Using Metocean Forecast Verification Information to Effectively Enhance Operational Decision-Making

2021 ◽  
Author(s):  
Edward Steele ◽  
Hannah Brown ◽  
Christopher Bunney ◽  
Philip Gill ◽  
Kenneth Mylne ◽  
...  
Keyword(s):  
Decision Making ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Forecast Verification ◽  
The Past ◽  
Adverse Weather ◽  
Operational Decision Making ◽  
Categorical Verification ◽  
The Impact ◽  
Verification Techniques

Abstract Metocean forecast verification statistics (or ‘skill scores’), for variables such as significant wave height, are typically computed as a means of assessing the (past) weather model performance over the particular area of interest. For developers, this information is important for the measurement of model improvement, while for consumers this is commonly applied for the comparison/evaluation of potential service providers. However, an opportunity missed by many is also its considerable benefit to users in enhancing operational decision-making on a real-time (future) basis, when combined with an awareness of the context of the specific decision being made. Here, we present two categorical verification techniques and demonstrate their application in simplifying the interpretation of ensemble (probabilistic) wave forecasts out to 15 days ahead, as pioneered – in operation – in Summer 2020 to support the recent weather sensitive installation of the first phase of a 36 km subsea pipeline in the Fenja field in the North Sea. Categorical verification information (based on whether forecast and observations exceed the user-defined operational weather limits) was constructed from 1460 archive wave forecasts, issued for the two-year period 2017 to 2018, and used to characterise the past performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) in the form of Receiver Operating Characteristic (ROC) and Relative Economic Value (REV) analysis. These data were then combined with a bespoke parameterization of the impact of adverse weather on the planned operation, allowing the relevant go/no-go ensemble probability threshold (i.e. the number of individual/constituent forecast members that must predict favourable/unfavourable conditions) for the interpretation of future forecasts to be determined. Following the computation of the probability thresholds for the Fenja location, trials on an unseen nine-month period of data from the site (Spring to Autumn 2019) confirm these approaches facilitate a simple technique for processing/interpreting the ensemble forecast, able to be readily tailored to the particular decision being made. The use of these methods achieves a considerably greater value (benefit) than equivalent deterministic (single) forecasts or traditional climate-based options at all lead times up to 15 days ahead, promising a more robust basis for effective planning than typically considered by the offshore industry. This is particularly important for tasks requiring early identification of long weather windows (e.g. for the Fenja tie-ins), but similarly relevant for maximising the exploitation of any ensemble forecast, providing a practical approach for how such data are handled and used to promote safe, efficient and successful operations.

scholarly journals Using probabilistic forecasts to effectively enhance operational marine industry decision-making

2021 ◽  
Author(s):  
Ken Mylne ◽  
Edward Steele ◽  
Hannah Brown ◽  
Christopher Bunney ◽  
Philip Gill ◽  
...  
Keyword(s):  
Decision Making ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Past Performance ◽  
Value Analysis ◽  
Probabilistic Forecasts ◽  
Adverse Weather ◽  
Probability Threshold ◽  
Offshore Industry ◽  
The Impact

<p>Ensemble Prediction Systems (EPSs) are now run routinely by many global weather centres but, despite the enormous potential these forecasts offer, their perceived complexity has long presented a barrier to effective adoption by many users; limiting the opportunity for early decision-making by industry. To facilitate the interpretation of a set of (potentially seemingly contradictory) forecasts, a sensible approach is to turn the prediction into a binary (yes/no) forecast by applying a user-relevant operational weather limit – with the decision to proceed with or postpone an operation based on whether a certain proportion of the members predict un-/favourable conditions. However, the question then remains as to how the appropriate probability threshold to achieve an optimum decision can be objectively defined. Here, we present two approaches for simplifying the interpretation of ensemble (probabilistic) ocean wave forecasts out to 15 days ahead, as pioneered – in operation – in Summer 2020 to support the recent weather sensitive installation of the first phase of a 36 km subsea pipeline in the North Sea. Categorical verification information was constructed from 1460 archive wave forecasts, issued for the two-year period 2017 to 2018, and used to characterise the past performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) EPS in the form of Receiver Operating Characteristic and Relative Economic Value analysis. These data were then combined with a bespoke parameterization of the impact of adverse weather on the planned operation, allowing the relevant go/no-go ensemble probability threshold for the interpretation of future forecasts to be determined. Trials on an unseen nine-month period of data from the same site (Spring to Autumn 2019) confirm the approaches facilitate a simple technique for processing/interpreting the ensemble forecast, able to be readily tailored to the particular decision being made. The use of these methods achieves a considerably greater value (benefit) than equivalent deterministic (single) forecasts or traditional climate-based options at all lead times up to 15 days ahead, promising a more robust basis for effective planning than typically considered by the offshore industry. This is particularly important for tasks requiring early identification of long weather windows (e.g. offshore pipeline installation), but similarly relevant for maximising the exploitation of any ensemble forecast –  by any sector – providing a practical approach for how such data are handled and used to promote safe, efficient and successful operations.</p>

