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Collaborating Authors
Steele, Edward
Abstract Accurate prediction of the frontal position and intensity of the Loop Current and associated Loop Current Eddies (LC/LCEs) in the Gulf of Mexico (GoM) is notoriously difficult, adversely impacting offshore planning and workability. Here, we review operational forecasting practices and propose a new, unifying, approach for the holistic analysis of oceanographic data, exploring the potential for establishing a new general-purpose set of GoM regimes for the objective classification of its ‘state’ into 1 of 40 discrete regime definitions, as generated through clustering of sea surface height fields obtained from reanalysis data for the period from 1994 to 2015. Drawing inspiration from present methods for the prediction of anticipated LC/LCE impacts, the approach delivers an automatic identification tool to complement operational decision-making, leveraging over 20 years of potential historical analogues to inform impact assessment. Furthermore, it is readily applicable to observational (e.g. satellite) and modelling (e.g. analysis/forecast) fields alike – making expansion to other equivalent spatial datasets simple – and capable of both simplifying the interpretation of dynamical forecasts and seeding the generation of statistical forecasts; opening the potential for a combined, hybrid, approach comprising multiple complementary data sources to be derived in the future. Although still at an early stage of refinement, this is deemed to be a highly important original contribution for operators seeking to improve the efficiency of their planning workflow when forecasting workability in the region.
Anticipated Verification: A Simple Method for Representing Confidence and Trust in Metocean Forecast Information to Effectively Enhance Operational Decision-Making
Steele, Edward (Met Office) | Mylne, Kenneth (Met Office) | Neal, Robert (Met Office) | Standen, Jessica (Met Office) | Geertsema, Gertie (KNMI) | Krikken, Folmer (KNMI) | de Vries, Hans (KNMI) | Amies, Jessica (Met Office) | Brown, Hannah (Met Office) | Upton, Jon (Societe des Petroles Shell)
Abstract It is easy to look back at a past forecast and assess how well it performed relative to the observed weather, but it is an obvious fact that such verification is only possible after the event has passed. Despite advances in skill in predicting variables such as wind speed or significant wave height, a perennial problem for offshore industry users interested in the incisive application of metocean forecast information to effectively enhance their operational decision-making is knowing how much ‘trust’ to place in these data at lead times beyond a few days ahead. Here, for the first time, we propose a method for the stratification of (past) verification statistics that applies unused information from the (future) forecast to anticipate how well the numerical weather prediction will perform a priori, providing a ‘weather-aware’ estimate of its skill (confidence) before the event has occurred. Stratification is a method for being able to identify differences in characteristics of a particular subset of data, rather than just considering the whole population together. Since forecast performance is partly determined by the ‘drivers of predictability’ at that particular forecast horizon (e.g. prevailing atmosphere/ocean regime), we apply stratification on the basis of the classification of the large-scale weather present at the time to ‘filter’ past verification statistics, as considered in terms of the continuous ranked probability skill score. When used in real-time with a future forecast, the technique offers a refined estimate of the skill by taking advantage of the context of the weather being predicted. The translation of this ‘anticipated verification’ information into a qualitative description offers a robust indicator of confidence, suitable for (dynamically) guiding user trust in the forecast. Using 2.75 years of archived forecast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) extended-range (monthly lead time) ensemble prediction system (EPS) for an example location in the North Sea, the derivation of a dynamic (conditionally sampled) ‘trust index’ is presented; the application of which being evaluated using trials on unseen data from the same site. The benefit of the approach demonstrates significant potential for improving confidence in decision-making from individual short- to medium- range forecasts, as well as building trust in longer-range forecasts. While not a substitute for meteorologist guidance regarding the comprehensiveness of the various drivers of predictability at sub-seasonal timescales, this is none-the-less an important step in making such forecasts usable, useful and used.
- North America > United States > Texas (0.29)
- Europe > United Kingdom > North Sea (0.25)
- Europe > Norway > North Sea (0.25)
- (2 more...)
Using Metocean Forecast Verification Information to Effectively Enhance Operational Decision-Making
Steele, Edward (Met Office) | Brown, Hannah (Met Office) | Bunney, Christopher (Met Office) | Gill, Philip (Met Office) | Mylne, Kenneth (Met Office) | Saulter, Andrew (Met Office) | Standen, Jessica (Met Office) | Blair, Liam (TechnipFMC) | Cruickshank, Stewart (TechnipFMC) | Gulbrandsen, Morten (TechnipFMC)
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. 2 OTC-31253-MS
- Europe > Norway > Norwegian Sea (0.34)
- North America > United States > Texas (0.28)
- Europe > United Kingdom > North Sea (0.25)
- (3 more...)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL S86 > Block 6406/12 > Fenja Field > Melke Formation (0.96)
- Europe > Norway > Norwegian Sea > Halten Terrace > Njord License > Block 6407/7 > Njord Field > Tilje Formation (0.94)
- Europe > Norway > Norwegian Sea > Halten Terrace > Njord License > Block 6407/7 > Njord Field > Ile Formation (0.94)
- (14 more...)
