Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations.

NASA Astrophysics Data System (ADS)

Ángel Prósper Fernández, Miguel; Casal, Carlos Otero; Canoura Fernández, Felipe; Miguez-Macho, Gonzalo

2017-04-01

Regional meteorological models are becoming a generalized tool for forecasting wind resource, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the production forecast and validation of a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex terrain region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting set-up. Power and wind predictions are obtained and compared with real data provided by the management company. Results show that WRF is able to yield good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

Using Bayes Model Averaging for Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Preede Revheim, Pål; Beyer, Hans Georg

2014-05-01

For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data does not contain information, but it has the disadvantage of nearly doubling the number of model parameters to be estimated. Second, the BMA procedure is run with group mean wind power as the response variable instead of group mean wind speed. This also solves the problem with longer consecutive periods without information in the input data, but it leaves the power curve to also be estimated from the data. [1] Raftery, A. E., et al. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133, 1155-1174. [2]Revheim, P. P. and H. G. Beyer (2013). Using Bayesian Model Averaging for wind farm group forecasts. EWEA Wind Power Forecasting Technology Workshop,Rotterdam, 4-5 December 2013. [3]Sloughter, J. M., T. Gneiting and A. E. Raftery (2010). Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging. Journal of the American Statistical Association, Vol. 105, No. 489, 25-35

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE PAGES

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.; ...

2017-07-11

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mendes, J.; Bessa, R.J.; Keko, H.

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highlymore » dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.« less

Wind power application research on the fusion of the determination and ensemble prediction

NASA Astrophysics Data System (ADS)

Lan, Shi; Lina, Xu; Yuzhu, Hao

2017-07-01

The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0-24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reducing the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multi-timescale unit commitment (UC) and economicmore » dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, FESTIV, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross-validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind fore-casting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple timescales, including the day-ahead UC, 4-hour-ahead UC, and 5-min real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.« less

Scaling forecast models for wind turbulence and wind turbine power intermittency

NASA Astrophysics Data System (ADS)

Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy

2017-04-01

The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Wu, Hongyu; Florita, Anthony R.

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE PAGES

Wang, Qin; Wu, Hongyu; Florita, Anthony R.; ...

2016-11-11

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

Short time ahead wind power production forecast

NASA Astrophysics Data System (ADS)

Sapronova, Alla; Meissner, Catherine; Mana, Matteo

2016-09-01

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

Overview and Meteorological Validation of the Wind Integration National Dataset toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, C.; Hodge, B. M.; Clifton, A.

2015-04-13

The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

A Novel Wind Speed Forecasting Model for Wind Farms of Northwest China

NASA Astrophysics Data System (ADS)

Wang, Jian-Zhou; Wang, Yun

2017-01-01

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon's Signed-Rank test, and Morgan-Granger-Newbold test tell us that the proposed model is different from the compared models.

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

NASA Astrophysics Data System (ADS)

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-01

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE PAGES

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-23

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Joseph C. Y.; Lundquist, Julie K.

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

NASA Astrophysics Data System (ADS)

Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

2010-12-01

Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on individual wind turbines. The information is utilized by several technologies including: a) the Weather Research and Forecasting (WRF) model, which generates finely detailed simulations of future atmospheric conditions, b) the Real-Time Four-Dimensional Data Assimilation System (RTFDDA), which performs continuous data assimilation providing the WRF model with continuous updates of the initial atmospheric state, 3) the Dynamic Integrated Forecast System (DICast®), which statistically optimizes the forecasts using all predictors, and 4) a suite of wind-to-power algorithms that convert wind speed to power for a wide range of wind farms with varying real-time data availability capabilities. In addition to these core wind energy prediction capabilities, NCAR implemented a high-resolution (10 km grid increment) 30-member ensemble RTFDDA prediction system that provides information on the expected range of wind power over a 72-hour forecast period covering Xcel Energy’s service areas. This talk will include descriptions of these capabilities and report on several topics including initial results of next-day forecasts and nowcasts of wind energy ramp events, influence of local observations on forecast skill, and overall lessons learned to date.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilczak, James M.; Finley, Cathy; Freedman, Jeff

The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collectionmore » of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.« less

Microgrid optimal scheduling considering impact of high penetration wind generation

NASA Astrophysics Data System (ADS)

Alanazi, Abdulaziz

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

Application of global weather and climate model output to the design and operation of wind-energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin Wilde, Principal Investigator

2012-12-31

ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most windmore » plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational setting. It shall be demonstrated that when used properly, the real-time offsite measurements materially improve wind ramp capture and prediction statistics, when compared to traditional wind forecasting techniques and to a simple persistence model.« less

IEA Wind Task 36 Forecasting

NASA Astrophysics Data System (ADS)

Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

2017-04-01

Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts, and one or more benchmark studies implemented on the Windbench platform hosted at CENER. Additionally, spreading of relevant information in both the forecasters and the users community is paramount. The poster also shows the work done in the first half of the Task, e.g. the collection of available datasets and the learnings from a public workshop on 9 June in Barcelona on Experiences with the Use of Forecasts and Gaps in Research. Participation is open for all interested parties in member states of the IEA Annex on Wind Power, see ieawind.org for the up-to-date list. For collaboration, please contact the author grgi@dtu.dk).

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

NASA Astrophysics Data System (ADS)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Improved Weather and Power Forecasts for Energy Operations - the German Research Project EWeLiNE

NASA Astrophysics Data System (ADS)

Lundgren, Kristina; Siefert, Malte; Hagedorn, Renate; Majewski, Detlev

2014-05-01

The German energy system is going through a fundamental change. Based on the energy plans of the German federal government, the share of electrical power production from renewables should increase to 35% by 2020. This means that, in the near future at certain times renewable energies will provide a major part of Germany's power production. Operating a power supply system with a large share of weather-dependent power sources in a secure way requires improved power forecasts. One of the most promising strategies to improve the existing wind power and PV power forecasts is to optimize the underlying weather forecasts and to enhance the collaboration between the meteorology and energy sectors. Deutscher Wetterdienst addresses these challenges in collaboration with Fraunhofer IWES within the research project EWeLiNE. The overarching goal of the project is to improve the wind and PV power forecasts by combining improved power forecast models and optimized weather forecasts. During the project, the numerical weather prediction models COSMO-DE and COSMO-DE-EPS (Ensemble Prediction System) by Deutscher Wetterdienst will be generally optimized towards improved wind power and PV forecasts. For instance, it will be investigated whether the assimilation of new types of data, e.g. power production data, can lead to improved weather forecasts. With regard to the probabilistic forecasts, the focus is on the generation of ensembles and ensemble calibration. One important aspect of the project is to integrate the probabilistic information into decision making processes by developing user-specified products. In this paper we give an overview of the project and present first results.

Hourly Wind Speed Interval Prediction in Arid Regions

NASA Astrophysics Data System (ADS)

Chaouch, M.; Ouarda, T.

2013-12-01

The long and extended warm and dry summers, the low rate of rain and humidity are the main factors that explain the increase of electricity consumption in hot arid regions. In such regions, the ventilating and air-conditioning installations, that are typically the most energy-intensive among energy consumption activities, are essential for securing healthy, safe and suitable indoor thermal conditions for building occupants and stored materials. The use of renewable energy resources such as solar and wind represents one of the most relevant solutions to overcome the increase of the electricity demand challenge. In the recent years, wind energy is gaining more importance among the researchers worldwide. Wind energy is intermittent in nature and hence the power system scheduling and dynamic control of wind turbine requires an estimate of wind energy. Accurate forecast of wind speed is a challenging task for the wind energy research field. In fact, due to the large variability of wind speed caused by the unpredictable and dynamic nature of the earth's atmosphere, there are many fluctuations in wind power production. This inherent variability of wind speed is the main cause of the uncertainty observed in wind power generation. Furthermore, producing wind power forecasts might be obtained indirectly by modeling the wind speed series and then transforming the forecasts through a power curve. Wind speed forecasting techniques have received substantial attention recently and several models have been developed. Basically two main approaches have been proposed in the literature: (1) physical models such as Numerical Weather Forecast and (2) statistical models such as Autoregressive integrated moving average (ARIMA) models, Neural Networks. While the initial focus in the literature has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. In short term context, probabilistic forecasts might be more relevant than point forecasts for the planner to build scenarios In this paper, we are interested in estimating predictive intervals of the hourly wind speed measures in few cities in United Arab emirates (UAE). More precisely, given a wind speed time series, our target is to forecast the wind speed at any specific hour during the day and provide in addition an interval with the coverage probability 0

A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Krishnan, Venkat K; Zhang, Jie

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertaintymore » and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.« less

Benefits of an ultra large and multiresolution ensemble for estimating available wind power

NASA Astrophysics Data System (ADS)

Berndt, Jonas; Hoppe, Charlotte; Elbern, Hendrik

2016-04-01

In this study we investigate the benefits of an ultra large ensemble with up to 1000 members including multiple nesting with a target horizontal resolution of 1 km. The ensemble shall be used as a basis to detect events of extreme errors in wind power forecasting. Forecast value is the wind vector at wind turbine hub height (~ 100 m) in the short range (1 to 24 hour). Current wind power forecast systems rest already on NWP ensemble models. However, only calibrated ensembles from meteorological institutions serve as input so far, with limited spatial resolution (˜10 - 80 km) and member number (˜ 50). Perturbations related to the specific merits of wind power production are yet missing. Thus, single extreme error events which are not detected by such ensemble power forecasts occur infrequently. The numerical forecast model used in this study is the Weather Research and Forecasting Model (WRF). Model uncertainties are represented by stochastic parametrization of sub-grid processes via stochastically perturbed parametrization tendencies and in conjunction via the complementary stochastic kinetic-energy backscatter scheme already provided by WRF. We perform continuous ensemble updates by comparing each ensemble member with available observations using a sequential importance resampling filter to improve the model accuracy while maintaining ensemble spread. Additionally, we use different ensemble systems from global models (ECMWF and GFS) as input and boundary conditions to capture different synoptic conditions. Critical weather situations which are connected to extreme error events are located and corresponding perturbation techniques are applied. The demanding computational effort is overcome by utilising the supercomputer JUQUEEN at the Forschungszentrum Juelich.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Characterizing Time Series Data Diversity for Wind Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Chartan, Erol Kevin; Feng, Cong

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the importantmore » information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.« less

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE PAGES

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa; ...

2017-09-14

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

A three-stage birandom program for unit commitment with wind power uncertainty.

PubMed

Zhang, Na; Li, Weidong; Liu, Rao; Lv, Quan; Sun, Liang

2014-01-01

The integration of large-scale wind power adds a significant uncertainty to power system planning and operating. The wind forecast error is decreased with the forecast horizon, particularly when it is from one day to several hours ahead. Integrating intraday unit commitment (UC) adjustment process based on updated ultra-short term wind forecast information is one way to improve the dispatching results. A novel three-stage UC decision method, in which the day-ahead UC decisions are determined in the first stage, the intraday UC adjustment decisions of subfast start units are determined in the second stage, and the UC decisions of fast-start units and dispatching decisions are determined in the third stage is presented. Accordingly, a three-stage birandom UC model is presented, in which the intraday hours-ahead forecasted wind power is formulated as a birandom variable, and the intraday UC adjustment event is formulated as a birandom event. The equilibrium chance constraint is employed to ensure the reliability requirement. A birandom simulation based hybrid genetic algorithm is designed to solve the proposed model. Some computational results indicate that the proposed model provides UC decisions with lower expected total costs.

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freedman, Jeffrey M.; Manobianco, John; Schroeder, John

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10more » - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.« less

Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

DTIC Science & Technology

2013-12-01

ahead scheduling, Weather forecast , Wind power , Photovoltaic Power 15. NUMBER OF PAGES 107 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...cost can be reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish...reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish day-ahead

How important is getting the land surface energy exchange correct in WRF for wind energy forecasting?

NASA Astrophysics Data System (ADS)

Wharton, S.; Simpson, M.; Osuna, J. L.; Newman, J. F.; Biraud, S.

2013-12-01

Wind power forecasting is plagued with difficulties in accurately predicting the occurrence and intensity of atmospheric conditions at the heights spanned by industrial-scale turbines (~ 40 to 200 m above ground level). Better simulation of the relevant physics would enable operational practices such as integration of large fractions of wind power into power grids, scheduling maintenance on wind energy facilities, and deciding design criteria based on complex loads for next-generation turbines and siting. Accurately simulating the surface energy processes in numerical models may be critically important for wind energy forecasting as energy exchange at the surface strongly drives atmospheric mixing (i.e., stability) in the lower layers of the planetary boundary layer (PBL), which in turn largely determines wind shear and turbulence at heights found in the turbine rotor-disk. We hypothesize that simulating accurate a surface-atmosphere energy coupling should lead to more accurate predictions of wind speed and turbulence at heights within the turbine rotor-disk. Here, we tested 10 different land surface model configurations in the Weather Research and Forecasting (WRF) model including Noah, Noah-MP, SSiB, Pleim-Xiu, RUC, and others to evaluate (1) the accuracy of simulated surface energy fluxes to flux tower measurements, (2) the accuracy of forecasted wind speeds to observations at rotor-disk heights, and (3) the sensitivity of forecasting hub-height rotor disk wind speed to the choice of land surface model. WRF was run for four, two-week periods covering both summer and winter periods over the Southern Great Plains ARM site in Oklahoma. Continuous measurements of surface energy fluxes and lidar-based wind speed, direction and turbulence were also available. The SGP ARM site provided an ideal location for this evaluation as it centrally located in the wind-rich Great Plains and multi-MW wind farms are rapidly expanding in the area. We found significant differences in simulated wind speeds at rotor-disk heights from WRF which indicated, in part, the sensitivity of lower PBL winds to surface energy exchange. We also found significant differences in energy partitioning between sensible heat and latent energy depending on choice of land surface model. Overall, the most consistent, accurate model results were produced using Noah-MP. Noah-MP was most accurate at simulating energy fluxes and wind shear. Hub-height wind speed, however, was predicted with most accuracy with Pleim-Xiu. This suggests that simulating wind shear in the surface layer is consistent with accurately simulating surface energy exchange while the exact magnitudes of wind speed may be more strongly influenced by the PBL dynamics. As the nation is working towards a 20% wind energy goal by 2030, increasing the accuracy of wind forecasting at rotor-disk heights becomes more important considering that utilities require wind farms to estimate their power generation 24 to 36 hours ahead and face penalties for inaccuracies in those forecasts.

Assessment of wind energy potential in Poland

NASA Astrophysics Data System (ADS)

Starosta, Katarzyna; Linkowska, Joanna; Mazur, Andrzej

2014-05-01

The aim of the presentation is to show the suitability of using numerical model wind speed forecasts for the wind power industry applications in Poland. In accordance with the guidelines of the European Union, the consumption of wind energy in Poland is rapidly increasing. According to the report of Energy Regulatory Office from 30 March 2013, the installed capacity of wind power in Poland was 2807MW from 765 wind power stations. Wind energy is strongly dependent on the meteorological conditions. Based on the climatological wind speed data, potential energy zones within the area of Poland have been developed (H. Lorenc). They are the first criterion for assessing the location of the wind farm. However, for exact monitoring of a given wind farm location the prognostic data from numerical model forecasts are necessary. For the practical interpretation and further post-processing, the verification of the model data is very important. Polish Institute Meteorology and Water Management - National Research Institute (IMWM-NRI) runs an operational model COSMO (Consortium for Small-scale Modelling, version 4.8) using two nested domains at horizontal resolutions of 7 km and 2.8 km. The model produces 36 hour and 78 hour forecasts from 00 UTC, for 2.8 km and 7 km domain resolutions respectively. Numerical forecasts were compared with the observation of 60 SYNOP and 3 TEMP stations in Poland, using VERSUS2 (Unified System Verification Survey 2) and R package. For every zone the set of statistical indices (ME, MAE, RMSE) was calculated. Forecast errors for aerological profiles are shown for Polish TEMP stations at Wrocław, Legionowo and Łeba. The current studies are connected with a topic of the COST ES1002 WIRE-Weather Intelligence for Renewable Energies.

Bulk electric system reliability evaluation incorporating wind power and demand side management

NASA Astrophysics Data System (ADS)

Huang, Dange

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework. Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits.

Towards more accurate wind and solar power prediction by improving NWP model physics

NASA Astrophysics Data System (ADS)

Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

2014-05-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during nighttime to well mixed conditions during the day presents a big challenge to NWP models. Fast decrease and successive increase in hub-height wind speed after sunrise, and the formation of nocturnal low level jets will be discussed. For PV, the life cycle of low stratus clouds and fog is crucial. Capturing these processes correctly depends on the accurate simulation of diffusion or vertical momentum transport and the interaction with other atmospheric and soil processes within the numerical weather model. Results from Single Column Model simulations and 3d case studies will be presented. Emphasis is placed on wind forecasts; however, some references to highlights concerning the PV-developments will also be given. *) ORKA: Optimierung von Ensembleprognosen regenerativer Einspeisung für den Kürzestfristbereich am Anwendungsbeispiel der Netzsicherheitsrechnungen **) EWeLiNE: Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger, www.projekt-eweline.de

Optimizing Microgrid Architecture on Department of Defense Installations

DTIC Science & Technology

2014-09-01

PPA power purchase agreement PV photovoltaic QDR Quadrennial Defense Review SNL Sandia National Laboratory SPIDERS Smart Power Infrastructure...a MILP that dispatches fuel-based generators with consideration to an ensemble of forecasted inputs from renewable power sources, subject to physical...wind power project costs by region: 2012 projects, from [30]. 6. Weather Forecasts Weather forecasts are often presented as a single prediction

A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble, which is a case-matching scheme. The presentation will provide (1) an overview of each method and the experimental design, (2) performance comparisons based on standard metrics such as bias, MAE and RMSE, (3) a summary of the performance characteristics of each approach and (4) a preview of further experiments to be conducted.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, M. H.; Giebel, G.; Nielsen, T. S.; Hahmann, A.; Sørensen, P.; Madsen, H.

2012-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the working title "Integrated Wind Power Planning Tool". The project commenced October 1, 2011, and the goal is to integrate a numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. With regard to the latter, one such simulation tool has been developed at the Wind Energy Division, Risø DTU, intended for long term power system planning. As part of the PSO project the inferior NWP model used at present will be replaced by the state-of-the-art Weather Research & Forecasting (WRF) model. Furthermore, the integrated simulation tool will be improved so it can handle simultaneously 10-50 times more turbines than the present ~ 300, as well as additional atmospheric parameters will be included in the model. The WRF data will also be input for a statistical short term prediction model to be developed in collaboration with ENFOR A/S; a danish company that specialises in forecasting and optimisation for the energy sector. This integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated prediction tool constitute scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator, and the need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2020, from the current 20%.

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP Smoothing Effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Hantao; Li, Fangxing; Fang, Xin

Our paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bi-level ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) and then recast intomore » a mixed-integer linear programming (MILP) using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bi-level model. The results from the conventional model and the bi-level model are compared under different ES power and energy ratings, and also various load and wind penetration levels.« less

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP Smoothing Effect

DOE PAGES

Cui, Hantao; Li, Fangxing; Fang, Xin; ...

2017-10-04

Our paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bi-level ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) and then recast intomore » a mixed-integer linear programming (MILP) using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bi-level model. The results from the conventional model and the bi-level model are compared under different ES power and energy ratings, and also various load and wind penetration levels.« less

A hybrid wavelet transform based short-term wind speed forecasting approach.

PubMed

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

PubMed Central

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy. PMID:25136699

Wind power generation and dispatch in competitive power markets

NASA Astrophysics Data System (ADS)

Abreu, Lisias

Wind energy is currently the fastest growing type of renewable energy. The main motivation is led by more strict emission constraints and higher fuel prices. In addition, recent developments in wind turbine technology and financial incentives have made wind energy technically and economically viable almost anywhere. In restructured power systems, reliable and economical operation of power systems are the two main objectives for the ISO. The ability to control the output of wind turbines is limited and the capacity of a wind farm changes according to wind speeds. Since this type of generation has no production costs, all production is taken by the system. Although, insufficient operational planning of power systems considering wind generation could result in higher system operation costs and off-peak transmission congestions. In addition, a GENCO can participate in short-term power markets in restructured power systems. The goal of a GENCO is to sell energy in such a way that would maximize its profitability. However, due to market price fluctuations and wind forecasting errors, it is essential for the wind GENCO to keep its financial risk at an acceptable level when constituting market bidding strategies. This dissertation discusses assumptions, functions, and methodologies that optimize short-term operations of power systems considering wind energy, and that optimize bidding strategies for wind producers in short-term markets. This dissertation also discusses uncertainties associated with electricity market environment and wind power forecasting that can expose market participants to a significant risk level when managing the tradeoff between profitability and risk.

Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Hayes, C.; Collier, C.

2014-12-01

The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.

Ensemble Data Assimilation of Wind and Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2016-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Wind speed and irradiation forecast from NWP system are however subject to several sources of error. The quality of the wind power prediction is mainly penalized by forecast error of the NWP model in the planetary boundary layer (PBL), which is characterized by high spatial and temporal fluctuations of the wind speed. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, the absorption of condensed water or aerosol optical depth are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, NWP model data can be corrected by post-processing techniques such as model output statistics and calibration using historical observational data. Additionally, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence the model error is reduced in the forecast. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. The numerous wind farm and PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation and wind speed through their power measurements. The accuracy of the NWP data may thus be enhanced by extending the observations in the assimilation by this new source of information. Wind power data can serve as indirect measurements of wind speed at hub height. The impact on the NWP model is potentially interesting since conventional observation network lacks measurements in this part of the PBL. Photovoltaic power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Additionally, since the latter kind of data is not limited to the vertical column above or below the detector. It may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the DA method (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first assimilation results are presented and discussed.

Short-term wind speed prediction based on the wavelet transformation and Adaboost neural network

NASA Astrophysics Data System (ADS)

Hai, Zhou; Xiang, Zhu; Haijian, Shao; Ji, Wu

2018-03-01

The operation of the power grid will be affected inevitably with the increasing scale of wind farm due to the inherent randomness and uncertainty, so the accurate wind speed forecasting is critical for the stability of the grid operation. Typically, the traditional forecasting method does not take into account the frequency characteristics of wind speed, which cannot reflect the nature of the wind speed signal changes result from the low generality ability of the model structure. AdaBoost neural network in combination with the multi-resolution and multi-scale decomposition of wind speed is proposed to design the model structure in order to improve the forecasting accuracy and generality ability. The experimental evaluation using the data from a real wind farm in Jiangsu province is given to demonstrate the proposed strategy can improve the robust and accuracy of the forecasted variable.

A probabilistic neural network based approach for predicting the output power of wind turbines

NASA Astrophysics Data System (ADS)

Tabatabaei, Sajad

2017-03-01

Finding the authentic predicting tools of eliminating the uncertainty of wind speed forecasts is highly required while wind power sources are strongly penetrating. Recently, traditional predicting models of generating point forecasts have no longer been trustee. Thus, the present paper aims at utilising the concept of prediction intervals (PIs) to assess the uncertainty of wind power generation in power systems. Besides, this paper uses a newly introduced non-parametric approach called lower upper bound estimation (LUBE) to build the PIs since the forecasting errors are unable to be modelled properly by applying distribution probability functions. In the present proposed LUBE method, a PI combination-based fuzzy framework is used to overcome the performance instability of neutral networks (NNs) used in LUBE. In comparison to other methods, this formulation more suitably has satisfied the PI coverage and PI normalised average width (PINAW). Since this non-linear problem has a high complexity, a new heuristic-based optimisation algorithm comprising a novel modification is introduced to solve the aforesaid problems. Based on data sets taken from a wind farm in Australia, the feasibility and satisfying performance of the suggested method have been investigated.

Wind Power predictability a risk factor in the design, construction and operation of Wind Generation Turbines

NASA Astrophysics Data System (ADS)

Thiesen, J.; Gulstad, L.; Ristic, I.; Maric, T.

2010-09-01

Summit: The wind power predictability is often a forgotten decision and planning factor for most major wind parks, both onshore and offshore. The results of the predictability are presented after having examined a number of European offshore and offshore parks power predictability by using three(3) mesoscale model IRIE_GFS and IRIE_EC and WRF. Full description: It is well known that the potential wind production is changing with latitude and complexity in terrain, but how big are the changes in the predictability and the economic impacts on a project? The concept of meteorological predictability has hitherto to some degree been neglected as a risk factor in the design, construction and operation of wind power plants. Wind power plants are generally built in places where the wind resources are high, but these are often also sites where the predictability of the wind and other weather parameters is comparatively low. This presentation addresses the question of whether higher predictability can outweigh lower average wind speeds with regard to the overall economy of a wind power project. Low predictability also tends to reduce the value of the energy produced. If it is difficult to forecast the wind on a site, it will also be difficult to predict the power production. This, in turn, leads to increased balance costs and a less reduced carbon emission from the renewable source. By investigating the output from three(3) mesoscale models IRIE and WRF, using ECMWF and GFS as boundary data over a forecasting period of 3 months for 25 offshore and onshore wind parks in Europe, the predictability are mapped. Three operational mesoscale models with two different boundary data have been chosen in order to eliminate the uncertainty with one mesoscale model. All mesoscale models are running in a 10 km horizontal resolution. The model output are converted into "day a head" wind turbine generation forecasts by using a well proven advanced physical wind power model. The power models are using a number of weather parameters like wind speed in different heights, friction velocity and DTHV. The 25 wind sites are scattered around in Europe and contains 4 offshore parks and 21 onshore parks in various terrain complexity. The "day a head" forecasts are compared with production data and predictability for the period February 2010-April 2010 are given in Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE). The power predictability results are mapped for each turbine giving a clear picture of the predictability in Europe. . Finally a economic analysis are shown for each wind parks in different regimes of predictability will be compared with regard to the balance costs that result from errors in the wind power prediction. Analysis shows that it may very well be profitable to place wind parks in regions of lower, but more predictable wind ressource. Authors: Ivan Ristic, CTO Weather2Umberlla D.O.O Tomislav Maric, Meteorologist at Global Flow Solutions Vestas Wind Technology R&D Line Gulstad, Manager Global Flow Solutions Vestas Wind Technology R&D Jesper Thiesen, CEO ConWx ApS

Wind-Friendly Flexible Ramping Product Design in Multi-Timescale Power System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Mingjian; Zhang, Jie; Wu, Hongyu

With increasing wind power penetration in the electricity grid, system operators are recognizing the need for additional flexibility, and some are implementing new ramping products as a type of ancillary service. However, wind is generally thought of as causing the need for ramping services, not as being a potential source for the service. In this paper, a multi-timescale unit commitment and economic dispatch model is developed to consider the wind power ramping product (WPRP). An optimized swinging door algorithm with dynamic programming is applied to identify and forecast wind power ramps (WPRs). Designed as positive characteristics of WPRs, the WPRPmore » is then integrated into the multi-timescale dispatch model that considers new objective functions, ramping capacity limits, active power limits, and flexible ramping requirements. Numerical simulations on the modified IEEE 118-bus system show the potential effectiveness of WPRP in increasing the economic efficiency of power system operations with high levels of wind power penetration. It is found that WPRP not only reduces the production cost by using less ramping reserves scheduled by conventional generators, but also possibly enhances the reliability of power system operations. Moreover, wind power forecasts play an important role in providing high-quality WPRP service.« less

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the loadmore » and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.« less

Climatology and trend of wind power resources in China and its surrounding regions: a revisit using Climate Forecast System Reanalysis data

Treesearch

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

2015-01-01

The mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33-year (1979–2011) wind data from the Climate Forecast System Reanalysis (CFSR) that has many improvements including higher spatial resolution over previous global reanalysis...

Research on Operation Strategy for Bundled Wind-thermal Generation Power Systems Based on Two-Stage Optimization Model

NASA Astrophysics Data System (ADS)

Sun, Congcong; Wang, Zhijie; Liu, Sanming; Jiang, Xiuchen; Sheng, Gehao; Liu, Tianyu

2017-05-01

Wind power has the advantages of being clean and non-polluting and the development of bundled wind-thermal generation power systems (BWTGSs) is one of the important means to improve wind power accommodation rate and implement “clean alternative” on generation side. A two-stage optimization strategy for BWTGSs considering wind speed forecasting results and load characteristics is proposed. By taking short-term wind speed forecasting results of generation side and load characteristics of demand side into account, a two-stage optimization model for BWTGSs is formulated. By using the environmental benefit index of BWTGSs as the objective function, supply-demand balance and generator operation as the constraints, the first-stage optimization model is developed with the chance-constrained programming theory. By using the operation cost for BWTGSs as the objective function, the second-stage optimization model is developed with the greedy algorithm. The improved PSO algorithm is employed to solve the model and numerical test verifies the effectiveness of the proposed strategy.

Managing Wind Power Uncertainty Through Strategic Reserve Purchasing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, Ershun; Zhang, Ning; Kang, Chongqing

With the rapidly increasing penetration of wind power, wind producers are becoming increasingly responsible for the deviation of the wind power output from the forecast. Such uncertainty results in revenue losses to the wind power producers (WPPs) due to penalties in ex-post imbalance settlements. This paper explores the opportunities available for WPPs if they can purchase or schedule some reserves to offset part of their deviation rather than being fully penalized in the real time market. The revenue for WPPs under such mechanism is modeled. The optimal strategy for managing the uncertainty of wind power by purchasing reserves to maximizemore » the WPP's revenue is analytically derived with rigorous optimality conditions. The amount of energy and reserves that should be bid in the market are explicitly quantified by the probabilistic forecast and the prices of the energy and reserves. A case study using the price data from ERCOT and wind power data from NREL is performed to verify the effectiveness of the derived optimal bidding strategy and the benefits of reserve purchasing. Additionally, the proposed bidding strategy can also reduce the risk of variations on WPP's revenue.« less

Control Strategy: Wind Energy Powered Variable Chiller with Thermal Ice Storage

DTIC Science & Technology

2014-12-01

New York, 2013. [8] A. Togelou et al., “Wind power forecasting in the absence of historical data,” IEEE trans. on sustainable energy, vol. 3, no...WIND ENERGY POWERED VARIABLE CHILLER WITH THERMAL ICE STORAGE by Rex A. Boonyobhas December 2014 Thesis Advisor: Anthony J. Gannon Co...AND DATES COVERED December 20 14 Master ’s Thesis 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS CONTROL STRATEGY: WIND ENERGY POWERED VARIABLE CHILLER

Power Flow Simulations of a More Renewable California Grid Utilizing Wind and Solar Insolation Forecasting

NASA Astrophysics Data System (ADS)

Hart, E. K.; Jacobson, M. Z.; Dvorak, M. J.

2008-12-01

Time series power flow analyses of the California electricity grid are performed with extensive addition of intermittent renewable power. The study focuses on the effects of replacing non-renewable and imported (out-of-state) electricity with wind and solar power on the reliability of the transmission grid. Simulations are performed for specific days chosen throughout the year to capture seasonal fluctuations in load, wind, and insolation. Wind farm expansions and new wind farms are proposed based on regional wind resources and time-dependent wind power output is calculated using a meteorological model and the power curves of specific wind turbines. Solar power is incorporated both as centralized and distributed generation. Concentrating solar thermal plants are modeled using local insolation data and the efficiencies of pre-existing plants. Distributed generation from rooftop PV systems is included using regional insolation data, efficiencies of common PV systems, and census data. The additional power output of these technologies offsets power from large natural gas plants and is balanced for the purposes of load matching largely with hydroelectric power and by curtailment when necessary. A quantitative analysis of the effects of this significant shift in the electricity portfolio of the state of California on power availability and transmission line congestion, using a transmission load-flow model, is presented. A sensitivity analysis is also performed to determine the effects of forecasting errors in wind and insolation on load-matching and transmission line congestion.

Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

The Wind Integration National Dataset (WIND) toolkit (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caroline Draxl: NREL

2014-01-01

Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

Ensemble forecasting for renewable energy applications - status and current challenges for their generation and verification

NASA Astrophysics Data System (ADS)

Pinson, Pierre

2016-04-01

The operational management of renewable energy generation in power systems and electricity markets requires forecasts in various forms, e.g., deterministic or probabilistic, continuous or categorical, depending upon the decision process at hand. Besides, such forecasts may also be necessary at various spatial and temporal scales, from high temporal resolutions (in the order of minutes) and very localized for an offshore wind farm, to coarser temporal resolutions (hours) and covering a whole country for day-ahead power scheduling problems. As of today, weather predictions are a common input to forecasting methodologies for renewable energy generation. Since for most decision processes, optimal decisions can only be made if accounting for forecast uncertainties, ensemble predictions and density forecasts are increasingly seen as the product of choice. After discussing some of the basic approaches to obtaining ensemble forecasts of renewable power generation, it will be argued that space-time trajectories of renewable power production may or may not be necessitate post-processing ensemble forecasts for relevant weather variables. Example approaches and test case applications will be covered, e.g., looking at the Horns Rev offshore wind farm in Denmark, or gridded forecasts for the whole continental Europe. Eventually, we will illustrate some of the limitations of current frameworks to forecast verification, which actually make it difficult to fully assess the quality of post-processing approaches to obtain renewable energy predictions.

Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pennock, K.

AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs.more » The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.« less

Using Information Processing Techniques to Forecast, Schedule, and Deliver Sustainable Energy to Electric Vehicles

NASA Astrophysics Data System (ADS)

Pulusani, Praneeth R.

As the number of electric vehicles on the road increases, current power grid infrastructure will not be able to handle the additional load. Some approaches in the area of Smart Grid research attempt to mitigate this, but those approaches alone will not be sufficient. Those approaches and traditional solution of increased power production can result in an insufficient and imbalanced power grid. It can lead to transformer blowouts, blackouts and blown fuses, etc. The proposed solution will supplement the ``Smart Grid'' to create a more sustainable power grid. To solve or mitigate the magnitude of the problem, measures can be taken that depend on weather forecast models. For instance, wind and solar forecasts can be used to create first order Markov chain models that will help predict the availability of additional power at certain times. These models will be used in conjunction with the information processing layer and bidirectional signal processing components of electric vehicle charging systems, to schedule the amount of energy transferred per time interval at various times. The research was divided into three distinct components: (1) Renewable Energy Supply Forecast Model, (2) Energy Demand Forecast from PEVs, and (3) Renewable Energy Resource Estimation. For the first component, power data from a local wind turbine, and weather forecast data from NOAA were used to develop a wind energy forecast model, using a first order Markov chain model as the foundation. In the second component, additional macro energy demand from PEVs in the Greater Rochester Area was forecasted by simulating concurrent driving routes. In the third component, historical data from renewable energy sources was analyzed to estimate the renewable resources needed to offset the energy demand from PEVs. The results from these models and components can be used in the smart grid applications for scheduling and delivering energy. Several solutions are discussed to mitigate the problem of overloading transformers, lack of energy supply, and higher utility costs.

Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

NASA Astrophysics Data System (ADS)

O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

2015-04-01

Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parks, K.; Wan, Y. H.; Wiener, G.

2011-10-01

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'),more » or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.« less

Three-model ensemble wind prediction in southern Italy

NASA Astrophysics Data System (ADS)

Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

2016-03-01

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

Wind speed time series reconstruction using a hybrid neural genetic approach

NASA Astrophysics Data System (ADS)

Rodriguez, H.; Flores, J. J.; Puig, V.; Morales, L.; Guerra, A.; Calderon, F.

2017-11-01

Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternatives. However, the wind speed is not constant, making the planning and operation at electric power systems a difficult activity. Knowing in advance the amount of raw material (wind speed) used for energy production allows us to estimate the energy to be generated by the power plant, helping the maintenance planning, the operational management, optimal operational cost. For these reasons, the forecast of wind speed becomes a necessary task. The forecast process involves the use of past observations from the variable to forecast (wind speed). To measure wind speed, weather stations use devices called anemometers, but due to poor maintenance, connection error, or natural wear, they may present false or missing data. In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series. The proposed methodology reconstructs the time series using a ANN defined by a Compact Genetic Algorithm.

