome of classification (the operating characteristic) is frequently robust to large changes in the sampling distribution, including estimates of the variance of pest counts, and to sample size. In contrast, the critical density, or critical incidence, about which classifications are made, has a large influence on the operating characteristic. We suggest that rather than investing resources in elaborate descriptions of sampling distributions, or in fine-tuning sample size to achieve desired levels of precision, greater emphasis should be placed on characterizing pest densities that signal the need for management action and on designing decision guides that will be adopted by practitioners.

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