scholarly journals Dynamic Modeling of Power Outages Caused by Thunderstorms

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 151-162 ◽  
Author(s):  
Berk A. Alpay ◽  
David Wanik ◽  
Peter Watson ◽  
Diego Cerrai ◽  
Guannan Liang ◽  
...  
Keyword(s):  
Dynamic Modeling ◽  
Prediction Models ◽  
Power Grid ◽  
Power Outages ◽  
Baseline Model ◽  
Weather Forecasts ◽  
Short Period ◽  
Storm Dynamics

Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitive outage prediction framework designed for models to learn the hourly dynamics of thunderstorm-caused outages directly from weather forecasts. Validation of several models built on this hour-by-hour prediction framework and comparison with a baseline model show abilities to accurately report temporal and storm-wide outage characteristics, which are vital for planning utility responses to storm-caused power grid damage.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia

2020 ◽  
Vol 12 (18) ◽  
pp. 2930 ◽  
Author(s):  
Anna del Moral ◽  
Tammy M. Weckwerth ◽  
Tomeu Rigo ◽  
Michael M. Bell ◽  
María Carmen Llasat
Keyword(s):  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Wind Fields ◽  
Severe Thunderstorm ◽  
Radar Network ◽  
Storm Dynamics ◽  
Storm Cells ◽  
First Time

Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ propagation mechanisms. Partly due to the local nature of the events, numerical weather prediction models are not able to accurately nowcast the complex mesoscale mechanisms (i.e., local influence of topography). This directly impacts the retrieved position and motion of the storms, and consequently, the likely associated storm severity. Although a successful warning system based on lightning and radar observations has been developed, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. The present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study the dynamics of Catalan thunderstorms. A severe thunderstorm that splits and a tornado-producing supercell that is channeled through a valley are used to demonstrate the capabilities of an advanced open source technique that retrieves dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, complete 3D storm-relative winds are obtained, providing information about the internal dynamics of the storms. This aids in the analyses of the interaction between different storm cells within a system and/or the interaction of the cells with the local topography.

scholarly journals Short period forecasting of catchment-scale precipitation. Part I: the role of Numerical Weather Prediction

2000 ◽  
Vol 4 (4) ◽  
pp. 627-633 ◽  
Author(s):  
M. A. Pedder ◽  
M. Haile ◽  
A. J. Thorpe
Keyword(s):  
Numerical Weather Prediction ◽  
Balance Equation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Phase Changes ◽  
Scale Model ◽  
Catchment Scale ◽  
Numerical Weather ◽  
Short Period ◽  
Moisture Balance

Abstract. A deterministic forecast of surface precipitation involves solving a time-dependent moisture balance equation satisfying conservation of total water substance. A realistic solution needs to take into account feedback between atmospheric dynamics and the diabatic sources of heat energy associated with phase changes, as well as complex microphysical processes controlling the conversion between cloud water (or ice) and precipitation. Such processes are taken into account either explicitly or via physical parameterisation schemes in many operational numerical weather prediction models; these can therefore generate precipitation forecasts which are fully consistent with the predicted evolution of the atmospheric state as measured by observations of temperature, wind, pressure and humidity. This paper reviews briefly the atmospheric moisture balance equation and how it may be solved in practice. Solutions are obtained using the Meteorological Office Mesoscale version of its operational Unified Numerical Weather Prediction (NWP) model; they verify predicted precipitation rates against catchment-scale values based on observations collected during an Intensive Observation Period (IOP) of HYREX. Results highlight some limitations of an operational NWP forecast in providing adequate time and space resolution, and its sensitivity to initial conditions. The large-scale model forecast can, nevertheless, provide important information about the moist dynamical environment which could be incorporated usefully into a higher resolution, ‘storm-resolving’ prediction scheme. Keywords: Precipitation forecasting; moisture budget; numerical weather prediction

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

scholarly journals Review of Sand Production Prediction Models

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Hossein Rahmati ◽  
Mahshid Jafarpour ◽  
Saman Azadbakht ◽  
Alireza Nouri ◽  
Hans Vaziri ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Prediction Models ◽  
Numerical Models ◽  
Element Model ◽  
Reservoir Rock ◽  
Sand Production ◽  
The Past ◽  
Short Period ◽  
Analytical Formulae ◽  
The Continuum