Wind Power Energy in Southern Brazil: evaluation using a mesoscale meteorological model

NASA Astrophysics Data System (ADS)

Krusche, Nisia; Stoevesandt, Bernhard; Chang, Chi-Yao; Peralta, Carlos

2015-04-01

In recent years, several wind farms were build in the coast of Rio Grande do Sul state. This region of Brazil was identified, in wind energy studies, as most favorable to the development of wind power energy, along with the Northeast part of the country. Site assessments of wind power, over long periods to estimate the power production and forecasts over short periods can be used for planning of power distribution and enhancements on Brazil's present capacity to use this resource. The computational power available today allows the simulation of the atmospheric flow in great detail. For instance, one of the authors participated in a research that demonstrated the interaction between the lake and maritime breeze in this region through the use of a atmospheric model. Therefore, we aim to evaluate simulations of wind conditions and its potential to generate energy in this region. The model applied is the Weather Research and Forecasting , which is the mesoscale weather forecast software. The calculation domain is centered in 32oS and 52oW, in the southern region of Rio Grande do Sul state. The initial conditions of the simulation are taken from the global weather forecast in the time period from October 1st to October 31st, 2006. The wind power potential was calculated for a generic turbine, with a blade length of 52 m, using the expression: P=1/2*d*A*Cp*v^3, where P is the wind power energy (in Watts), d is the density (equal to 1.23 kg/m^3), A is the area section, which is equal to 8500 m2 , and v is the intensity of the velocity. The evaluation was done for a turbine placed at 50 m and 150 m of height. A threshold was chosen for a turbine production of 1.5 MW to estimate the potential of the site. In contrast to northern Brazilian region, which has a rather constant wind condition, this region shows a great variation of power output due to the weather variability. During the period of the study, at least three frontal systems went over the region, and thre was a associated variation of wind intensity. The monthly average indicate several small regions with a higher value of energy. Average production higher than 1.5 MW, for the area inland, was of 72.9% for a turbine at 150 m height but only 13.1% for one at 50 m height. This initial study indicates the variability of the region in terms of wind power availability. It can be extended to the study of extreme situations, as the case of very strong winds that knocked down 8 wind turbines in this region on the 20 of December of 2014. Simulations with high degree of spacial details will be the next step in this investigation.

Wind Integration National Dataset (WIND) Toolkit; NREL (National Renewable Energy Laboratory)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, Caroline; Hodge, Bri-Mathias

A webinar about the Wind Integration National Dataset (WIND) Toolkit was presented by Bri-Mathias Hodge and Caroline Draxl on July 14, 2015. It was hosted by the Southern Alliance for Clean Energy. The toolkit is a grid integration data set that contains meteorological and power data at a 5-minute resolution across the continental United States for 7 years and hourly power forecasts.

Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

2012-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

Power control and management of the grid containing largescale wind power systems

NASA Astrophysics Data System (ADS)

Aula, Fadhil Toufick

The ever increasing demand for electricity has driven many countries toward the installation of new generation facilities. However, concerns such as environmental pollution and global warming issues, clean energy sources, high costs associated with installation of new conventional power plants, and fossil fuels depletion have created many interests in finding alternatives to conventional fossil fuels for generating electricity. Wind energy is one of the most rapidly growing renewable power sources and wind power generations have been increasingly demanded as an alternative to the conventional fossil fuels. However, wind power fluctuates due to variation of wind speed. Therefore, large-scale integration of wind energy conversion systems is a threat to the stability and reliability of utility grids containing these systems. They disturb the balance between power generation and consumption, affect the quality of the electricity, and complicate load sharing and load distribution managing and planning. Overall, wind power systems do not help in providing any services such as operating and regulating reserves to the power grid. In order to resolve these issues, research has been conducted in utilizing weather forecasting data to improve the performance of the wind power system, reduce the influence of the fluctuations, and plan power management of the grid containing large-scale wind power systems which consist of doubly-fed induction generator based energy conversion system. The aims of this research, my dissertation, are to provide new methods for: smoothing the output power of the wind power systems and reducing the influence of their fluctuations, power managing and planning of a grid containing these systems and other conventional power plants, and providing a new structure of implementing of latest microprocessor technology for controlling and managing the operation of the wind power system. In this research, in order to reduce and smooth the fluctuations, two methods are presented. The first method is based on a de-loaded technique while the other method is based on utilizing multiple storage facilities. The de-loaded technique is based on characteristics of the power of a wind turbine and estimation of the generated power according to weather forecasting data. The technique provides a reference power by which the wind power system will operate and generate a smooth power. In contrast, utilizing storage facilities will allow the wind power system to operate at its maximum tracking power points' strategy. Two types of energy storages are considered in this research, battery energy storage system (BESS) and pumped-hydropower storage system (PHSS), to suppress the output fluctuations and to support the wind power system to follow the system load demands. Furthermore, this method provides the ability to store energy when there is a surplus of the generated power and to reuse it when there is a shortage of power generation from wind power systems. Both methods are new in terms of utilizing of the techniques and wind speed data. A microprocessor embedded system using an IntelRTM Atom(TM) processor is presented for controlling the wind power system and for providing the remote communication for enhancing the operation of the individual wind power system in a wind farm. The embedded system helps the wind power system to respond and to follow the commands of the central control of the power system. Moreover, it enhances the performance of the wind power system through self-managing, self-functioning, and self-correcting. Finally, a method of system power management and planning is modeled and studied for a grid containing large-scale wind power systems. The method is based on a new technique through constructing a new load demand curve (NLDC) from merging the estimation of generated power from wind power systems and forecasting of the load. To summarize, the methods and their results presented in this dissertation, enhance the operation of the large-scale wind power systems and reduce their drawbacks on the operation of the power grid.

Comparative Study of Standards for Grid-Connected Wind Power Plant in China and the U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Wenzhong; Tian, Tian; Muljadi, Eduard

2015-10-06

The rapid deployment of wind power has made grid integration and operational issues focal points in industry discussions and research. Compliance with grid connection standards for wind power plants (WPP) is crucial to ensuring the safe and stable operation of the electric power grid. The standards for grid-connected WPPs in China and the United States are compared in this paper to facilitate further improvements to the standards and enhance the development of wind power equipment. Detailed analyses in power quality, low-voltage ride-through capability, active power control, reactive power control, voltage control, and wind power forecasting are provided to enhance themore » understanding of grid codes in the two largest markets of wind power.« less

Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

Systematic Evaluation of Stochastic Methods in Power System Scheduling and Dispatch with Renewable Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yishen; Zhou, Zhi; Liu, Cong

2016-08-01

As more wind power and other renewable resources are being integrated into the electric power grid, the forecast uncertainty brings operational challenges for the power system operators. In this report, different operational strategies for uncertainty management are presented and evaluated. A comprehensive and consistent simulation framework is developed to analyze the performance of different reserve policies and scheduling techniques under uncertainty in wind power. Numerical simulations are conducted on a modified version of the IEEE 118-bus system with a 20% wind penetration level, comparing deterministic, interval, and stochastic unit commitment strategies. The results show that stochastic unit commitment provides amore » reliable schedule without large increases in operational costs. Moreover, decomposition techniques, such as load shift factor and Benders decomposition, can help in overcoming the computational obstacles to stochastic unit commitment and enable the use of a larger scenario set to represent forecast uncertainty. In contrast, deterministic and interval unit commitment tend to give higher system costs as more reserves are being scheduled to address forecast uncertainty. However, these approaches require a much lower computational effort Choosing a proper lower bound for the forecast uncertainty is important for balancing reliability and system operational cost in deterministic and interval unit commitment. Finally, we find that the introduction of zonal reserve requirements improves reliability, but at the expense of higher operational costs.« less

Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less

The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

NASA Astrophysics Data System (ADS)

Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

2017-08-01

The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

Equilibrium pricing in electricity markets with wind power

NASA Astrophysics Data System (ADS)

Rubin, Ofir David

Estimates from the World Wind Energy Association assert that world total wind power installed capacity climbed from 18 Gigawatt (GW) to 152 GW from 2000 to 2009. Moreover, according to their predictions, by the end of 2010 global wind power capacity will reach 190 GW. Since electricity is a unique commodity, this remarkable expansion brings forward several key economic questions regarding the integration of significant amount of wind power capacity into deregulated electricity markets. The overall dissertation objective is to develop a comprehensive theoretical framework that enables the modeling of the performance and outcome of wind-integrated electricity markets. This is relevant because the state of knowledge of modeling electricity markets is insufficient for the purpose of wind power considerations. First, there is a need to decide about a consistent representation of deregulated electricity markets. Surprisingly, the related body of literature does not agree on the very economic basics of modeling electricity markets. That is important since we need to capture the fundamentals of electricity markets before we introduce wind power to our study. For example, the structure of the electric industry is a key. If market power is present, the integration of wind power has large consequences on welfare distribution. Since wind power uncertainty changes the dynamics of information it also impacts the ability to manipulate market prices. This is because the quantity supplied by wind energy is not a decision variable. Second, the intermittent spatial nature of wind over a geographical region is important because the market value of wind power capacity is derived from its statistical properties. Once integrated into the market, the distribution of wind will impact the price of electricity produced from conventional sources of energy. Third, although wind power forecasting has improved in recent years, at the time of trading short-term electricity forwards, forecasting precision is still low. Therefore, it is crucial that the uncertainty in forecasting wind power is considered when modeling trading behavior. Our theoretical framework is based on finding a symmetric Cournot-Nash equilibrium in double-sided auctions in both forwards and spot electricity markets. The theoretical framework allows for the first time, to the best of our knowledge, a model of electricity markets that explain two main empirical findings; the existence of forwards premium and spot market mark-ups. That is a significant contribution since so far forward premiums have been explained exclusively by the assumption of risk-averse behavior while spot mark-ups are the outcome of the body of literature assuming oligopolistic competition. In the next step, we extend the theoretical framework to account for deregulated electricity markets with wind power. Modeling a wind-integrated electricity market allows us to analyze market outcomes with respect to three main factors; the introduction of uncertainty from the supply side, ownership of wind power capacity and the geographical diversification of wind power capacity. For the purpose of modeling trade in electricity forwards one should simulate the information agents have regarding future availability of aggregate wind power. This is particularly important for modeling accurately traders' ability to predict the spot price distribution. We develop a novel numerical methodology for the simulation of the conditional distribution of regional wind power at the time of trading short-term electricity forwards. Finally, we put the theoretical framework and the numerical methodology developed in this study to work by providing a detailed computational experiment examining electricity market outcomes for a particular expansion path of wind power capacity.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Security, protection, and control of power systems with large-scale wind power penetration

NASA Astrophysics Data System (ADS)

Acharya, Naresh

As the number of wind generation facilities in the utility system is fast increasing, many issues associated with their integration into the power system are beginning to emerge. Of the various issues, this dissertation deals with the development of new concepts and computational methods to handle the transmission issues and voltage issues caused by large-scale integration of wind turbines. This dissertation also formulates a probabilistic framework for the steady-state security assessment of wind power incorporating the forecast uncertainty and correlation. Transmission issues are mainly related to the overloading of transmission lines, when all the wind power generated cannot be delivered in full due to prior outage conditions. To deal with this problem, a method to curtail the wind turbine outputs through Energy Management System facilities in the on-line operational environment is proposed. The proposed method, which is based on linear optimization, sends the calculated control signals via the Supervisory Control and Data Acquisition system to wind farm controllers. The necessary ramping of the wind farm outputs is implemented either by the appropriate blade pitch angle control at the turbine level or by switching a certain number of turbines. The curtailment strategy is tested with an equivalent system model of MidAmerican Energy Company. The results show that the line overload in high wind areas can be alleviated by controlling the outputs of the wind farms step-by-step over an allowable period of time. A low voltage event during a system fault can cause a large number of wind turbines to trip, depending on voltages at the wind turbine terminals during the fault and the under-voltage protection setting of wind turbines. As a result, an N-1 contingency may evolve into an N-(K+1) contingency, where K is the number of wind farms tripped due to low voltage conditions. Losing a large amount of wind power following a line contingency might lead to system instabilities. It is important for the system operator to be aware of such limiting events during system operation and be prepared to take proper control actions. This can be achieved by incorporating the wind farm tripping status for each contingency as part of the static security assessment. A methodology to calculate voltages at the wind farm buses during a worst case line fault is proposed, which, along with the protection settings of wind turbines, can be used to determine the tripping of wind farms. The proposed algorithm is implemented in MATLAB and tested with MidAmerican Energy reduced network. The result shows that a large amount of wind capacity can be tripped due to a fault in the lines. Therefore, the technique will find its application in the static security assessment where each line fault can be associated with the tripping of wind farms as determined from the proposed method. A probabilistic framework to handle the uncertainty in day-ahead forecast error in order to correctly assess the steady-state security of the power system is presented. Stochastic simulations are conducted by means of Latin hypercube sampling along with the consideration of correlations. The correlation is calculated from the historical distribution of wind power forecast errors. The results from the deterministic simulation based on point forecast and the stochastic simulation show that security assessment based solely on deterministic simulations can lead to incorrect assessment of system security. With stochastic simulations, each outcome can be assigned a probability and the decision regarding control actions can be made based on the associated probability.

Comparison of Standards and Technical Requirements of Grid-Connected Wind Power Plants in China and the United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, David Wenzhong; Muljadi, Eduard; Tian, Tian

The rapid deployment of wind power has made grid integration and operational issues focal points in industry discussions and research. Compliance with grid connection standards for wind power plants (WPPs) is crucial to ensuring the reliable and stable operation of the electric power grid. This report compares the standards for grid-connected WPPs in China to those in the United States to facilitate further improvements in wind power standards and enhance the development of wind power equipment. Detailed analyses of power quality, low-voltage ride-through capability, active power control, reactive power control, voltage control, and wind power forecasting are provided to enhancemore » the understanding of grid codes in the two largest markets of wind power. This study compares WPP interconnection standards and technical requirements in China to those in the United States.« less

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Ying; Zhou, Zhi; Botterud, Audun

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixedmore » integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.« less

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

Weather Research and Forecasting model simulation of an onshore wind farm: assessment against LiDAR and SCADA data

NASA Astrophysics Data System (ADS)

Santoni, Christian; Garcia-Cartagena, Edgardo J.; Zhan, Lu; Iungo, Giacomo Valerio; Leonardi, Stefano

2017-11-01

The integration of wind farm parameterizations into numerical weather prediction models is essential to study power production under realistic conditions. Nevertheless, recent models are unable to capture turbine wake interactions and, consequently, the mean kinetic energy entrainment, which are essential for the development of power optimization models. To address the study of wind turbine wake interaction, one-way nested mesoscale to large-eddy simulation (LES) were performed using the Weather Research and Forecasting model (WRF). The simulation contains five nested domains modeling the mesoscale wind on the entire North Texas Panhandle region to the microscale wind fluctuations and turbine wakes of a wind farm located at Panhandle, Texas. The wind speed, direction and boundary layer profile obtained from WRF were compared against measurements obtained with a sonic anemometer and light detection and ranging system located within the wind farm. Additionally, the power production were assessed against measurements obtained from the supervisory control and data acquisition system located in each turbine. Furthermore, to incorporate the turbines into very coarse LES, a modification to the implementation of the wind farm parameterization by Fitch et al. (2012) is proposed. This work was supported by the NSF, Grants No. 1243482 (WINDINSPIRE) and IIP 1362033 (WindSTAR), and TACC.

Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit; Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, C.; Hodge, B. M.; Orwig, K.

2013-10-01

Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under high-penetration scenarios. The wind data sets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time synchronized with available load profiles. The Wind Integration National Dataset (WIND) Toolkit described in this paper fulfills these requirements. A wind resource dataset, wind power production time series, and simulated forecasts from a numerical weather predictionmore » model run on a nationwide 2-km grid at 5-min resolution will be made publicly available for more than 110,000 onshore and offshore wind power production sites.« less

A Comparison of Synoptic Classification Methods for Application to Wind Power Prediction

NASA Astrophysics Data System (ADS)

Fowler, P.; Basu, S.

2008-12-01

Wind energy is a highly variable resource. To make it competitive with other sources of energy for integration on the power grid, at the very least, a day-ahead forecast of power output must be available. In many grid operations worldwide, next-day power output is scheduled in 30 minute intervals and grid management routinely occurs at real time. Maintenance and repairs require costly time to complete and must be scheduled along with normal operations. Revenue is dependent on the reliability of the entire system. In other words, there is financial and managerial benefit to short-term prediction of wind power. One approach to short-term forecasting is to combine a data centric method such as an artificial neural network with a physically based approach like numerical weather prediction (NWP). The key is in associating high-dimensional NWP model output with the most appropriately trained neural network. Because neural networks perform the best in the situations they are designed for, one can hypothesize that if one can identify similar recurring states in historical weather data, this data can be used to train multiple custom designed neural networks to be used when called upon by numerical prediction. Identifying similar recurring states may offer insight to how a neural network forecast can be improved, but amassing the knowledge and utilizing it efficiently in the time required for power prediction would be difficult for a human to master, thus showing the advantage of classification. Classification methods are important tools for short-term forecasting because they can be unsupervised, objective, and computationally quick. They primarily involve categorizing data sets in to dominant weather classes, but there are numerous ways to define a class and a great variety in interpretation of the results. In the present study a collection of classification methods are used on a sampling of atmospheric variables from the North American Regional Reanalysis data set. The results will be discussed in relation to their use for short-term wind power forecasting by neural networks.

NREL Integrate: RCS-4-42326

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hudgins, Andrew P.; Waight, Jim; Grover, Shailendra

OMNETRIC Corp., Duke Energy, CPS Energy, and the University of Texas at San Antonio (UTSA) created a project team to execute the project 'OpenFMB Reference Architecture Demonstration.' The project included development and demonstration of concepts that will enable the electric utility grid to host larger penetrations of renewable resources. The project concept calls for the aggregation of renewable resources and loads into microgrids and the control of these microgrids with an implementation of the OpenFMB Reference Architecture. The production of power from the renewable resources that are appearing on the grid today is very closely linked to the weather. Themore » difficulty of forecasting the weather, which is well understood, leads to difficulty in forecasting the production of renewable resources. The current state of the art in forecasting the power production from renewables (solar PV and wind) are accuracies in the range of 12-25 percent NMAE. In contrast the demand for electricity aggregated to the system level, is easier to predict. The state of the art of demand forecasting done, 24 hours ahead, is about 2-3% MAPE. Forecasting the load to be supplied from conventional resources (demand minus generation from renewable resources) is thus very hard to forecast. This means that even a few hours before the time of consumption, there can be considerable uncertainty over what must be done to balance supply and demand. Adding to the problem of difficulty of forecasting, is the reality of the variability of the actual production of power from renewables. Due to the variability of wind speeds and solar insolation, the actual output of power from renewable resources can vary significantly over a short period of time. Gusts of winds result is variation of power output of wind turbines. The shadows of clouds moving over solar PV arrays result in the variation of power production of the array. This compounds the problem of balancing supply and demand in real time. Establishing a control system that can manage distribution systems with large penetrations of renewable resources is difficult due to two major issues: (1) the lack of standardization and interoperability between the vast array of equipment in operation and on the market, most of which use different and proprietary means of communication and (2) the magnitude of the network and the information it generates and consumes. The objective of this project is to provide the industry with a design concept and tools that will enable the electric power grid to overcome these barriers and support a larger penetration of clean energy from renewable resources.« less

Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, John L.; Maxwell, Reed M.; Monache, Luca Delle

2013-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.

High Quality Data for Grid Integration Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clifton, Andrew; Draxl, Caroline; Sengupta, Manajit

As variable renewable power penetration levels increase in power systems worldwide, renewable integration studies are crucial to ensure continued economic and reliable operation of the power grid. The existing electric grid infrastructure in the US in particular poses significant limitations on wind power expansion. In this presentation we will shed light on requirements for grid integration studies as far as wind and solar energy are concerned. Because wind and solar plants are strongly impacted by weather, high-resolution and high-quality weather data are required to drive power system simulations. Future data sets will have to push limits of numerical weather predictionmore » to yield these high-resolution data sets, and wind data will have to be time-synchronized with solar data. Current wind and solar integration data sets are presented. The Wind Integration National Dataset (WIND) Toolkit is the largest and most complete grid integration data set publicly available to date. A meteorological data set, wind power production time series, and simulated forecasts created using the Weather Research and Forecasting Model run on a 2-km grid over the continental United States at a 5-min resolution is now publicly available for more than 126,000 land-based and offshore wind power production sites. The National Solar Radiation Database (NSRDB) is a similar high temporal- and spatial resolution database of 18 years of solar resource data for North America and India. The need for high-resolution weather data pushes modeling towards finer scales and closer synchronization. We also present how we anticipate such datasets developing in the future, their benefits, and the challenges with using and disseminating such large amounts of data.« less

On the skill of various ensemble spread estimators for probabilistic short range wind forecasting

NASA Astrophysics Data System (ADS)

Kann, A.

2012-05-01

A variety of applications ranging from civil protection associated with severe weather to economical interests are heavily dependent on meteorological information. For example, a precise planning of the energy supply with a high share of renewables requires detailed meteorological information on high temporal and spatial resolution. With respect to wind power, detailed analyses and forecasts of wind speed are of crucial interest for the energy management. Although the applicability and the current skill of state-of-the-art probabilistic short range forecasts has increased during the last years, ensemble systems still show systematic deficiencies which limit its practical use. This paper presents methods to improve the ensemble skill of 10-m wind speed forecasts by combining deterministic information from a nowcasting system on very high horizontal resolution with uncertainty estimates from a limited area ensemble system. It is shown for a one month validation period that a statistical post-processing procedure (a modified non-homogeneous Gaussian regression) adds further skill to the probabilistic forecasts, especially beyond the nowcasting range after +6 h.

Wind-Driven Ecological Flow Regimes Downstream from Hydropower Dams

NASA Astrophysics Data System (ADS)

Kern, J.; Characklis, G. W.

2012-12-01

Conventional hydropower can be turned on and off quicker and less expensively than thermal generation (coal, nuclear, or natural gas). These advantages enable hydropower utilities to respond to rapid fluctuations in energy supply and demand. More recently, a growing renewable energy sector has underlined the need for flexible generation capacity that can complement intermittent renewable resources such as wind power. While wind power entails lower variable costs than other types of generation, incorporating it into electric power systems can be problematic. Due to variable and unpredictable wind speeds, wind power is difficult to schedule and must be used when available. As a result, integrating large amounts of wind power into the grid may result in atypical, swiftly changing demand patterns for other forms of generation, placing a premium on sources that can be rapidly ramped up and down. Moreover, uncertainty in wind power forecasts will stipulate increased levels of 'reserve' generation capacity that can respond quickly if real-time wind supply is less than expected. These changes could create new hourly price dynamics for energy and reserves, altering the short-term financial signals that hydroelectric dam operators use to schedule water releases. Traditionally, hourly stream flow patterns below hydropower dams have corresponded in a very predictable manner to electricity demand, whose primary factors are weather (hourly temperature) and economic activity (workday hours). Wind power integration has the potential to yield more variable, less predictable flows at hydro dams, flows that at times could resemble reciprocal wind patterns. An existing body of research explores the impacts of standard, demand-following hydroelectric dams on downstream ecological flows; but weighing the benefits of increased reliance on wind power against further impacts to ecological flows may be a novel challenge for the environmental community. As a preliminary step in meeting this challenge, the following study was designed to investigate the potential for wind power integration to alter riparian flow regimes below hydroelectric dams. A hydrological model of a three-dam cascade in the Roanoke River basin (Virginia, USA) is interfaced with a simulated electricity market (i.e. a unit commitment problem) representing the Dominion Zone of PJM Interconnection. Incorporating forecasts of electricity demand, hydro capacity and wind availability, a mixed-integer optimization program minimizes the system cost of meeting hourly demand and reserve requirements by means of a diverse generation portfolio (e.g. nuclear, fossil, hydro, and biomass). A secondary 'balancing' energy market is executed if real-time wind generation is less than the day-ahead forecast, calling upon reserved generation resources to meet the supply shortfall. Hydropower release schedules are determined across a range of wind development scenarios (varying wind's fraction of total installed generating capacity, as well as its geographical source region). Flow regimes for each wind development scenario are compared against both historical and simulated flows under current operations (negligible wind power), as well as simulated natural flows (dam removal), in terms of ecologically relevant flow metrics. Results quantify the ability of wind power development to alter within-week stream flows downstream from hydropower dams.

Evaluation of the performance of a meso-scale NWP model to forecast solar irradiance on Reunion Island for photovoltaic power applications

NASA Astrophysics Data System (ADS)

Kalecinski, Natacha; Haeffelin, Martial; Badosa, Jordi; Periard, Christophe

2013-04-01

Solar photovoltaic power is a predominant source of electrical power on Reunion Island, regularly providing near 30% of electrical power demand for a few hours per day. However solar power on Reunion Island is strongly modulated by clouds in small temporal and spatial scales. Today regional regulations require that new solar photovoltaic plants be combined with storage systems to reduce electrical power fluctuations on the grid. Hence cloud and solar irradiance forecasting becomes an important tool to help optimize the operation of new solar photovoltaic plants on Reunion Island. Reunion Island, located in the South West of the Indian Ocean, is exposed to persistent trade winds, most of all in winter. In summer, the southward motion of the ITCZ brings atmospheric instabilities on the island and weakens trade winds. This context together with the complex topography of Reunion Island, which is about 60 km wide, with two high summits (3070 and 2512 m) connected by a 1500 m plateau, makes cloudiness very heterogeneous. High cloudiness variability is found between mountain and coastal areas and between the windward, leeward and lateral regions defined with respect to the synoptic wind direction. A detailed study of local dynamics variability is necessary to better understand cloud life cycles around the island. In the presented work, our approach to explore the short-term solar irradiance forecast at local scales is to use the deterministic output from a meso-scale numerical weather prediction (NWP) model, AROME, developed by Meteo France. To start we evaluate the performance of the deterministic forecast from AROME by using meteorological measurements from 21 meteorological ground stations widely spread around the island (and with altitudes from 8 to 2245 m). Ground measurements include solar irradiation, wind speed and direction, relative humidity, air temperature, precipitation and pressure. Secondly we study in the model the local dynamics and thermodynamics that control cloud development and solar irradiance in order to define new predictors to improve probabilistic forecast of solar irradiance.

Economic Benefits, Carbon Dioxide (CO2) Emissions Reductions, and Water Conservation Benefits from 1,000 Megawatts (MW) of New Wind Power in Maine (Fact Sheet)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

2008-10-01

The U.S. Department of Energy?s Wind Powering America Program is committed to educating state-level policymakers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Maine. Although construction and operation of 1000 MW of wind power is a significant effort, six states have already reached the 1000-MW mark. We forecast the cumulative economic benefits from 1000 MW of development in Maine to be $1.3 billion, annual CO2 reductions are estimated at 2.8 million tons, and annual water savings are 1,387 million gallons.

Economic Benefits, Carbon Dioxide (CO2) Emissions Reductions, and Water Conservation Benefits from 1,000 Megawatts (MW) of New Wind Power in Wisconsin (Fact Sheet)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

2008-10-01

The U.S. Department of Energy?s Wind Powering America Program is committed to educating state-level policymakers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Wisconsin. Although construction and operation of 1000 MW of wind power is a significant effort, six states have already reached the 1000-MW mark. We forecast the cumulative economic benefits from 1000 MW of development in Wisconsin to be $1.1 billion, annual CO2 reductions are estimated at 3.2 million tons, and annual water savings are 1,476 million gallons.

Opportunities for ice storage to provide ancillary services to power grids incorporating wind turbine generation

NASA Astrophysics Data System (ADS)

Finley, Christopher

Power generation using wind turbines increases the electrical system balancing, regulation and ramp rate requirements due to the minute to minute variability in wind speed and the difficulty in accurately forecasting wind speeds. The addition of thermal energy storage, such as ice storage, to a building's space cooling equipment increases the operational flexibility of the equipment by allowing the owner to choose when the chiller is run. The ability of the building owner to increase the power demand from the chiller (e.g. make ice) or to decrease the power demand (e.g. melt ice) to provide electrical system ancillary services was evaluated.

ANEMOS: Development of a next generation wind power forecasting system for the large-scale integration of onshore and offshore wind farms.

NASA Astrophysics Data System (ADS)

Kariniotakis, G.; Anemos Team

2003-04-01

Objectives: Accurate forecasting of the wind energy production up to two days ahead is recognized as a major contribution for reliable large-scale wind power integration. Especially, in a liberalized electricity market, prediction tools enhance the position of wind energy compared to other forms of dispatchable generation. ANEMOS, is a new 3.5 years R&D project supported by the European Commission, that resembles research organizations and end-users with an important experience on the domain. The project aims to develop advanced forecasting models that will substantially outperform current methods. Emphasis is given to situations like complex terrain, extreme weather conditions, as well as to offshore prediction for which no specific tools currently exist. The prediction models will be implemented in a software platform and installed for online operation at onshore and offshore wind farms by the end-users participating in the project. Approach: The paper presents the methodology of the project. Initially, the prediction requirements are identified according to the profiles of the end-users. The project develops prediction models based on both a physical and an alternative statistical approach. Research on physical models gives emphasis to techniques for use in complex terrain and the development of prediction tools based on CFD techniques, advanced model output statistics or high-resolution meteorological information. Statistical models (i.e. based on artificial intelligence) are developed for downscaling, power curve representation, upscaling for prediction at regional or national level, etc. A benchmarking process is set-up to evaluate the performance of the developed models and to compare them with existing ones using a number of case studies. The synergy between statistical and physical approaches is examined to identify promising areas for further improvement of forecasting accuracy. Appropriate physical and statistical prediction models are also developed for offshore wind farms taking into account advances in marine meteorology (interaction between wind and waves, coastal effects). The benefits from the use of satellite radar images for modeling local weather patterns are investigated. A next generation forecasting software, ANEMOS, will be developed to integrate the various models. The tool is enhanced by advanced Information Communication Technology (ICT) functionality and can operate both in stand alone, or remote mode, or be interfaced with standard Energy or Distribution Management Systems (EMS/DMS) systems. Contribution: The project provides an advanced technology for wind resource forecasting applicable in a large scale: at a single wind farm, regional or national level and for both interconnected and island systems. A major milestone is the on-line operation of the developed software by the participating utilities for onshore and offshore wind farms and the demonstration of the economic benefits. The outcome of the ANEMOS project will help consistently the increase of wind integration in two levels; in an operational level due to better management of wind farms, but also, it will contribute to increasing the installed capacity of wind farms. This is because accurate prediction of the resource reduces the risk of wind farm developers, who are then more willing to undertake new wind farm installations especially in a liberalized electricity market environment.

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-09-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and windmore » forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.« less

WIRE: Weather Intelligence for Renewable Energies

NASA Astrophysics Data System (ADS)

Heimo, A.; Cattin, R.; Calpini, B.

2010-09-01

Renewable energies such as wind and solar energy will play an important, even decisive role in order to mitigate and adapt to the projected dramatic consequences to our society and environment due to climate change. Due to shrinking fossil resources, the transition to more and more renewable energy shares is unavoidable. But, as wind and solar energy are strongly dependent on highly variable weather processes, increased penetration rates will also lead to strong fluctuations in the electricity grid which need to be balanced. Proper and specific forecasting of ‘energy weather' is a key component for this. Therefore, it is today appropriate to scientifically address the requirements to provide the best possible specific weather information for forecasting the energy production of wind and solar power plants within the next minutes up to several days. Towards such aims, Weather Intelligence will first include developing dedicated post-processing algorithms coupled with weather prediction models and with past and/or online measurement data especially remote sensing observations. Second, it will contribute to investigate the difficult relationship between the highly intermittent weather dependent power production and concurrent capacities such as transport and distribution of this energy to the end users. Selecting, resp. developing surface-based and satellite remote sensing techniques well adapted to supply relevant information to the specific post-processing algorithms for solar and wind energy production short-term forecasts is a major task with big potential. It will lead to improved energy forecasts and help to increase the efficiency of the renewable energy productions while contributing to improve the management and presumably the design of the energy grids. The second goal will raise new challenges as this will require first from the energy producers and distributors definitions of the requested input data and new technologies dedicated to the management of power plants and electricity grids and second from the meteorological measurement community to deliver suitable, short term high quality forecasts to fulfill these requests with emphasis on highly variable weather conditions and spatially distributed energy productions often located in complex terrain. This topic has been submitted for a new COST Action under the title "Short-Term High Resolution Wind and Solar Energy Production Forecasts".

System-wide emissions implications of increased wind power penetration.

PubMed

Valentino, Lauren; Valenzuela, Viviana; Botterud, Audun; Zhou, Zhi; Conzelmann, Guenter

2012-04-03

This paper discusses the environmental effects of incorporating wind energy into the electric power system. We present a detailed emissions analysis based on comprehensive modeling of power system operations with unit commitment and economic dispatch for different wind penetration levels. First, by minimizing cost, the unit commitment model decides which thermal power plants will be utilized based on a wind power forecast, and then, the economic dispatch model dictates the level of production for each unit as a function of the realized wind power generation. Finally, knowing the power production from each power plant, the emissions are calculated. The emissions model incorporates the effects of both cycling and start-ups of thermal power plants in analyzing emissions from an electric power system with increasing levels of wind power. Our results for the power system in the state of Illinois show significant emissions effects from increased cycling and particularly start-ups of thermal power plants. However, we conclude that as the wind power penetration increases, pollutant emissions decrease overall due to the replacement of fossil fuels.

Wind Resource Assessment of Gujarat (India)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, C.; Purkayastha, A.; Parker, Z.

India is one of the largest wind energy markets in the world. In 1986 Gujarat was the first Indian state to install a wind power project. In February 2013, the installed wind capacity in Gujarat was 3,093 MW. Due to the uncertainty around existing wind energy assessments in India, this analysis uses the Weather Research and Forecasting (WRF) model to simulate the wind at current hub heights for one year to provide more precise estimates of wind resources in Gujarat. The WRF model allows for accurate simulations of winds near the surface and at heights important for wind energy purposes.more » While previous resource assessments published wind power density, we focus on average wind speeds, which can be converted to wind power densities by the user with methods of their choice. The wind resource estimates in this study show regions with average annual wind speeds of more than 8 m/s.« less

Probabilistic Weather Information Tailored to the Needs of Transmission System Operators

NASA Astrophysics Data System (ADS)

Alberts, I.; Stauch, V.; Lee, D.; Hagedorn, R.

2014-12-01

Reliable and accurate forecasts for wind and photovoltaic (PV) power production are essential for stable transmission systems. A high potential for improving the wind and PV power forecasts lies in optimizing the weather forecasts, since these energy sources are highly weather dependent. For this reason the main objective of the German research project EWeLiNE is to improve the quality the underlying numerical weather predictions towards energy operations. In this project, the German Meteorological Service (DWD), the Fraunhofer Institute for Wind Energy and Energy System Technology, and three of the German transmission system operators (TSOs) are working together to improve the weather and power forecasts. Probabilistic predictions are of particular interest, as the quantification of uncertainties provides an important tool for risk management. Theoretical considerations suggest that it can be advantageous to use probabilistic information to represent and respond to the remaining uncertainties in the forecasts. However, it remains a challenge to integrate this information into the decision making processes related to market participation and power systems operations. The project is planned and carried out in close cooperation with the involved TSOs in order to ensure the usability of the products developed. It will conclude with a demonstration phase, in which the improved models and newly developed products are combined into a process chain and used to provide information to TSOs in a real-time decision support tool. The use of a web-based development platform enables short development cycles and agile adaptation to evolving user needs. This contribution will present the EWeLiNE project and discuss ideas on how to incorporate probabilistic information into the users' current decision making processes.

Wind power prediction based on genetic neural network

NASA Astrophysics Data System (ADS)

Zhang, Suhan

2017-04-01

The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Gallego, C.; Costa, A.; Cuerva, A.