scholarly journals A review of the past, the present, and the future of fishers' knowledge research: a challenge to established fisheries science

2014 ◽  
Vol 72 (2) ◽  
pp. 341-358 ◽  
Author(s):  
Edward J. Hind
Keyword(s):  
Decision Making ◽  
Productive Activity ◽  
Fisheries Science ◽  
The Past ◽  
Knowledge Cultures ◽  
The Future ◽  
Qualitative Knowledge ◽  
The Impact

Abstract Fishers' knowledge research is an approach to fisheries research that has a relatively long history, yet has generally failed to become integrated into the fisheries science mainstream alongside approaches that rely primarily on the knowledge of professional scientists. Its continued position on the margins of fisheries science has not however stopped fishers' knowledge researchers from publishing an expanding literature, which they often use to advocate for the greater consideration of fishers' knowledge by fisheries scientists and managers. They believe that the unique and often highly qualitative knowledge of fishers could inform better decision-making, resulting in improved socio-ecological outcomes for fisheries. This review first outlines the scope of the fishers' knowledge literature, before outlining five waves of fishers' knowledge research that have developed over the last century. For each wave, the nature of the fishers' knowledge documented during it is noted, as is the research and dissemination approach taken by its practitioners. The impact of that wave on mainstream fisheries science is then assessed. Overall, it is found that only one wave of fishers' knowledge research is beginning to have consistent success integrating with mainstream fisheries science, a wave that omits the research of many of the unique elements of fishers' knowledge. Other waves have died out, or are in danger of dying out, either because they have failed to be noticed by mainstream fisheries scientists or because mainstream fisheries scientists have not welcomed their outputs. It is summarized that fishers' knowledge research will only continue as a productive activity if mainstream fisheries scientists begin to open their discipline to other knowledge cultures and if fishers' knowledge researchers facilitate this action by disseminating their research so that it is more accessible to these scientists.

Still Crazy after All These Years: The Polarized Politics of the Roberts Court Continue

The Forum ◽  
2014 ◽  
Vol 12 (4) ◽  
Author(s):  
Cornell W. Clayton ◽  
Michael F. Salamone
Keyword(s):  
Decision Making ◽  
Supreme Court ◽  
Presidential Election ◽  
Electoral Politics ◽  
Federal Courts ◽  
The Past ◽  
Swing Vote ◽  
Electoral Regime ◽  
Roberts Court ◽  
The Impact

AbstractThis article examines the Roberts Court and its relationship to the Obama administration following the 2014 midterm election. We begin by analyzing how the Court has been structured by electoral politics during the past 40 years, arguing that the Court’s more conservative, divided, and polarized decision-making reflects the politics of the post-1968 electoral regime. We then consider the impact of the 2014 midterm election. Republican control of the Senate will constrain the president’s ability to shape the federal courts going forward. It will most likely leave the composition of the current Supreme Court intact, leave Justice Kennedy as the pivotal swing vote, while elevating the Court as a campaign issue in the 2016 presidential election.

Pluvial flood forecasting in urban data-scarce regions: Influence of rainfall spatio-temporal data (in)accuracy on decision-making

2020 ◽  
Author(s):  
Adele Young ◽  
Biswa Bhattacharya ◽  
Chris Zevenbergen
Keyword(s):  
Decision Making ◽  
Input Data ◽  
Early Warning Systems ◽  
Flood Forecasting ◽  
Ensemble Forecast ◽  
Water Levels ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Flood Forecast ◽  
Urban Data