Sand production in oil and gas wells can occur if fluid flow exceeds a certain threshold governed by factors such as consistency of the reservoir rock, stress state and the type of completion used around the well. The amount of solids can be less than a few grams per cubic meter of reservoir fluid, posing only minor problems, or a substantial amount over a short period of time, resulting in erosion and in some cases filling and blocking of the wellbore. This paper provides a review of selected approaches and models that have been developed for sanding prediction. Most of these models are based on the continuum assumption, while a few have recently been developed based on discrete element model. Some models are only capable of assessing the conditions that lead to the onset of sanding, while others are capable of making volumetric predictions. Some models use analytical formulae, particularly those for estimating the onset of sanding while others use numerical models, particularly in calculating sanding rate. Although major improvements have been achieved in the past decade, sanding tools are still unable to predict the sand mass and the rate of sanding for all field problems in a reliable form.

scholarly journals Poleward Atmospheric Energy Transports and Their Variability as Evaluated from ECMWF Reanalysis Data

2012 ◽  
Vol 25 (2) ◽  
pp. 734-752 ◽  
Author(s):  
Michael Mayer ◽  
Leopold Haimberger
Keyword(s):  
Energy Budget ◽  
Energy Transport ◽  
Southern Oscillation ◽  
Forecast Model ◽  
Reanalysis Data ◽  
Energy Budgets ◽  
Global Energy ◽  
Weather Forecasts ◽  
Short Period ◽  
Global Energy Budget

Abstract The vertically integrated global energy budget is evaluated with a direct and an indirect method (both corrected for mass inconsistencies of the forecast model), mainly using the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) data. A new estimate for the net poleward total energy transport is given. Comparison to satellite-derived radiation data proves that ERA-Interim is better suited for investigation of interannual variations of the global energy budget than available satellite data since these either cover a relatively short period of time or are too inhomogeneous in time. While much improved compared to the 40-yr ECMWF Re-Analysis (ERA-40), regionally averaged energy budgets of ERA-Interim show that strong anomalies of forecasted vertical fluxes tend to be partly compensated by unrealistically large forecasted energy storage rates. Discrepancies between observed and forecasted monthly mean tendencies can be taken as rough measure for the uncertainties involved in the ERA-Interim energy budget. El Niño–Southern Oscillation (ENSO) is shown to have large impact on regional energy budgets, but strong compensation occurs between the western and eastern Pacific, leading to only small net variations of the total poleward energy transports (similar magnitude as the uncertainty of the computations). However, Hovmöller longitude–time plots of tropical energy exports show relatively strong slowly eastward-moving poleward transport anomalies in connection with ENSO. Verification of these findings using independent estimates still needs to be done.

LIMITED AREA WEATHER PREDICTION MODELS VIEWED AS DYNAMICAL SYSTEMS

1999 ◽  
Vol 09 (05) ◽  
pp. 831-842 ◽  
Author(s):  
F. CHOMÉ ◽  
C. NICOLIS
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Dynamical Behavior ◽  
Limited Area ◽  
Initial State ◽  
Weather Forecasts ◽  
Fine Mesh ◽  
Area Of Interest ◽  
Variable Mesh ◽  
High Resolution Models

Different strategies for building high-resolution models providing a more detailed description of a limited area of interest as for example, in regional weather forecasts are developed. They are subsequently compared, on the basis of the dynamical behavior generated by the corresponding models. The statistical properties of the relevant fields are analyzed, and predictability experiments are performed on statistical ensembles of close lying trajectories whose mean distance represents the uncertainty in the initial state of the system. The results show that a global, variable-mesh model performs much better than a limited area fine mesh one embedded into a coarser global model.

scholarly journals OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2668 ◽  
Author(s):  
Rongheng Lin ◽  
Zezhou Ye ◽  
Yingying Zhao
Keyword(s):  
Power Consumption ◽  
Power Grid ◽  
Electricity Consumption ◽  
Economic Benefits ◽  
Consumption Behavior ◽  
Evolutionary Clustering ◽  
Power Load ◽  
Short Period ◽  
Clustering Quality ◽  
Daily Load

