scholarly journals Coupling urban drainage–wastewater systems and electric smart grids during dry periods: a gain/loss framework using the relative economic value with ensemble flow forecasts to predict the switch between management objectives

2016 ◽  
Author(s):  
Vianney Courdent ◽  
Morten Grum ◽  
Thomas Munk-Nielsen ◽  
Peter S. Mikkelsen
Keyword(s):  
Smart Grids ◽  
Weather Prediction ◽  
Economic Value ◽  
Decision Makers ◽  
Low Flow ◽  
Mitigation Measures ◽  
Ensemble Prediction ◽  
Urban Drainage ◽  
Wastewater Systems ◽  
Gain Loss

Abstract. Precipitation is the major perturbation to the flow in urban drainage and wastewater systems. Flow forecast, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimise the operation of Integrated Urban Drainage–Wastewater Systems (IUDWS) during both wet and dry weather periods. Numerical Weather Prediction (NWP) models have significantly improved in recent years; increasing their spatial and temporal resolution. Finer resolution NWP are suitable for urban catchment scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with Ensemble Prediction Systems (EPS). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain therefore errors will be made, decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The Relative Economic Value (REV) approach associates economic values to the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecasted). The specificity of this study is to optimise the energy consumption in IUDWS during low flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecasted). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial use.

scholarly journals A gain–loss framework based on ensemble flow forecasts to switch the urban drainage–wastewater system management towards energy optimization during dry periods

2017 ◽  
Vol 21 (5) ◽  
pp. 2531-2544 ◽  
Author(s):  
Vianney Courdent ◽  
Morten Grum ◽  
Thomas Munk-Nielsen ◽  
Peter S. Mikkelsen
Keyword(s):  
Weather Prediction ◽  
Economic Value ◽  
Decision Makers ◽  
Low Flow ◽  
Mitigation Measures ◽  
Ensemble Prediction ◽  
Urban Drainage ◽  
Catchment Scale ◽  
Wastewater Systems ◽  
Gain Loss

Abstract. Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage–wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain–loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial uses.

scholarly journals Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

2022 ◽  
Vol 22 (1) ◽  
pp. 577-596
Author(s):  
Susan J. Leadbetter ◽  
Andrew R. Jones ◽  
Matthew C. Hort
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Weather Prediction ◽  
Skill Score ◽  
Decision Makers ◽  
Radioactive Material ◽  
Ensemble Prediction ◽  
Ensemble Simulations ◽  
Model Predictions ◽  
Flight Level

Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

scholarly journals Verification of forecasted winter precipitation in complex terrain

2014 ◽  
Vol 8 (6) ◽  
pp. 5727-5762 ◽  
Author(s):  
M. Schirmer ◽  
B. Jamieson
Keyword(s):  
Low Cost ◽  
Weather Prediction ◽  
Economic Value ◽  
Winter Season ◽  
Model Performance ◽  
Mitigation Measures ◽  
Depth Sensors ◽  
Mountainous Regions ◽  
Precipitation Analysis ◽  
Analysis System

Abstract. Numerical Weather Prediction (NWP) models are rarely verified for mountainous regions during the winter season, although avalanche forecasters and other decision makers frequently rely on NWP models. We verified two NWP models (GEM-LAM and GEM15) and a precipitation analysis system (CaPA) at approximately 100 stations in the mountains of western Canadian and northwestern US. Ultrasonic snow depth sensors and snow pillows were used to observe daily precipitation amounts. For the first time, a detailed objective validation scheme was performed highlighting many aspects of forecast quality. Overall, the models underestimated precipitation amounts, although low precipitation categories were overestimated. The finer resolution model GEM-LAM performed best in all analysed aspects of model performance, while the precipitation analysis system performed worst. An analysis of the economic value of large precipitation categories showed that only mitigation measures with low cost/loss ratios (i.e. measures that can be performed often) will benefit from these NWP models. This means that measures with large associated costs (relative to anticipated losses when the measure is not performed) should not or not primarily depend on forecasted precipitation.

scholarly journals Can we assess the value meteorological ensembles add to dispersion modelling using hypothetical releases?