2010-09-01

Since nowadays wind energy can't be neither scheduled nor large-scale storaged, wind power forecasting has been useful to minimize the impact of wind fluctuations. In particular, short-term forecasting (characterised by prediction horizons from minutes to a few days) is currently required by energy producers (in a daily electricity market context) and the TSO's (in order to keep the stability/balance of an electrical system). Within the short-term background, time-series based models (i.e., statistical models) have shown a better performance than NWP models for horizons up to few hours. These models try to learn and replicate the dynamic shown by the time series of a certain variable. When considering the power output of wind farms, ramp events are usually observed, being characterized by a large positive gradient in the time series (ramp-up) or negative (ramp-down) during relatively short time periods (few hours). Ramp events may be motivated by many different causes, involving generally several spatial scales, since the large scale (fronts, low pressure systems) up to the local scale (wind turbine shut-down due to high wind speed, yaw misalignment due to fast changes of wind direction). Hence, the output power may show unexpected dynamics during ramp events depending on the underlying processes; consequently, traditional statistical models considering only one dynamic for the hole power time series may be inappropriate. This work proposes a Regime Switching (RS) model based on Artificial Neural Nets (ANN). The RS-ANN model gathers as many ANN's as different dynamics considered (called regimes); a certain ANN is selected so as to predict the output power, depending on the current regime. The current regime is on-line updated based on a gradient criteria, regarding the past two values of the output power. 3 Regimes are established, concerning ramp events: ramp-up, ramp-down and no-ramp regime. In order to assess the skillness of the proposed RS-ANN model, a single-ANN model (without regime classification) is adopted as a reference model. Both models are evaluated in terms of Improvement over Persistence on the Mean Square Error basis (IoP%) when predicting horizons form 1 time-step to 5. The case of a wind farm located in the complex terrain of Alaiz (north of Spain) has been considered. Three years of available power output data with a hourly resolution have been employed: two years for training and validation of the model and the last year for assessing the accuracy. Results showed that the RS-ANN overcame the single-ANN model for one step-ahead forecasts: the overall IoP% was up to 8.66% for the RS-ANN model (depending on the gradient criterion selected to consider the ramp regime triggered) and 6.16% for the single-ANN. However, both models showed similar accuracy for larger horizons. A locally-weighted evaluation during ramp events for one-step ahead was also performed. It was found that the IoP% during ramps-up increased from 17.60% (case of single-ANN) to 22.25% (case of RS-ANN); however, during the ramps-down events this improvement increased from 18.55% to 19.55%. Three main conclusions are derived from this case study: It highlights the importance of considering statistical models capable of differentiate several regimes showed by the output power time series in order to improve the forecasting during extreme events like ramps. On-line regime classification based on available power output data didn't seem to contribute to improve forecasts for horizons beyond one-step ahead. Tacking into account other explanatory variables (local wind measurements, NWP outputs) could lead to a better understanding of ramp events, improving the regime assessment also for further horizons. The RS-ANN model slightly overcame the single-ANN during ramp-down events. If further research reinforce this effect, special attention should be addressed to understand the underlying processes during ramp-down events.

High-quality weather data for grid integration studies

NASA Astrophysics Data System (ADS)

Draxl, C.

2016-12-01

As variable renewable power penetration levels increase in power systems worldwide, renewable integration studies are crucial to ensure continued economic and reliable operation of the power grid. In this talk we will shed light on requirements for grid integration studies as far as wind and solar energy are concerned. Because wind and solar plants are strongly impacted by weather, high-resolution and high-quality weather data are required to drive power system simulations. Future data sets will have to push limits of numerical weather prediction to yield these high-resolution data sets, and wind data will have to be time-synchronized with solar data. Current wind and solar integration data sets will be presented. The Wind Integration National Dataset (WIND) Toolkit is the largest and most complete grid integration data set publicly available to date. A meteorological data set, wind power production time series, and simulated forecasts created using the Weather Research and Forecasting Model run on a 2-km grid over the continental United States at a 5-min resolution is now publicly available for more than 126,000 land-based and offshore wind power production sites. The Solar Integration National Dataset (SIND) is available as time synchronized with the WIND Toolkit, and will allow for combined wind-solar grid integration studies. The National Solar Radiation Database (NSRDB) is a similar high temporal- and spatial resolution database of 18 years of solar resource data for North America and India. Grid integration studies are also carried out in various countries, which aim at increasing their wind and solar penetration through combined wind and solar integration data sets. We will present a multi-year effort to directly support India's 24x7 energy access goal through a suite of activities aimed at enabling large-scale deployment of clean energy and energy efficiency. Another current effort is the North-American-Renewable-Integration-Study, with the aim of providing a seamless data set across borders for a whole continent, to simulate and analyze the impacts of potential future large wind and solar power penetrations on bulk power system operations.

Wind Power Forecasting Error Distributions over Multiple Timescales: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Milligan, M.

2011-03-01

In this paper, we examine the shape of the persistence model error distribution for ten different wind plants in the ERCOT system over multiple timescales. Comparisons are made between the experimental distribution shape and that of the normal distribution.

Ensemble Data Assimilation of Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2017-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Irradiation forecasts from NWP systems are however subject to several sources of error. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, absorption of condensed water or aerosol optical depths are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence forecast errors are reduced. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly by means of remote sensing such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. Numerous PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation through their power measurements. Forecast accuracy may thus be enhanced by extending the observations in the assimilation by this new source of information. PV power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Since these data are not limited to the vertical column above or below the detector, it may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the used DA technique (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first results are presented and discussed.

Forecasting Global Horizontal Irradiance Using the LETKF and a Combination of Advected Satellite Images and Sparse Ground Sensors

NASA Astrophysics Data System (ADS)

Harty, T. M.; Lorenzo, A.; Holmgren, W.; Morzfeld, M.

2017-12-01

The irradiance incident on a solar panel is the main factor in determining the power output of that panel. For this reason, accurate global horizontal irradiance (GHI) estimates and forecasts are critical when determining the optimal location for a solar power plant, forecasting utility scale solar power production, or forecasting distributed, behind the meter rooftop solar power production. Satellite images provide a basis for producing the GHI estimates needed to undertake these objectives. The focus of this work is to combine satellite derived GHI estimates with ground sensor measurements and an advection model. The idea is to use accurate but sparsely distributed ground sensors to improve satellite derived GHI estimates which can cover large areas (the size of a city or a region of the United States). We use a Bayesian framework to perform the data assimilation, which enables us to produce irradiance forecasts and associated uncertainties which incorporate both satellite and ground sensor data. Within this framework, we utilize satellite images taken from the GOES-15 geostationary satellite (available every 15-30 minutes) as well as ground data taken from irradiance sensors and rooftop solar arrays (available every 5 minutes). The advection model, driven by wind forecasts from a numerical weather model, simulates cloud motion between measurements. We use the Local Ensemble Transform Kalman Filter (LETKF) to perform the data assimilation. We present preliminary results towards making such a system useful in an operational context. We explain how localization and inflation in the LETKF, perturbations of wind-fields, and random perturbations of the advection model, affect the accuracy of our estimates and forecasts. We present experiments showing the accuracy of our forecasted GHI over forecast-horizons of 15 mins to 1 hr. The limitations of our approach and future improvements are also discussed.

Darrieus wind-turbine and pump performance for low-lift irrigation pumping

NASA Astrophysics Data System (ADS)

Hagen, L. J.; Sharif, M.

1981-10-01

In the Great Plains about 15 percent of the irrigation water pumped on farms comes from surface water sources; for the United States as a whole, the figure is about 22 percent. Because of forecast fuel shortages, there is a need to develop alternative energy sources such as wind power for surface water pumping. Specific objectives of this investigation were to: design and assemble a prototype wind powered pumping system for low lift irrigation pumping; determine performance of the prototype system; design and test an irrigation system using the wind powered prototype in a design and test an farm application; and determine the size combinations of wind turbines, tailwater pits, and temporary storage reservoirs needed for successful farm application of wind powered tailwater pumping systems in western Kansas. The power source selected was a two bladed, 6 m diameter, 9 m tall Darrieus vertical axis wind turbine with 0.10 solidity and 36.1 M(2) swept area.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zack, J; Natenberg, E J; Knowe, G V

The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region, which encompasses the Bonneville Power Administration (BPA) wind generation area (Figure 1) that includes the Klondike, Stateline, and Hopkins Ridge wind plants. There are two tasks in the current project effort designed to validate themore » Ensemble Sensitivity Analysis (ESA) observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach. The results of this task are presented in a separate report. (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. This report presents the results of the OSSE task. The specific objective is to test strategies for future deployment of observing systems in order to suggest the best and most efficient ways to improve wind forecasting at BPA wind farm locations. OSSEs have been used for many years in meteorology to evaluate the potential impact of proposed observing systems, determine tradeoffs in instrument design, and study the most effective data assimilation methodologies to incorporate the new observations into numerical weather prediction (NWP) models (Atlas 1997; Lord 1997). For this project, a series of OSSEs will allow consideration of the impact of new observing systems of various types and in various locations.« less

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, WanYin; Zhang, Jie; Florita, Anthony

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance,more » cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.« less

Improvement of short-term numerical wind predictions

NASA Astrophysics Data System (ADS)

Bedard, Joel

Geophysic Model Output Statistics (GMOS) are developed to optimize the use of NWP for complex sites. GMOS differs from other MOS that are widely used by meteorological centers in the following aspects: it takes into account the surrounding geophysical parameters such as surface roughness, terrain height, etc., along with wind direction; it can be directly applied without any training, although training will further improve the results. The GMOS was applied to improve the Environment Canada GEM-LAM 2.5km forecasts at North Cape (PEI, Canada): It improves the predictions RMSE by 25-30% for all time horizons and almost all meteorological conditions; the topographic signature of the forecast error due to insufficient grid refinement is eliminated and the NWP combined with GMOS outperform the persistence from a 2h horizon, instead of 4h without GMOS. Finally, GMOS was applied at another site (Bouctouche, NB, Canada): similar improvements were observed, thus showing its general applicability. Keywords: wind energy, wind power forecast, numerical weather prediction, complex sites, model output statistics

The useful potential of using existing data to uniquely identify predictable wind events and regimes, part 1

NASA Technical Reports Server (NTRS)

Trettel, D. W.; Aquino, J. T.; Piazza, T. R.; Taylor, L. E.; Trask, D. C.

1982-01-01

Correlations between standard meteorological data and wind power generation potential were developed. Combined with appropriate wind forecasts, these correlations can be useful to load dispatchers to supplement conventional energy sources. Hourly wind data were analyzed for four sites, each exhibiting a unique physiography. These sites are Amarillo, Texas; Ludington, Michigan; Montauk Point, New York; and San Gorgonio, California. Synoptic weather maps and tables are presented to illustrate various wind 'regimes' at these sites.

Low Probability Tail Event Analysis and Mitigation in BPA Control Area: Task 2 Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Shuai; Makarov, Yuri V.; McKinstry, Craig A.

Task report detailing low probability tail event analysis and mitigation in BPA control area. Tail event refers to the situation in a power system when unfavorable forecast errors of load and wind are superposed onto fast load and wind ramps, or non-wind generators falling short of scheduled output, causing the imbalance between generation and load to become very significant.

A High Resolution Tropical Cyclone Power Outage Forecasting Model for the Continental United States

NASA Astrophysics Data System (ADS)

Pino, J. V.; Quiring, S. M.; Guikema, S.; Shashaani, S.; Linger, S.; Backhaus, S.

2017-12-01

Tropical cyclones cause extensive damage to the power infrastructure system throughout the United States. This damage can leave millions without power for extended periods of time, as most recently seen with Hurricane Matthew (2016). Accurate and timely prediction of power outages are essential for utility companies, emergency management agencies, and governmental organizations. Here we present a high-resolution (250 m x 250 m) hurricane power outage model for the United States. The model uses only publicly-available data to make predictions. It uses forecasts of storm variables such as maximum 3-second wind gust, duration of strong winds > 20 m s-2, soil moisture, and precipitation. It also incorporates static environmental variables such as elevation characteristics, land cover type, population density, tree species data, and root zone depth. A web tool was established for use by the Department of Energy (DOE) so that the model can be used for real-time outage forecasting or for synthetic tropical cyclones as an exercise in emergency management. This web tool provides DOE decision-makers with high impact analytic results and products that can be disseminated to federal, local, and state agencies. The results then aid utility companies in their pre- and post-storm activities, thus decreasing restoration times and lowering costs.

A novel application of artificial neural network for wind speed estimation

NASA Astrophysics Data System (ADS)

Fang, Da; Wang, Jianzhou

2017-05-01

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

Multi-Temporal Decomposed Wind and Load Power Models for Electric Energy Systems

NASA Astrophysics Data System (ADS)

Abdel-Karim, Noha

This thesis is motivated by the recognition that sources of uncertainties in electric power systems are multifold and may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, however, the uncertain price of electricity and hard-to-predict power produced by renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. The near-real-time power imbalances are compensated by means of frequency regulation and generally require fast-responding costly resources. Because of this, a more accurate forecast and look-ahead scheduling would result in a reduced need for expensive power balancing. Similarly, long-term planning and seasonal maintenance need to take into account long-term demand forecast as well as how the short-term generation scheduling is done. The better the demand forecast, the more efficient planning will be as well. Moreover, computer algorithms for scheduling and planning are essential in helping the system operators decide what to schedule and planners what to build. This is needed given the overall complexity created by different abilities to adjust the power output of generation technologies, demand uncertainties and by the network delivery constraints. Given the growing presence of major uncertainties, it is likely that the main control applications will use more probabilistic approaches. Today's predominantly deterministic methods will be replaced by methods which account for key uncertainties as decisions are made. It is well-understood that although demand and wind power cannot be predicted at very high accuracy, taking into consideration predictions and scheduling in a look-ahead way over several time horizons generally results in more efficient and reliable utilization, than when decisions are made assuming deterministic, often worst-case scenarios. This change is in approach is going to ultimately require new electricity market rules capable of providing the right incentives to manage uncertainties and of differentiating various technologies according to the rate at which they can respond to ever changing conditions. Given the overall need for modeling uncertainties in electric energy systems, we consider in this thesis the problem of multi-temporal modeling of wind and demand power, in particular. Historic data is used to derive prediction models for several future time horizons. Short-term prediction models derived can be used for look-ahead economic dispatch and unit commitment, while the long-term annual predictive models can be used for investment planning. As expected, the accuracy of such predictive models depends on the time horizons over which the predictions are made, as well as on the nature of uncertain signals. It is shown that predictive models obtained using the same general modeling approaches result in different accuracy for wind than for demand power. In what follows, we introduce several models which have qualitatively different patterns, ranging from hourly to annual. We first transform historic time-stamped data into the Fourier Transform (Fr) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions using extracted spectral techniques. The short-term results are interpreted next as a Linear Prediction Coding Model (LPC) and its accuracy is analyzed. Next, a new Markov-Based Sensitivity Model (MBSM) for short term prediction has been proposed and the dispatched costs of uncertainties for different predictive models with comparisons have been developed. Moreover, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. In addition, this thesis discusses operational impacts of wind power integration in different scenario levels by performing more than 9,000 AC Optimal Power Flow runs. The effects of both wind and load variations on system constraints and costs are presented. The limitations of DC Optimal Power Flow (DCOPF) vs. ACOPF are emphasized by means of system convergence problems due to the effect of wind power on changing line flows and net power injections. By studying the effect of having wind power on line flows, we found that the divergence problem applies in areas with high wind and hydro generation capacity share (cheap generations). (Abstract shortened by UMI.).

Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2013-01-01

Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of themore » day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.« less

Peak Wind Forecasts for the Launch-Critical Wind Towers on Kennedy Space Center/Cape Canaveral Air Force Station, Phase IV

NASA Technical Reports Server (NTRS)

Crawford, Winifred

2011-01-01

This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds arc an important forecast clement for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to update the statistics in the current peak-wind forecast tool to assist in forecasting LCC violations. The tool includes onshore and offshore flow climatologies of the 5-minute mean and peak winds and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Towards a More Accurate Solar Power Forecast By Improving NWP Model Physics

NASA Astrophysics Data System (ADS)

Köhler, C.; Lee, D.; Steiner, A.; Ritter, B.

2014-12-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the uncertainties associated with the large share of weather-dependent power sources. Precise power forecast, well-timed energy trading on the stock market, and electrical grid stability can be maintained. The research project EWeLiNE is a collaboration of the German Weather Service (DWD), the Fraunhofer Institute (IWES) and three German transmission system operators (TSOs). Together, wind and photovoltaic (PV) power forecasts shall be improved by combining optimized NWP and enhanced power forecast models. The conducted work focuses on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. Not only the representation of the model cloud characteristics, but also special events like Sahara dust over Germany and the solar eclipse in 2015 are treated and their effect on solar power accounted for. An overview of the EWeLiNE project and results of the ongoing research will be presented.

Statistical Short-Range Guidance for Peak Wind Speed Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase I Results

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.; Merceret, Francis J. (Technical Monitor)

2002-01-01

This report describes the results of the ANU's (Applied Meteorology Unit) Short-Range Statistical Forecasting task for peak winds. The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The Keith Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A 7 year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. In all climatologies, the average and peak wind speeds were highly variable in time. This indicated that the development of a peak wind forecasting tool would be difficult. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. The climatologies and PDFs provide tools with which to make peak wind forecasts that are critical to safe operations.

A Numerical Simulation (Study) of a Strong West Coast December 2014 Winter Storm

NASA Astrophysics Data System (ADS)

Smelser, I.; Xu, L.; Amerault, C. M.; Baker, N. L.; Satterfield, E.; Chua, B.

2016-12-01

From December 10 through December 13, 2014, a powerful winter storm swept across the western US coastal states bringing widespread power outages, numerous downed trees and power lines, heavy rains, flooding and even a tornado in the Los Angeles basin. This windstorm was the strongest since October 2009, and was similar to classic wind storms such as the 1962 Columbus Day Storm (Read, 2015).The storm started developing over the Pacific Ocean north of Hawaii on Nov. 30, and formed an atmospheric river that eventually stretched from Hawaii to the west coast. The storm initially hit the Pacific Northwest on Dec. 9th and then split. The highest precipitation amounts started in British Colombia and moved south along the coast. By the Dec. 11th, the highest precipitation amounts were near San Francisco (CA). The peak wind gust (14.4 ms-1) for Monterey (CA) occurred at 1116Z on Dec. 11th while the heaviest 6-hr precipitation (42.9 mm) occurred between 18Z on Dec. 11th to 00Z on Dec. 12th. By Dec. 12th, the storm was centered over Southern California.This storm was poorly forecast by many operational NWP models even 2-3 days in advance (Mass, 2014). The NCEP Global Forecast System (GFS) showed considerably variability between successive model runs, and significant differences existed between Environment Canada, UK Met Office and ECMWF model forecasts. To study this extreme weather event, we used the Navy global (NAVGEM) and mesoscale (COAMPS®) NWP models, and compared the resulting forecasts to observations, satellite imagery and ECMWF (TIGGE) forecasts. NAVGEM, with Hybrid 4DVar, was run with a resolution of 31 km, and generated the boundary conditions for COAMPS® 4DVar and forecasts, that were run with triple-nested grids of 27, 9, and 3 km. The MesoWest data from the University of Utah were used for forecast verification, and to locate the times of highest precipitation and wind speed for different points along the coast. Both the online API and the python module were used to access and pull information from the data base. Overall, both NAVGEM and COAMPS® predicted the storm well. NAVGEM predicted the storm to be slower and more powerful than the analyses. The NAVGEM analysis and corresponding 5-day forecast accumulated 6-hr precipitation (Fig. 1) for Dec. 12th at 00Z agree well with the observed precipitation (4.29 cm) for Monterey (KMRY).

Developing a Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Lambert, WInifred; Roeder, William

2007-01-01

This conference presentation describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations. The tool will include climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Statistical Short-Range Guidance for Peak Wind Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station, Phase III

NASA Technical Reports Server (NTRS)

Crawford, Winifred

2010-01-01

This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations.The tool includes climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

A Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Crawford, Winifred; Roeder, William

2008-01-01

This conference abstract describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violatioas.The tool will include climatologies of the 5-minute mean end peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Post-processing method for wind speed ensemble forecast using wind speed and direction

NASA Astrophysics Data System (ADS)

Sofie Eide, Siri; Bjørnar Bremnes, John; Steinsland, Ingelin

2017-04-01

Statistical methods are widely applied to enhance the quality of both deterministic and ensemble NWP forecasts. In many situations, like wind speed forecasting, most of the predictive information is contained in one variable in the NWP models. However, in statistical calibration of deterministic forecasts it is often seen that including more variables can further improve forecast skill. For ensembles this is rarely taken advantage of, mainly due to that it is generally not straightforward how to include multiple variables. In this study, it is demonstrated how multiple variables can be included in Bayesian model averaging (BMA) by using a flexible regression method for estimating the conditional means. The method is applied to wind speed forecasting at 204 Norwegian stations based on wind speed and direction forecasts from the ECMWF ensemble system. At about 85 % of the sites the ensemble forecasts were improved in terms of CRPS by adding wind direction as predictor compared to only using wind speed. On average the improvements were about 5 %, but mainly for moderate to strong wind situations. For weak wind speeds adding wind direction had more or less neutral impact.

Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation

DOE PAGES

Siohansi, Ramteen

2010-05-01

One of the costs associated with integrating wind generation into a power system is the cost of redispatching the system in real-time due to day-ahead wind resource forecast errors. One possible way of reducing these redispatch costs is to introduce demand response in the form of real-time pricing (RTP), which could allow electricity demand to respond to actual real-time wind resource availability using price signals. A day-ahead unit commitment model with day-ahead wind forecasts and a real-time dispatch model with actual wind resource availability is used to estimate system operations in a high wind penetration scenario. System operations are comparedmore » to a perfect foresight benchmark, in which actual wind resource availability is known day-ahead. The results show that wind integration costs with fixed demands can be high, both due to real-time redispatch costs and lost load. It is demonstrated that introducing RTP can reduce redispatch costs and eliminate loss of load events. Finally, social surplus with wind generation and RTP is compared to a system with neither and the results demonstrate that introducing wind and RTP into a market can result in superadditive surplus gains.« less

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine.

PubMed

Hernandez, Wilmar; Méndez, Alfredo; Maldonado-Correa, Jorge L; Balleteros, Francisco

2016-12-07

Having an accurate model of the power curve of a wind turbine allows us to better monitor its operation and planning of storage capacity. Since wind speed and direction is of a highly stochastic nature, the forecasting of the power generated by the wind turbine is of the same nature as well. In this paper, a method for obtaining a robust confidence band containing the power curve of a wind turbine under test conditions is presented. Here, the confidence band is bound by two curves which are estimated using parametric statistical inference techniques. However, the observations that are used for carrying out the statistical analysis are obtained by using the binning method, and in each bin, the outliers are eliminated by using a censorship process based on robust statistical techniques. Then, the observations that are not outliers are divided into observation sets. Finally, both the power curve of the wind turbine and the two curves that define the robust confidence band are estimated using each of the previously mentioned observation sets.

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

PubMed Central

Hernandez, Wilmar; Méndez, Alfredo; Maldonado-Correa, Jorge L.; Balleteros, Francisco

2016-01-01

Having an accurate model of the power curve of a wind turbine allows us to better monitor its operation and planning of storage capacity. Since wind speed and direction is of a highly stochastic nature, the forecasting of the power generated by the wind turbine is of the same nature as well. In this paper, a method for obtaining a robust confidence band containing the power curve of a wind turbine under test conditions is presented. Here, the confidence band is bound by two curves which are estimated using parametric statistical inference techniques. However, the observations that are used for carrying out the statistical analysis are obtained by using the binning method, and in each bin, the outliers are eliminated by using a censorship process based on robust statistical techniques. Then, the observations that are not outliers are divided into observation sets. Finally, both the power curve of the wind turbine and the two curves that define the robust confidence band are estimated using each of the previously mentioned observation sets. PMID:27941604

Planning a Target Renewable Portfolio using Atmospheric Modeling and Stochastic Optimization

NASA Astrophysics Data System (ADS)

Hart, E.; Jacobson, M. Z.

2009-12-01

A number of organizations have suggested that an 80% reduction in carbon emissions by 2050 is a necessary step to mitigate climate change and that decarbonization of the electricity sector is a crucial component of any strategy to meet this target. Integration of large renewable and intermittent generators poses many new problems in power system planning. In this study, we attempt to determine an optimal portfolio of renewable resources to meet best the fluctuating California load while also meeting an 80% carbon emissions reduction requirement. A stochastic optimization scheme is proposed that is based on a simplified model of the California electricity grid. In this single-busbar power system model, the load is met with generation from wind, solar thermal, photovoltaic, hydroelectric, geothermal, and natural gas plants. Wind speeds and insolation are calculated using GATOR-GCMOM, a global-through-urban climate-weather-air pollution model. Fields were produced for California and Nevada at 21km SN by 14 km WE spatial resolution every 15 minutes for the year 2006. Load data for 2006 were obtained from the California ISO OASIS database. Maximum installed capacities for wind and solar thermal generation were determined using a GIS analysis of potential development sites throughout the state. The stochastic optimization scheme requires that power balance be achieved in a number of meteorological and load scenarios that deviate from the forecasted (or modeled) data. By adjusting the error distributions of the forecasts, the model describes how improvements in wind speed and insolation forecasting may affect the optimal renewable portfolio. Using a simple model, we describe the diversity, size, and sensitivities of a renewable portfolio that is best suited to the resources and needs of California and that contributes significantly to reduction of the state’s carbon emissions.

Robust optimization-based DC optimal power flow for managing wind generation uncertainty

NASA Astrophysics Data System (ADS)

Boonchuay, Chanwit; Tomsovic, Kevin; Li, Fangxing; Ongsakul, Weerakorn

2012-11-01

Integrating wind generation into the wider grid causes a number of challenges to traditional power system operation. Given the relatively large wind forecast errors, congestion management tools based on optimal power flow (OPF) need to be improved. In this paper, a robust optimization (RO)-based DCOPF is proposed to determine the optimal generation dispatch and locational marginal prices (LMPs) for a day-ahead competitive electricity market considering the risk of dispatch cost variation. The basic concept is to use the dispatch to hedge against the possibility of reduced or increased wind generation. The proposed RO-based DCOPF is compared with a stochastic non-linear programming (SNP) approach on a modified PJM 5-bus system. Primary test results show that the proposed DCOPF model can provide lower dispatch cost than the SNP approach.

ICE CONTROL - Towards optimizing wind energy production during icing events

NASA Astrophysics Data System (ADS)

Dorninger, Manfred; Strauss, Lukas; Serafin, Stefano; Beck, Alexander; Wittmann, Christoph; Weidle, Florian; Meier, Florian; Bourgeois, Saskia; Cattin, René; Burchhart, Thomas; Fink, Martin

2017-04-01

Forecasts of wind power production loss caused by icing weather conditions are produced by a chain of physical models. The model chain consists of a numerical weather prediction model, an icing model and a production loss model. Each element of the model chain is affected by significant uncertainty, which can be quantified using targeted observations and a probabilistic forecasting approach. In this contribution, we present preliminary results from the recently launched project ICE CONTROL, an Austrian research initiative on measurements, probabilistic forecasting, and verification of icing on wind turbine blades. ICE CONTROL includes an experimental field phase, consisting of measurement campaigns in a wind park in Rhineland-Palatinate, Germany, in the winters 2016/17 and 2017/18. Instruments deployed during the campaigns consist of a conventional icing detector on the turbine hub and newly devised ice sensors (eologix Sensor System) on the turbine blades, as well as meteorological sensors for wind, temperature, humidity, visibility, and precipitation type and spectra. Liquid water content and spectral characteristics of super-cooled water droplets are measured using a Fog Monitor FM-120. Three cameras document the icing conditions on the instruments and on the blades. Different modelling approaches are used to quantify the components of the model-chain uncertainties. The uncertainty related to the initial conditions of the weather prediction is evaluated using the existing global ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Furthermore, observation system experiments are conducted with the AROME model and its 3D-Var data assimilation to investigate the impact of additional observations (such as Mode-S aircraft data, SCADA data and MSG cloud mask initialization) on the numerical icing forecast. The uncertainty related to model formulation is estimated from multi-physics ensembles based on the Weather Research and Forecasting model (WRF) by perturbing parameters in the physical parameterization schemes. In addition, uncertainties of the icing model and of its adaptations to the rotating turbine blade are addressed. The model forecasts combined with the suite of instruments and their measurements make it possible to conduct a step-wise verification of all the components of the model chain - a novel aspect compared to similar ongoing and completed forecasting projects.

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station (CCAFS)

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

Peak wind speed is important element in 24-Hour and Weekly Planning Forecasts issued by 45th Weather Squadron (45 WS). Forecasts issued for planning operations at KSC/CCAFS. 45 WS wind advisories issued for wind gusts greater than or equal to 25 kt. 35 kt and 50 kt from surface to 300 ft. AMU developed cool-season (Oct - Apr) tool to help 45 WS forecast: daily peak wind speed, 5-minute average speed at time of peak wind, and probability peak speed greater than or equal to 25 kt, 35 kt, 50 kt. AMU tool also forecasts daily average wind speed from 30 ft to 60 ft. Phase I and II tools delivered as a Microsoft Excel graphical user interface (GUI). Phase II tool also delivered as Meteorological Interactive Data Display System (MIDDS) GUI. Phase I and II forecast methods were compared to climatology, 45 WS wind advisories and North American Mesoscale model (MesoNAM) forecasts in a verification data set.

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Dreher, Joseph G.; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry

2009-01-01

The peak winds near the surface are an important forecast element for space shuttle landings. As defined in the Flight Rules (FR), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings, and is required to issue surface average and 10-minute peak wind speed forecasts. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMU) developed a PC-based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC; Lambert 2003). However, the shuttle occasionally may land at Edwards Air Force Base (EAFB) in southern California when weather conditions at KSC in Florida are not acceptable, so SMG forecasters requested a similar tool be developed for EAFB.

Solving the Meteorological Challenges of Creating a Sustainable Energy System (Invited)

NASA Astrophysics Data System (ADS)

Marquis, M.

2010-12-01

Global energy demand is projected to double from 13 TW at the start of this century to 28 TW by the middle of the century. This translates into obtaining 1000 MW (1 GW, the amount produced by an average nuclear or coal power plant) of new energy every single day for the next 40 years. The U.S. Department of Energy has conducted three feasibility studies in the last two years identifying the costs, challenges, impacts, and benefits of generating large portions of the nation’s electricity from wind and solar energy, in the new two decades. The 20% Wind by 2030 report found that the nation could meet one-fifth of its electricity demand from wind energy by 2030. The second report, the Eastern Wind Integration and Transmission Study, considered similar costs, challenges, and benefits, but considered 20% wind energy in the Eastern Interconnect only, with a target date of 2024. The third report, the Western Wind and Solar Integration Study, considered the operational impact of up to 35% penetration of wind, photovoltaics (PVs) and, concentrating solar power (CSP) on the power system operated by the WestConnect group, with a target date of 2017. All three studies concluded that it is technically feasible to obtain these high penetration levels of renewable energy, but that increases in the balancing area cooperation or coordination, increased utilization of transmission and building of transmission in some cases, and improved weather forecasts are needed. Current energy systems were designed for dispatchable fuels, such as coal, natural gas and nuclear energy. Fitting weather-driven renewable energy into today's energy system is like fitting a square peg into a round hole. If society chooses to meet a significant portion of new energy demand from weather-driven renewable energy, such as wind and solar energy, a number of obstacles must be overcome. Some of these obstacles are meteorological and climatological issues that are amenable to scientific research. For variable renewable energy sources to reach high penetration levels, electric system operators and utilities need better atmo¬spheric observations, models, and forecasts. Current numerical weather prediction models have not been optimized to help the nation use renewable energy. Improved meteorological observations (e.g., wind turbine hub-height wind speeds, surface direct and diffuse solar radiation), as well as observations through a deeper layer of the atmosphere for assimilation into NWP models, are needed. Particularly urgent is the need for improved forecasts of ramp events. Longer-term predictions of renewable resources, on the seasonal to decadal scale, are also needed. Improved understanding of the variability and co-variability of wind and solar energy, as well as their correlations with large-scale climate drivers, would assist decision-makers in long-term planning. This talk with discuss the feasibility and benefits of developing enhanced weather forecasts and climate information specific to the needs of a growing renewable energy infrastructure.

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

Mid-Atlantic Offshore Wind Interconnection and Transmission (MAOWIT)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kempton, Willett

This project has carried out a detailed analysis to evaluate the pros and cons of offshore transmission, a possible method to decrease balance-of-system costs and permitting time identified in the DOE Office Wind Strategic Plan (DOE, 2011). It also addresses questions regarding the adequacy of existing transmission infrastructure and the ability of existing generating resources to provide the necessary Ancillary Services (A/S) support (spinning and contingency reserves) in the ISO territory. This project has completed the tasks identified in the proposal: 1. Evaluation of the offshore wind resource off PJM, then examination of offshore wind penetrations consistent with U.S. Departmentmore » of Energy’s (DOE) targets and with their assumed resource size (DOE, 2011). 2. Comparison of piecemeal radial connections to the Independent System Operator (ISO) with connections via a high-voltage direct current (HVDC) offshore network similar to a team partner. 3. High-resolution examination of power fluctuations at each node due to wind energy variability 4. Analysis of wind power production profiles over the Eastern offshore region of the regional ISO to assess the effectiveness of long-distance, North- South transmission for leveling offshore wind energy output 5. Analysis of how the third and fourth items affect the need for ISO grid upgrades, congestion management, and demand for Ancillary Services (A/S) 6. Analysis of actual historic 36-hr and 24-hr forecasts to solve the unit commitment problem and determine the optimal mix of generators given the need to respond to both wind variability and wind forecasting uncertainties.« less

Current and Future Opportunities for Wind Power in the Southeast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tinnesand, Heidi; Roberts, Owen; Lantz, Eric

This presentation discusses future wind opportunities in the Southeast including factors such as changes in wind turbine technology, historical innovation trends, and forecast demand growth among regions. The presentation covers the current status of wind in the United States at 80-m hub height and the near-future outlook with a hub height at 110 to 140 meters. Future cost reductions in 2030 and beyond are also explored. Heidi Tinnesand presented this information to a utility advisory group meeting in Charlotte, North Carolina, on October 5, 2016.

Low Probability Tail Event Analysis and Mitigation in BPA Control Area: Task One Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Shuai; Makarov, Yuri V.

This is a report for task one of the tail event analysis project for BPA. Tail event refers to the situation in a power system when unfavorable forecast errors of load and wind are superposed onto fast load and wind ramps, or non-wind generators falling short of scheduled output, the imbalance between generation and load becomes very significant. This type of events occurs infrequently and appears on the tails of the distribution of system power imbalance; therefore, is referred to as tail events. This report analyzes what happened during the Electric Reliability Council of Texas (ERCOT) reliability event on Februarymore » 26, 2008, which was widely reported because of the involvement of wind generation. The objective is to identify sources of the problem, solutions to it and potential improvements that can be made to the system. Lessons learned from the analysis include the following: (1) Large mismatch between generation and load can be caused by load forecast error, wind forecast error and generation scheduling control error on traditional generators, or a combination of all of the above; (2) The capability of system balancing resources should be evaluated both in capacity (MW) and in ramp rate (MW/min), and be procured accordingly to meet both requirements. The resources need to be able to cover a range corresponding to the variability of load and wind in the system, additional to other uncertainties; (3) Unexpected ramps caused by load and wind can both become the cause leading to serious issues; (4) A look-ahead tool evaluating system balancing requirement during real-time operations and comparing that with available system resources should be very helpful to system operators in predicting the forthcoming of similar events and planning ahead; and (5) Demand response (only load reduction in ERCOT event) can effectively reduce load-generation mismatch and terminate frequency deviation in an emergency situation.« less

Final Report for Project: Impacts of stratification and non-equilibrium winds and waves on hub-height winds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patton, Edward G.