Customers’ electricity consumption behavior can be studied from daily load data. Studying the daily load data for user behavior pattern analysis is an emerging research area in smart grid. Traditionally, the daily load data can be clustered into different clusters, to reveal the different categories of consumption. However, as user’s electricity consumption behavior changes over time, classical clustering algorithms are not suitable for tracing the changes, as they rebuild the clusters when clustering at any timestamp but never consider the relationship with the clusters in the previous state. To understand the changes of consumption behavior, we proposed an optimized evolutionary clustering (OPEC) algorithm, which optimized the existing evolutionary clustering algorithm by joining the Proper Restart (PR) Framework. OPEC relied on the basic fact that user’s energy consumption behavior would not abruptly change significantly, so the clusters would change progressively and remain similar in adjacent periods, except for an emergency. The newly added PR framework can deal with a situation where data changes dramatically in a short period of time, and where the former frameworks of evolutionary clustering do not work well. We evaluated the OPEC based on daily load data from Shanghai, China and the power load diagram data from UCI machine learning repository. We also carefully discussed the adjustment of the parameter in the optimized algorithm and gave an optimal value for reference. OPEC can be implemented to adapt to this situation and improve clustering quality. By understanding the changes of the users’ power consumption modes, we can detect abnormal power consumption behaviors, and also analyze the changing trend to improve the operations of the power system. This is significant for the regulation of peak load in the power grid. In addition, it can bring certain economic benefits to the operation of the power grid.

scholarly journals The Gridding of MOS

2009 ◽  
Vol 24 (2) ◽  
pp. 520-529 ◽  
Author(s):  
Bob Glahn ◽  
Kathryn Gilbert ◽  
Rebecca Cosgrove ◽  
David P. Ruth ◽  
Kari Sheets
Keyword(s):  
United States ◽  
Prediction Models ◽  
Weather Prediction ◽  
Correction Method ◽  
Maximum Temperature ◽  
Weather Variables ◽  
Weather Forecasts ◽  
Surface Weather ◽  
Model Output Statistics ◽  
Numerical Weather Prediction Models

Abstract Model output statistics (MOS) guidance forecasts have been produced at stations and provided to National Weather Service forecasters and private entities for over three decades. As the numerical weather prediction models became more accurate, MOS followed that trend. Up until a few years ago, the MOS produced at observation locations met the basic need for guidance. With the advent of the Interactive Forecast Preparation System and the National Digital Forecast Database, gridded MOS forecasts became needed as guidance for forecasters. One method of providing such grids is to objectively analyze the MOS forecasts for points. A basic successive correction method has been extended to analyze MOS forecasts and surface weather variables. This method is being applied to MOS forecasts to provide guidance for producing grids of sensible weather elements such as temperature, clouds, and snow amount. Guidance forecasts have been implemented for the conterminous United States for most weather elements contained in routine weather forecasts. This paper describes the method applied to daytime maximum temperature over the conterminous United States and gives example results.

scholarly journals Improving Business Pages Recommendation in Social Network Using Link Prediction Methods

2021 ◽  
pp. 1-12
Author(s):  
Watare Asaph ◽  
Shaowei Sun
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Area Under Curve ◽  
Prediction Models ◽  
Modern Society ◽  
Prediction Methods ◽  
Baseline Model ◽  
Exchange Information ◽  
Internet Link ◽  
A Company

Recently Social Network has become one of the favorite means for a modern society to perform social interaction and exchange information via the internet. Link prediction is a common problem that has broad application in such social networks, ranging from predicting unobserved interaction to recommending related items. In this paper, we investigate link recommendations over business pages on Facebook Social Network. More specifically, given a company in thenetwork, we want to recommend potential companies to connect with. We start by introducing existing work in link recommendations and some link prediction models as our baseline. We then talk about the Graph Neural Network model SEAL to make a link recommendations in the network. Our results show that SEAL outperformed the compared baseline model while reaching above 94% Area Under Curve accuracy in link recommendations.

Sign in / Sign up

Export Citation Format

Share Document