2021 ◽  
Author(s):  
Susan Janet Leadbetter ◽  
Andrew R. Jones ◽  
Matthew C. Hort
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Weather Prediction ◽  
Skill Score ◽  
Decision Makers ◽  
Radioactive Material ◽  
Ensemble Prediction ◽  
Ensemble Simulations ◽  
Model Predictions ◽  
Flight Level

Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

scholarly journals The benefits of ensemble prediction for forecasting an extreme event: The Queensland Floods of February 2019

2021 ◽  
Author(s):  
Matt Hawcroft ◽  
Sally Lavender ◽  
Dan Copsey ◽  
Sean Milton ◽  
José Rodríguez ◽  
...  
Keyword(s):  
Extreme Event ◽  
Weather Prediction ◽  
Decision Makers ◽  
Ensemble Prediction ◽  
Maximum Temperature ◽  
Forecast Errors ◽  
Ensemble Prediction System ◽  
Ocean Coupling ◽  
Strong Winds ◽  
The Uk

AbstractFrom late January to early February 2019, a quasi-stationary monsoon depression situated over northeast Australia caused devastating floods. During the first week of February, when the event had its greatest impact in northwest Queensland, record-breaking precipitation accumulations were observed in several locations, accompanied by strong winds, substantial cold maximum temperature anomalies and related wind chill. In spite of the extreme nature of the event, the monthly rainfall outlook for February issued by Australia’s Bureau of Meteorology on 31st January provided no indication of the event. In this study, we evaluate the dynamics of the event and assess how predictable it was across a suite of ensemble model forecasts using the UK Met Office numerical weather prediction (NWP) system, focussing on a one week lead time. In doing so, we demonstrate the skill of the NWP system in predicting the possibility of such an extreme event occurring. We further evaluate the benefits derived from running the ensemble prediction system at higher resolution than used operationally at the Met Office and with a fully coupled dynamical ocean. We show that the primary forecast errors are generated locally, with key sources of these errors including atmosphere-ocean coupling and a known bias associated with the behaviour of the convection scheme around the coast. We note that a relatively low resolution ensemble approach requires limited computing resource, yet has the capacity in this event to provide useful information to decision makers with over aweek’s notice, beyond the duration of many operational deterministic forecasts.

strategi dalam pengambilan keputusan

2019 ◽  
Author(s):  
Suci Handayani Handayani ◽  
Hade Afriansyah
Keyword(s):  
Decision Making ◽  
Imperfect Information ◽  
Economic Value ◽  
Decision Makers ◽  
Decision Making Process ◽  
Administrative Process ◽  
Left Behind ◽  
Information Time ◽  
Complex Matter ◽  
General Individuals

Decision making is one element of economic value, especially in the era of globalization, and if it is not acceptable in the decision making process, we will be left behind. According to Robins, (2003: 173), Salusu, (2000: 47), and Razik and Swanson, (1995: 476) say that decision making can be interpreted as a process of choosing a number of alternatives, how to act in accordance with concepts, or rules in solving problems to achieve individual or group goals that have been formulated using a number of specific techniques, approaches and methods and achieve optimal levels of acceptance.Decision making in organizations whether a decision is made for a person or group, the nature of the decision is often determined by rules, policies, prescribed, instructions that have been derived or practices that apply. To understand decision making within the organization it is useful to view decision making as part of the overall administrative process. In general, individuals tend to use simple strategies, even if in any complex matter, to get the desired solution, because the solution is limited by imperfect information, time and costs, limited thinking and psychological stress experienced by decision makers.

scholarly journals Time Evolution of Initial Errors in Lorenz’s 05 Chaotic Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Hynek Bednář ◽  
Aleš Raidl ◽  
Jiří Mikšovský
Keyword(s):  
Weather Prediction ◽  
Prediction Method ◽  
Atmospheric Model ◽  
Ensemble Prediction ◽  
Asymptotic Value ◽  
Time Limits ◽  
Initial Errors ◽  
Asymptotic Values ◽  
Chaotic Model ◽  
Almost All

Initial errors in weather prediction grow in time and, as they become larger, their growth slows down and then stops at an asymptotic value. Time of reaching this saturation point represents the limit of predictability. This paper studies the asymptotic values and time limits in a chaotic atmospheric model for five initial errors, using ensemble prediction method (model’s data) as well as error approximation by quadratic and logarithmic hypothesis and their modifications. We show that modified hypotheses approximate the model’s time limits better, but not without serious disadvantages. We demonstrate how hypotheses can be further improved to achieve better match of time limits with the model. We also show that quadratic hypothesis approximates the model’s asymptotic value best and that, after improvement, it also approximates the model’s time limits better for almost all initial errors and time lengths.

Statistical methods and weather prediction for ampacity forecasting in smart grids

2016 ◽  
Author(s):  
Rafael Alberdi ◽  
Elvira Fernandez ◽  
Igor Albizu ◽  
Victor Valverde ◽  
Miren T. Bedialauneta ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Smart Grids ◽  
Weather Prediction
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