Accurate forecasts of coastal erosion are essential for the effective management (operation and protection) of critical infrastructure such as gas terminals and shallow-buried nearshore pipelines, preventing the costly losses of production associated with storm damage or exposure. Traditionally, these predictions were the preserve of computationally-expensive, morphodynamic simulations of the three-dimensional structure of the beach surface, however recent developments in reduced-complexity ‘equilibrium’ models have been shown to skilfully hindcast coastal change in cross-shore and long-shore transport dominated environments more accurately, over much longer time-scales. The simplicity and stability of these models – expressed as a function of the incident wave power and the relative equilibrium in dimensionless fall velocity – make them particularly appropriate for assessing the current ‘health’ of the coastline in actionable terms, while unlocking their potential use in forecast mode. Here, we present such a system, forced by data from the Met Office Wave Ensemble Prediction System, capable of providing real-time probabilistic forecasts of important coastal indices (e.g. beach volume and shoreline position) out to seven days ahead. The system is calibrated using an extended Kalman Filter and becomes more accurate over time as it assimilates more observational measurements. Once calibrated, tests on unseen data from the University of Plymouth coastal monitoring station at Perranporth, UK, during Winter 2017/18 confirm it can accurately predict the impact of an extreme storm sequence on coastal erosion and subsequent recovery. This promises the potential for a new coastal management tool, able to be applied to other vulnerable locations.
- Europe > United Kingdom > England (0.28)
- North America > United States > Texas (0.28)
- Energy > Oil & Gas (1.00)
- Government > Regional Government > North America Government > United States Government (0.46)
- Energy > Renewable > Ocean Energy (0.35)
- South America > Atlantic Basin (0.99)
- North America > United States > Colorado > Ice Field (0.99)
- North America > Atlantic Basin (0.99)
- (7 more...)
Using Weather Pattern Typology to Identify Calm Weather Windows for Local Marine Operations
Steele, Edward (Met Office) | Neal, Robert (Met Office) | Dankers, Rutger (Met Office) | Fournier, Nicolas (Met Office) | Mylne, Kenneth (Met Office) | Newell, Paul (Met Office) | Saulter, Andrew (Met Office) | Skea, Alasdair (Met Office) | Upton, Jon (Shell U.K.)
Abstract The cost and complexity of offshore operations, combined with the vulnerability of equipment to prevailing conditions, requires weather-sensitive decisions to be made to ensure the continued accessibility and availability of marine assets. In the current economic context, this is especially important since the robust (timely) identification of calm weather windows has the potential to save many thousands of dollars per day in unplanned downtime and vessel contracting, allowing large efficiencies in sequencing, mobilisation and demobilisation costs if decisions are taken at the earliest possible opportunity. As forecasts extend weeks to months ahead, it is well known that predictability limits make the direct characterisation of small-scale weather events all but impossible, but there still remains a considerable amount of useful information contained within the large-scale weather circulation types. These circulation types, termed ‘weather patterns’, have a strong influence on the variability of marine wind and wave fields. Here, we present a new method for the tracking of calm weather windows out to several weeks ahead. Using a 34-year hindcast to elicit the daily maximum significant wave height experienced at the location of interest – and an analysis of the associated weather pattern under which they occurred – the circulation types are linked to the viability of offshore operations on a local scale. When subsequently applied in forecast mode, this method can enable earlier decision-making than is typically done at present. As weather patterns are more predictable than the actual weather itself at long lead-times, knowledge of the corresponding historic wave heights can enable identification of expected conditions within a probabilistic weather pattern forecasting system. In addition, the approach facilitates contingency planning; further supporting improved decision-making and reduced operational costs for the offshore oil and gas and marine renewable energy sector.
- Europe > United Kingdom (0.70)
- North America > United States > Texas (0.28)
- Management > Risk Management and Decision-Making (1.00)
- Health, Safety, Environment & Sustainability > Environment (0.88)
- Data Science & Engineering Analytics > Information Management and Systems (0.69)
- Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (0.34)
Making the Most of Probabilistic Marine Forecasts on Timescales of Days, Weeks and Months Ahead
Steele, Edward (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom) | Neal, Robert (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom) | Bunney, Christopher (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom) | Evans, Benjamin (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom) | Fournier, Nicolas (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom) | Gill, Philip (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom) | Mylne, Kenneth (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom) | Saulter, Andrew (Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, United Kingdom)
Abstract Marine forecasts are essential to operational planning, with decisions able to be guided by a host of different weather products spanning a period of days, weeks and even months ahead. The correct selection and subsequent application of these different types of weather products has the potential to save many thousands of dollars per day in operational downtime, however this is only possible when the science underpinning these marine forecasts is properly understood by the user. In the current economic context, this is especially relevant to the offshore industry – whose use of forecasting technology is traditionally very conservative, and therefore whose planning is often more reactive – allowing large savings (e.g. mobilization / demobilisation costs) if robust decisions are made as early as possible. Two established methods for the interpretation of probabilistic data based on cost-loss and weather regime analysis are described and applied to ocean wave forecasting. It is suggested the selection of methods will be dependant on the timescales of interest, with the cost-loss analysis optimised for supporting decisions at timescales on days to weeks ahead and the weather regime analysis optimized for supporting decisions at timescales of weeks to months ahead. The application of these methods are illustrated from the point of view of a North Sea asset manager planning the mobilization of equipment / personnel under conditions of calm weather, and the protection of equipment / personnel under conditions of severe weather. For such a user, efficient operational planning will be best supported by the use of marine forecasts across all such timescales, from days to months ahead. It is intended that this will enable more informed decision-making, and help reduce operational costs, by promoting increased confidence in longer-range forecasts than are typically used by the offshore oil & gas and marine renewable energy sector.
- Europe > United Kingdom > North Sea (0.35)
- North America > United States > Texas (0.28)
- Europe > Norway > North Sea (0.25)
- (2 more...)