<p>Pluvial flooding is on the rise as more cities are challenged by a changing climate and local drivers: increased urbanisation and inadequate sewer system capacity. Flood forecasting and early warning systems have been proposed as a “low regret” measure to reduce flood risk and increase preparedness through forecast-based actions.  However there are multiple sources of uncertainty from meteorological forecast, model parameters and structure and inadequate calibration.  In data-scarce cities, there are additional challenges to produce high-quality rainfall forecast and well-calibrated flood forecast (timing, water levels, extent and impact). As a result, there is a cascading effect on the ability to make and provide good reliable decisions given the uncertainty in the forecast or inaccuracy in the input data.</p><p>Ensemble prediction systems (EPS) have been proposed as a means to quantify uncertainty in forecast and compared to deterministic forecast, facilitate a probabilistic framework in decision making. Probabilistic information has been applied to cost loss ratio approaches and Bayesian decision under uncertainty. However, to what extent inherent spatiotemporal inaccuracies of meteorological inputs influence this posterior probability and the resultant decision has not been considered in data scare regions. In this regard, this research focuses on providing understanding on how the influence of the varying degrees of input data, particularly forecast rainfall spatial and temporal distributions will ultimately affect the ability to make an optimal decision; i.e. the recommended decision given the information available at the time of the forecast.</p><p>Using a study area in the Alexandria city, Egypt, this research proposes a framework for decision making under uncertainty in an urban data-scarce city using a Weather Research Forecast (WRF) model to simulate downscaled rainfall ensemble forecast and remotely sensed rainfall products to supplement data gaps. Adopting a probabilistic approach, uncertainty in the flood forecast predictions will be represented from an urban rainfall-runoff model driven by ensemble precipitation forecast. The objective of this research is not to make forecast more accurate but rather to highlight the interdependences of the flood forecast and decision-making chain in order to address what decision can be made given the quality of forecast.</p><p>Keywords: Pluvial flood forecasting, Ensemble forecast, Decision making, Data-scarce Alexandria, Egypt</p>

Regional Ministers, National Policies and the Administrative State in Canada: The Regional Dimension in Cabinet Decision-Making, 1980–1984

1988 ◽  
Vol 21 (3) ◽  
pp. 539-567 ◽  
Author(s):  
Herman Bakvis
Keyword(s):  
Decision Making ◽  
Regional Development ◽  
The Political ◽  
Administrative State ◽  
Employment Creation ◽  
National Policies ◽  
Pierre Trudeau ◽  
The Past ◽  
Economic Climate ◽  
The Impact

AbstractRegional ministers, it is said, have declined in importance over the past three decades. While granting the disappearance of figures whose influence spanned broad regions, this article argues that in the last cabinet of Pierre Trudeau (1980–1984) the regional minister system was to a degree revived and formalized within the context of cabinet decision-making. The impact of this system is examined with respect to regional development and employment creation programmes. To account for the renewed influence of regional ministers, attention is focussed on changes in the machinery of government and on the political and economic climate of the time. The case of one minister in particular, Lloyd Axworthy, suggests that a contemporary regional minister's success is dependent primarily on the ability to mobilize the resources of the administrative state.

scholarly journals Big Tech and the Right to Freedom of Thought

2020 ◽  
Vol 5 (1) ◽  
pp. 35-40
Author(s):  
Brittany Morison
Keyword(s):  
Decision Making ◽  
Critical Analysis ◽  
Individual Decision ◽  
Individual Decision Making ◽  
The Past ◽  
Freedom Of Thought ◽  
Independent Decision ◽  
The Right ◽  
The Impact ◽  
Insight Into

Over the past few decades technology has become ubiquitous, with technology companies gaining increasing insight into the lives of individuals. This paper explores how technology companies use these insights to influence the ability to exercise free and independent decision-making. Through a critical analysis of social nudging, I establish the subtle but significant ways in which individuals can be susceptible to manipulation. Through this lens, I highlight some notable examples of how big tech companies have manipulated individual decision-making and the impact this may have on our democracy. 

scholarly journals Evaluation of bias-correction methods for ensemble streamflow volume forecasts

2006 ◽  
Vol 3 (2) ◽  
pp. 561-594 ◽  
Author(s):  
T. Hashino ◽  
A. A. Bradley ◽  
S. S. Schwartz
Keyword(s):  
Decision Making ◽  
Bias Correction ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Historical Simulation ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Operational Decision Making ◽  
Prediction Systems ◽  
Forecast Quality

Abstract. Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for the Des Moines River, issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

