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>

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 Initial perturbations based on Ensemble Transfrom Kalman Filter with rescaling method for ensemble forecast

2021 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Hanbin Zhang ◽  
Hua Tian ◽  
Yining Shi
Keyword(s):  
Growth Rate ◽  
Kalman Filter ◽  
Weather Forecasting ◽  
Initial Perturbation ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Model Uncertainties ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts

AbstractEnsemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

scholarly journals The Met Office Weather Game: investigating how different methods for presenting probabilistic weather forecasts influence decision-making

2019 ◽  
Vol 2 (2) ◽  
pp. 101-116 ◽  
Author(s):  
Elisabeth M. Stephens ◽  
David J. Spiegelhalter ◽  
Ken Mylne ◽  
Mark Harrison
Keyword(s):  
Decision Making ◽  
Weather Forecasting ◽  
Weather Forecast ◽  
Online Game ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Presentation Formats ◽  
The Uk ◽  
Mass Participation ◽  
The Impact

Abstract. To inform the way probabilistic forecasts would be displayed on their website, the UK Met Office ran an online game as a mass participation experiment to highlight the best methods of communicating uncertainty in rainfall and temperature forecasts, and to widen public engagement in uncertainty in weather forecasting. The game used a hypothetical “ice-cream seller” scenario and a randomized structure to test decision-making ability using different methods of representing uncertainty and to enable participants to experience being “lucky” or “unlucky” when the most likely forecast scenario did not occur. Data were collected on participant age, gender, educational attainment, and previous experience of environmental modelling. The large number of participants (n>8000) that played the game has led to the collation of a unique large dataset with which to compare the impact on the decision-making ability of different weather forecast presentation formats. This analysis demonstrates that within the game the provision of information regarding forecast uncertainty greatly improved decision-making ability and did not cause confusion in situations where providing the uncertainty added no further information.

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

2007 ◽  
Vol 11 (2) ◽  
pp. 939-950 ◽  
Author(s):  
T. Hashino ◽  
A. A. Bradley ◽  
S. S. Schwartz
Keyword(s):  
Decision Making ◽  
Bias Correction ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Historical Simulation ◽  
Probabilistic Forecasts ◽  
Central United States ◽  
Streamflow Forecasts ◽  
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 a north-central United States basin (Des Moines River, Iowa), 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.

scholarly journals Analysis of the Relative Operating Characteristic and Economic Value Using the LAPS Ensemble Prediction System in Taiwan

2015 ◽  
Vol 143 (5) ◽  
pp. 1833-1848 ◽  
Author(s):  
Hui-Ling Chang ◽  
Shu-Chih Yang ◽  
Huiling Yuan ◽  
Pay-Liam Lin ◽  
Yu-Chieng Liou
Keyword(s):  
Decision Making ◽  
Operating Characteristic ◽  
Economic Value ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast System ◽  
Relative Operating Characteristic ◽  
Ensemble Prediction System ◽  
Discrimination Ability ◽  
Probabilistic Forecasts

Abstract Measurement of the usefulness of numerical weather prediction considers not only the forecast quality but also the possible economic value (EV) in the daily decision-making process of users. Discrimination ability of an ensemble prediction system (EPS) can be assessed by the relative operating characteristic (ROC), which is closely related to the EV provided by the same forecast system. Focusing on short-range probabilistic quantitative precipitation forecasts (PQPFs) for typhoons, this study demonstrates the consistent and strongly related characteristics of ROC and EV based on the Local Analysis and Prediction System (LAPS) EPS operated at the Central Weather Bureau in Taiwan. Sensitivity experiments including the effect of terrain, calibration, and forecast uncertainties on ROC and EV show that the potential EV provided by a forecast system is mainly determined by the discrimination ability of the same system. The ROC and maximum EV (EVmax) of an EPS are insensitive to calibration, but the optimal probability threshold to achieve the EVmax becomes more reliable after calibration. In addition, the LAPS ensemble probabilistic forecasts outperform deterministic forecasts in respect to both ROC and EV, and such an advantage grows with increasing precipitation intensity. Also, even without explicitly knowing the cost–loss ratio, one can still optimize decision-making and obtain the EVmax by using ensemble probabilistic forecasts.

scholarly journals Setting up a water quality ensemble forecast for coastal ecosystems: a case study of the southern North Sea

2018 ◽  
Vol 20 (4) ◽  
pp. 846-863 ◽  
Author(s):  
Lőrinc Mészáros ◽  
Ghada El Serafy
Keyword(s):  
Water Quality ◽  
Coastal Ecosystems ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Ecological Processes ◽  
Forecast Performance ◽  
Probabilistic Forecasts ◽  
Forecasting System

Abstract Prediction systems, such as the coastal ecosystem models, often incorporate complex non-linear ecological processes. There is an increasing interest in the use of probabilistic forecasts instead of deterministic forecasts in cases where the inherent uncertainties in the prediction system are important. The primary goal of this study is to set up an operational ensemble forecasting system for the prediction of the Chlorophyll-a concentration in coastal waters, using the Generic Ecological Model. The input ensemble is generated from perturbed model process parameters and external forcings through Latin Hypercube Sampling with Dependence. The forecast performance of the ensemble prediction is assessed using several forecast verification metrics that can describe the forecast accuracy, reliability and discrimination. The verification is performed against in-situ measurements and remote sensing data. The ensemble forecast moderately outperforms the deterministic prediction at the coastal in-situ measurement stations. The proposed ensemble forecasting system is therefore a promising tool to provide enhanced water quality prediction for coastal ecosystems which, with further inclusion of other uncertainty sources, could be used for operational forecasting.

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>

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.

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