This project used a combination of turbulence-resolving large-eddy simulations, single-column modeling (where turbulence is parameterized), and currently available observations to improve, assess, and develop a parameterization of the impact of non-equilibrium wave states and stratification on the buoy-observed winds to establish reliable wind data at the turbine hub-height level. Analysis of turbulence-resolving simulations and observations illuminates the non-linear coupling between the atmosphere and the undulating sea surface. This analysis guides modification of existing boundary layer parameterizations to include wave influences for upward extrapolation of surface-based observations through the turbine layer. Our surface roughness modifications account for the interaction between stratificationmore » and the effects of swell’s amplitude and wavelength as well as swell’s relative motion with respect to the mean wind direction. The single-column version of the open source Weather and Research Forecasting (WRF) model (Skamarock et al., 2008) serves as our platform to test our proposed planetary boundary layer parameterization modifications that account for wave effects on marine atmospheric boundary layer flows. WRF has been widely adopted for wind resource analysis and forecasting. The single column version is particularly suitable to development, analysis, and testing of new boundary layer parameterizations. We utilize WRF’s single-column version to verify and validate our proposed modifications to the Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization (Nakanishi and Niino, 2004). We explore the implications of our modifications for two-way coupling between WRF and wave models (e.g.,Wavewatch III). The newly implemented parameterization accounting for marine atmospheric boundary layer-wave coupling is then tested in three-dimensional WRF simulations at grid sizes near 1 km. These simulations identify the behavior of simulated winds at the wind plant scale. Overall project conclusions include; In the presence of fast-moving swell (significant wave height Hs = 6.4 m, and phase speed cp = 18 ms -1), the atmospheric boundary layer grows more rapidly when waves propagate opposite to the winds compared to when winds and waves are aligned. Pressure drag increases by nearly a factor of 2 relative to the turbulent stress for the extreme case where waves propagate at 180° compared to the pressure gradient forcing. Net wind speed reduces by nearly 15% at hub-height for the 180°-case compared to the 0°-case, and turbulence intensities increase by nearly a factor of 2. These impacts diminish with decreasing wave age; Stratification increases hub height wind speeds and increases the vertical shear of the mean wind across the rotor plane. Fortuitously, this stability-induced enhanced shear does not influence turbulence intensity at hub height, but does increase (decrease) turbulence intensity below (above) hub height. Increased stability also increases the wave-induced pressure stress by ~ 10%; Off the East Coast of the United States during Coupled Boundary Layers Air-Sea Transfer - Low Wind (CBLAST-Low), cases with short fetch include thin stable boundary layers with depths of only a few tens of meters. In the coastal zone, the relationship between the mean wind and the surface fiction velocity (u*(V )) is significantly related to wind direction for weak winds but is not systematically related to the air sea difference of virtual potential temperature, δθv; since waves generally propagate from the south at the Air-Sea Interaction Tower (ASIT) tower, these results suggest that under weak wind conditions waves likely influence surface stress more than stratification does; and Winds and waves are frequently misaligned in the coastal zone. Stability conditions persist for long duration. Over a four year period, the Forschungsplattformen in Nord- und Ostsee Nr. 1 (FINO1) tower (a site with long fetch) primarily experienced weakly-unstable conditions, while stability at the ASIT tower (with a larger influence of offshore winds) experiences a mix of both unstable and stable conditions, where the summer months are predominantly stable. Wind-wave misalignment likely explains the large scatter in observed non-dimensional surface roughness under swell-dominated conditions. Andreas et al.’s (2012) relationship between u* and the 10-m wind speed under predicts the increased u* produced by wave-induced pressure drag produced by misaligned winds and waves. Incorporating wave-state (speed and direction) influences in parameterizations improves predictive skill. In a broad sense, these results suggest that one needs information on winds, temperature, and wave state to upscale buoy measurements to hub-height and across the rotor plane. Our parameterization of wave-state influences on surface drag has been submitted for inclusion in the next publicly available release. In combination, our project elucidates the impacts of two important physical processes (non-equilibrium wind/waves and stratification) on the atmosphere within which offshore turbines operate. This knowledge should help guide and inform manufacturers making critical decisions surrounding design criteria of future turbines to be deployed in the coastal zone. Reductions in annually averaged hub height wind speed error using our new wave-state-aware surface layer parameterization are relatively modest. However since wind turbine power production depends on the wind speed cubed, the error in estimated power production is close to 5%; which is significant and can substantially impact wind resource assessment and decision making with regards to the viability of particular location for a wind plant location. For a single 30-hour forecast, significant reductions in wind speed prediction errors can yield substantially improved wind power forecast skill, thereby mitigating costs and/or increasing revenue through improved; forecasting for maintenance operations and planning; day-ahead forecasting for power trading and resource allocation; and short-term forecasting for dispatch and grid balancing.« less

Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance

DTIC Science & Technology

2015-08-19

industries to manage the service life of equipment, and also to detect precursors to the failure of components found in nuclear power plants, wind turbines ...detection methods for predictive maintenance 5a. CONTRACT NUMBER FA2386-14-1-4096 5b. GRANT NUMBER Grant 14IOA015 AOARD-144096 5c. PROGRAM ELEMENT...sensitive to changes related to abnormality. 15. SUBJECT TERMS predictive maintenance , predictive maintenance , forecasting 16

Spatial optimization of an ideal wind energy system as a response to the intermittency of renewable energies?

NASA Astrophysics Data System (ADS)

Lassonde, Sylvain; Boucher, Olivier; Breon, François-Marie; Tobin, Isabelle; Vautard, Robert

2016-04-01

The share of renewable energies in the mix of electricity production is increasing worldwide. This trend is driven by environmental and economic policies aiming at a reduction of greenhouse gas emissions and an improvement of energy security. It is expected to continue in the forthcoming years and decades. Electricity production from renewables is related to weather and climate factors such as the diurnal and seasonal cycles of sunlight and wind, but is also linked to variability on all time scales. The intermittency in the renewable electricity production (solar, wind power) could eventually hinder their future deployment. Intermittency is indeed a challenge as demand and supply of electricity need to be balanced at any time. This challenge can be addressed by the deployment of an overcapacity in power generation (from renewable and/or thermal sources), a large-scale energy storage system and/or improved management of the demand. The main goal of this study is to optimize a hypothetical renewable energy system at the French and European scales in order to investigate if spatial diversity of the production (here electricity from wind energy) could be a response to the intermittency. We use ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-interim meteorological reanalysis and meteorological fields from the Weather Research and Forecasts (WRF) model to estimate the potential for wind power generation. Electricity demand and production are provided by the French electricity network (RTE) at the scale of administrative regions for years 2013 and 2014. Firstly we will show how the simulated production of wind power compares against the measured production at the national and regional scale. Several modelling and bias correction methods of wind power production will be discussed. Secondly, we will present results from an optimization procedure that aims to minimize some measure of the intermittency of wind energy. For instance we estimate the optimal distribution between French regions (with or without cross-border inputs) that minimizes the impact of low-production periods computed in a running mean sense and its sensitivity to the period considered. We will also assess which meteorological situations are the most problematic over the 35-year ERA-interim climatology(1980-2015).

A large-eddy simulation based power estimation capability for wind farms over complex terrain

NASA Astrophysics Data System (ADS)

Senocak, I.; Sandusky, M.; Deleon, R.

2017-12-01

There has been an increasing interest in predicting wind fields over complex terrain at the micro-scale for resource assessment, turbine siting, and power forecasting. These capabilities are made possible by advancements in computational speed from a new generation of computing hardware, numerical methods and physics modelling. The micro-scale wind prediction model presented in this work is based on the large-eddy simulation paradigm with surface-stress parameterization. The complex terrain is represented using an immersed-boundary method that takes into account the parameterization of the surface stresses. Governing equations of incompressible fluid flow are solved using a projection method with second-order accurate schemes in space and time. We use actuator disk models with rotation to simulate the influence of turbines on the wind field. Data regarding power production from individual turbines are mostly restricted because of proprietary nature of the wind energy business. Most studies report percentage drop of power relative to power from the first row. There have been different approaches to predict power production. Some studies simply report available wind power in the upstream, some studies estimate power production using power curves available from turbine manufacturers, and some studies estimate power as torque multiplied by rotational speed. In the present work, we propose a black-box approach that considers a control volume around a turbine and estimate the power extracted from the turbine based on the conservation of energy principle. We applied our wind power prediction capability to wind farms over flat terrain such as the wind farm over Mower County, Minnesota and the Horns Rev offshore wind farm in Denmark. The results from these simulations are in good agreement with published data. We also estimate power production from a hypothetical wind farm in complex terrain region and identify potential zones suitable for wind power production.

Predicting Near-surface Winds with WindNinja for Wind Energy Applications

NASA Astrophysics Data System (ADS)

Wagenbrenner, N. S.; Forthofer, J.; Shannon, K.; Butler, B.

2016-12-01

WindNinja is a high-resolution diagnostic wind model widely used by operational wildland fire managers to predict how near-surface winds may influence fire behavior. Many of the features which have made WindNinja successful for wildland fire are also important for wind energy applications. Some of these features include flexible runtime options which allow the user to initialize the model with coarser scale weather model forecasts, sparse weather station observations, or a simple domain-average wind for what-if scenarios; built-in data fetchers for required model inputs, including gridded terrain and vegetation data and operational weather model forecasts; relatively fast runtimes on simple hardware; an extremely user-friendly interface; and a number of output format options, including KMZ files for viewing in Google Earth and GeoPDFs which can be viewed in a GIS. The recent addition of a conservation of mass and momentum solver based on OpenFOAM libraries further increases the utility of WindNinja to modelers in the wind energy sector interested not just in mean wind predictions, but also in turbulence metrics. Here we provide an evaluation of WindNinja forecasts based on (1) operational weather model forecasts and (2) weather station observations provided by the MesoWest API. We also compare the high-resolution WindNinja forecasts to the coarser operational weather model forecasts. For this work we will use the High Resolution Rapid Refresh (HRRR) model and the North American Mesoscale (NAM) model. Forecasts will be evaluated with data collected in the Birch Creek valley of eastern Idaho, USA between June-October 2013. Near-surface wind, turbulence data, and vertical wind and temperature profiles were collected at very high spatial resolution during this field campaign specifically for use in evaluating high-resolution wind models like WindNinja. This work demonstrates the ability of WindNinja to generate very high-resolution wind forecasts for wind energy applications and evaluates the forecasts produced by two different initialization methods with data collected in a broad valley surrounded by complex terrain.

14 CFR 121.201 - Nontransport category airplanes: En route limitations: One engine inoperative.

Code of Federal Regulations, 2011 CFR

2011-01-01

... concerned: (1) The reliability of wind and weather forecasting. (2) The location and kinds of navigation... operating at the maximum continuous power available; (5) The airplane is operating in standard atmosphere...

Forecasting for a Remote Island: A Class Exercise.

NASA Astrophysics Data System (ADS)

Riordan, Allen J.

2003-06-01

Students enrolled in a satellite meteorology course at North Carolina State University, Raleigh, recently had an unusual opportunity to apply their forecast skills to predict wind and weather conditions for a remote site in the Southern Hemisphere. For about 40 days starting in early February 2001, students used satellite and model guidance to develop forecasts to support a research team stationed on Bouvet Island (54°26S, 3°24E). Internet products together with current output from NCEP's Aviation (AVN) model supported the activity. Wind forecasts were of particular interest to the Bouvet team because violent winds often developed unexpectedly and posed a safety hazard.Results were encouraging in that 24-h wind speed forecasts showed reasonable reliability over a wide range of wind speeds. Forecasts for 48 h showed only marginal skill, however. Two critical events were well forecasted-the major February storm with wind speeds of over 120 kt and a brief calm period following several days of strong winds in early March. The latter forecast proved instrumental in recovering the research team.

Short-term Wind Forecasting at Wind Farms using WRF-LES and Actuator Disk Model

NASA Astrophysics Data System (ADS)

Kirkil, Gokhan

2017-04-01

Short-term wind forecasts are obtained for a wind farm on a mountainous terrain using WRF-LES. Multi-scale simulations are also performed using different PBL parameterizations. Turbines are parameterized using Actuator Disc Model. LES models improved the forecasts. Statistical error analysis is performed and ramp events are analyzed. Complex topography of the study area affects model performance, especially the accuracy of wind forecasts were poor for cross valley-mountain flows. By means of LES, we gain new knowledge about the sources of spatial and temporal variability of wind fluctuations such as the configuration of wind turbines.

A Comparison Of Primitive Model Results Of The Short Term Wind Energy Prediction System (Sweps): WRF vs MM5

NASA Astrophysics Data System (ADS)

Unal, E.; Tan, E.; Mentes, S. S.; Caglar, F.; Turkmen, M.; Unal, Y. S.; Onol, B.; Ozdemir, E. T.

2012-04-01

Although discontinuous behavior of wind field makes energy production more difficult, wind energy is the fastest growing renewable energy sector in Turkey which is the 6th largest electricity market in Europe. Short-term prediction systems, which capture the dynamical and statistical nature of the wind field in spatial and time scales, need to be advanced in order to increase the wind power prediction accuracy by using appropriate numerical weather forecast models. Therefore, in this study, performances of the next generation mesoscale Numerical Weather Forecasting model, WRF, and The Fifth-Generation NCAR/Penn State Mesoscale Model, MM5, have been compared for the Western Part of Turkey. MM5 has been widely used by Turkish State Meteorological Service from which MM5 results were also obtained. Two wind farms of the West Turkey have been analyzed for the model comparisons by using two different model domain structures. Each model domain has been constructed by 3 nested domains downscaling from 9km to 1km resolution by the ratio of 3. Since WRF and MM5 models have no exactly common boundary layer, cumulus, and microphysics schemes, the similar physics schemes have been chosen for these two models in order to have reasonable comparisons. The preliminary results show us that, depending on the location of the wind farms, MM5 wind speed RMSE values are 1 to 2 m/s greater than that of WRF values. Since 1 to 2 m/s errors can be amplified when wind speed is converted to wind power; it is decided that the WRF model results are going to be used for the rest of the project.

Oceanographic and meteorological research based on the data products of SEASAT

NASA Technical Reports Server (NTRS)

Pierson, W. J., Jr.

1985-01-01

Reservations were expressed concerning the sum of squares wind recovery algorithm and the power law model function. The SAS sum of squares (SOS) method for recovering winds from backscatter data leads to inconsistent results when V pol and H pol winds are compared. A model function that does not use a power law and that accounts for sea surface temperature is needed and is under study both theoretically and by means of the SASS mode 4 data. Aspects of the determination of winds by means of scatterometry and of the utilization of vector wind data for meteorological forecasts are elaborated. The operational aspect of an intermittent assimilation scheme currently utilized for the specification of the initial value field is considered with focus on quantifying the absolute 12-hour linear displacement error of the movement of low centers.

Probabilistic Solar Wind and Geomagnetic Forecasting Using an Analogue Ensemble or "Similar Day" Approach

NASA Astrophysics Data System (ADS)

Owens, M. J.; Riley, P.; Horbury, T. S.

2017-05-01

Effective space-weather prediction and mitigation requires accurate forecasting of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar wind, driven by remote solar observations, are gaining skill at forecasting the large-scale solar-wind features that give rise to near-Earth variations over days and weeks. There remains a need for accurate short-term (hours to days) solar-wind forecasts, however. In this study we investigate the analogue ensemble (AnEn), or "similar day", approach that was developed for atmospheric weather forecasting. The central premise of the AnEn is that past variations that are analogous or similar to current conditions can be used to provide a good estimate of future variations. By considering an ensemble of past analogues, the AnEn forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We show that forecasts of solar-wind speed can be improved by considering both speed and density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic field [BN] are improved by also considering the in-ecliptic magnetic-field components. In general, the best forecasts are found by considering only the previous 6 - 12 hours of observations. Using these parameters, the AnEn provides a valuable probabilistic forecast for solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours to around four days. For BN, which is central to space-weather disturbance, the AnEn only provides a valuable forecast out to around six to seven hours. As the inherent predictability of this parameter is low, this is still likely a marked improvement over other forecast methods. We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp. The AnEn provides a valuable probabilistic forecast of both indices out to around four days. We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind and geomagnetic conditions.

Appendix I1-2 to Wind HUI Initiative 1: Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

John Zack; Deborah Hanley; Dora Nakafuji

This report is an appendix to the Hawaii WindHUI efforts to dev elop and operationalize short-term wind forecasting and wind ramp event forecasting capabilities. The report summarizes the WindNET field campaign deployment experiences and challenges. As part of the WindNET project on the Big Island of Hawaii, AWS Truepower (AWST) conducted a field campaign to assess the viability of deploying a network of monitoring systems to aid in local wind energy forecasting. The data provided at these monitoring locations, which were strategically placed around the Big Island of Hawaii based upon results from the Oahu Wind Integration and Transmission Studymore » (OWITS) observational targeting study (Figure 1), provided predictive indicators for improving wind forecasts and developing responsive strategies for managing real-time, wind-related system events. The goal of the field campaign was to make measurements from a network of remote monitoring devices to improve 1- to 3-hour look ahead forecasts for wind facilities.« less

Adequacy assessment of composite generation and transmission systems incorporating wind energy conversion systems

NASA Astrophysics Data System (ADS)

Gao, Yi

The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems. A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator.

Integration of Wind Turbines with Compressed Air Energy Storage

NASA Astrophysics Data System (ADS)

Arsie, I.; Marano, V.; Rizzo, G.; Moran, M.

2009-08-01

Some of the major limitations of renewable energy sources are represented by their low power density and intermittent nature, largely depending upon local site and unpredictable weather conditions. These problems concur to increase the unit costs of wind power, so limiting their diffusion. By coupling storage systems with a wind farm, some of the major limitations of wind power, such as a low power density and an unpredictable nature, can be overcome. After an overview on storage systems, the Compressed Air Energy Storage (CAES) is analyzed, and the state of art on such systems is discussed. A Matlab/Simulink model of a hybrid power plant consisting of a wind farm coupled with CAES is then presented. The model has been successfully validated starting from the operating data of the McIntosh CAES Plant in Alabama. Time-series neural network-based wind speed forecasting are employed to determine the optimal daily operation strategy for the storage system. A detailed economic analysis has been carried out: investment and maintenance costs are estimated based on literature data, while operational costs and revenues are calculated according to energy market prices. As shown in the paper, the knowledge of the expected available energy is a key factor to optimize the management strategies of the proposed hybrid power plant, allowing to obtain environmental and economic benefits.

14 CFR 135.391 - Large nontransport category airplanes: En route limitations: One engine inoperative.

Code of Federal Regulations, 2011 CFR

2011-01-01

... reliability of wind and weather forecasting. (2) The location and kinds of navigation aids. (3) The prevailing... power available; (5) The airplane is operating in standard atmosphere; and (6) The weight of the...

Offshore Wind Power Integration in severely fluctuating Wind Conditions

NASA Astrophysics Data System (ADS)

von Bremen, L.

2010-09-01

Strong power fluctuations from offshore wind farms that are induced by wind speed fluctuations pose a severe problem to the save integration of offshore wind power into the power supply system. Experience at the first large-scale offshore wind farm Horns Rev showed that spatial smoothing of power fluctuations within a single wind farm is significantly smaller than onshore results suggest when distributed wind farms of 160 MW altogether are connected to a single point of common-coupling. Wind power gradients larger than 10% of the rated capacity within 5 minutes require large amount of regulation power that is very expensive for the grid operator. It must be noted that a wind speed change of only 0.5m/s result in a wind power change of 10% (within the range of 9-11 m/s where the wind power curve is steepest). Hence, it is very important for the grid operator to know if strong fluctuations are likely or not. Observed weather conditions at the German wind energy research platform FINO1 in the German bight are used to quantify wind fluctuations. With a standard power curve these wind fluctuations are transfered to wind power. The aim is to predict the probability of exceedence of certain wind power gradients that occur in a time interval of e.g. 12 hours. During 2006 and 2009 the distribution of wind power fluctuations looks very similar giving hope that distinct atmospheric processes can be determined that act as a trigger. Most often high wind power fluctuations occur in a range of wind speeds between 9-12 m/s as can be expected from the shape of the wind power curve. A cluster analysis of the 500 hPa geopotential height to detect predominant weather regimes shows that high fluctuations are more likely in north-western flow. It is shown that most often high fluctuations occur in non-stable atmospheric stratification. The description of stratification by means of the vertical gradient of the virtual potential temperature is chosen to be indicative for convection, i.e. it can be assumed that a negative gradient indicates convection which leads to strong wind fluctuations in the updraft and downdraft of the cloud. Neural Networks are used to determine the probability of exceedence of wind power gradients from a set of atmospheric parameters that are taken from Numerical Weather Prediction Models. Parameters describing atmospheric stability, that are related to convection (e.g. rain rate) and that forecast wind gusts tend to carry most information to estimate expected wind power fluctuations.

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

Extended Statistical Short-Range Guidance for Peak Wind Speed Analyses at the Shuttle Landing Facility: Phase II Results

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.

2003-01-01

This report describes the results from Phase II of the AMU's Short-Range Statistical Forecasting task for peak winds at the Shuttle Landing Facility (SLF). The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The 45th Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A seven year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. A PC-based Graphical User Interface (GUI) tool was created to display the data quickly.

Two-step forecast of geomagnetic storm using coronal mass ejection and solar wind condition

PubMed Central

Kim, R-S; Moon, Y-J; Gopalswamy, N; Park, Y-D; Kim, Y-H

2014-01-01

To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz ≤ −5 nT or Ey ≥ 3 mV/m for t≥ 2 h for moderate storms with minimum Dst less than −50 nT) and a Dst model developed by Temerin and Li (2002, 2006) (TL model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90%) than the forecasts based on the TL model (87%). However, the latter produces better forecasts for 24 nonstorm events (88%), while the former correctly forecasts only 71% of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80%) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (∩), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81%) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (∪), all geomagnetic storms are correctly forecasted. PMID:26213515

Two-step forecast of geomagnetic storm using coronal mass ejection and solar wind condition.

PubMed

Kim, R-S; Moon, Y-J; Gopalswamy, N; Park, Y-D; Kim, Y-H

2014-04-01

To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study ( B z  ≤ -5 nT or E y  ≥ 3 mV/m for t ≥ 2 h for moderate storms with minimum Dst less than -50 nT) and a Dst model developed by Temerin and Li (2002, 2006) (TL model). Using 55 CME- Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90%) than the forecasts based on the TL model (87%). However, the latter produces better forecasts for 24 nonstorm events (88%), while the former correctly forecasts only 71% of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80%) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (∩), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81%) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (∪), all geomagnetic storms are correctly forecasted.

Five Indisputable Facts on Modern Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bloom, Aaron P; Brinkman, Gregory L; Lopez, Anthony J

This presentation overviews five indisputable facts about modern power systems: Fact one: The grid can handle more renewable generation than previously thought. Fact two: Geographic and resource diversity provide additional reliability to the system. Fact three: Wind and solar forecasting provide significant value. Fact four: Our electric power markets were not originally designed for variable renewables -- but they could be adapted. Fact five: Modern power electronics are creating new sources of essential reliability services.

Assessing the Operational Robustness of the Homer Model for Marine Corps Use in Expeditionary Environments

DTIC Science & Technology

2014-06-01

systems. It can model systems including both conventional, diesel powered generators and renewable power sources such as photovoltaic arrays and wind...conducted an experiment where he assessed the capabilities of the HOMER model in forecasting the power output of a solar panel at NPS [32]. In his ex...energy efficiency in expeditionary operations, the HOMER micropower optimization model provides potential to serve as a powerful tool for improving

Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

NASA Astrophysics Data System (ADS)

Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

2017-12-01

A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

Flexible reserve markets for wind integration

NASA Astrophysics Data System (ADS)

Fernandez, Alisha R.

The increased interconnection of variable generation has motivated the use of improved forecasting to more accurately predict future production with the purpose to lower total system costs for balancing when the expected output exceeds or falls short of the actual output. Forecasts are imperfect, and the forecast errors associated with utility-scale generation from variable generators need new balancing capabilities that cannot be handled by existing ancillary services. Our work focuses on strategies for integrating large amounts of wind generation under the flex reserve market, a market that would called upon for short-term energy services during an under or oversupply of wind generation to maintain electric grid reliability. The flex reserve market would be utilized for time intervals that fall in-between the current ancillary services markets that would be longer than second-to-second energy services for maintaining system frequency and shorter than reserve capacity services that are called upon for several minutes up to an hour during an unexpected contingency on the grid. In our work, the wind operator would access the flex reserve market as an energy service to correct for unanticipated forecast errors, akin to paying the generators participating in the market to increase generation during a shortfall or paying the other generators to decrease generation during an excess of wind generation. Such a market does not currently exist in the Mid-Atlantic United States. The Pennsylvania-New Jersey-Maryland Interconnection (PJM) is the Mid-Atlantic electric grid case study that was used to examine if a flex reserve market can be utilized for integrating large capacities of wind generation in a lowcost manner for those providing, purchasing and dispatching these short-term balancing services. The following work consists of three studies. The first examines the ability of a hydroelectric facility to provide short-term forecast error balancing services via a flex reserve market, identifying the operational constraints that inhibit a multi-purpose dam facility to meet the desired flexible energy demand. The second study transitions from the hydroelectric facility as the decision maker providing flex reserve services to the wind plant as the decision maker purchasing these services. In this second study, methods for allocating the costs of flex reserve services under different wind policy scenarios are explored that aggregate farms into different groupings to identify the least-cost strategy for balancing the costs of hourly day-ahead forecast errors. The least-cost strategy may be different for an individual wind plant and for the system operator, noting that the least-cost strategy is highly sensitive to cost allocation and aggregation schemes. The latter may also cause cross-subsidies in the cost for balancing wind forecast errors among the different wind farms. The third study builds from the second, with the objective to quantify the amount of flex reserves needed for balancing future forecast errors using a probabilistic approach (quantile regression) to estimating future forecast errors. The results further examine the usefulness of separate flexible markets PJM could use for balancing oversupply and undersupply events, similar to the regulation up and down markets used in Europe. These three studies provide the following results and insights to large-scale wind integration using actual PJM wind farm data that describe the markets and generators within PJM. • Chapter 2 provides an in-depth analysis of the valuable, yet highly-constrained, energy services multi-purpose hydroelectric facilities can provide, though the opportunity cost for providing these services can result in large deviations from the reservoir policies with minimal revenue gain in comparison to dedicating the whole of dam capacity to providing day-ahead, baseload generation. • Chapter 3 quantifies the system-wide efficiency gains and the distributive effects of PJM's decision to act as a single balancing authority, which means that it procures ancillary services across its entire footprint simultaneously. This can be contrasted to Midwest Independent System Operator (MISO), which has several balancing authorities operating under its footprint. • Chapter 4 uses probabilistic methods to estimate the uncertainty in the forecast errors and the quantity of energy needed to balance these forecast errors at a certain percentile. Current practice is to use a point forecast that describes the conditional expectation of the dependent variable at each time step. The approach here uses quantile regression to describe the relationship between independent variable and the conditional quantiles (equivalently the percentiles) of the dependent variable. An estimate of the conditional density is performed, which contains information about the covariate relationship of the sign of the forecast errors (negative for too much wind generation and positive for too little wind generation) and the wind power forecast. This additional knowledge may be implemented in the decision process to more accurately schedule day-ahead wind generation bids and provide an example for using separate markets for balancing an oversupply and undersupply of generation. Such methods are currently used for coordinating large footprints of wind generation in Europe.

Performance of Trajectory Models with Wind Uncertainty

NASA Technical Reports Server (NTRS)

Lee, Alan G.; Weygandt, Stephen S.; Schwartz, Barry; Murphy, James R.

2009-01-01

Typical aircraft trajectory predictors use wind forecasts but do not account for the forecast uncertainty. A method for generating estimates of wind prediction uncertainty is described and its effect on aircraft trajectory prediction uncertainty is investigated. The procedure for estimating the wind prediction uncertainty relies uses a time-lagged ensemble of weather model forecasts from the hourly updated Rapid Update Cycle (RUC) weather prediction system. Forecast uncertainty is estimated using measures of the spread amongst various RUC time-lagged ensemble forecasts. This proof of concept study illustrates the estimated uncertainty and the actual wind errors, and documents the validity of the assumed ensemble-forecast accuracy relationship. Aircraft trajectory predictions are made using RUC winds with provision for the estimated uncertainty. Results for a set of simulated flights indicate this simple approach effectively translates the wind uncertainty estimate into an aircraft trajectory uncertainty. A key strength of the method is the ability to relate uncertainty to specific weather phenomena (contained in the various ensemble members) allowing identification of regional variations in uncertainty.

Does NASA SMAP Improve the Accuracy of Power Outage Models?

NASA Astrophysics Data System (ADS)

Quiring, S. M.; McRoberts, D. B.; Toy, B.; Alvarado, B.

2016-12-01

Electric power utilities make critical decisions in the days prior to hurricane landfall that are primarily based on the estimated impact to their service area. For example, utilities must determine how many repair crews to request from other utilities, the amount of material and equipment they will need to make repairs, and where in their geographically expansive service area to station crews and materials. Accurate forecasts of the impact of an approaching hurricane within their service area are critical for utilities in balancing the costs and benefits of different levels of resources. The Hurricane Outage Prediction Model (HOPM) are a family of statistical models that utilize predictions of tropical cyclone windspeed and duration of strong winds, along with power system and environmental variables (e.g., soil moisture, long-term precipitation), to forecast the number and location of power outages. This project assesses whether using NASA SMAP soil moisture improves the accuracy of power outage forecasts as compared to using model-derived soil moisture from NLDAS-2. A sensitivity analysis is employed since there have been very few tropical cyclones making landfall in the United States since SMAP was launched. The HOPM is used to predict power outages for 13 historical tropical cyclones and the model is run using twice, once with NLDAS soil moisture and once with SMAP soil moisture. Our results demonstrate that using SMAP soil moisture can have a significant impact on power outage predictions. SMAP has the potential to enhance the accuracy of power outage forecasts. Improved outage forecasts reduce the duration of power outages which reduces economic losses and accelerates recovery.

Modelling utility-scale wind power plants. Part 2: Capacity credit

NASA Astrophysics Data System (ADS)

Milligan, Michael R.

2000-10-01

As the worldwide use of wind turbine generators in utility-scale applications continues to increase, it will become increasingly important to assess the economic and reliability impact of these intermittent resources. Although the utility industry appears to be moving towards a restructured environment, basic economic and reliability issues will continue to be relevant to companies involved with electricity generation. This article is the second in a two-part series that addresses modelling approaches and results that were obtained in several case studies and research projects at the National Renewable Energy Laboratory (NREL). This second article focuses on wind plant capacity credit as measured with power system reliability indices. Reliability-based methods of measuring capacity credit are compared with wind plant capacity factor. The relationship between capacity credit and accurate wind forecasting is also explored. Published in 2000 by John Wiley & Sons, Ltd.

The probability forecast evaluation of hazard and storm wind over the territories of Russia and Europe

NASA Astrophysics Data System (ADS)

Perekhodtseva, E. V.

2012-04-01

The results of the probability forecast methods of summer storm and hazard wind over territories of Russia and Europe are submitted at this paper. These methods use the hydrodynamic-statistical model of these phenomena. The statistical model was developed for the recognition of the situation involving these phenomena. For this perhaps the samples of the values of atmospheric parameters (n=40) for the presence and for the absence of these phenomena of storm and hazard wind were accumulated. The compressing of the predictors space without the information losses was obtained by special algorithm (k=7<19m/s, the values of 65%24m/s, the values of 75%29m/s or the area of the tornado and strong squalls. The evaluation of this probability forecast was provided by criterion of Brayer. The estimation was successful and was equal for the European part of Russia B=0,37. The application of the probability forecast of storm and hazard winds allows to mitigate the economic losses when the errors of the first and second kinds of storm wind categorical forecast are not so small. A lot of examples of the storm wind probability forecast are submitted at this report.

Challenges in Understanding and Forecasting Winds in Complex Terrain.

NASA Astrophysics Data System (ADS)

Mann, J.; Fernando, J.; Wilczak, J. M.

2017-12-01

An overview will be given of some of the challenges in understanding and forecasting winds in complex terrain. These challenges can occur for several different reasons including 1) gaps in our understanding of fundamental physical boundary layer processes occurring in complex terrain; 2) a lack of adequate parameterizations and/or numerical schemes in NWP models; and 3) inadequate observations for initialization of NWP model forecasts. Specific phenomena that will be covered include topographic wakes/vortices, cold pools, gap flows, and mountain-valley winds, with examples taken from several air quality and wind energy related field programs in California as well as from the recent Second Wind Forecast Improvement Program (WFIP2) field campaign in the Columbia River Gorge/Basin area of Washington and Oregon States. Recent parameterization improvements discussed will include those for boundary layer turbulence, including 3D turbulence schemes, and gravity wave drag. Observational requirements for improving wind forecasting in complex terrain will be discussed, especially in the context of forecasting pressure gradient driven gap flow events.

Forecasting Cool Season Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe, III; Short, David; Roeder, William

2008-01-01

The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning Forecasts issued by the 45th Weather Squadron (45 WS) for planning operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The morning outlook for peak speeds also begins the warning decision process for gusts ^ 35 kt, ^ 50 kt, and ^ 60 kt from the surface to 300 ft. The 45 WS forecasters have indicated that peak wind speeds are a challenging parameter to forecast during the cool season (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. The tool must only use data available by 1200 UTC to support the issue time of the Planning Forecasts. Based on observations from the KSC/CCAFS wind tower network, surface observations from the Shuttle Landing Facility (SLF), and CCAFS upper-air soundings from the cool season months of October 2002 to February 2007, the AMU created multiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence, the temperature inversion depth, strength, and wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft. Six synoptic patterns were identified: 1) surface high near or over FL, 2) surface high north or east of FL, 3) surface high south or west of FL, 4) surface front approaching FL, 5) surface front across central FL, and 6) surface front across south FL. The following six predictors were selected: 1) inversion depth, 2) inversion strength, 3) wind gust factor, 4) synoptic weather pattern, 5) occurrence of precipitation at the SLF, and 6) strongest wind in the lowest 3000 ft. The forecast tool was developed as a graphical user interface with Microsoft Excel to help the forecaster enter the variables, and run the appropriate regression equations. Based on the forecaster's input and regression equations, a forecast of the day's peak and average wind is generated and displayed. The application also outputs the probability that the peak wind speed will be ^ 35 kt, 50 kt, and 60 kt.

Winds over Japan.

NASA Astrophysics Data System (ADS)

Plumley, William J.

1994-01-01

Before World War II, weather forecasters had little knowledge of upper-air wind patterns above 20000 feet. Data were seldom avai able at these heights, and the need was not great because commercial aircraft seldom flew at these altitudes. The war in the Pacific changed all that. Wind forecasts for 30000 feet plus became urgent to support the XXI Bomber Command in its bombing mission over Japan.The U.S. Army Air Force Pacific Ocean Area (AAFPOA) placed a Weather Central in the Marianas Islands in 1944 (Saipan in 1944 and Guam in 1945) to provide forecasting support for this mission. A forecasting procedure was put into operation that combined the elements known as "single-station forecasting" and an advanced procedure that used "altirmeter corrections" to analyze upper-airdata and make prognoses. Upper-air charts were drawn for constant pressure surfaces rather than constant height surfaces. The constant pressure surfaces were tied together by means of the atmospheric temperature field represented by specific temperature anomalies between pressure surfaces. Wind forecasts over the Marianas-Japan route made use of space cross sections that provided the data to forecast winds at each 5000-ft level to 35000 ft along the mission flight path. The new procedures allowed the forecaster to construct internally consistent meteorological charts in three dimensions in regions of sparse data.Army air force pilots and their crews from the Marianas were among the first to experience the extreme wind conditions now known as the "jet stream". Air force forecasters demonstrated that, with experience, such winds could reasonably be forecast under difficult operational conditions.

Coupling the Weather Research and Forecasting (WRF) model and Large Eddy Simulations with Actuator Disk Model: predictions of wind farm power production

NASA Astrophysics Data System (ADS)

Garcia Cartagena, Edgardo Javier; Santoni, Christian; Ciri, Umberto; Iungo, Giacomo Valerio; Leonardi, Stefano

2015-11-01

A large-scale wind farm operating under realistic atmospheric conditions is studied by coupling a meso-scale and micro-scale models. For this purpose, the Weather Research and Forecasting model (WRF) is coupled with an in-house LES solver for wind farms. The code is based on a finite difference scheme, with a Runge-Kutta, fractional step and the Actuator Disk Model. The WRF model has been configured using seven one-way nested domains where the child domain has a mesh size one third of its parent domain. A horizontal resolution of 70 m is used in the innermost domain. A section from the smallest and finest nested domain, 7.5 diameters upwind of the wind farm is used as inlet boundary condition for the LES code. The wind farm consists in six-turbines aligned with the mean wind direction and streamwise spacing of 10 rotor diameters, (D), and 2.75D in the spanwise direction. Three simulations were performed by varying the velocity fluctuations at the inlet: random perturbations, precursor simulation, and recycling perturbation method. Results are compared with a simulation on the same wind farm with an ideal uniform wind speed to assess the importance of the time varying incoming wind velocity. Numerical simulations were performed at TACC (Grant CTS070066). This work was supported by NSF, (Grant IIA-1243482 WINDINSPIRE).