Impact of weather conditions on airport arrival delay and throughput

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Álvaro Rodríguez-Sanz ◽  
Javier Cano ◽  
Beatriz Rubio Fernández
Keyword(s):  
Decision Making ◽  
Weather Conditions ◽  
Weather Data ◽  
Arrival Process ◽  
Data Set ◽  
Content Type ◽  
Weather Events ◽  
Adverse Weather ◽  
Arrival Processes ◽  
The Impact

Purpose Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed. Design/methodology/approach This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results. Findings The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration. Originality/value This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

Integrating Carbon Footprint into Coordination of Constant Pricing and Lot Sizing Problem

2012 ◽  
Vol 518-523 ◽  
pp. 3959-3967 ◽  
Author(s):  
Dao Ming Dai
Keyword(s):  
Decision Making ◽  
Inventory Management ◽  
Carbon Footprint ◽  
Carbon Tax ◽  
Lot Sizing ◽  
Regulatory Policies ◽  
First Case ◽  
Product Pricing ◽  
Operational Decision Making ◽  
The Impact

The purpose of this paper is to improve our understanding of how carbon emission concerns could be integrated into operational decision-making with regard to procurement, production, and inventory management. Lot sizing model with constant product pricing can be modified to support decision-making that accounts for both profit and carbon footprint, by combining carbon footprint parameters with various decision variables. In the first case, the strict carbon cap is introduced into coordination of product pricing and lot sizing. The second case with carbon trade can be achieved by the Lagrangian relaxation of the carbon cap in the first case. In particular, the model with carbon trade is transformed into the third model with carbon tax when the carbon cap equals zero. The results show a series of insights that highlight the impact of operational decisions on carbon emissions and the importance of operational models in evaluation the impact of different regulatory policies and in assessing the benefits of investments in more carbon efficient technologies.

Climate services for water resources – the Australian experience

2020 ◽  
Author(s):  
Louise Wilson ◽  
Chantal Donnelly ◽  
Pandora Hope ◽  
Elisabeth Vogel ◽  
Wendy Sharples ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Water Resources ◽  
Climate Services ◽  
Australian Water ◽  
Water Service ◽  
The Past ◽  
Hydrological Impact ◽  
Operational Decision Making ◽  
Regional Downscaling

<p>Climate change is already impacting on Australian water resources with step changes in rainfall regimes, changes in catchment functioning and drier, hotter conditions creating major challenges for water resource management.  Water resources in most parts of the country are influenced by high interannual variability. Thus Australia's operational water management, as well as water policy and infrastructure development decisions require high resolution information that realistically defines this variability both for the past, at seasonal scales, and into the future.</p><p>In Australia, water information accounting for climate change that is available to planners and resource managers, exists for limited geographical regions such as single catchments, urban regions or states. It is typically sourced from multiple regional downscaling efforts and using different methods to interpret this data for hydrological impacts. These regional downscaling and hydrological impact data collections are either not application-ready or tailored for specific purposes only, which poses additional barriers to their use across the water and other sectors. The needs of the water sector in managing this resource over vast river basins which cross jurisdictional boundaries, such as the Murray Darling Basin, have provided a challenge for providers of climate projection information and climate services. Consistent, agreed upon approaches across impacts at the national scale are yet to be developed. However, an accessible and consistent set of climate projections for water will help ensure that climate change risks are properly factored into decision-making in the water sector.</p><p>The Australian Bureau of Meteorology is developing a seamless national landscape water service, combining historical data on water availability with forecast products, as well as hydrological impact projections. This system uses a consistent methodology based upon the Australian Water resources Assessment (AWRA-L) hydrological model across all time scales. Once delivered, these new products will contribute towards comparable water services for the water, agricultural, energy, and other sectors, providing data across timescales. From a user's perspective the service will facilitate understanding of both past and future variability across multiple timescales of interest including the associated impacts of a changing climate. Providing a seamless service will improve operational decision making by putting short- and medium-term forecasts in the context of the past and future climate variability. Operational decision making can therefore be better integrated with longer-term strategic decision making on climate change.</p><p>For services to meet user needs they must be designed in consultation with these users. An extensive user centred design (UCD) process underpins the scope and nature of the new service. Insights will be shared from the UCD outcomes including user-defined data requirements of past and future variability. Users clearly expressed needs for guidance material and information about skill, confidence and uncertainty to accompany and contextualise climate information which is a major focus of this seamless water service. To engage users and ensure useful outputs, co-design principles are being employed as part of the confidence and uncertainty assessment process to be undertaken as part of the hydrological projections service, which will underpin development of guidance to assist users navigate multiple datasets.</p>

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