The effect of real-time pricing on load shifting in a highly renewable power system dominated by generation from the renewable sources of wind and photovoltaics

NASA Astrophysics Data System (ADS)

Kies, Alexander; Brown, Tom; Schlachtberger, David; Schramm, Stefan

2017-04-01

The supply-demand imbalance is a major concern in the presence of large shares of highly variable renewable generation from sources like wind and photovoltaics (PV) in power systems. Other than the measures on the generation side, such as flexible backup generation or energy storage, sector coupling or demand side management are the most likely option to counter imbalances, therefore to ease the integration of renewable generation. Demand side management usually refers to load shifting, which comprises the reaction of electricity consumers to price fluctuations. In this work, we derive a novel methodology to model the interplay of load shifting and provided incentives via real-time pricing in highly renewable power systems. We use weather data to simulate generation from the renewable sources of wind and photovoltaics, as well as historical load data, split into different consumption categories, such as, heating, cooling, domestic, etc., to model a simplified power system. Together with renewable power forecast data, a simple market model and approaches to incorporate sector coupling [1] and load shifting [2,3], we model the interplay of incentives and load shifting for different scenarios (e.g., in dependency of the risk-aversion of consumers or the forecast horizon) and demonstrate the practical benefits of load shifting. First, we introduce the novel methodology and compare it with existing approaches. Secondly, we show results of numerical simulations on the effects of load shifting: It supports the integration of PV power by providing a storage, which characteristics can be described as "daily" and provides a significant amount of balancing potential. Lastly, we propose an experimental setup to obtain empirical data on end-consumer load-shifting behaviour in response to price incentives. References [1] Brown, T., Schlachtberger, D., Kies. A., Greiner, M., Sector coupling in a highly renewable European energy system, Proc. of the 15th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Vienna, Austria, 15.-17. November 2016 [2] Kleinhans, D.: Towards a systematic characterization of the potential of demand side management, arXiv preprint arXiv:1401.4121, 2014 [3] Kies, A., Schyska, B. U., von Bremen, L., The Demand Side Management Potential to Balance a Highly Renewable European Power System. Energies, 9(11), 955, 2016

Market protocols in ERCOT and their effect on wind generation

DOE PAGES

Sioshansi, Ramteen; Hurlbut, David

2009-08-22

Integrating wind generation into power systems and wholesale electricity markets presents unique challenges due to the characteristics of wind power, including its limited dispatchability, variability in generation, difficulty in forecasting resource availability, and the geographic location of wind resources. Texas has had to deal with many of these issues beginning in 2002 when it restructured its electricity industry and introduced aggressive renewable portfolio standards that helped spur major investments in wind generation. In this paper we discuss the issues that have arisen in designing market protocols that take account of these special characteristics of wind generation and survey the regulatorymore » and market rules that have been developed in Texas. We discuss the perverse incentives some of the rules gave wind generators to overschedule generation in order to receive balancing energy payments, and steps that have been taken to mitigate those incentive effects. Lastly, we discuss more recent steps taken by the market operator and regulators to ensure transmission capacity is available for new wind generators that are expected to come online in the future.« less

Evaluation of the Wind Flow Variability Using Scanning Doppler Lidar Measurements

NASA Astrophysics Data System (ADS)

Sand, S. C.; Pichugina, Y. L.; Brewer, A.

2016-12-01

Better understanding of the wind flow variability at the heights of the modern turbines is essential to accurately assess of generated wind power and efficient turbine operations. Nowadays the wind energy industry often utilizes scanning Doppler lidar to measure wind-speed profiles at high spatial and temporal resolution.The study presents wind flow features captured by scanning Doppler lidars during the second Wind Forecast and Improvement Project (WFIP 2) sponsored by the Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA). This 18-month long experiment in the Columbia River Basin aims to improve model wind forecasts complicated by mountain terrain, coastal effects, and numerous wind farms.To provide a comprehensive dataset to use for characterizing and predicting meteorological phenomena important to Wind Energy, NOAA deployed scanning, pulsed Doppler lidars to two sites in Oregon, one at Wasco, located upstream of all wind farms relative to the predominant westerly flow in the region, and one at Arlington, located in the middle of several wind farms.In this presentation we will describe lidar scanning patterns capable of providing data in conical, or vertical-slice modes. These individual scans were processed to obtain 15-min averaged profiles of wind speed and direction in real time. Visualization of these profiles as time-height cross sections allows us to analyze variability of these parameters with height, time and location, and reveal periods of rapid changes (ramp events). Examples of wind flow variability between two sites of lidar measurements along with examples of reduced wind velocity downwind of operating turbines (wakes) will be presented.

Wet snow hazard for power lines: a forecast and alert system applied in Italy

NASA Astrophysics Data System (ADS)

Bonelli, P.; Lacavalla, M.; Marcacci, P.; Mariani, G.; Stella, G.

2011-09-01

Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

A new aircraft hurricane wind climatology and applications in assessing the predictive skill of tropical cyclone intensity using high-resolution ensemble forecasts

NASA Astrophysics Data System (ADS)

Judt, Falko; Chen, Shuyi S.

2015-07-01

Hurricane surface wind is a key measure of storm intensity. However, a climatology of hurricane winds is lacking to date, largely because hurricanes are relatively rare events and difficult to observe over the open ocean. Here we present a new hurricane wind climatology based on objective surface wind analyses, which are derived from Stepped Frequency Microwave Radiometer measurements acquired by NOAA WP-3D and U.S. Air Force WC-130J hurricane hunter aircraft. The wind data were collected during 72 aircraft reconnaissance missions into 21 western Atlantic hurricanes from 1998 to 2012. This climatology provides an opportunity to validate hurricane intensity forecasts beyond the simplistic maximum wind speed metric and allows evaluating the predictive skill of probabilistic hurricane intensity forecasts using high-resolution model ensembles. An example of application is presented here using a 1.3 km grid spacing Weather Research and Forecasting model ensemble forecast of Hurricane Earl (2010).

Project Ukko - Design of a climate service visualisation interface for seasonal wind forecasts

NASA Astrophysics Data System (ADS)

Hemment, Drew; Stefaner, Moritz; Makri, Stephann; Buontempo, Carlo; Christel, Isadora; Torralba-Fernandez, Veronica; Gonzalez-Reviriego, Nube; Doblas-Reyes, Francisco; de Matos, Paula; Dykes, Jason

2016-04-01

Project Ukko is a prototype climate service to visually communicate probabilistic seasonal wind forecasts for the energy sector. In Project Ukko, an interactive visualisation enhances the accessibility and readability to the latests advances in seasonal wind speed predictions developed as part of the RESILIENCE prototype of the EUPORIAS (EC FP7) project. Climate services provide made-to-measure climate information, tailored to the specific requirements of different users and industries. In the wind energy sector, understanding of wind conditions in the next few months has high economic value, for instance, for the energy traders. Current energy practices use retrospective climatology, but access to reliable seasonal predictions based in the recent advances in global climate models has potential to improve their resilience to climate variability and change. Despite their potential benefits, a barrier to the development of commercially viable services is the complexity of the probabilistic forecast information, and the challenge of communicating complex and uncertain information to decision makers in industry. Project Ukko consists of an interactive climate service interface for wind energy users to explore probabilistic wind speed predictions for the coming season. This interface enables fast visual detection and exploration of interesting features and regions likely to experience unusual changes in wind speed in the coming months.The aim is not only to support users to better understand the future variability in wind power resources, but also to bridge the gap between practitioners' traditional approach and the advanced prediction systems developed by the climate science community. Project Ukko is presented as a case study of cross-disciplinary collaboration between climate science and design, for the development of climate services that are useful, usable and effective for industry users. The presentation will reflect on the challenge of developing a climate service for industry users in the wind energy sector, the background to this challenge, our approach, and the evaluation of the visualisation interface.

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk.

PubMed

Tonn, Gina L; Guikema, Seth D; Ferreira, Celso M; Quiring, Steven M

2016-10-01

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind-related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography. © 2016 Society for Risk Analysis.

Evaluation of NOAA's High Resolution Rapid Refresh (HRRR), 12 km North America Model (NAM12) and 4km North America Model (NAM 4) hub-height wind speed forecasts

NASA Astrophysics Data System (ADS)

Pendergrass, W.; Vogel, C. A.

2013-12-01

As an outcome of discussions between Duke Energy Generation and NOAA/ARL following the 2009 AMS Summer Community Meeting, in Norman Oklahoma, ARL and Duke Energy Generation (Duke) signed a Cooperative Research and Development Agreement (CRADA) which allows NOAA to conduct atmospheric boundary layer (ABL) research using Duke renewable energy sites as research testbeds. One aspect of this research has been the evaluation of forecast hub-height winds from three NOAA atmospheric models. Forecasts of 10m (surface) and 80m (hub-height) wind speeds from (1) NOAA/GSD's High Resolution Rapid Refresh (HRRR) model, (2) NOAA/NCEP's 12 km North America Model (NAM12) and (3) NOAA/NCEP's 4k high resolution North America Model (NAM4) were evaluated against 18 months of surface-layer wind observations collected at the joint NOAA/Duke Energy research station located at Duke Energy's West Texas Ocotillo wind farm over the period April 2011 through October 2012. HRRR, NAM12 and NAM4 10m wind speed forecasts were compared with 10m level wind speed observations measured on the NOAA/ATDD flux-tower. Hub-height (80m) HRRR , NAM12 and NAM4 forecast wind speeds were evaluated against the 80m operational PMM27-28 meteorological tower supporting the Ocotillo wind farm. For each HRRR update, eight forecast hours (hour 01, 02, 03, 05, 07, 10, 12, 15) plus the initialization hour (hour 00), evaluated. For the NAM12 and NAM4 models forecast hours 00-24 from the 06z initialization were evaluated. Performance measures or skill score based on absolute error 50% cumulative probability were calculated for each forecast hour. HRRR forecast hour 01 provided the best skill score with an absolute wind speed error within 0.8 m/s of observed 10m wind speed and 1.25 m/s for hub-height wind speed at the designated 50% cumulative probability. For both NAM4 and NAM12 models, skill scores were diurnal with comparable best scores observed during the day of 0.7 m/s of observed 10m wind speed and 1.1 m/s for hub-height wind speed at the designated 50% cumulative probability level.

Peak Wind Tool for General Forecasting

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III

2010-01-01

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III

2008-01-01

NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

Stratospheric wind errors, initial states and forecast skill in the GLAS general circulation model

NASA Technical Reports Server (NTRS)

Tenenbaum, J.

1983-01-01

Relations between stratospheric wind errors, initial states and 500 mb skill are investigated using the GLAS general circulation model initialized with FGGE data. Erroneous stratospheric winds are seen in all current general circulation models, appearing also as weak shear above the subtropical jet and as cold polar stratospheres. In this study it is shown that the more anticyclonic large-scale flows are correlated with large forecast stratospheric winds. In addition, it is found that for North America the resulting errors are correlated with initial state jet stream accelerations while for East Asia the forecast winds are correlated with initial state jet strength. Using 500 mb skill scores over Europe at day 5 to measure forecast performance, it is found that both poor forecast skill and excessive stratospheric winds are correlated with more anticyclonic large-scale flows over North America. It is hypothesized that the resulting erroneous kinetic energy contributes to the poor forecast skill, and that the problem is caused by a failure in the modeling of the stratospheric energy cycle in current general circulation models independent of vertical resolution.

Impact of scatterometer wind (ASCAT-A/B) data assimilation on semi real-time forecast system at KIAPS

NASA Astrophysics Data System (ADS)

Han, H. J.; Kang, J. H.

2016-12-01

Since Jul. 2015, KIAPS (Korea Institute of Atmospheric Prediction Systems) has been performing the semi real-time forecast system to assess the performance of their forecast system as a NWP model. KPOP (KIAPS Protocol for Observation Processing) is a part of KIAPS data assimilation system and has been performing well in KIAPS semi real-time forecast system. In this study, due to the fact that KPOP would be able to treat the scatterometer wind data, we analyze the effect of scatterometer wind (ASCAT-A/B) on KIAPS semi real-time forecast system. O-B global distribution and statistics of scatterometer wind give use two information which are the difference between background field and observation is not too large and KPOP processed the scatterometer wind data well. The changes of analysis increment because of O-B global distribution appear remarkably at the bottom of atmospheric field. It also shows that scatterometer wind data cover wide ocean where data would be able to short. Performance of scatterometer wind data can be checked through the vertical error reduction against IFS between background and analysis field and vertical statistics of O-A. By these analysis result, we can notice that scatterometer wind data will influence the positive effect on lower level performance of semi real-time forecast system at KIAPS. After, long-term result based on effect of scatterometer wind data will be analyzed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zack, J; Natenberg, E J; Knowe, G V

The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region which encompasses the Bonneville Power Administration (BPA) wind generation area shown in Figure 1 that includes Klondike, Stateline, and Hopkins Ridge wind plants. The Ensemble Sensitivity Analysis (ESA) approach uses data generated by a setmore » (ensemble) of perturbed numerical weather prediction (NWP) simulations for a sample time period to statistically diagnose the sensitivity of a specified forecast variable (metric) for a target location to parameters at other locations and prior times referred to as the initial condition (IC) or state variables. The ESA approach was tested on the large-scale atmospheric prediction problem by Ancell and Hakim 2007 and Torn and Hakim 2008. ESA was adapted and applied at the mesoscale by Zack et al. (2010a, b, and c) to the Tehachapi Pass, CA (warm and cools seasons) and Mid-Colombia Basin (warm season only) wind generation regions. In order to apply the ESA approach at the resolution needed at the mesoscale, Zack et al. (2010a, b, and c) developed the Multiple Observation Optimization Algorithm (MOOA). MOOA uses a multivariate regression on a few select IC parameters at one location to determine the incremental improvement of measuring multiple variables (representative of the IC parameters) at various locations. MOOA also determines how much information from each IC parameter contributes to the change in the metric variable at the target location. The Zack et al. studies (2010a, b, and c), demonstrated that forecast sensitivity can be characterized by well-defined, localized patterns for a number of IC variables such as 80-m wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective.« less

Impacts of Severe Weather, Climate Zone, and Energy Factors on Base Realignment and Closure (BRAC)

DTIC Science & Technology

2015-03-26

hydroelectric, solar photovoltaic , and wind power . Aside from locations and facilities that use electricity to heat, natural gas is the only...have large photovoltaic solar arrays with unique buy-back contracts or power -purchase agreements. These renewable energy projects benefit primarily...these costs, a Monte Carlo simulation is used to forecast annual costs and account for uncertainty with tornado and hurricane risks, along with

Stochastic Multi-Timescale Power System Operations With Variable Wind Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Hongyu; Krad, Ibrahim; Florita, Anthony

This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conductedmore » in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.« less

Two methods for estimating limits to large-scale wind power generation

PubMed Central

Miller, Lee M.; Brunsell, Nathaniel A.; Mechem, David B.; Gans, Fabian; Monaghan, Andrew J.; Vautard, Robert; Keith, David W.; Kleidon, Axel

2015-01-01

Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way. PMID:26305925

Applied Meteorology Unit (AMU) Quarterly Report First Quarter FY-14

NASA Technical Reports Server (NTRS)

Bauman, William Henry; Crawford, Winifred C.; Shafer, Jaclyn A.; Watson, Leela R.; Huddleston, Lisa L.; Decker, Ryan K.

2014-01-01

NASA's LSP and other programs at Vandenberg Air Force Base (VAFB) use wind forecasts issued by the 30th Operational Support Squadron (30 OSS) to determine if they need to limit activities or protect property such as a launch vehicle due to the occurrence of warning level winds at VAFB in California. The 30 OSS tasked the AMU to provide a wind forecasting capability to improve wind warning forecasts and enhance the safety of their customers' operations. This would allow 30 OSS forecasters to evaluate pressure gradient thresholds between pairs of regional observing stations to help determine the onset and duration of warning category winds. Development of such a tool will require that solid relationships exist between wind speed and the pressure gradient of one or more station pairs. As part of this task, the AMU will also create a statistical climatology of meteorological observations from the VAFB wind towers.

Initialization of a mesoscale model for April 10, 1979, using alternative data sources

NASA Technical Reports Server (NTRS)

Kalb, M. W.

1984-01-01

A 35 km grid limited area mesoscale model was initialized with high density SESAME radiosonde data and high density TIROS-N satellite temperature profiles for April 10, 1979. These data sources were used individually and with low level wind fields constructed from surface wind observations. The primary objective was to examine the use of satellite temperature data for initializing a mesoscale model by comparing the forecast results with similar experiments employing radiosonde data. The impact of observed low level winds on the model forecasts was also investigated with experiments varying the method of insertion. All forecasts were compared with each other and with mesoscale observations for precipitation, mass and wind structure. Several forecasts produced convective precipitation systems with characteristics satisfying criteria for a mesoscale convective complex. High density satellite temperature data and balanced winds can be used in a mesoscale model to produce forecasts which verify favorably with observations.

Two-Step Forecast of Geomagnetic Storm Using Coronal Mass Ejection and Solar Wind Condition

NASA Technical Reports Server (NTRS)

Kim, R.-S.; Moon, Y.-J.; Gopalswamy, N.; Park, Y.-D.; Kim, Y.-H.

2014-01-01

To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz = -5 nT or Ey = 3 mV/m for t = 2 h for moderate storms with minimum Dst less than -50 nT) (i.e. Magnetic Field Magnitude, B (sub z) less than or equal to -5 nanoTeslas or duskward Electrical Field, E (sub y) greater than or equal to 3 millivolts per meter for time greater than or equal to 2 hours for moderate storms with Minimum Disturbance Storm Time, Dst less than -50 nanoTeslas) and a Dst model developed by Temerin and Li (2002, 2006) (TL [i.e. Temerin Li] model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90 percent) than the forecasts based on the TL model (87 percent). However, the latter produces better forecasts for 24 nonstorm events (88 percent), while the former correctly forecasts only 71 percent of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80 percent) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (n, i.e. cap operator - the intersection set that is comprised of all the elements that are common to both), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81 percent) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (?, i.e. cup operator - the union set that is comprised of all the elements of either or both), all geomagnetic storms are correctly forecasted.

Minimum Energy Routing through Interactive Techniques (MERIT) modeling

NASA Technical Reports Server (NTRS)

Wylie, Donald P.

1988-01-01

The MERIT program is designed to demonstrate the feasibility of fuel savings by airlines through improved route selection using wind observations from their own fleet. After a discussion of weather and aircraft data, manually correcting wind fields, automatic corrections to wind fields, and short-range prediction models, it is concluded that improvements in wind information are possible if a system is developed for analyzing wind observations and correcting the forecasts made by the major models. One data handling system, McIDAS, can easily collect and display wind observations and model forecasts. Changing the wind forecasts beyond the time of the most recent observations is more difficult; an Australian Mesoscale Model was tested with promising but not definitive results.

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Dreher, Joseph; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry

2008-01-01

The peak winds near the surface are an important forecast element for Space Shuttle landings. As defined in the Shuttle Flight Rules (FRs), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMTJ) developed a personal computer based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak-wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center. However, the shuttle must land at Edwards Air Force Base (EAFB) in southern California when weather conditions at Kennedy Space Center in Florida are not acceptable, so SMG forecasters requested that a similar tool be developed for EAFB. Marshall Space Flight Center (MSFC) personnel archived and performed quality control of 2-minute average and 10-minute peak wind speeds at each tower adjacent to the main runway at EAFB from 1997- 2004. They calculated wind climatologies and probabilities of average peak wind occurrence based on the average speed. The climatologies were calculated for each tower and month, and were stratified by hour, direction, and direction/hour. For the probabilities of peak wind occurrence, MSFC calculated empirical and modeled probabilities of meeting or exceeding specific 10-minute peak wind speeds using probability density functions. The AMU obtained and reformatted the data into Microsoft Excel PivotTables, which allows users to display different values with point-click-drag techniques. The GUT was then created from the PivotTables using Visual Basic for Applications code. The GUI is run through a macro within Microsoft Excel and allows forecasters to quickly display and interpret peak wind climatology and likelihoods in a fast-paced operational environment. A summary of how the peak wind climatologies and probabilities were created and an overview of the GUT will be presented.

2015 Wind Technologies Market Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiser, Ryan; Bolinger, Mark; Barbose, Galen

Annual wind power capacity additions in the United States surged in 2015 and are projected to continue at a rapid clip in the coming five years. Recent and projected near-term growth is supported by the industry’s primary federal incentive—the production tax credit (PTC)—having been extended for several years (though with a phase-down schedule, described further on pages 68-69), as well as a myriad of state-level policies. Wind additions are also being driven by improvements in the cost and performance of wind power technologies, yielding low power sales prices for utility, corporate, and other purchasers. At the same time, the prospectsmore » for growth beyond the current PTC cycle remain uncertain: growth could be blunted by declining federal tax support, expectations for low natural gas prices, and modest electricity demand growth. This annual report—now in its tenth year—provides a detailed overview of developments and trends in the U.S. wind power market, with a particular focus on 2015. The report begins with an overview of key installation-related trends: trends in U.S. wind power capacity growth; how that growth compares to other countries and generation sources; the amount and percentage of wind energy in individual states; the status of offshore wind power development; and the quantity of proposed wind power capacity in various interconnection queues in the United States. Next, the report covers an array of wind power industry trends: developments in turbine manufacturer market share; manufacturing and supply-chain developments; wind turbine and component imports into and exports from the United States; project financing developments; and trends among wind power project owners and power purchasers. The report then turns to a summary of wind turbine technology trends: turbine size, hub height, rotor diameter, specific power, and IEC Class. After that, the report discusses wind power performance, cost, and pricing trends. In so doing, it describes trends in project performance, wind turbine transaction prices, installed project costs, and operations and maintenance (O&M) expenses. It also reviews the prices paid for wind power in the United States and how those prices compare to short-term wholesale electricity prices and forecasts of future natural gas prices. Next, the report examines policy and market factors impacting the domestic wind power market, including federal and state policy drivers as well as transmission and grid integration issues. The report concludes with a preview of possible near-term market developments. This edition of the annual report updates data presented in previous editions while highlighting key trends and important new developments from 2015. The report concentrates on larger, utility-scale wind turbines, defined here as individual turbines that exceed 100 kW in size.« less

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 3

NASA Technical Reports Server (NTRS)

Keitz, J. F.

1982-01-01

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 3 of the four major tasks included in the study. Task 3 compares flight plans developed on the Suitland forecast with actual data observed by the aircraft (and averaged over 10 degree segments). The results show that the average difference between the forecast and observed wind speed is 9 kts. without considering direction, and the average difference in the component of the forecast wind parallel to the direction of the observed wind is 13 kts. - both indicating that the Suitland forecast underestimates the wind speeds. The Root Mean Square (RMS) vector error is 30.1 kts. The average absolute difference in direction between the forecast and observed wind is 26 degrees and the temperature difference is 3 degree Centigrade. These results indicate that the forecast model as well as the verifying analysis used to develop comparison flight plans in Tasks 1 and 2 is a limiting factor and that the average potential fuel savings or penalty are up to 3.6 percent depending on the direction of flight.

Statistical Post-Processing of Wind Speed Forecasts to Estimate Relative Economic Value

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2013-04-01

The objective of this research is to get the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) which is underdispersive and hence uncalibrated. We aim to produce wind speed forecasts that are more accurate and calibrated than the EPS. The 51 members of the EPS are clustered to 8 weighted representative members (RMs), chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. This process creates four distinguishable ensembles which are used as input to statistical post-processes requiring multi-model forecasts. Two such processes are presented here. The first, Bayesian Model Averaging, has been proven to provide more calibrated and accurate wind speed forecasts than the ECMWF EPS using this multi-model input data. The second, heteroscedastic censored regression is indicating positive results also. We compare the two post-processing methods, applied to a year of hindcast wind speed data around Ireland, using an array of deterministic and probabilistic verification techniques, such as MAE, CRPS, probability transform integrals and verification rank histograms, to show which method provides the most accurate and calibrated forecasts. However, the value of a forecast to an end-user cannot be fully quantified by just the accuracy and calibration measurements mentioned, as the relationship between skill and value is complex. Capturing the full potential of the forecast benefits also requires detailed knowledge of the end-users' weather sensitive decision-making processes and most importantly the economic impact it will have on their income. Finally, we present the continuous relative economic value of both post-processing methods to identify which is more beneficial to the wind energy industry of Ireland.

3 CFR 8523 - Proclamation 8523 of May 20, 2010. National Hurricane Preparedness Week, 2010

Code of Federal Regulations, 2011 CFR

2011-01-01

... coastal and inland communities face the danger of these powerful storms. From high winds and storm surges... Preparedness Week, I urge individuals, families, communities, and businesses to take time to plan for the storm season before it begins. While hurricane forecasting has improved, storms may still develop with little...

How can monthly to seasonal forecasts help to better manage power systems? (Invited)

NASA Astrophysics Data System (ADS)

Dubus, L.; Troccoli, A.

2013-12-01

The energy industry increasingly depends on weather and climate, at all space and time scales. This is especially true in countries with volunteer renewable energies development policies. There is no doubt that Energy and Meteorology is a burgeoning inter-sectoral discipline. It is also clear that the catalyst for the stronger interaction between these two sectors is the renewed and fervent interest in renewable energies, especially wind and solar power. Recent progress in meteorology has led to a marked increase in the knowledge of the climate system and in the ability to forecast climate on monthly to seasonal time scales. Several studies have already demonstrated the effectiveness of using these forecasts for energy operations, for instance for hydro-power applications. However, it is also obvious that scientific progress on its own is not sufficient to increase the value of weather forecasts. The process of integration of new meteorological products into operational tools and decision making processes is not straightforward but it is at least as important as the scientific discovery. In turn, such integration requires effective communication between users and providers of these products. We will present some important aspects of energy systems in which monthly to seasonal forecasts can bring useful, if not vital, information, and we will give some examples of encouraging energy/meteorology collaborations. We will also provide some suggestions for a strengthened collaboration into the future.

Test of wind predictions for peak fire-danger stations in Oregon and Washington.

Treesearch

Owen P. Cramer

1957-01-01

Relative accuracy of several wind-speed forecasting methods was tested during the forest fire seasons of 1950 and 1951. For the study, three fire-weather forecast centers of the U. S. Weather Bureau prepared individual station forecasts for 11 peak stations within the national. forests of Oregon and Washington. These spot forecasts were considered...

Efficient, space-based, PM 100W thulium fiber laser for pumping Q-switched 2μm Ho:YLF for global winds and carbon dioxide lidar

NASA Astrophysics Data System (ADS)

Engin, Doruk; Mathason, Brian; Storm, Mark

2017-08-01

Global wind measurements are critically needed to improve and extend NOAA weather forecasting that impacts U.S. economic activity such as agriculture crop production, as well as hurricane forecasting, flooding, and FEMA disaster planning.1 NASA and the 2007 National Research Council (NRC) Earth Science Decadal Study have also identified global wind measurements as critical for global change research. NASA has conducted aircraft-based wind lidar measurements using 2 um Ho:YLF lasers, which has shown that robust wind measurements can be made. Fibertek designed and demonstrated a high-efficiency, 100 W average power continuous wave (CW) 1940 nm thulium (Tm)- doped fiber laser bread-board system meeting all requirements for a NASA Earth Science spaceflight 2 μm Ho:YLF pump laser. Our preliminary design shows that it is possible to package the laser for high-reliability spaceflight operation in an ultra-compact 2″x8″x14″ size and weight <8.5 lbs. A spaceflight 100 W polarization maintaining (PM) Tm laser provides a path to space for a pulsed, Q-switched 2 μm Ho:YLF laser with 30-80 mJ/pulse range at 100-200 Hz repletion rates.

Added value of non-calibrated and BMA calibrated AEMET-SREPS probabilistic forecasts: the 24 January 2009 extreme wind event over Catalonia

NASA Astrophysics Data System (ADS)

Escriba, P. A.; Callado, A.; Santos, D.; Santos, C.; Simarro, J.; García-Moya, J. A.

2009-09-01

At 00 UTC 24 January 2009 an explosive ciclogenesis originated over the Atlantic Ocean reached its maximum intensity with observed surface pressures lower than 970 hPa on its center and placed at Gulf of Vizcaya. During its path through southern France this low caused strong westerly and north-westerly winds over the Iberian Peninsula higher than 150 km/h at some places. These extreme winds leaved 10 casualties in Spain, 8 of them in Catalonia. The aim of this work is to show whether exists an added value in the short range prediction of the 24 January 2009 strong winds when using the Short Range Ensemble Prediction System (SREPS) of the Spanish Meteorological Agency (AEMET), with respect to the operational forecasting tools. This study emphasizes two aspects of probabilistic forecasting: the ability of a 3-day forecast of warn an extreme windy event and the ability of quantifying the predictability of the event so that giving value to deterministic forecast. Two type of probabilistic forecasts of wind are carried out, a non-calibrated and a calibrated one using Bayesian Model Averaging (BMA). AEMET runs daily experimentally SREPS twice a day (00 and 12 UTC). This system consists of 20 members that are constructed by integrating 5 local area models, COSMO (COSMO), HIRLAM (HIRLAM Consortium), HRM (DWD), MM5 (NOAA) and UM (UKMO), at 25 km of horizontal resolution. Each model uses 4 different initial and boundary conditions, the global models GFS (NCEP), GME (DWD), IFS (ECMWF) and UM. By this way it is obtained a probabilistic forecast that takes into account the initial, the contour and the model errors. BMA is a statistical tool for combining predictive probability functions from different sources. The BMA predictive probability density function (PDF) is a weighted average of PDFs centered on the individual bias-corrected forecasts. The weights are equal to posterior probabilities of the models generating the forecasts and reflect the skill of the ensemble members. Here BMA is applied to provide probabilistic forecasts of wind speed. In this work several forecasts for different time ranges (H+72, H+48 and H+24) of 10 meters wind speed over Catalonia are verified subjectively at one of the instants of maximum intensity, 12 UTC 24 January 2009. On one hand, three probabilistic forecasts are compared, ECMWF EPS, non-calibrated SREPS and calibrated SREPS. On the other hand, the relationship between predictability and skill of deterministic forecast is studied by looking at HIRLAM 0.16 deterministic forecasts of the event. Verification is focused on location and intensity of 10 meters wind speed and 10-minutal measures from AEMET automatic ground stations are used as observations. The results indicate that SREPS is able to forecast three days ahead mean winds higher than 36 km/h and that correctly localizes them with a significant probability of ocurrence in the affected area. The probability is higher after BMA calibration of the ensemble. The fact that probability of strong winds is high allows us to state that the predictability of the event is also high and, as a consequence, deterministic forecasts are more reliable. This is confirmed when verifying HIRLAM deterministic forecasts against observed values.

Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme

NASA Astrophysics Data System (ADS)

Owens, Mathew J.; Riley, Pete

2017-11-01

Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme.

PubMed

Owens, Mathew J; Riley, Pete

2017-11-01

Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

Probabilistic Solar Wind Forecasting Using Large Ensembles of Near‐Sun Conditions With a Simple One‐Dimensional “Upwind” Scheme

PubMed Central

Riley, Pete

2017-01-01

Abstract Long lead‐time space‐weather forecasting requires accurate prediction of the near‐Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near‐Sun solar wind and magnetic field conditions provide the inner boundary condition to three‐dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics‐based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near‐Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near‐Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near‐Sun solar wind speed at a range of latitudes about the sub‐Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun‐Earth line. Propagating these conditions to Earth by a three‐dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one‐dimensional “upwind” scheme is used. The variance in the resulting near‐Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996–2016, the upwind ensemble is found to provide a more “actionable” forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large). PMID:29398982

Statistical modeling to support power system planning

NASA Astrophysics Data System (ADS)

Staid, Andrea

This dissertation focuses on data-analytic approaches that improve our understanding of power system applications to promote better decision-making. It tackles issues of risk analysis, uncertainty management, resource estimation, and the impacts of climate change. Tools of data mining and statistical modeling are used to bring new insight to a variety of complex problems facing today's power system. The overarching goal of this research is to improve the understanding of the power system risk environment for improved operation, investment, and planning decisions. The first chapter introduces some challenges faced in planning for a sustainable power system. Chapter 2 analyzes the driving factors behind the disparity in wind energy investments among states with a goal of determining the impact that state-level policies have on incentivizing wind energy. Findings show that policy differences do not explain the disparities; physical and geographical factors are more important. Chapter 3 extends conventional wind forecasting to a risk-based focus of predicting maximum wind speeds, which are dangerous for offshore operations. Statistical models are presented that issue probabilistic predictions for the highest wind speed expected in a three-hour interval. These models achieve a high degree of accuracy and their use can improve safety and reliability in practice. Chapter 4 examines the challenges of wind power estimation for onshore wind farms. Several methods for wind power resource assessment are compared, and the weaknesses of the Jensen model are demonstrated. For two onshore farms, statistical models outperform other methods, even when very little information is known about the wind farm. Lastly, chapter 5 focuses on the power system more broadly in the context of the risks expected from tropical cyclones in a changing climate. Risks to U.S. power system infrastructure are simulated under different scenarios of tropical cyclone behavior that may result from climate change. The scenario-based approach allows me to address the deep uncertainty present by quantifying the range of impacts, identifying the most critical parameters, and assessing the sensitivity of local areas to a changing risk. Overall, this body of work quantifies the uncertainties present in several operational and planning decisions for power system applications.

An Evaluation of QuikSCAT data over Tropical Cyclones as Determined in an Operational Environment

NASA Astrophysics Data System (ADS)

Hawkins, J. D.; Edson, R. T.

2001-12-01

QuikSCAT data over all global tropical cyclones were examined during the past 3 1/2 years in conjunction with the development of a user¡_s guide to the forecasters at the Joint Typhoon Warning Center, Pearl Harbor, Hawaii. The active microwave scatterometer has greatly enhanced the forecaster's ability to evaluate surface winds over the data poor regions of the tropical oceans. The QuikSCAT scatterometer¡_s unique ability to provide both wind speed and direction on a nearly bi-daily basis has greatly increased the forecaster¡_s near real-time knowledge of tropical cyclone genesis, intensification potential, outer wind structure, and a ¡rminimum estimate¡_ for a tropical cyclone¡_s maximum sustained winds. Scatterometer data were compared with data available to the forecasters in a near real-time environment including ship, land and buoy reports. In addition, comparisons were also made with aircraft measurements (for Atlantic and East Pacific systems), numerical weather model wind fields, and various remote sensing techniques. Wind speeds were found to be extremely useful, especially for the radius of gale force winds. However, in rain-contaminated areas, light winds were often greatly overestimated while in heavy winds, wind speeds were often quite reasonable if not slightly underestimated. The largest issues are still focused on the correct wind direction selection. In these cases, rain-flagged wind vector cells greatly affected the results from the direction ambiguity selection procedure. The ambiguity selection algorithm often had difficulties resolving a circulation center when large areas of the tropical cyclone¡_s center were flagged. Often a block of winds would occur perpendicular to the swath irregardless of the circulation¡_s position. These winds caused considerable confusion for the operational forecasters. However, it was determined that in many cases, an accurate center position could still be obtained by using methods to incorporate the more accurate wind speeds and the outer wind field vectors that were not as seriously affected. Quantitative results and comparisons will be shown in this presentation. In addition, guides to the operational forecasters to determine system centers inspite of the ambiguity selection problems will also be discussed.

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

NASA Technical Reports Server (NTRS)

Hoffman, Ross N.

1993-01-01

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Synoptic versus regional causes of icing on wind turbines at an exposed wind farm site in Germany

NASA Astrophysics Data System (ADS)

Weissinger, Maximilian; Strauss, Lukas; Serafin, Stefano; Dorninger, Manfred; Burchhart, Thomas; Fink, Martin

2017-04-01

Ice accretion on wind turbine blades can lead to significant power production loss or even permanent structural damage on the turbine. With the ongoing construction of wind farms at sites with increased icing potential in cold climates, accurate icing predictions are needed to optimise power plant operation. To this end, the frequency of occurrence and the causes of meteorological icing need to be better understood. The project ICE CONTROL, an Austrian research initiative, aims to improve icing forecasts through measurements, probabilistic forecasting, and verification of icing on wind turbine blades. The project focuses on a wind farm site near Ellern, Germany, located on the Hunsrück, a hilly terrain rising above the surrounding plain by 200-300 metres. Production data from the last three winters show that icing events tend to occur more often at the wind turbines on top of the highest hills. The present study aims to investigate historical cases of wind turbine icing and their meteorological causes at the Ellern wind farm. The data available consists of a three-year period (2013-2016) of operational data from the Ellern wind farm as well as meteorological measurements at nearby stations operated by the German Weather Service (DWD). In addition, radiosondes and weather charts are taken into account. The main objective of this work is, first, to link the local and regional weather conditions to larger-scale weather patterns and prevailing air masses, and second, to determine the types of icing (in-cloud vs. freezing precipation). Results show that in most icing cases the cloud base height was below the hub height while the temperature was just below the freezing point. Precipitation was absent in most cases. This suggests that most of the observed icing events were due to in-cloud icing. Icing conditions occurred often (but not exclusively) under specific synoptic-scale weather conditions, such as north-westerly flow advecting maritime polar air masses to Central Europe. In other cases, icing events were favoured by the development of low-level thermal inversions during weak south-easterly flow conditions.

Advancing Embedded and Extrinsic Solutions for Optimal Control and Efficiency of Energy Systems in Buildings

NASA Astrophysics Data System (ADS)

Bay, Christopher Joseph

Massachusetts' Act to Promote Energy Diversity requires distribution companies to solicit contracts for up to 1600 MW of offshore wind. To test whether offshore wind projects can meet the Act's requirement to reduce C02 emissions, the Oak Ridge Competitive Electricity Dispatch Model was used to forecast changes in ISO New England's resource mix under five different wind capacity levels and calculate avoided C02 emissions attributable to offshore wind. With 1600 MW of installed capacity, representing full solicitation under the Act, reliance on natural gas is reduced by ˜10% and carbon emissions decline by ˜9%. This represents significant progress towards the goals of the Global Warming Solutions Act and the Clean Power Plan. The 5000 MW scenario reduces emissions enough to meet the Clean Power Plan's 2030 goals. This study's application of a dispatch model provides an example for policymakers of a simple and cost-effective approach for assessing a project's value.

Improvements and Lingering Challenges with Modeling Low-Level Winds Over Complex Terrain during the Wind Forecast Improvement Project 2

NASA Astrophysics Data System (ADS)

Olson, J.; Kenyon, J.; Brown, J. M.; Angevine, W. M.; Marquis, M.; Pichugina, Y. L.; Choukulkar, A.; Bonin, T.; Banta, R. M.; Bianco, L.; Djalalova, I.; McCaffrey, K.; Wilczak, J. M.; Lantz, K. O.; Long, C. N.; Redfern, S.; McCaa, J. R.; Stoelinga, M.; Grimit, E.; Cline, J.; Shaw, W. J.; Lundquist, J. K.; Lundquist, K. A.; Kosovic, B.; Berg, L. K.; Kotamarthi, V. R.; Sharp, J.; Jiménez, P.

2017-12-01

The Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) are NOAA real-time operational hourly updating forecast systems run at 13- and 3-km grid spacing, respectively. Both systems use the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) as the model component of the forecast system. During the second installment of the Wind Forecast Improvement Project (WFIP 2), the RAP/HRRR have been targeted for the improvement of low-level wind forecasts in the complex terrain within the Columbia River Basin (CRB), which requires much finer grid spacing to resolve important terrain peaks in the Cascade Mountains as well as the Columbia River Gorge. Therefore, this project provides a unique opportunity to test and develop the RAP/HRRR physics suite within a very high-resolution nest (Δx = 750 m) over the northwestern US. Special effort is made to incorporate scale-aware aspects into the model physical parameterizations to improve RAP/HRRR wind forecasts for any application at any grid spacing. Many wind profiling and scanning instruments have been deployed in the CRB in support the WFIP 2 field project, which spanned 01 October 2015 to 31 March 2017. During the project, several forecast error modes were identified, such as: (1) too-shallow cold pools during the cool season, which can mix-out more frequently than observed and (2) the low wind speed bias in thermal trough-induced gap flows during the warm season. Development has been focused on the column-based turbulent mixing scheme to improve upon these biases, but investigating the effects of horizontal (and 3D) mixing has also helped improve some of the common forecast failure modes. This presentation will highlight the testing and development of various model components, showing the improvements over original versions for temperature and wind profiles. Examples of case studies and retrospective periods will be presented to illustrate the improvements. We will demonstrate that the improvements made in WFIP 2 will be extendable to other regions, complex or flat terrain. Ongoing and future challenges in RAP/HRRR physics development will be touched upon.

Impacts of Typhoon Megi (2010) on the South China Sea

DTIC Science & Technology

2014-06-01

investigations. To obtain realistic typhoon-strength atmospheric forcing, the EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind...EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind field blended with global weather forecast winds from the U.S. Navy...only 1C. Sequential SST snapshots, of which only a Figure 1. The EASNFS model domain with topography and an inset covered by WRF model. Typhoon Megi’s

Ramping and Uncertainty Prediction Tool - Analysis and Visualization of Wind Generation Impact on Electrical Grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel; Makarov, PNNL Yuri; Subbarao, PNNL Kris

RUT software is designed for use by the Balancing Authorities to predict and display additional requirements caused by the variability and uncertainty in load and generation. The prediction is made for the next operating hours as well as for the next day. The tool predicts possible deficiencies in generation capability and ramping capability. This deficiency of balancing resources can cause serious risks to power system stability and also impact real-time market energy prices. The tool dynamically and adaptively correlates changing system conditions with the additional balancing needs triggered by the interplay between forecasted and actual load and output of variablemore » resources. The assessment is performed using a specially developed probabilistic algorithm incorporating multiple sources of uncertainty including wind, solar and load forecast errors. The tool evaluates required generation for a worst case scenario, with a user-specified confidence level.« less

Variable Generation Power Forecasting as a Big Data Problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen; Kosovic, Branko

To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the physical and dynamical knowledge as well as computational intelligence. Accurate prediction is a Big Data problem that requires disparate data, multiple models that are each applicable for a specific time frame, and application of computational intelligence techniques to successfully blend all of the model andmore » observational information in real-time and deliver it to the decision makers at utilities and grid operators. This paper describes an example system that has been used for utility applications and how it has been configured to meet utility needs while addressing the Big Data issues.« less

Variable Generation Power Forecasting as a Big Data Problem

DOE PAGES

Haupt, Sue Ellen; Kosovic, Branko

2016-10-10

To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the physical and dynamical knowledge as well as computational intelligence. Accurate prediction is a Big Data problem that requires disparate data, multiple models that are each applicable for a specific time frame, and application of computational intelligence techniques to successfully blend all of the model andmore » observational information in real-time and deliver it to the decision makers at utilities and grid operators. This paper describes an example system that has been used for utility applications and how it has been configured to meet utility needs while addressing the Big Data issues.« less

Spherical Harmonics Analysis of the ECMWF Global Wind Fields at the 10-Meter Height Level During 1985: A Collection of Figures Illustrating Results

NASA Technical Reports Server (NTRS)

Sanchez, Braulio V.; Nishihama, Masahiro

1997-01-01

Half-daily global wind speeds in the east-west (u) and north-south (v) directions at the 10-meter height level were obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) data set of global analyses. The data set covered the period 1985 January to 1995 January. A spherical harmonic expansion to degree and order 50 was used to perform harmonic analysis of the east-west (u) and north-south (v) velocity field components. The resulting wind field is displayed, as well as the residual of the fit, at a particular time. The contribution of particular coefficients is shown. The time variability of the coefficients up to degree and order 3 is presented. Corresponding power spectrum plots are given. Time series analyses were applied also to the power associated with degrees 0-10; the results are included.

Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

NASA Technical Reports Server (NTRS)

MacNeice, Peter; Taktakishvili, Alexandra; Jackson, Bernard; Clover, John; Bisi, Mario; Odstrcil, Dusan

2011-01-01

The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

Treesearch

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

2015-01-01

This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

Using particle swarm optimization to enhance PI controller performances for active and reactive power control in wind energy conversion systems

NASA Astrophysics Data System (ADS)

Taleb, M.; Cherkaoui, M.; Hbib, M.

2018-05-01

Recently, renewable energy sources are impacting seriously power quality of the grids in term of frequency and voltage stability, due to their intermittence and less forecasting accuracy. Among these sources, wind energy conversion systems (WECS) received a great interest and especially the configuration with Doubly Fed Induction Generator. However, WECS strongly nonlinear, are making their control not easy by classical approaches such as a PI. In this paper, we continue deepen study of PI controller used in active and reactive power control of this kind of WECS. Particle Swarm Optimization (PSO) is suggested to improve its dynamic performances and its robustness against parameters variations. This work highlights the performances of PSO optimized PI control against classical PI tuned with poles compensation strategy. Simulations are carried out on MATLAB-SIMULINK software.

DOE SBIR Phase II Final Technical Report - Assessing Climate Change Effects on Wind Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whiteman, Cameron; Capps, Scott

Specialized Vertum Partners software tools were prototyped, tested and commercialized to allow wind energy stakeholders to assess the uncertainties of climate change on wind power production and distribution. This project resulted in three commercially proven products and a marketing tool. The first was a Weather Research and Forecasting Model (WRF) based resource evaluation system. The second was a web-based service providing global 10m wind data from multiple sources to wind industry subscription customers. The third product addressed the needs of our utility clients looking at climate change effects on electricity distribution. For this we collaborated on the Santa Ana Wildfiremore » Threat Index (SAWTi), which was released publicly last quarter. Finally to promote these products and educate potential users we released “Gust or Bust”, a graphic-novel styled marketing publication.« less

Scientific breakthroughs necessary for the commercial success of renewable energy (Invited)

NASA Astrophysics Data System (ADS)

Sharp, J.

2010-12-01

In recent years the wind energy industry has grown at an unprecedented rate, and in certain regions has attained significant penetration into the power infrastructure. This growth has been both a result of, and a precursor to, significant advances in the science and business of wind energy. But as a result of this growth and increasing penetration, further advances and breakthroughs will become increasingly important. These advances will be required in a number of different aspects of wind energy, including: resource assessment, operations and performance analysis, forecasting, and the impacts of increased wind energy development. Resource assessment has benefited from the development of tools specifically designed for this purpose. Despite this, the atmosphere is often portrayed in an extremely simplified manner by these tools. New methodologies should rely upon more sophisticated application of the physics of fluid flows. There will need to be an increasing reliance and acceptance of improved measurement techniques (remote sensing, volume rather than point measurements, etc), and more sophisticated and higher-resolution numerical methods for micrositing. The goals of resource assessment will have to include a better understanding of the variability and forecastability of potential sites. Operational and performance analysis are vital to quantifying how well all aspects of the business are being carried out. Operational wind farms generate large amounts of meteorological and mechanical data. Data mining and detailed analysis of this data has proven to be invaluable to shed light upon poorly understood aspects of the science and industry. Future analysis will need to be even more rigorous and creative. Worthy topics of study include the impact of turbine wakes upon downstream turbine performance, how to utilize operational data to improve resource assessment and forecasting, and what the impacts of large-scale wind energy development might be. Forecasting is an area in which there have been great advances, and yet even greater advances will be required in the future. Until recently, the scale of wind energy made forecasting relatively unimportant - something that could be handled by automated systems augmented with limited observations. Recently, however, the use of human forecasting teams and specialized observation networks has greatly advanced the state of the art. Further advances will need to include dense networks of observations, providing timely and reliable observations over a much deeper layer of the boundary layer. High resolution rapid refresh models incorporating these observations via data assimilation should advance the state of the art further. Finally, understanding potential impacts of increasing wind energy development is an area where there has been significant interest lately. Preliminary studies have raised concerns of possible unintended climatological consequences upon downwind areas. A policy breakthrough was the inclusion of language into SB 1462, providing for research into these concerns. Advances will be required in the areas of transmission system improvements. The generation of large amounts of wind energy itself will impact the energy infrastructure, and will require breakthroughs within all of the topics above, and thus be a breakthrough in its own right.

Operation of Power Grids with High Penetration of Wind Power

NASA Astrophysics Data System (ADS)

Al-Awami, Ali Taleb

The integration of wind power into the power grid poses many challenges due to its highly uncertain nature. This dissertation involves two main components related to the operation of power grids with high penetration of wind energy: wind-thermal stochastic dispatch and wind-thermal coordinated bidding in short-term electricity markets. In the first part, a stochastic dispatch (SD) algorithm is proposed that takes into account the stochastic nature of the wind power output. The uncertainty associated with wind power output given the forecast is characterized using conditional probability density functions (CPDF). Several functions are examined to characterize wind uncertainty including Beta, Weibull, Extreme Value, Generalized Extreme Value, and Mixed Gaussian distributions. The unique characteristics of the Mixed Gaussian distribution are then utilized to facilitate the speed of convergence of the SD algorithm. A case study is carried out to evaluate the effectiveness of the proposed algorithm. Then, the SD algorithm is extended to simultaneously optimize the system operating costs and emissions. A modified multi-objective particle swarm optimization algorithm is suggested to identify the Pareto-optimal solutions defined by the two conflicting objectives. A sensitivity analysis is carried out to study the effect of changing load level and imbalance cost factors on the Pareto front. In the second part of this dissertation, coordinated trading of wind and thermal energy is proposed to mitigate risks due to those uncertainties. The problem of wind-thermal coordinated trading is formulated as a mixed-integer stochastic linear program. The objective is to obtain the optimal tradeoff bidding strategy that maximizes the total expected profits while controlling trading risks. For risk control, a weighted term of the conditional value at risk (CVaR) is included in the objective function. The CVaR aims to maximize the expected profits of the least profitable scenarios, thus improving trading risk control. A case study comparing coordinated with uncoordinated bidding strategies depending on the trader's risk attitude is included. Simulation results show that coordinated bidding can improve the expected profits while significantly improving the CVaR.

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

Wind and Solar Energy Resource Assessment for Navy Installations in the Midwestern US

NASA Astrophysics Data System (ADS)

Darmenova, K.; Apling, D.; Higgins, G. J.; Carnes, J.; Smith, C.

2012-12-01

A stable supply of energy is critical for sustainable economic development and the ever-increasing demand for energy resources drives the need for alternative weather-driven renewable energy solutions such as solar and wind-generated power. Recognizing the importance of energy as a strategic resource, the Department of the Navy has focused on energy efficient solutions aiming to increase tactical and shore energy security and reduce greenhouse gas emissions. Implementing alternative energy solutions will alleviate the Navy installations demands on the National power grid, however transitioning to renewable energy sources is a complex multi-stage process that involves initial investment in resource assessment and feasibility of building solar and wind power systems in Navy's facilities. This study focuses on the wind and solar energy resource assessment for Navy installations in the Midwestern US. We use the dynamically downscaled datasets at 12 km resolution over the Continental US generated with the Weather Research and Forecasting (WRF) model to derive the wind climatology in terms of wind speed, direction, and wind power at 20 m above the surface for 65 Navy facilities. In addition, we derived the transmissivity of the atmosphere, diffuse radiation fraction, cloud cover and seasonal energy potential for a zenith facing surface with unobstructed horizon for each installation location based on the results of a broadband radiative transfer model and our cloud database based on 17-years of GOES data. Our analysis was incorporated in a GIS framework in combination with additional infrastructure data that enabled a synergistic resource assessment based on the combination of climatological and engineering factors.

Accuracy of National Weather Service wind-direction forecasts at Macon and Augusta, Georgia

Treesearch

Leonidas G. Lavdas

1997-01-01

National Weather Service wind forecasts and observations over a nine-year period (1985 to 1993) were analyzed to determine the usefulness of these forecasts for forestry smoke management. Data from Macon, GA indicated that forecasts were accurate to within plus or minus 22.5E about 38 percent of the time. When a wider plus or minus 67.5E window was used, accuracy...

Nesting large-eddy simulations within mesoscale simulations for wind energy applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lundquist, J K; Mirocha, J D; Chow, F K

2008-09-08

With increasing demand for more accurate atmospheric simulations for wind turbine micrositing, for operational wind power forecasting, and for more reliable turbine design, simulations of atmospheric flow with resolution of tens of meters or higher are required. These time-dependent large-eddy simulations (LES), which resolve individual atmospheric eddies on length scales smaller than turbine blades and account for complex terrain, are possible with a range of commercial and open-source software, including the Weather Research and Forecasting (WRF) model. In addition to 'local' sources of turbulence within an LES domain, changing weather conditions outside the domain can also affect flow, suggesting thatmore » a mesoscale model provide boundary conditions to the large-eddy simulations. Nesting a large-eddy simulation within a mesoscale model requires nuanced representations of turbulence. Our group has improved the Weather and Research Forecasting model's (WRF) LES capability by implementing the Nonlinear Backscatter and Anisotropy (NBA) subfilter stress model following Kosovic (1997) and an explicit filtering and reconstruction technique to compute the Resolvable Subfilter-Scale (RSFS) stresses (following Chow et al, 2005). We have also implemented an immersed boundary method (IBM) in WRF to accommodate complex terrain. These new models improve WRF's LES capabilities over complex terrain and in stable atmospheric conditions. We demonstrate approaches to nesting LES within a mesoscale simulation for farms of wind turbines in hilly regions. Results are sensitive to the nesting method, indicating that care must be taken to provide appropriate boundary conditions, and to allow adequate spin-up of turbulence in the LES domain.« less

915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, M.; Bartholomew, M. J.; Giangrande, S.

When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds onmore » the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.« less

915-Mhz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, M.; Bartholomew, M. J.; Giangrande, S.

When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds onmore » the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.« less

Arab energy: prospects to 2000

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1982-01-01

The energy situation of 21 Arab countries for the period between 1960 and 2000 is examined. Attempts to forecast the demand and supply of energy in the Arab world for 1985, 1990 and 2000 are discussed. Following a description of the methodology employed, crude petroleum, petroleum production, natural gas and electricity are explored in detail. The national programs of the Arab countries for electric-power generation include conventional thermal electricity, hydroelectricity, nuclear power, solar energy, biomass conversion, and geothermal and wind energy. 23 references.

Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route

PubMed Central

Inoue, Jun; Yamazaki, Akira; Ono, Jun; Dethloff, Klaus; Maturilli, Marion; Neuber, Roland; Edwards, Patti; Yamaguchi, Hajime

2015-01-01

During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR. PMID:26585690

Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

NASA Astrophysics Data System (ADS)

AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

2014-12-01

Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using nonparametric kernel methods. In addition, to the pointwise hourly wind speed forecasts, a confidence interval is also provided which allows to quantify the uncertainty around the forecasts.

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Flinn, Clay

2013-01-01

On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers. During launch operations, the payload/launch team sometimes asks the LWOs if they expect the upper-level winds to change during the countdown. The LWOs used numerical weather prediction model point forecasts to provide the information, but did not have the capability to quickly retrieve or adequately display the upper-level observations and compare them directly in the same display to the model point forecasts to help them determine which model performed the best. The LWOs requested the Applied Meteorology Unit (AMU) develop a graphical user interface (GUI) that will plot upper-level wind speed and direction observations from the Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Profiling System (AMPS) rawinsondes with point forecast wind profiles from the National Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM), Rapid Refresh (RAP) and Global Forecast System (GFS) models to assess the performance of these models. The AMU suggested adding observations from the NASA 50 MHz wind profiler and one of the US Air Force 915 MHz wind profilers, both located near the Kennedy Space Center (KSC) Shuttle Landing Facility, to supplement the AMPS observations with more frequent upper-level profiles. Figure 1 shows a map of KSC/CCAFS with the locations of the observation sites and the model point forecasts.

Use of ground-based wind profiles in mesoscale forecasting

NASA Technical Reports Server (NTRS)

Schlatter, Thomas W.

1985-01-01

A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

The propagation of wind errors through ocean wave hindcasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holthuijsen, L.H.; Booij, N.; Bertotti, L.

1996-08-01

To estimate uncertainties in wave forecast and hindcasts, computations have been carried out for a location in the Mediterranean Sea using three different analyses of one historic wind field. These computations involve a systematic sensitivity analysis and estimated wind field errors. This technique enables a wave modeler to estimate such uncertainties in other forecasts and hindcasts if only one wind analysis is available.

How to Integrate Variable Power Source into a Power Grid

NASA Astrophysics Data System (ADS)

Asano, Hiroshi

This paper discusses how to integrate variable power source such as wind power and photovoltaic generation into a power grid. The intermittent renewable generation is expected to penetrate for less carbon intensive power supply system, but it causes voltage control problem in the distribution system, and supply-demand imbalance problem in a whole power system. Cooperative control of customers' energy storage equipment such as water heater with storage tank for reducing inverse power flow from the roof-top PV system, the operation technique using a battery system and the solar radiation forecast for stabilizing output of variable generation, smart charging of plug-in hybrid electric vehicles for load frequency control (LFC), and other methods to integrate variable power source with improving social benefits are surveyed.

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Flinn, Clay

2013-01-01

On the day-of-launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers to include NASA's Launch Services Program and NASA's Ground Systems Development and Operations Program. They currently do not have the capability to display and overlay profiles of upper-level observations and numerical weather prediction model forecasts. The LWOs requested the Applied Meteorology Unit (AMU) develop a tool in the form of a graphical user interface (GUI) that will allow them to plot upper-level wind speed and direction observations from the Kennedy Space Center (KSC) 50 MHz tropospheric wind profiling radar, KSC Shuttle Landing Facility 915 MHz boundary layer wind profiling radar and Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Processing System (AMPS) radiosondes, and then overlay forecast wind profiles from the model point data including the North American Mesoscale (NAM) model, Rapid Refresh (RAP) model and Global Forecast System (GFS) model to assess the performance of these models. The AMU developed an Excel-based tool that provides an objective method for the LWOs to compare the model-forecast upper-level winds to the KSC wind profiling radars and CCAFS AMPS observations to assess the model potential to accurately forecast changes in the upperlevel profile through the launch count. The AMU wrote Excel Visual Basic for Applications (VBA) scripts to automatically retrieve model point data for CCAFS (XMR) from the Iowa State University Archive Data Server (http://mtarchive.qeol.iastate.edu) and the 50 MHz, 915 MHz and AMPS observations from the NASA/KSC Spaceport Weather Data Archive web site (http://trmm.ksc.nasa.gov). The AMU then developed code in Excel VBA to automatically ingest and format the observations and model point data in Excel to ready the data for generating Excel charts for the LWO's. The resulting charts allow the LWOs to independently initialize the three models 0-hour forecasts against the observations to determine which is the best performing model and then overlay the model forecasts on time-matched observations during the launch countdown to further assess the model performance and forecasts. This paper will demonstrate integration of observed and predicted atmospheric conditions into a decision support tool and demonstrate how the GUI is implemented in operations.

Tower Mesonetwork Climatology and Interactive Display Tool

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Bauman, William H., III

2004-01-01

Forecasters at the 45th Weather Squadron and Spaceflight Meteorology Group use data from the tower network over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to evaluate Launch Commit Criteria, and issue and verify forecasts for ground operations. Systematic biases in these parameters could adversely affect an analysis, forecast, or verification. Also, substantial geographical variations in temperature and wind speed can occur under specific wind directions. To address these concerns, the Applied Meteorology Unit (AMU) developed a climatology of temperatures and winds from the tower network, and identified the geographical variation and significant tower biases. The mesoclimate is largely driven by the complex land-water interfaces across KSC/CCAFS. Towers with close proximity to water typically had much warmer nocturnal temperatures and higher wind speeds throughout the year. The strongest nocturnal wind speeds occurred from October to March whereas the strongest mean daytime wind speeds occurred from February to May. These results of this project can be viewed by forecasters through an interactive graphical user interface developed by the AMU. The web-based interface includes graphical and map displays of mean, standard deviation, bias, and data availability for any combination of towers, variables, months, hours, and wind directions.

Implementation of a state of the art automated system for the production of cloud/water vapor motion winds from geostationary satellites

NASA Technical Reports Server (NTRS)

Velden, Christopher S.

1994-01-01

The thrust of the proposed effort under this contract is aimed at improving techniques to track water vapor data in sequences of imagery from geostationary satellites. In regards to this task, significant testing, evaluation, and progress was accomplished during this period. Sets of winds derived from Meteosat data were routinely produced during Atlantic hurricane events in the 1993 season. These wind sets were delivered via Internet in real time to the Hurricane Research Division in Miami for their evaluation in a track forecast model. For eighteen cases in which 72-hour forecasts were produced, thirteen resulted in track forecast improvements (some quite significant). In addition, quality-controlled Meteosat water vapor winds produced by NESDIS were validated against rawinsondes, yielding an 8 m/s RMS. This figure is comparable to upper-level cloud drift wind accuracies. Given the complementary horizontal coverage in cloud-free areas, we believe that water vapor vectors can supplement cloud-drift wind information to provide good full-disk coverage of the upper tropospheric flow. The impact of these winds on numerical analysis and forecasts will be tested in the next reporting period.

Quantifying Uncertainty of Wind Power Production Through an Analog Ensemble

NASA Astrophysics Data System (ADS)

Shahriari, M.; Cervone, G.

2016-12-01

The Analog Ensemble (AnEn) method is used to generate probabilistic weather forecasts that quantify the uncertainty in power estimates at hypothetical wind farm locations. The data are from the NREL Eastern Wind Dataset that includes more than 1,300 modeled wind farms. The AnEn model uses a two-dimensional grid to estimate the probability distribution of wind speed (the predictand) given the values of predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind. The meteorological data is taken from the NCEP GFS which is available on a 0.25 degree grid resolution. The methodology first divides the data into two classes: training period and verification period. The AnEn selects a point in the verification period and searches for the best matching estimates (analogs) in the training period. The predictand value at those analogs are the ensemble prediction for the point in the verification period. The model provides a grid of wind speed values and the uncertainty (probability index) associated with each estimate. Each wind farm is associated with a probability index which quantifies the degree of difficulty to estimate wind power. Further, the uncertainty in estimation is related to other factors such as topography, land cover and wind resources. This is achieved by using a GIS system to compute the correlation between the probability index and geographical characteristics. This study has significant applications for investors in renewable energy sector especially wind farm developers. Lower level of uncertainty facilitates the process of submitting bids into day ahead and real time electricity markets. Thus, building wind farms in regions with lower levels of uncertainty will reduce the real-time operational risks and create a hedge against volatile real-time prices. Further, the links between wind estimate uncertainty and factors such as topography and wind resources, provide wind farm developers with valuable information regarding wind farm siting.

Scientific motivation for ADM/Aeolus mission

NASA Astrophysics Data System (ADS)

Källén, Erland

2018-04-01

The ADM/Aeolus wind lidar mission will provide a global coverage of atmospheric wind profiles. Atmospheric wind observations are required for initiating weather forecast models and for predicting and monitoring long term climate change. Improved knowledge of the global wind field is widely recognised as fundamental to advancing the understanding and prediction of weather and climate. In particular over tropical areas there is a need for better wind data leading to improved medium range (3-10 days) weather forecasts over the whole globe.

Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model

NASA Astrophysics Data System (ADS)

Lim, Kyo-Sun Sunny; Lim, Jong-Myoung; Shin, Hyeyum Hailey; Hong, Jinkyu; Ji, Young-Yong; Lee, Wanno

2018-06-01

A substantial over-prediction bias at low-to-moderate wind speeds in the Weather Research and Forecasting (WRF) model has been reported in the previous studies. Low-level wind fields play an important role in dispersion of air pollutants, including radionuclides, in a high-resolution WRF framework. By implementing two subgrid-scale orography parameterizations (Jimenez and Dudhia in J Appl Meteorol Climatol 51:300-316, 2012; Mass and Ovens in WRF model physics: problems, solutions and a new paradigm for progress. Preprints, 2010 WRF Users' Workshop, NCAR, Boulder, Colo. http://www.mmm.ucar.edu/wrf/users/workshops/WS2010/presentations/session%204/4-1_WRFworkshop2010Final.pdf, 2010), we tried to compare the performance of parameterizations and to enhance the forecast skill of low-level wind fields over the central western part of South Korea. Even though both subgrid-scale orography parameterizations significantly alleviated the positive bias at 10-m wind speed, the parameterization by Jimenez and Dudhia revealed a better forecast skill in wind speed under our modeling configuration. Implementation of the subgrid-scale orography parameterizations in the model did not affect the forecast skills in other meteorological fields including 10-m wind direction. Our study also brought up the problem of discrepancy in the definition of "10-m" wind between model physics parameterizations and observations, which can cause overestimated winds in model simulations. The overestimation was larger in stable conditions than in unstable conditions, indicating that the weak diurnal cycle in the model could be attributed to the representation error.

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE PAGES

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

2018-03-01

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Comparative Analysis of NOAA REFM and SNB3GEO Tools for the Forecast of the Fluxes of High-Energy Electrons at GEO

NASA Technical Reports Server (NTRS)

Balikhin, M. A.; Rodriguez, J. V.; Boynton, R. J.; Walker, S. N.; Aryan, Homayon; Sibeck, D. G.; Billings, S. A.

2016-01-01

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field B(sub z) observations at L1. The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast.

Comparative analysis of NOAA REFM and SNB3GEO tools for the forecast of the fluxes of high-energy electrons at GEO.

PubMed

Balikhin, M A; Rodriguez, J V; Boynton, R J; Walker, S N; Aryan, H; Sibeck, D G; Billings, S A

2016-01-01

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB 3 GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB 3 GEO forecasts use solar wind density and interplanetary magnetic field B z observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB 3 GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB 3 GEO forecast.

SASS wind forecast impact studies using the GLAS and NEPRF systems: Preliminary conclusions

NASA Technical Reports Server (NTRS)

Kalnay, E.; Atlas, R.; Baker, W. E.; Duffy, D.; Halem, M.; Helfand, M.

1984-01-01

For this project, a version of the GLAS Analysis/Forecast System was developed that includes an objective dealiasing scheme as an integral part of the analysis cycle. With this system the (100 sq km) binned SASS wind data generated by S. Peteherych of AER, Canada corresponding of the period 0000 GMT 7 September 1978 to 1200 GMT 13 September 1978 was objectively dealiased. The dealiased wind fields have been requested and received by JPL, NMC and the British Meteorological Office. The first 3.5 days of objectively dealiased fields were subjectively enhanced on the McIDAS system. Approximately 20% of the wind directions were modified, and of these, about 70% were changed by less than 90 deg. Two SASS forecast impact studies, were performed using the dealiased fields, with the GLAS and the NEPRF (Navy Environmental Prediction Research Facility) analysis/forecast systems.

Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

NASA Astrophysics Data System (ADS)

Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei

2018-01-01

This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.

Could Crop Height Impact the Wind Resource at Agriculturally Productive Wind Farm Sites?

NASA Astrophysics Data System (ADS)

Vanderwende, B. J.; Lundquist, J. K.

2013-12-01

The agriculture-intensive United States Midwest and Great Plains regions feature some of the best wind resources in the nation. Collocation of cropland and wind turbines introduces complex meteorological interactions that could affect both agriculture and wind power production. Crop management practices may modify the wind resource through alterations of land-surface properties. In this study, we used the Weather Research and Forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. We parameterized a hypothetical array of 121 1.8 MW turbines at the site of the 2011 Crop/Wind-energy Experiment field campaign using the WRF wind farm parameterization. We estimated the impact of crop choices on power production by altering the aerodynamic roughness length in a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 10 cm and 25 cm represent a mature soy crop and a mature corn crop respectively. Results suggest that the presence of the mature corn crop reduces hub-height wind speeds and increases rotor-layer wind shear, even in the presence of a large wind farm which itself modifies the flow. During the night, the influence of the surface was dependent on the boundary layer stability, with strong stability inhibiting the surface drag from modifying the wind resource aloft. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop management practices.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Automated Statistical Forecast Method to 36-48H ahead of Storm Wind and Dangerous Precipitation at the Mediterranean Region

NASA Astrophysics Data System (ADS)

Perekhodtseva, E. V.

2009-09-01

Development of successful method of forecast of storm winds, including squalls and tornadoes and heavy rainfalls, that often result in human and material losses, could allow one to take proper measures against destruction of buildings and to protect people. Well-in-advance successful forecast (from 12 hours to 48 hour) makes possible to reduce the losses. Prediction of the phenomena involved is a very difficult problem for synoptic till recently. The existing graphic and calculation methods still depend on subjective decision of an operator. Nowadays in Russia there is no hydrodynamic model for forecast of the maximal precipitation and wind velocity V> 25m/c, hence the main tools of objective forecast are statistical methods using the dependence of the phenomena involved on a number of atmospheric parameters (predictors). Statistical decisive rule of the alternative and probability forecast of these events was obtained in accordance with the concept of "perfect prognosis" using the data of objective analysis. For this purpose the different teaching samples of present and absent of this storm wind and rainfalls were automatically arranged that include the values of forty physically substantiated potential predictors. Then the empirical statistical method was used that involved diagonalization of the mean correlation matrix R of the predictors and extraction of diagonal blocks of strongly correlated predictors. Thus for these phenomena the most informative predictors were selected without loosing information. The statistical decisive rules for diagnosis and prognosis of the phenomena involved U(X) were calculated for choosing informative vector-predictor. We used the criterion of distance of Mahalanobis and criterion of minimum of entropy by Vapnik-Chervonenkis for the selection predictors. Successful development of hydrodynamic models for short-term forecast and improvement of 36-48h forecasts of pressure, temperature and others parameters allowed us to use the prognostic fields of those models for calculations of the discriminant functions in the nodes of the grid 150x150km and the values of probabilities P of dangerous wind and thus to get fully automated forecasts. In order to change to the alternative forecast the author proposes the empirical threshold values specified for this phenomenon and advance period 36 hours. In the accordance to the Pirsey-Obukhov criterion (T), the success of these automated statistical methods of forecast of squalls and tornadoes to 36 -48 hours ahead and heavy rainfalls in the warm season for the territory of Italy, Spain and Balkan countries is T = 1-a-b=0,54: 0,78 after author experiments. A lot of examples of very successful forecasts of summer storm wind and heavy rainfalls over the Italy and Spain territory are submitted at this report. The same decisive rules were applied to the forecast of these phenomena during cold period in this year too. This winter heavy snowfalls in Spain and in Italy and storm wind at this territory were observed very often. And our forecasts are successful.

Assessing Upper-Level Winds on Day-of-Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Wheeler, Mark M.

2012-01-01

On the day-or-launch. the 45th Weather Squadron Launch Weather Officers (LWOS) monitor the upper-level winds for their launch customers to include NASA's Launch Services Program (LSP). During launch operations, the payload launch team sometimes asks the LWO if they expect the upper level winds to change during the countdown but the LWOs did not have the capability to quickly retrieve or display the upper-level observations and compare them to the numerical weather prediction model point forecasts. The LWOs requested the Applied Meteorology Unit (AMU) develop a capability in the form of a graphical user interface (GUI) that would allow them to plot upper-level wind speed and direction observations from the Kennedy Space Center Doppler Radar Wind Profilers and Cape Canaveral Air Force Station rawinsondes and then overlay model point forecast profiles on the observation profiles to assess the performance of these models and graphically display them to the launch team. The AMU developed an Excel-based capability for the LWOs to assess the model forecast upper-level winds and compare them to observations. They did so by creating a GUI in Excel that allows the LWOs to first initialize the models by comparing the O-hour model forecasts to the observations and then to display model forecasts in 3-hour intervals from the current time through 12 hours.

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosenthal, William Steven; Tartakovsky, Alex; Huang, Zhenyu

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integratemore » them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.« less

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

DOE PAGES

Rosenthal, William Steven; Tartakovsky, Alex; Huang, Zhenyu

2017-10-31

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integratemore » them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.« less

Learning-based Wind Estimation using Distant Soundings for Unguided Aerial Delivery

NASA Astrophysics Data System (ADS)

Plyler, M.; Cahoy, K.; Angermueller, K.; Chen, D.; Markuzon, N.

2016-12-01

Delivering unguided, parachuted payloads from aircraft requires accurate knowledge of the wind field inside an operational zone. Usually, a dropsonde released from the aircraft over the drop zone gives a more accurate wind estimate than a forecast. Mission objectives occasionally demand releasing the dropsonde away from the drop zone, but still require accuracy and precision. Barnes interpolation and many other assimilation methods do poorly when the forecast error is inconsistent in a forecast grid. A machine learning approach can better leverage non-linear relations between different weather patterns and thus provide a better wind estimate at the target drop zone when using data collected up to 100 km away. This study uses the 13 km resolution Rapid Refresh (RAP) dataset available through NOAA and subsamples to an area around Yuma, AZ and up to approximately 10km AMSL. RAP forecast grids are updated with simulated dropsondes taken from analysis (historical weather maps). We train models using different data mining and machine learning techniques, most notably boosted regression trees, that can accurately assimilate the distant dropsonde. The model takes a forecast grid and simulated remote dropsonde data as input and produces an estimate of the wind stick over the drop zone. Using ballistic winds as a defining metric, we show our data driven approach does better than Barnes interpolation under some conditions, most notably when the forecast error is different between the two locations, on test data previously unseen by the model. We study and evaluate the model's performance depending on the size, the time lag, the drop altitude, and the geographic location of the training set, and identify parameters most contributing to the accuracy of the wind estimation. This study demonstrates a new approach for assimilating remotely released dropsondes, based on boosted regression trees, and shows improvement in wind estimation over currently used methods.

Application of data assimilation to solar wind forecasting models

NASA Astrophysics Data System (ADS)

Innocenti, M.; Lapenta, G.; Vrsnak, B.; Temmer, M.; Veronig, A.; Bettarini, L.; Lee, E.; Markidis, S.; Skender, M.; Crespon, F.; Skandrani, C.; Soteria Space-Weather Forecast; Data Assimilation Team

2010-12-01

Data Assimilation through Kalman filtering [1,2] is a powerful statistical tool which allows to combine modeling and observations to increase the degree of knowledge of a given system. We apply this technique to the forecast of solar wind parameters (proton speed, proton temperature, absolute value of the magnetic field and proton density) at 1 AU, using the model described in [3] and ACE data as observations. The model, which relies on GOES 12 observations of the percentage of the meridional slice of the sun covered by coronal holes, grants 1-day and 6-hours in advance forecasts of the aforementioned quantities in quiet times (CMEs are not taken into account) during the declining phase of the solar cycle and is tailored for specific time intervals. We show that the application of data assimilation generally improves the quality of the forecasts during quiet times and, more notably, extends the periods of applicability of the model, which can now provide reliable forecasts also in presence of CMEs and for periods other than the ones it was designed for. Acknowledgement: The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under the grant agreement N. 218816 (SOTERIA project: http://www.soteria-space.eu). References: [1] R. Kalman, J. Basic Eng. 82, 35 (1960); [2] G. Welch and G. Bishop, Technical Report TR 95-041, University of North Carolina, Department of Computer Science (2001); [3] B. Vrsnak, M. Temmer, and A. Veronig, Solar Phys. 240, 315 (2007).

Forecast and Specification of Radiation Belt Electrons Based on Solar Wind Measurements

NASA Astrophysics Data System (ADS)

Li, X.; Barker, A.; Burin Des Roziers, E.

2003-12-01

Relativistic electrons in the Earth's magnetosphere are of considerable practical importance because of their effect on spacecraft and because of their radiation hazard to astronauts who perform extravehicular activity. The good correlation between solar wind velocity and MeV electron fluxes at geosynchronous orbit has long been established. We have developed a radial diffusion model, using solar wind parameters as the only input, to reproduce the variation of the MeV electrons at geosynchronous orbit. Based on this model, we have constructed a real-time model that forecasts one to two days in advance the daily averaged >2 MeV electron flux at geosynchronous orbit using real-time solar wind data from ACE. The forecasts from this model are available on the web in real time. A natural extension of our current model is to create a system for making quantitative forecasts and specifications of radiation belt electrons at different radial distances and different local times based on the solar wind conditions. The successes and obstacles associated with this extension will be discussed in this presentation.

Wind Information Uplink to Aircraft Performing Interval Management Operations

NASA Technical Reports Server (NTRS)

Ahmad, Nashat; Barmore, Bryan; Swieringa, Kurt

2015-01-01

The accuracy of the wind information used to generate trajectories for aircraft performing Interval Management (IM) operations is critical to the success of an IM operation. There are two main forms of uncertainty in the wind information used by the Flight Deck Interval Management (FIM) equipment. The first is the accuracy of the forecast modeling done by the weather provider. The second is that only a small subset of the forecast data can be uplinked to the aircraft for use by the FIM equipment, resulting in loss of additional information. This study focuses on what subset of forecast data, such as the number and location of the points where the wind is sampled should be made available to uplink to the aircraft.

Impact of the initial specification of moisture and vertical motion on precipitation forecasts with a mesoscale model Implications for a satellite mesoscale data base

NASA Technical Reports Server (NTRS)

Mlynczak, Pamela E.; Houghton, David D.; Diak, George R.

1986-01-01

Using a numerical mesoscale model, four simulations were performed to determine the effects of suppressing the initial mesoscale information in the moisture and wind fields on the precipitation forecasts. The simulations included a control forecast 12-h simulation that began at 1200 GMT March 1982 and three experiment simulations with modifications to the moisture and vertical motion fields incorporated at 1800 GMT. The forecasts from 1800 GMT were compared to the second half of the control forecast. It was found that, compared to the control forecast, suppression of the moisture and/or wind initial field(s) produces a drier forecast. However, the characteristics of the precipitation forecasts of the experiments were not different enough to conclude that either mesoscale moisture or mesoscale vertical velocity at the initial time are more important for producing a forecast closer to that of the control.

An online mineral dust model within the global/regional NMMB: current progress and plans

NASA Astrophysics Data System (ADS)

Perez, C.; Haustein, K.; Janjic, Z.; Jorba, O.; Baldasano, J. M.; Black, T.; Nickovic, S.

2008-12-01

While mineral dust distribution and effects are important on global scales, they strongly depend on dust emissions that are occurring on small spatial and temporal scales. Indeed, the accuracy of surface wind speed used in dust models is crucial. Due to the high-order power dependency on wind friction velocity and the threshold behaviour of dust emissions, small errors in surface wind speed lead to large dust emission errors. Most global dust models use prescribed wind fields provided by major meteorological centres (e.g., NCEP and ECMWF) and their spatial resolution is currently about 1 degree x 1 degree . Such wind speeds tend to be strongly underestimated over arid and semi-arid areas and do not account for mesoscale systems responsible for a significant fraction of dust emissions regionally and globally. Other significant uncertainties in dust emissions resulting from such approaches are related to the misrepresentation of high subgrid-scale spatial heterogeneity in soil and vegetation boundary conditions, mainly in semi-arid areas. In order to significantly reduce these uncertainties, the Barcelona Supercomputing Center is currently implementing a mineral dust model coupled on-line with the new global/regional NMMB atmospheric model using the ESMF framework under development in NOAA/NCEP/EMC. The NMMB is an evolution of the operational WRF-NMME extending from meso to global scales, and including non-hydrostatic option and improved tracer advection. This model is planned to become the next-generation NCEP mesoscale model for operational weather forecasting in North America. Current implementation is based on the well established regional dust model and forecast system Eta/DREAM (http://www.bsc.es/projects/earthscience/DREAM/). First successful global simulations show the potentials of such an approach and compare well with DREAM regionally. Ongoing developments include improvements in dust size distribution representation, sedimentation, dry deposition, wet scavenging and dust-radiation feedback, as well as the efficient implementation of the model on High Performance Supercomputers for global simulations and forecasts at high resolution.

Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, Michael P; Giangrande, Scott E; Bartholomew, Mary Jane

The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf] campaign was scheduled to take place from 15 July 2013 through 15 July 2015 (or until shipped for the next U.S. Department of Energy Atmospheric Radiation Measurement [ARM] Climate Research Facility first Mobile Facility [AMF1] deployment). The campaign involved the deployment of the AMF1 Scintec 915 MHz Radar Wind Profiler (RWP) at BNL, in conjunction with several other ARM, BNL and National Weather Service (NWS) instruments. The two main scientific foci of the campaign were: 1) To provide profiles of the horizontal wind to be used tomore » test and validate short-term cloud advection forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. This campaign was a serendipitous opportunity that arose following the deployment of the RWP at the Two-Column Aerosol Project (TCAP) campaign in Cape Cod, Massachusetts and restriction from participation in the Green Ocean Amazon 2014/15 (GoAmazon 2014/15) campaign due to radio-frequency allocation restriction for international deployments. The RWP arrived at BNL in the fall of 2013, but deployment was delayed until fall of 2014 as work/safety planning and site preparation were completed. The RWP further encountered multiple electrical failures, which eventually required several shipments of instrument power supplies and the final amplifier to the vendor to complete repairs. Data collection began in late January 2015. The operational modes of the RWP were changed such that in addition to collecting traditional profiles of the horizontal wind, a vertically pointing mode was also included for the purpose of precipitation sensing and estimation of vertical velocities. The RWP operated well until the end of the campaign in July 2015 and collected observations for more than 20 precipitation events.« less

A vector auto-regressive model for onshore and offshore wind synthesis incorporating meteorological model information

NASA Astrophysics Data System (ADS)

Hill, D.; Bell, K. R. W.; McMillan, D.; Infield, D.

2014-05-01

The growth of wind power production in the electricity portfolio is striving to meet ambitious targets set, for example by the EU, to reduce greenhouse gas emissions by 20% by 2020. Huge investments are now being made in new offshore wind farms around UK coastal waters that will have a major impact on the GB electrical supply. Representations of the UK wind field in syntheses which capture the inherent structure and correlations between different locations including offshore sites are required. Here, Vector Auto-Regressive (VAR) models are presented and extended in a novel way to incorporate offshore time series from a pan-European meteorological model called COSMO, with onshore wind speeds from the MIDAS dataset provided by the British Atmospheric Data Centre. Forecasting ability onshore is shown to be improved with the inclusion of the offshore sites with improvements of up to 25% in RMS error at 6 h ahead. In addition, the VAR model is used to synthesise time series of wind at each offshore site, which are then used to estimate wind farm capacity factors at the sites in question. These are then compared with estimates of capacity factors derived from the work of Hawkins et al. (2011). A good degree of agreement is established indicating that this synthesis tool should be useful in power system impact studies.

The combined value of wind and solar power forecasting improvements and electricity storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Bri-Mathias; Brancucci Martinez-Anido, Carlo; Wang, Qin

As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies aremore » used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.« less

The combined value of wind and solar power forecasting improvements and electricity storage

DOE PAGES

Hodge, Bri-Mathias; Brancucci Martinez-Anido, Carlo; Wang, Qin; ...

2018-02-12

As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies aremore » used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.« less

Sensor network based solar forecasting using a local vector autoregressive ridge framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, J.; Yoo, S.; Heiser, J.

2016-04-04

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations duemore » to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.« less

Operational warnings issued by the SMC in the 8th March snow event in Catalonia

NASA Astrophysics Data System (ADS)

Vilaclara, E.; Segalà, S.; Andrés, A.; Aran, M.

2010-09-01

The snowfall event of 8th March 2010 was one of the most important of the last years in Catalonia, with high societal impact in the communication and in the power distribution. Since 2002, after an agreement between Meteorological Service of Catalonia (SMC) and Civil Protection authority, the SMC is the agency responsible to carry out meteorological warnings in Catalonia. These warnings are issued depending on the thresholds which are expected to be exceeded (three different probability degrees are defined), and are characterized by a high spatial and temporal resolution. In the snow event of 8th March, forecasting team of the SMC did the first meteorological warning three days before, on 5th March. During the two following days broadcasted four different warnings, taking into account the high probability of exceeding 2 cm of snow above 200 meters, 15 above 400 m and 30 above 800 m. Furthermore, the SMC disseminated also two special press releases with the aim to extend the forecast to the public. In the afternoon of Sunday, 7th March, the snow precipitation started in some areas of Catalonia. From this moment, the SMC followed the surveillance of situation with the remote sensing tools and the Meteorological Observers Network data. This network is formed by a hundred of spotters with mobile phones able to transmit observations in real time to our web site. This surveillance and the conceptual model of snow events in Catalonia allowed the forecasters to improve the estimation of the snow level forecasted by mesoscale meteorological models. In this event, the snow level obtained from these models was higher than the real one. In spite of the accuracy of the forecast, the impact was very important in Catalonia. On one hand, it was due to the exceptionality of the event, with 3 cm of snow in Barcelona in the afternoon of a working day. On the other hand, the large amount of wet snow precipitation (until 100 mm), and the wind, contributed to an important snow accumulation in the electric power lines. The snow weight and the wind effect broke down a lot of electrical towers in the north-east of Catalonia and more than 450,000 customers were affected by power outages in the following days.

Inventory of File dvrtma.t12z.ndgd_alaska.grib2

Science.gov Websites

Number of Records: 6 Number Level/Layer Parameter Forecast Valid Description 001 anl PRES ENS=low-res c Pressure [Pa]:surface analysis/forecast error 002 anl UGRD ENS=low-res c U-Component of Wind [m/s]:10 m above ground analysis/forecast error 003 anl VGRD ENS=low-res c V-Component of Wind [m/s]:10 m above

Inventory of File dvrtma.t12z.ndgd_conus.grib2

Science.gov Websites

Number of Records: 6 Number Level/Layer Parameter Forecast Valid Description 001 anl PRES ENS=low-res c Pressure [Pa]:surface analysis/forecast error 002 anl UGRD ENS=low-res c U-Component of Wind [m/s]:10 m above ground analysis/forecast error 003 anl VGRD ENS=low-res c V-Component of Wind [m/s]:10 m above

Using Seasonal Forecasting Data for Vessel Routing

NASA Astrophysics Data System (ADS)

Bell, Ray; Kirtman, Ben

2017-04-01

We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Mailhot, J.; Milbrandt, J. A.; Giguère, A.; McTaggart-Cowan, R.; Erfani, A.; Denis, B.; Glazer, A.; Vallée, M.

2014-01-01

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM-LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.

A high resolution WRF model for wind energy forecasting

NASA Astrophysics Data System (ADS)

Vincent, Claire Louise; Liu, Yubao

2010-05-01

The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the diffusion constant caused damping of the unrealistic fluctuations, but did not completely solve the problem. Using two-way nesting also mitigated the unrealistic fluctuations significantly. It can be concluded that for real case LES modelling of wind farm circulations, care should be taken to ensure the consistency between the mesoscale weather forcing and LES models to avoid exciting spurious noise along the forcing boundary. The development of algorithms that adequately model the sub-grid-scale mixing that cannot be resolved by LES models is an important area for further research. References Liu, Y. Y._W. Liu, W. Y.Y. Cheng, W. Wu, T. T. Warner and K. Parks, 2009: Simulating intra-farm wind variations with the WRF-RTFDDA-LES modeling system. 10th WRF Users' Workshop, Boulder, C, USA. June 23 - 26, 2009. Skamarock, W., J. Dudhia, D.O. Gill, D.M. Barker, M.G.Duda, X-Y. Huang, W. Wang and J.G. Powers, A Description of the Advanced Research WRF version 3, NCAR Technical Note TN-475+STR, NCAR, Boulder, Colorado, 2008.

Impact of the Assimilation of Hyperspectral Infrared Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, Emily B.; Zavodsky, Bradley T; Jedlovec, Gary J.; Elmer, Nicholas J.

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), North American Regional Reanalysis (NARR) reanalysis, and Rapid Refresh analyses.

A Project Assessment of Stabilizing System of WT Generation using Rechargeable Battery

NASA Astrophysics Data System (ADS)

Kojima, Yasuhiro; Takano, Tomihiro; Tanikawa, Ryoichi; Takagi, Tetsuro; Hirooka, Koutaro; Kumagai, Sadatoshi

The expansion of the renewable energy introduction is examined as measures for controlling global warming. Wind power generation is expected as effective power resource, but the negative impact from the difficulty of an unstable output is concerned. In recent years, WT generation with contract of cut-of with shorting adjusting power and with rechargeable battery for stabilizing control are examined, but the introduction has not been accelerated yet because there is an influence in WT generation entrepreneur's business. In this paper, we make a brief summary of relation between the fluctuation of wind power generation and stability of electric power operation, and two types of approach; cut-off contract and stabilization using rechargeable battery. For the stabilization using battery, there are two methods, one is reduction control and the other is constant control. We propose a new control method for constant control based on profit optimization considering WT generation forecast and its risk of deviation. We also propose the estimation method for the .limitation of battery installation. Simulation results show the efficiency of our proposed methods.

Optimizing Lidar Scanning Strategies for Wind Energy Measurements (Invited)

NASA Astrophysics Data System (ADS)

Newman, J. F.; Bonin, T. A.; Klein, P.; Wharton, S.; Chilson, P. B.

2013-12-01

Environmental concerns and rising fossil fuel prices have prompted rapid development in the renewable energy sector. Wind energy, in particular, has become increasingly popular in the United States. However, the intermittency of available wind energy makes it difficult to integrate wind energy into the power grid. Thus, the expansion and successful implementation of wind energy requires accurate wind resource assessments and wind power forecasts. The actual power produced by a turbine is affected by the wind speeds and turbulence levels experienced across the turbine rotor disk. Because of the range of measurement heights required for wind power estimation, remote sensing devices (e.g., lidar) are ideally suited for these purposes. However, the volume averaging inherent in remote sensing technology produces turbulence estimates that are different from those estimated by a sonic anemometer mounted on a standard meteorological tower. In addition, most lidars intended for wind energy purposes utilize a standard Doppler beam-swinging or Velocity-Azimuth Display technique to estimate the three-dimensional wind vector. These scanning strategies are ideal for measuring mean wind speeds but are likely inadequate for measuring turbulence. In order to examine the impact of different lidar scanning strategies on turbulence measurements, a WindCube lidar, a scanning Halo lidar, and a scanning Galion lidar were deployed at the Southern Great Plains Atmospheric Radiation Measurement (ARM) site in Summer 2013. Existing instrumentation at the ARM site, including a 60-m meteorological tower and an additional scanning Halo lidar, were used in conjunction with the deployed lidars to evaluate several user-defined scanning strategies. For part of the experiment, all three scanning lidars were pointed at approximately the same point in space and a tri-Doppler analysis was completed to calculate the three-dimensional wind vector every 1 second. In another part of the experiment, one of the scanning lidars ran a Doppler beam-swinging technique identical to that used by the WindCube lidar while another scanning lidar used a novel six-beam technique that has been presented in the literature as a better alternative for measuring turbulence. In this presentation, turbulence measurements from these techniques are compared to turbulence measured by the WindCube lidar and sonic anemometers on the 60-m meteorological tower. In addition, recommendations are made for lidar measurement campaigns for wind energy applications.

Energy: An annotated bibliography

NASA Technical Reports Server (NTRS)

Blow, S. J. (Compiler)

1975-01-01

This bibliography is the first update of a previous energy bibliography dated August 1974. It contains approximately 3,300 selected references on energy and energy related topics from bibliographic sources dated August 1974 through December 1974. The references are arranged by date, with the latest works first, in subject categories. (1) Energy and power - general; resources, supply/demand, and forecasting; policy, legislation, and regulation; research and development, environment; consumption and economics; conservation; and systems analysis. (2) Energy and power sources - general; fossil fuels; hydrogen and other fuels; organic wastes and waste heat; nuclear; geothermal; solar; wind; ocean/water; magnetohydrodynamics and electrohydrodynamics; and gas and steam turbines. (3) Energy and power storage and transmission.

Energy: An annotated bibliography

NASA Technical Reports Server (NTRS)

Blow, S. J. (Compiler)

1974-01-01

This bibliography is a compilation of approximately 4,300 selected references on energy and energy related topics. The references are arranged by date, with the latest works first, in the following subject categories: (1) energy and power - general; resources, supply/demand, and forecasting; policy, legislation, and regulation; research and development; environment; consumption and economics; and conservation, (2) energy and power sources - general, fossil fuels, hydrogen and methanol, organic wastes and waste heat, nuclear, geothermal, solar, wind, ocean/water, magnetohydrodynamics and electrohydrodynamics, and gas and steam turbines, and (3) energy and power storage and transmission. Literature from bibliographic sources dated January 1972 through July 1974 is covered, with some pertinent literature prior to 1972 included.

Impacts of an offshore wind farm on the lower marine atmosphere

NASA Astrophysics Data System (ADS)

Volker, P. J.; Huang, H.; Capps, S. B.; Badger, J.; Hahmann, A. N.; Hall, A. D.

2013-12-01

Due to a continuing increase in energy demand and heightened environmental consciousness, the State of California is seeking out more environmentally-friendly energy resources. Strong and persistent winds along California's coast can be harnessed effectively by current wind turbine technology, providing a promising source of alternative energy. Using an advanced wind farm parameterization implemented in the Weather Research & Forecast model, we investigate the potential impacts of a large offshore wind farm on the lower marine atmosphere. Located offshore of the Sonoma Coast in northern California, this theoretical wind farm includes 200-7 megawatt, 125 m hub height wind turbines which are able to provide a total of 1.4 TW of power for use in neighboring cities. The wind turbine model (i.e., the Explicit Wake Parameterization originally developed at the Danish Technical University) acts as a source of drag where the sub-grid scale velocity deficit expansion is explicitly described. A swath consisting of hub-height velocity deficits and temperature and moisture anomalies extends more than 100 km downstream of the wind farm location. The presence of the large modern wind farm also creates flow distortion upstream in conjunction with an enhanced vertical momentum and scalar transport.

Hydrodaynamic - Statistical Forecast Method To 36-48h Ahead Of Storm Wind And Tornadoes Over The Territory Of Europe And Siberia

NASA Astrophysics Data System (ADS)

Perekhodtseva, Elvira V.

2010-05-01

Development of successful method of forecast of storm winds, including squalls and tornadoes, that often result in human and material losses, could allow one to take proper measures against destruction of buildings and to protect people. Well-in-advance successful forecast (from 12 hours to 48 hour) makes possible to reduce the losses. Prediction of the phenomena involved is a very difficult problem for synoptic till recently. The existing graphic and calculation methods still depend on subjective decision of an operator. Nowadays in Russia there is no hydrodynamic model for forecast of the maximal wind velocity V> 25m/c, hence the main tools of objective forecast are statistical methods using the dependence of the phenomena involved on a number of atmospheric parameters (predictors). . Statistical decisive rule of the alternative and probability forecast of these events was obtained in accordance with the concept of "perfect prognosis" using the data of objective analysis. For this purpose the different teaching samples of present and absent of this storm wind and rainfalls were automatically arranged that include the values of forty physically substantiated potential predictors. Then the empirical statistical method was used that involved diagonalization of the mean correlation matrix R of the predictors and extraction of diagonal blocks of strongly correlated predictors. Thus for these phenomena the most informative predictors were selected without loosing information. The statistical decisive rules for diagnosis and prognosis of the phenomena involved U(X) were calculated for choosing informative vector-predictor. We used the criterion of distance of Mahalanobis and criterion of minimum of entropy by Vapnik-Chervonenkis for the selection predictors. Successful development of hydrodynamic models for short-term forecast and improvement of 36-48h forecasts of pressure, temperature and others parameters allowed us to use the prognostic fields of those models for calculations of the discriminant functions in the nodes of the grid 75x75km and the values of probabilities P of dangerous wind and thus to get fully automated forecasts. . In order to apply the alternative forecast to European part of Russia and Europe the author proposes the empirical threshold values specified for this phenomenon and advance period 36 hours. According to the Pirsey-Obukhov criterion (T), the success of this hydrometeorological-statistical method of forecast of storm wind and tornadoes to 36 -48 hours ahead in the warm season for the territory of Europe part of Russia and Siberia is T = 1-a-b=0,54-0,78 after independent and author experiments during the period 2004-2009 years. A lot of examples of very successful forecasts are submitted at this report for the territory of Europe and Russia. The same decisive rules were applied to the forecast of these phenomena during cold period in 2009-2010 years too. On the first month of 2010 a lot of cases of storm wind with heavy snowfall were observed and were forecasting over the territory of France, Italy and Germany.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muljadi, Eduard; Gevorgian, Vahan; Hoke, Andy

The total inertia stored in all rotating masses (synchronous generators, induction motors, etc.) connected to a power system grid is an essential force that keeps the system stable after disturbances. Power systems have been experiencing reduced inertia during the past few decades [1]. This trend will continue as the level of renewable generation (e.g., wind and solar) increases. Wind power plants (WPPs) and other renewable power plants with power electronic interfaces are capable of delivering frequency response (both droop and/or inertial response) by a control action; thus, the reduction in available online inertia can be compensated by designing the plantmore » control to include frequency response. The source of energy to be delivered as inertial response is determined by the type of generation (wind, photovoltaic, concentrating solar power, etc.) and the control strategy chosen. The importance of providing ancillary services to ensure frequency control within a power system is evidenced from many recent publications with different perspectives (manufacturer, system operator, regulator, etc.) [2]-[6]. This paper is intended to provide operators with a method for the real-time assessment of the available inertia of a WPP. This is critical to managing power system stability and the reserve margin. In many states, modern WPPs are required to provide ancillary services (e.g., frequency regulation via governor response and inertial response) to the grid. This paper describes the method of estimating the available inertia and the profile of the forecasted response from a WPP.« less

30 WS North Base Wind Study

NASA Technical Reports Server (NTRS)

Wheeler, Mark

2011-01-01

The 30 Weather Squadron (30 WS) is concerned about strong winds observed at their northern towers without advance warning. They state that terrain influences along the extreme northern fringes of Vandenberg Air Force Base (VAFB) make it difficult for forecasters to issue timely and accurate high wind warnings for northeasterly wind events. These events tend to occur during the winter or early spring when they are under the influence of the Great Basin high pressure weather regime. The Launch Weather Officers (LWOs) have seen these rapid wind increases in the current northern Towers 60, 70 and 71 in excess of their 35 kt operational warning threshold. For this task, the 30 WS requested the Applied Meteorology Unit (AMU) analyze data from days when these towers reported winds in excess of 35 kt and determine if there were any precursors in the observations that would allow the LWOs to better forecast and warn their operational customers for these wind events. The 30 WS provided wind tower data for the cool season (October - March) from the period January 2004-March 20 IO. The AMU decoded and evaluated the wind tower data for 66 days identified by the 30 WS as having high-wind events. Out of the 66 event days, only 30 had wind speed observations of > or =35 kt from at least one of the three northern towers. The AMU analyzed surface and upper air charts to determine the synoptic conditions for each event day along with tower peak wind speed and direction time series and wind rose charts for all 30 event days. The analysis revealed a trend on all event days in which the tower winds shifted to the northeast for a period of time before the first recorded > or =35 kt wind speed. The time periods for the 30 event days ranged from 20 minutes to several hours, with a median value of 110 minutes. This trend, if monitored, could give the 30 WS forecasters a precursor to assist in issuing an operational warning before a high wind event occurs. The AMU recommends developing a high-wind alert capability for VAFB using a local mesoscale model to forecast these wind events. The model should incorporate all of the VAFB local data sets and have a forecast capability of between 2 to 24 hours. Such a model would allow the meteorologists at VAFB to alert the operational customers of high wind events in a timely manner so protective action could be taken.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

2009-01-01

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Land-Breeze Forecasting

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Wheeler, Mark M.; Merceret, Francis J. (Technical Monitor)

2002-01-01

The nocturnal land breeze at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) is both operationally significant and challenging to forecast. The occurrence and timing of land breezes impact low-level winds, atmospheric stability, low temperatures, and fog development. Accurate predictions of the land breeze are critical for toxic material dispersion forecasts associated with space launch missions, since wind direction and low-level stability can change noticeably with the onset of a land breeze. This report presents a seven-year observational study of land breezes over east-central Florida from 1995 to 2001. This comprehensive analysis was enabled by the high-resolution tower observations over KSC/CCAFS. Five-minute observations of winds, temperature, and moisture along with 9 15-MHz Doppler Radar Wind Profiler data were used to analyze specific land-breeze cases, while the tower data were used to construct a composite climatology. Utilities derived from this climatology were developed to assist forecasters in determining the land-breeze occurrence, timing, and movement based on predicted meteorological conditions.

Predicting tropical cyclone intensity using satellite measured equivalent blackbody temperatures of cloud tops. [regression analysis

NASA Technical Reports Server (NTRS)

Gentry, R. C.; Rodgers, E.; Steranka, J.; Shenk, W. E.

1978-01-01

A regression technique was developed to forecast 24 hour changes of the maximum winds for weak (maximum winds less than or equal to 65 Kt) and strong (maximum winds greater than 65 Kt) tropical cyclones by utilizing satellite measured equivalent blackbody temperatures around the storm alone and together with the changes in maximum winds during the preceding 24 hours and the current maximum winds. Independent testing of these regression equations shows that the mean errors made by the equations are lower than the errors in forecasts made by the peristence techniques.

The impact of scatterometer wind data on global weather forecasting

NASA Technical Reports Server (NTRS)

Atlas, D.; Baker, W. E.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.

1984-01-01

The impact of SEASAT-A scatterometer (SASS) winds on coarse resolution atmospheric model forecasts was assessed. The scatterometer provides high resolution winds, but each wind can have up to four possible directions. One wind direction is correct; the remainder are ambiguous or "aliases'. In general, the effect of objectively dealiased-SASS data was found to be negligible in the Northern Hemisphere. In the Southern Hemisphere, the impact was larger and primarily beneficial when vertical temperature profile radiometer (VTPR) data was excluded. However, the inclusion of VTPR data eliminates the positive impact, indicating some redundancy between the two data sets.

Better hurricane forecasts

NASA Astrophysics Data System (ADS)

Friebele, Elaine

People living in coastal areas can rely on better hurricane predictions because forecasters now have nearly instant access to global wind data. Measurements of wind speed and direction over the world's oceans are available within 3 hours of measurement from the Japanese satellite ADEOS (Advanced Earth Observing Satellite).Wind parameters at 25-km resolution are being measured by NASA's scatterometer traveling on the Japanese satellite ADEOS (Advanced Earth Observing Satellite). “The high accuracy and spatial resolution of the data were quickly recognized by our forecasters, who have been starved for data over significant expanses of the world's oceans,” said Jim Hoke, director of NOAA's Marine Prediction Center.

Optimizing Wind And Hydropower Generation Within Realistic Reservoir Operating Policy

NASA Astrophysics Data System (ADS)

Magee, T. M.; Clement, M. A.; Zagona, E. A.

2012-12-01

Previous studies have evaluated the benefits of utilizing the flexibility of hydropower systems to balance the variability and uncertainty of wind generation. However, previous hydropower and wind coordination studies have simplified non-power constraints on reservoir systems. For example, some studies have only included hydropower constraints on minimum and maximum storage volumes and minimum and maximum plant discharges. The methodology presented here utilizes the pre-emptive linear goal programming optimization solver in RiverWare to model hydropower operations with a set of prioritized policy constraints and objectives based on realistic policies that govern the operation of actual hydropower systems, including licensing constraints, environmental constraints, water management and power objectives. This approach accounts for the fact that not all policy constraints are of equal importance. For example target environmental flow levels may not be satisfied if it would require violating license minimum or maximum storages (pool elevations), but environmental flow constraints will be satisfied before optimizing power generation. Additionally, this work not only models the economic value of energy from the combined hydropower and wind system, it also captures the economic value of ancillary services provided by the hydropower resources. It is recognized that the increased variability and uncertainty inherent with increased wind penetration levels requires an increase in ancillary services. In regions with liberalized markets for ancillary services, a significant portion of hydropower revenue can result from providing ancillary services. Thus, ancillary services should be accounted for when determining the total value of a hydropower system integrated with wind generation. This research shows that the end value of integrated hydropower and wind generation is dependent on a number of factors that can vary by location. Wind factors include wind penetration level, variability due to geographic distribution of wind resources, and forecast error. Electric power system factors include the mix of thermal generation resources, available transmission, demand patterns, and market structures. Hydropower factors include relative storage capacity, reservoir operating policies and hydrologic conditions. In addition, the wind, power system, and hydropower factors are often interrelated because stochastic weather patterns can simultaneously influence wind generation, power demand, and hydrologic inflows. One of the central findings is that the sensitivity of the model to changes cannot be performed one factor at a time because the impact of the factors is highly interdependent. For example, the net value of wind generation may be very sensitive to changes in transmission capacity under some hydrologic conditions, but not at all under others.

Turbulence-driven Coronal Heating and Improvements to Empirical Forecasting of the Solar Wind

NASA Astrophysics Data System (ADS)

Woolsey, Lauren N.; Cranmer, Steven R.

2014-06-01

Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvén waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

On using scatterometer and altimeter data to improve storm surge forecasting in the Adriatic Sea

NASA Astrophysics Data System (ADS)

Bajo, Marco; Umgiesser, Georg; De Biasio, Francesco; Vignudelli, Stefano; Zecchetto, Stefano

2017-04-01

Satellite data are seldom used in storm surge forecasting. Among the most important issues related to the storm surge forecasting are the quality of the model wind forcing and the initial condition of the sea surface elevation. In this work, focused on storm surge forecasting in the Adriatic Sea, satellite scatterometer wind data are used to correct the wind speed and direction biases of the ECMWF global atmospheric model by tuning the spatial fields, as an alternative to data assimilation. The capability of such an unbiased wind is tested against that of a high resolution wind, produced by a regional non-hydrostatic model. On the other hand, altimeter Total Water Level Envelope (TWLE) data, which provide the sea level elevation, are used to improve the accuracy of the initial state of the model simulations. This is done by assimilating into a storm surge model the TWLE obtained by the altimeter observations along ground tracks, after subtraction of the tidal components. In order to test the methodology, eleven storm surge events recorded in Venice, from 2008 to 2012, have been simulated using different configurations of forcing wind and altimeter data assimilation. Results show that the relative error on the estimation of the maximum surge peak, averaged over the cases considered, decreases from 13% to 7% using both the unbiased wind and the altimeter data assimilation, while forcing the hydrodynamic model with the high resolution wind (no tuning), the altimeter data assimilation reduces the error from 9% to 6%.

Near-surface wind speed statistical distribution: comparison between ECMWF System 4 and ERA-Interim

NASA Astrophysics Data System (ADS)

Marcos, Raül; Gonzalez-Reviriego, Nube; Torralba, Verónica; Cortesi, Nicola; Young, Doo; Doblas-Reyes, Francisco J.

2017-04-01

In the framework of seasonal forecast verification, knowing whether the characteristics of the climatological wind speed distribution, simulated by the forecasting systems, are similar to the observed ones is essential to guide the subsequent process of bias adjustment. To bring some light about this topic, this work assesses the properties of the statistical distributions of 10m wind speed from both ERA-Interim reanalysis and seasonal forecasts of ECMWF system 4. The 10m wind speed distribution has been characterized in terms of the four main moments of the probability distribution (mean, standard deviation, skewness and kurtosis) together with the coefficient of variation and goodness of fit Shapiro-Wilks test, allowing the identification of regions with higher wind variability and non-Gaussian behaviour at monthly time-scales. Also, the comparison of the predicted and observed 10m wind speed distributions has been measured considering both inter-annual and intra-seasonal variability. Such a comparison is important in both climate research and climate services communities because it provides useful climate information for decision-making processes and wind industry applications.

Medium-range fire weather forecasts

Treesearch

J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

1991-01-01

The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

An Initial Assessment of the Impact of CYGNSS Ocean Surface Wind Assimilation on Navy Global and Mesoscale Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Baker, N. L.; Tsu, J.; Swadley, S. D.

2017-12-01

We assess the impact of assimilation of CYclone Global Navigation Satellite System (CYGNSS) ocean surface winds observations into the NAVGEM[i] global and COAMPS®[ii] mesoscale numerical weather prediction (NWP) systems. Both NAVGEM and COAMPS® used the NRL 4DVar assimilation system NAVDAS-AR[iii]. Long term monitoring of the NAVGEM Forecast Sensitivity Observation Impact (FSOI) indicates that the forecast error reduction for ocean surface wind vectors (ASCAT and WindSat) are significantly larger than for SSMIS wind speed observations. These differences are larger than can be explained by simply two pieces of information (for wind vectors) versus one (wind speed). To help understand these results, we conducted a series of Observing System Experiments (OSEs) to compare the assimilation of ASCAT wind vectors with the equivalent (computed) ASCAT wind speed observations. We found that wind vector assimilation was typically 3 times more effective at reducing the NAVGEM forecast error, with a higher percentage of beneficial observations. These results suggested that 4DVar, in the absence of an additional nonlinear outer loop, has limited ability to modify the analysis wind direction. We examined several strategies for assimilating CYGNSS ocean surface wind speed observations. In the first approach, we assimilated CYGNSS as wind speed observations, following the same methodology used for SSMIS winds. The next two approaches converted CYGNSS wind speed to wind vectors, using NAVGEM sea level pressure fields (following Holton, 1979), and using NAVGEM 10-m wind fields with the AER Variational Analysis Method. Finally, we compared these methods to CYGNSS wind speed assimilation using multiple outer loops with NAVGEM Hybrid 4DVar. Results support the earlier studies suggesting that NAVDAS-AR wind speed assimilation is sub-optimal. We present detailed results from multi-month NAVGEM assimilation runs along with case studies using COAMPS®. Comparisons include the fit of analyses and forecasts with in-situ observations and analyses from other NWP centers (e.g. ECMWF and GFS). [i] NAVy Global Environmental Model [ii] COAMPS® is a registered trademark of the Naval Research Laboratory for the Navy's Coupled Ocean Atmosphere Mesoscale Prediction System. [iii] NRL Atmospheric Variational Data Assimilation System

National Weather Service Forecast Office - Honolulu, Hawai`i

Science.gov Websites

Locations - Coastal Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel Oahu Forecast Oahu Surf Forecast Coastal Wind Observations Buoy Reports, and current weather conditions for selected locations tides, sunrise and sunset information Coastal Waters Forecast general weather

Mixture EMOS model for calibrating ensemble forecasts of wind speed.

PubMed

Baran, S; Lerch, S

2016-03-01

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.

Analysis of winter weather conditions and their potential impact on wind farm operations

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Treinish, L. A.; Praino, A.

2009-12-01

Severe weather conditions have two primary impacts on wind farm operations. The first relates to understanding potential damage to the turbines themselves and what actions are required to mitigate the effects. The second is recognizing what conditions may lead to a full or partial shutdown of the wind farm with sufficient lead time to determine the likely inability to meet energy generation committments. Ideally, wind forecasting suitable for wind farm operations should be of sufficient fidelity to resolve features within the boundary layer that lead to either damaging conditions or useful power generation. Given the complexity of the site-specific factors that effect the boundary layer at the scale of typical land-based wind farm locations such as topography, vegetation, land use, soil conditions, etc., which may vary with turbine design and layout within the farm, enabling reliable forecasts of too little or too much wind is challenging. A potential solution should involve continuous updates of alert triggering criteria through analysis of local wind patterns and probabilistic risk assessment for each location. To evaluate this idea, we utilize our operational mesoscale prediction system, dubbed “Deep Thunder”, developed at the IBM Thomas J. Watson Research Center. In particular, we analyze winter-time near-surface winds in upstate New York, where four similar winds farms are located. Each of these farms were built at roughly the same time and utilize similar turbines. Given the relative uncertainty associated with numerical weather prediction at this scale, and the difference in risk assessment due to the two primary impacts of severe weather, probabilistic forecasts are a prerequisite. Hence, we have employed ensembles of weather scenarios, which are based on the NCAR WRF-ARW modelling system. The set of ensemble members was composed with variations in the choices of physics and parameterization schemes, and source of background fields for initial conditions with horizontal grid resolutions in the one to two km range. In addition, the vertical grid structure was defined to ensure at least ten levels within the boundary layer and two from the bottom to the top of the turbine. This approach enables us to estimate the variability of winds at the farms and how it is distributed over the region. Further, we analyze the potential differences in structural risks at these farms during the 2009 winter season, and whether such differences in wind and weather patterns should be considered in choice of turbine design, installation and operations. We believe that this methodology can be extended to provide an estimate for mean annual energy production at a wind farm with the potential to improve the quality of siting and layout.

Forecasting surface-layer atmospheric parameters at the Large Binocular Telescope site

NASA Astrophysics Data System (ADS)

Turchi, Alessio; Masciadri, Elena; Fini, Luca

2017-04-01

In this paper, we quantify the performance of an automated weather forecast system implemented on the Large Binocular Telescope (LBT) site at Mt Graham (Arizona) in forecasting the main atmospheric parameters close to the ground. The system employs a mesoscale non-hydrostatic numerical model (Meso-Nh). To validate the model, we compare the forecasts of wind speed, wind direction, temperature and relative humidity close to the ground with the respective values measured by instrumentation installed on the telescope dome. The study is performed over a large sample of nights uniformly distributed over 2 yr. The quantitative analysis is done using classical statistical operators [bias, root-mean-square error (RMSE) and σ] and contingency tables, which allows us to extract complementary key information, such as the percentage of correct detections (PC) and the probability of obtaining a correct detection within a defined interval of values (POD). The results of our study indicate that the model performance in forecasting the atmospheric parameters we have just cited are very good, in some cases excellent: RMSE for temperature is below 1°C, for relative humidity it is 14 per cent and for the wind speed it is around 2.5 m s-1. The relative error of the RMSE for wind direction varies from 9 to 17 per cent depending on the wind speed conditions. This work is performed in the context of the ALTA (Advanced LBT Turbulence and Atmosphere) Center project, whose final goal is to provide forecasts of all the atmospheric parameters and the optical turbulence to support LBT observations, adaptive optics facilities and interferometric facilities.

Smooth Sailing for Weather Forecasting

NASA Technical Reports Server (NTRS)

2002-01-01

Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.

Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

NASA Astrophysics Data System (ADS)

Henley, E. M.; Pope, E. C. D.

2017-12-01

This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

The Use of Red Green Blue Air Mass Imagery to Investigate the Role of Stratospheric Air in a Non-convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, E. B.; Zavodsky, B. T.; Jedlovec, G. J.; Molthan, A. L.

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and Rapid Refresh analyses.

The Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, Emily; Zavodsky, Bradley; Jedlovec, Gary; Elmer, Nicholas

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and Rapid Refresh analyses.

Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, E. B.; Zavodsky, B. T.; Folmer, M. J.; Jedlovec, G. J.

2014-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), 32-km North American Regional Reanalysis (NARR) interpolated to a 12-km grid, and 13-km Rapid Refresh analyses.

Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, E. B.; Zavodsky, B. T.; Jedlovec, G. J.

2014-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and Rapid Refresh analyses.

Novel Atmospheric and Sea State Modeling in Ocean Energy Applications

NASA Astrophysics Data System (ADS)

Kallos, George; Galanis, George; Kalogeri, Christina; Larsen, Xiaoli Guo

2013-04-01

The rapidly increasing use of renewable energy sources poses new challenges for the research and technological community today. The integration of the, usually, highly variable wind and wave energy amounts into the general grid, the optimization of energy transition and the forecast of extreme values that could lead to instabilities and failures of the system can be listed among them. In the present work, novel methodologies based on state of the art numerical wind/wave simulation systems and advanced statistical techniques addressing such type of problems are discussed. In particular, extremely high resolution modeling systems simulating the atmospheric and sea state conditions with spatial resolution of 100 meters or less and temporal discretization of a few seconds are utilized in order to simulate in the most detailed way the combined wind-wave energy potential at offshore sites. In addition, a statistical analysis based on a variety of mean and variation measures as well as univariate and bivariate probability distributions is used for the estimation of the variability of the power potential revealing the advantages of the use of combined forms of energy by offshore platforms able to produce wind and wave power simultaneously. The estimation and prediction of extreme wind/wave conditions - a critical issue both for site assessment and infrastructure maintenance - is also studied by means of the 50-year return period over areas with increased power potential. This work has been carried out within the framework of the FP7 project MARINA Platform (http://www.marina-platform.info/index.aspx).

Multi-step-ahead Method for Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data

NASA Astrophysics Data System (ADS)

Wang, Han; Yan, Jie; Liu, Yongqian; Han, Shuang; Li, Li; Zhao, Jing

2017-11-01

Increasing the accuracy of wind speed prediction lays solid foundation to the reliability of wind power forecasting. Most traditional correction methods for wind speed prediction establish the mapping relationship between wind speed of the numerical weather prediction (NWP) and the historical measurement data (HMD) at the corresponding time slot, which is free of time-dependent impacts of wind speed time series. In this paper, a multi-step-ahead wind speed prediction correction method is proposed with consideration of the passing effects from wind speed at the previous time slot. To this end, the proposed method employs both NWP and HMD as model inputs and the training labels. First, the probabilistic analysis of the NWP deviation for different wind speed bins is calculated to illustrate the inadequacy of the traditional time-independent mapping strategy. Then, support vector machine (SVM) is utilized as example to implement the proposed mapping strategy and to establish the correction model for all the wind speed bins. One Chinese wind farm in northern part of China is taken as example to validate the proposed method. Three benchmark methods of wind speed prediction are used to compare the performance. The results show that the proposed model has the best performance under different time horizons.

Sources of Wind Variability at a Single Station in Complex Terrain During Tropical Cyclone Passage

DTIC Science & Technology

2013-12-01

Mesoscale Prediction System CPA Closest point of approach ET Extratropical transition FNMOC Fleet Numerical Meteorology and Oceanography Center...forecasts. However, 2 the TC forecast tracks and warnings they issue necessarily focus on the large-scale structure of the storm , and are not...winds at one station. Also, this technique is a storm - centered forecast and even if the grid spacing is on order of one kilometer, it is unlikely

Tests of high-resolution simulations over a region of complex terrain in Southeast coast of Brazil

NASA Astrophysics Data System (ADS)

Chou, Sin Chan; Luís Gomes, Jorge; Ristic, Ivan; Mesinger, Fedor; Sueiro, Gustavo; Andrade, Diego; Lima-e-Silva, Pedro Paulo

2013-04-01

The Eta Model is used operationally by INPE at the Centre for Weather Forecasts and Climate Studies (CPTEC) to produce weather forecasts over South America since 1997. The model has gone through upgrades along these years. In order to prepare the model for operational higher resolution forecasts, the model is configured and tested over a region of complex topography located near the coast of Southeast Brazil. The model domain includes the two Brazilians cities, Rio de Janeiro and Sao Paulo, urban areas, preserved tropical forest, pasture fields, and complex terrain where it can rise from sea level up to about 1000 m. Accurate near-surface wind direction and magnitude are needed for the power plant emergency plan. Besides, the region suffers from frequent events of floods and landslides, therefore accurate local forecasts are required for disaster warnings. The objective of this work is to carry out a series of numerical experiments to test and evaluate high resolution simulations in this complex area. Verification of model runs uses observations taken from the nuclear power plant and higher resolution reanalyses data. The runs were tested in a period when flow was predominately forced by local conditions and in a period forced by frontal passage. The Eta Model was configured initially with 2-km horizontal resolution and 50 layers. The Eta-2km is a second nesting, it is driven by Eta-15km, which in its turn is driven by Era-Interim reanalyses. The series of experiments consists of replacing surface layer stability function, adjusting cloud microphysics scheme parameters, further increasing vertical and horizontal resolutions. By replacing the stability function for the stable conditions substantially increased the katabatic winds and verified better against the tower wind data. Precipitation produced by the model was excessive in the region. Increasing vertical resolution to 60 layers caused a further increase in precipitation production. This excessive precipitation was reduced by adjusting some parameters in the cloud microphysics scheme. Precipitation overestimate still occurs and further tests are still necessary. The increase of horizontal resolution to 1 km required adjusting model diffusion parameters and refining divergence calculations. Available observations in the region for a thorough evaluation is a major constraint.

An OSSE on Mesoscale Model Assimilation of Simulated HIRAD-Observed Hurricane Surface Winds

NASA Technical Reports Server (NTRS)

Albers, Cerese; Miller, Timothy; Uhlhorn, Eric; Krishnamurti, T. N.

2012-01-01

The hazards of landfalling hurricanes are well known, but progress on improving the intensity forecasts of these deadly storms at landfall has been slow. Many cite a lack of high-resolution data sets taken inside the core of a hurricane, and the lack of reliable measurements in extreme conditions near the surface of hurricanes, as possible reasons why even the most state-of-the-art forecasting models cannot seem to forecast intensity changes better. The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for observing hurricanes, and is operated and researched by NASA Marshall Space Flight Center in partnership with the NOAA Atlantic Oceanographic and Meteorological Laboratory/Hurricane Research Division, the University of Central Florida, the University of Michigan, and the University of Alabama in Huntsville. This instrument?s purpose is to study the wind field of a hurricane, specifically observing surface wind speeds and rain rates, in what has traditionally been the most difficult areas for other instruments to study; the high wind and heavy rain regions. Dr. T. N. Krishnamurti has studied various data assimilation techniques for hurricane and monsoon rain rates, and this study builds off of results obtained from utilizing his style of physical initializations of rainfall observations, but obtaining reliable observations in heavy rain regions has always presented trouble to our research of high-resolution rainfall forecasting. Reliable data from these regions at such a high resolution and wide swath as HIRAD provides is potentially very valuable to mesoscale forecasting of hurricane intensity. This study shows how the data assimilation technique of Ensemble Kalman Filtering (EnKF) in the Weather Research and Forecasting (WRF) model can be used to incorporate wind, and later rain rate, data into a mesoscale model forecast of hurricane intensity. The study makes use of an Observing System Simulation Experiment (OSSE) with a simulated HIRAD dataset sampled during a hurricane and uses EnKF to forecast the track and intensity prediction of the hurricane. Comparisons to truth and error metrics are used to assess the model?s forecast performance.

Forecast of solar wind parameters according to STOP magnetograph observations

NASA Astrophysics Data System (ADS)

Tlatov, A. G.; Pashchenko, M. P.; Ponyavin, D. I.; Svidskii, P. M.; Peshcherov, V. S.; Demidov, M. L.

2016-12-01

The paper discusses the results of the forecast of solar wind parameters at a distance of 1 AU made according to observations made by the STOP telescope magnetograph during 2014-2015. The Wang-Sheeley-Arge (WSA) empirical model is used to reconstruct the magnetic field topology in the solar corona and estimate the solar wind speed in the interplanetary medium. The proposed model is adapted to STOP magnetograph observations. The results of the calculation of solar wind parameters are compared with ACE satellite measurements. It is shown that the use of STOP observations provides a significant correlation of predicted solar wind speed values with the observed ones.

The Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Brendt. Emily; Zavodsky, Bradley; Jedlovec, Gary; Elmer, Nicholas

2014-01-01

Tropopause folds are identified by warm, dry, high-potential vorticity, ozone-rich air and are one explanation for damaging non-convective wind events. Could improved model representation of stratospheric air and associated tropopause folding improve non-convective wind forecasts and high wind warnings? The goal of this study is to assess the impact of assimilating Hyperspectral Infrared (IR) profiles on forecasting stratospheric air, tropopause folds, and associated non-convective winds: (1) AIRS: Atmospheric Infrared Sounder (2) IASI: Infrared Atmospheric Sounding Interferometer (3) CrIMSS: Cross-track Infrared and Microwave Sounding Suite

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

NASA Astrophysics Data System (ADS)

Bodini, Nicola; Zardi, Dino; Lundquist, Julie K.

2017-08-01

The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

DOE PAGES

Bodini, Nicola; Zardi, Dino; Lundquist, Julie K.

2017-08-14

The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions.more » Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. As a result, these insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.« less

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bodini, Nicola; Zardi, Dino; Lundquist, Julie K.

The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions.more » Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. As a result, these insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.« less

A Real-time 3D Visualization of Global MHD Simulation for Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Murata, K.; Matsuoka, D.; Kubo, T.; Shimazu, H.; Tanaka, T.; Fujita, S.; Watari, S.; Miyachi, H.; Yamamoto, K.; Kimura, E.; Ishikura, S.

2006-12-01

Recently, many satellites for communication networks and scientific observation are launched in the vicinity of the Earth (geo-space). The electromagnetic (EM) environments around the spacecraft are always influenced by the solar wind blowing from the Sun and induced electromagnetic fields. They occasionally cause various troubles or damages, such as electrification and interference, to the spacecraft. It is important to forecast the geo-space EM environment as well as the ground weather forecasting. Owing to the recent remarkable progresses of super-computer technologies, numerical simulations have become powerful research methods in the solar-terrestrial physics. For the necessity of space weather forecasting, NICT (National Institute of Information and Communications Technology) has developed a real-time global MHD simulation system of solar wind-magnetosphere-ionosphere couplings, which has been performed on a super-computer SX-6. The real-time solar wind parameters from the ACE spacecraft at every one minute are adopted as boundary conditions for the simulation. Simulation results (2-D plots) are updated every 1 minute on a NICT website. However, 3D visualization of simulation results is indispensable to forecast space weather more accurately. In the present study, we develop a real-time 3D webcite for the global MHD simulations. The 3-D visualization results of simulation results are updated every 20 minutes in the following three formats: (1)Streamlines of magnetic field lines, (2)Isosurface of temperature in the magnetosphere and (3)Isoline of conductivity and orthogonal plane of potential in the ionosphere. For the present study, we developed a 3-D viewer application working on Internet Explorer browser (ActiveX) is implemented, which was developed on the AVS/Express. Numerical data are saved in the HDF5 format data files every 1 minute. Users can easily search, retrieve and plot past simulation results (3D visualization data and numerical data) by using the STARS (Solar-terrestrial data Analysis and Reference System). The STARS is a data analysis system for satellite and ground-based observation data for solar-terrestrial physics.

The impacts of wind power integration on sub-daily variation in river flows downstream of hydroelectric dams.

PubMed

Kern, Jordan D; Patino-Echeverri, Dalia; Characklis, Gregory W

2014-08-19

Due to their operational flexibility, hydroelectric dams are ideal candidates to compensate for the intermittency and unpredictability of wind energy production. However, more coordinated use of wind and hydropower resources may exacerbate the impacts dams have on downstream environmental flows, that is, the timing and magnitude of water flows needed to sustain river ecosystems. In this paper, we examine the effects of increased (i.e., 5%, 15%, and 25%) wind market penetration on prices for electricity and reserves, and assess the potential for altered price dynamics to disrupt reservoir release schedules at a hydroelectric dam and cause more variable and unpredictable hourly flow patterns (measured in terms of the Richards-Baker Flashiness (RBF) index). Results show that the greatest potential for wind energy to impact downstream flows occurs at high (∼25%) wind market penetration, when the dam sells more reserves in order to exploit spikes in real-time electricity prices caused by negative wind forecast errors. Nonetheless, compared to the initial impacts of dam construction (and the dam's subsequent operation as a peaking resource under baseline conditions) the marginal effects of any increased wind market penetration on downstream flows are found to be relatively minor.

Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woolsey, Lauren N.; Cranmer, Steven R.

Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvén waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPESTmore » is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.« less

Validating the WRF-Chem model for wind energy applications using High Resolution Doppler Lidar data from a Utah 2012 field campaign

NASA Astrophysics Data System (ADS)

Mitchell, M. J.; Pichugina, Y. L.; Banta, R. M.

2015-12-01

Models are important tools for assessing potential of wind energy sites, but the accuracy of these projections has not been properly validated. In this study, High Resolution Doppler Lidar (HRDL) data obtained with high temporal and spatial resolution at heights of modern turbine rotors were compared to output from the WRF-chem model in order to help improve the performance of the model in producing accurate wind forecasts for the industry. HRDL data were collected from January 23-March 1, 2012 during the Uintah Basin Winter Ozone Study (UBWOS) field campaign. A model validation method was based on the qualitative comparison of the wind field images, time-series analysis and statistical analysis of the observed and modeled wind speed and direction, both for case studies and for the whole experiment. To compare the WRF-chem model output to the HRDL observations, the model heights and forecast times were interpolated to match the observed times and heights. Then, time-height cross-sections of the HRDL and WRF-Chem wind speed and directions were plotted to select case studies. Cross-sections of the differences between the observed and forecasted wind speed and directions were also plotted to visually analyze the model performance in different wind flow conditions. A statistical analysis includes the calculation of vertical profiles and time series of bias, correlation coefficient, root mean squared error, and coefficient of determination between two datasets. The results from this analysis reveals where and when the model typically struggles in forecasting winds at heights of modern turbine rotors so that in the future the model can be improved for the industry.

Applications products of aviation forecast models

NASA Technical Reports Server (NTRS)

Garthner, John P.

1988-01-01

A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.

Forecasting intense geomagnetic activity using interplanetary magnetic field data

NASA Astrophysics Data System (ADS)

Saiz, E.; Cid, C.; Cerrato, Y.

2008-12-01

Southward interplanetary magnetic fields are considered traces of geoeffectiveness since they are a main agent of magnetic reconnection of solar wind and magnetosphere. The first part of this work revises the ability to forecast intense geomagnetic activity using different procedures available in the literature. The study shows that current methods do not succeed in making confident predictions. This fact led us to develop a new forecasting procedure, which provides trustworthy results in predicting large variations of Dst index over a sample of 10 years of observations and is based on the value Bz only. The proposed forecasting method appears as a worthy tool for space weather purposes because it is not affected by the lack of solar wind plasma data, which usually occurs during severe geomagnetic activity. Moreover, the results obtained guide us to provide a new interpretation of the physical mechanisms involved in the interaction between the solar wind and the magnetosphere using Faraday's law.

2016 Wind Technologies Market Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiser, Ryan H.; Bolinger, Mark

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. You can find the report, a presentation, and a data file on the Files tab, below. Additionally, several data visualizations are available in the Data Visualizations tab. Highlights of this year’s report include: -Wind power additions continued at a rapid clip in 2016: $13 billion was invested in new wind power plants in 2016. In 2016, wind energy contributed 5.6% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and 29% tomore » 37% in three of those states—Iowa, South Dakota, and Kansas. -Bigger turbines are enhancing wind project performance: Increased blade lengths, in particular, have dramatically increased wind project capacity factors, one measure of project performance. For example, the average 2016 capacity factor among projects built in 2014 and 2015 was 42.6%, compared to an average of 32.1% among projects built from 2004 to 2011 and 25.4% among projects built from 1998 to 2001. -Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen from their highs in 2008, to $800–$1,100/kW. Overall, the average installed cost of wind projects in 2016 was $1,590/kW, down $780/kW from the peak in 2009 and 2010. -Wind energy prices remain low: After topping out at nearly 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to around 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country (such as Texas, Iowa, Oklahoma). These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. -The supply chain continued to adjust to swings in domestic demand for wind equipment: Wind sector employment reached a new high of more than 101,000 full-time workers at the end of 2016. For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (65-80%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the turbine. -Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, though declining, various forecasts for the domestic market show expected wind power capacity additions averaging more than 9,000 MW/year from 2017 to 2020.« less

An Approach to Remove the Systematic Bias from the Storm Surge forecasts in the Venice Lagoon

NASA Astrophysics Data System (ADS)

Canestrelli, A.

2017-12-01

In this work a novel approach is proposed for removing the systematic bias from the storm surge forecast computed by a two-dimensional shallow-water model. The model covers both the Adriatic and Mediterranean seas and provides the forecast at the entrance of the Venice Lagoon. The wind drag coefficient at the water-air interface is treated as a calibration parameter, with a different value for each range of wind velocities and wind directions. This sums up to a total of 16-64 parameters to be calibrated, depending on the chosen resolution. The best set of parameters is determined by means of an optimization procedure, which minimizes the RMS error between measured and modeled water level in Venice for the period 2011-2015. It is shown that a bias is present, for which the peaks of wind velocities provided by the weather forecast are largely underestimated, and that the calibration procedure removes this bias. When the calibrated model is used to reproduce events not included in the calibration dataset, the forecast error is strongly reduced, thus confirming the quality of our procedure. The proposed approach it is not site-specific and could be applied to different situations, such as storm surges caused by intense hurricanes.

AE Geomagnetic Index Predictability for High Speed Solar Wind Streams: A Wavelet Decomposition Technique

NASA Technical Reports Server (NTRS)

Guarnieri, Fernando L.; Tsurutani, Bruce T.; Hajra, Rajkumar; Echer, Ezequiel; Gonzalez, Walter D.; Mannucci, Anthony J.

2014-01-01

High speed solar wind streams cause geomagnetic activity at Earth. In this study we have applied a wavelet interactive filtering and reconstruction technique on the solar wind magnetic field components and AE index series to allowed us to investigate the relationship between the two. The IMF Bz component was found as the most significant solar wind parameter responsible by the control of the AE activity. Assuming magnetic reconnection associated to southward directed Bz is the main mechanism transferring energy into the magnetosphere, we adjust parameters to forecast the AE index. The adjusted routine is able to forecast AE, based only on the Bz measured at the L1 Lagrangian point. This gives a prediction approximately 30-70 minutes in advance of the actual geomagnetic activity. The correlation coefficient between the observed AE data and the forecasted series reached values higher than 0.90. In some cases the forecast reproduced particularities observed in the signal very well.The high correlation values observed and the high efficacy of the forecasting can be taken as a confirmation that reconnection is the main physical mechanism responsible for the energy transfer during HILDCAAs. The study also shows that the IMF Bz component low frequencies are most important for AE prediction.

Extra-tropical Cyclones and Windstorms in Seasonal Forecasts

NASA Astrophysics Data System (ADS)

Leckebusch, Gregor C.; Befort, Daniel J.; Weisheimer, Antje; Knight, Jeff; Thornton, Hazel; Roberts, Julia; Hermanson, Leon

2015-04-01

Severe damages and large insured losses over Europe related to natural phenomena are mostly caused by extra-tropical cyclones and their related windstorm fields. Thus, an adequate representation of these events in seasonal prediction systems and reliable forecasts up to a season in advance would be of high value for society and economy. In this study, state-of-the-art seasonal forecast prediction systems are analysed (ECMWF, UK Met Office) regarding the general climatological representation and the seasonal prediction of extra-tropical cyclones and windstorms during the core winter season (DJF) with a lead time of up to four months. Two different algorithms are used to identify cyclones and windstorm events in these datasets. Firstly, we apply a cyclone identification and tracking algorithm based on the Laplacian of MSLP and secondly, we use an objective wind field tracking algorithm to identify and track continuous areas of extreme high wind speeds (cf. Leckebusch et al., 2008), which can be related to extra-tropical winter cyclones. Thus, for the first time, we can analyse the forecast of severe wind events near to the surface caused by extra-tropical cyclones. First results suggest a successful validation of the spatial climatological distributions of wind storm and cyclone occurrence in the seasonal forecast systems in comparison with reanalysis data (ECMWF-ERA40 & ERAInterim) in general. However, large biases are found for some areas. The skill of the seasonal forecast systems in simulating the year-to-year variability of the frequency of severe windstorm events and cyclones is investigated using the ranked probability skill score. Positive skill is found over large parts of the Northern Hemisphere as well as for the most intense extra-tropical cyclones and its related wind fields.

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 4

NASA Technical Reports Server (NTRS)

Keitz, J. F.

1982-01-01

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 4 of the four major tasks included in the study. Task 4 uses flight plan segment wind and temperature differences as indicators of dates and geographic areas for which significant forecast errors may have occurred. An in-depth analysis is then conducted for the days identified. The analysis show that significant errors occur in the operational forecast on 15 of the 33 arbitrarily selected days included in the study. Wind speeds in an area of maximum winds are underestimated by at least 20 to 25 kts. on 14 of these days. The analysis also show that there is a tendency to repeat the same forecast errors from prog to prog. Also, some perceived forecast errors from the flight plan comparisons could not be verified by visual inspection of the corresponding National Meteorological Center forecast and analyses charts, and it is likely that they are the result of weather data interpolation techniques or some other data processing procedure in the airlines' flight planning systems.

Interaction Between the Atmospheric Boundary Layer and Wind Energy: From Continental-Scale to Turbine-Scale

NASA Astrophysics Data System (ADS)

St. Martin, Clara Mae

Wind turbines and groups of wind turbines, or "wind plants", interact with the complex and heterogeneous boundary layer of the atmosphere. We define the boundary layer as the portion of the atmosphere directly influenced by the surface, and this layer exhibits variability on a range of temporal and spatial scales. While early developments in wind energy could ignore some of this variability, recent work demonstrates that improved understanding of atmosphere-turbine interactions leads to the discovery of new ways to approach turbine technology development as well as processes such as performance validation and turbine operations. This interaction with the atmosphere occurs at several spatial and temporal scales from continental-scale to turbine-scale. Understanding atmospheric variability over continental-scales and across plants can facilitate reliance on wind energy as a baseload energy source on the electrical grid. On turbine scales, understanding the atmosphere's contribution to the variability in power production can improve the accuracy of power production estimates as we continue to implement more wind energy onto the grid. Wind speed and directional variability within a plant will affect wind turbine wakes within the plants and among neighboring plants, and a deeper knowledge of these variations can help mitigate effects of wakes and possibly even allow the manipulation of these wakes for increased production. Herein, I present the extent of my PhD work, in which I studied outstanding questions at these scales at the intersections of wind energy and atmospheric science. My work consists of four distinct projects. At the coarsest scales, I analyze the separation between wind plant sites needed for statistical independence in order to reduce variability for grid-integration of wind. At lower wind speeds, periods of unstable and more turbulent conditions produce more power than periods of stable and less turbulent conditions, while at wind speeds closer to rated wind speed, periods of unstable and more turbulent conditions produce less power than periods of stable and less turbulent conditions. Using these new, stability- and turbulence-specific power curves to calculate annual energy production (AEP) estimates results in smaller AEPs than if calculated using no stability and turbulence filters, which could have implications for manufacturers and operators. In my third project, I address the problem of expensive power production validation. Rather than erecting towers to provide upwind wind measurements, I explore the utility of using nacelle-mounted anemometers for power curve verification studies. I calculate empirical nacelle transfer functions (NTFs) with upwind tower and turbine measurements. The fifth-order and second-order NTFs show a linear relationship between upwind wind speed and nacelle wind speed at wind speeds less than about 9 m s-1 , but this relationship becomes non-linear at wind speeds higher than about 9 m s-1. The use of NTFs results in AEPs within 1 % of an AEP using upwind wind speeds. Additionally, during periods of unstable conditions as well as during more turbulent conditions, the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of stable conditions and less turbulence conditions at some wind speed bins below rated speed. Finally, in my fourth project, I consider spatial scales on the order of a wind plant. Using power production data from over 300 turbines from four neighboring wind farms in the western US along with simulations using the Weather Research and Forecasting model's Wind Farm Parameterization (WRF-WFP), I investigate the advantage of using the WFP to simulate wakes. During this case, winds from the west and north-northwest range from about 5 to 11 m s-1. A down-ramp occurs in this case study, which WRF predicts too early. The early prediction of the down-ramp likely affects the error in WRF-predicted power, the results of which show exaggerated wake effects. While these projects span a range of spatio-temporal scales, a unifying theme is the important aspect of atmospheric variation on wind power production, wind power production estimates, and means for facilitating the integration of wind-generated electricity into power grids. Future work, such as universal NTFs for sites with similar characteristics, NTFs for waked turbines, or the deployment of lidars on turbine nacelles for operation purposes, should continue to study the mutually-important interconnections between these two fields. (Abstract shortened by ProQuest.).

Assimilation of GMS-5 satellite winds using nudging method with MM5

NASA Astrophysics Data System (ADS)

Gao, Shanhong; Wu, Zengmao; Yang, Bo

2006-09-01

With the aid of Meteorological Information Composite and Processing System (MICAPS), satellite wind vectors derived from the Geostationary Meteorological Statellite-5 (GMS-5) and retrieved by National Satellite Meteorology Center of China (NSMC) can be obtained. Based on the nudging method built in the fifth-generation Mesoscale Model (MM5) of Pennsylvania State University and National Center for Atmospheric Research, a data preprocessor is developed to convert these satellite wind vectors to those with specified format required in MM5. To examine the data preprocessor and evaluate the impact of satellite winds from GMS-5 on MM5 simulations, a series of numerical experimental forecasts consisting of four typhoon cases in 2002 are designed and implemented. The results show that the preprocessor can process satellite winds smoothly and MM5 model runs successfully with a little extra computational load during ingesting these winds, and that assimilation of satellite winds by MM5 nudging method can obviously improve typhoon track forecast but contributes a little to typhoon intensity forecast. The impact of the satellite winds depends heavily upon whether the typhoon bogussing scheme in MM5 was turned on or not. The data preprocessor developed in this paper not only can treat GMS-5 satellite winds but also has capability with little modification to process derived winds from other geostationary satellites.

Comparison of Artificial Neural Networks and ARIMA statistical models in simulations of target wind time series

NASA Astrophysics Data System (ADS)

Kolokythas, Kostantinos; Vasileios, Salamalikis; Athanassios, Argiriou; Kazantzidis, Andreas

2015-04-01

The wind is a result of complex interactions of numerous mechanisms taking place in small or large scales, so, the better knowledge of its behavior is essential in a variety of applications, especially in the field of power production coming from wind turbines. In the literature there is a considerable number of models, either physical or statistical ones, dealing with the problem of simulation and prediction of wind speed. Among others, Artificial Neural Networks (ANNs) are widely used for the purpose of wind forecasting and, in the great majority of cases, outperform other conventional statistical models. In this study, a number of ANNs with different architectures, which have been created and applied in a dataset of wind time series, are compared to Auto Regressive Integrated Moving Average (ARIMA) statistical models. The data consist of mean hourly wind speeds coming from a wind farm on a hilly Greek region and cover a period of one year (2013). The main goal is to evaluate the models ability to simulate successfully the wind speed at a significant point (target). Goodness-of-fit statistics are performed for the comparison of the different methods. In general, the ANN showed the best performance in the estimation of wind speed prevailing over the ARIMA models.

Energy Storage Applications in Power Systems with Renewable Energy Generation

NASA Astrophysics Data System (ADS)

Ghofrani, Mahmoud

In this dissertation, we propose new operational and planning methodologies for power systems with renewable energy sources. A probabilistic optimal power flow (POPF) is developed to model wind power variations and evaluate the power system operation with intermittent renewable energy generation. The methodology is used to calculate the operating and ramping reserves that are required to compensate for power system uncertainties. Distributed wind generation is introduced as an operational scheme to take advantage of the spatial diversity of renewable energy resources and reduce wind power fluctuations using low or uncorrelated wind farms. The POPF is demonstrated using the IEEE 24-bus system where the proposed operational scheme reduces the operating and ramping reserve requirements and operation and congestion cost of the system as compared to operational practices available in the literature. A stochastic operational-planning framework is also proposed to adequately size, optimally place and schedule storage units within power systems with high wind penetrations. The method is used for different applications of energy storage systems for renewable energy integration. These applications include market-based opportunities such as renewable energy time-shift, renewable capacity firming, and transmission and distribution upgrade deferral in the form of revenue or reduced cost and storage-related societal benefits such as integration of more renewables, reduced emissions and improved utilization of grid assets. A power-pool model which incorporates the one-sided auction market into POPF is developed. The model considers storage units as market participants submitting hourly price bids in the form of marginal costs. This provides an accurate market-clearing process as compared to the 'price-taker' analysis available in the literature where the effects of large-scale storage units on the market-clearing prices are neglected. Different case studies are provided to demonstrate our operational-planning framework and economic justification for different storage applications. A new reliability model is proposed for security and adequacy assessment of power networks containing renewable resources and energy storage systems. The proposed model is used in combination with the operational-planning framework to enhance the reliability and operability of wind integration. The proposed framework optimally utilizes the storage capacity for reliability applications of wind integration. This is essential for justification of storage deployment within regulated utilities where the absence of market opportunities limits the economic advantage of storage technologies over gas-fired generators. A control strategy is also proposed to achieve the maximum reliability using energy storage systems. A cost-benefit analysis compares storage technologies and conventional alternatives to reliably and efficiently integrate different wind penetrations and determines the most economical design. Our simulation results demonstrate the necessity of optimal storage placement for different wind applications. This dissertation also proposes a new stochastic framework to optimally charge and discharge electric vehicles (EVs) to mitigate the effects of wind power uncertainties. Vehicle-to-grid (V2G) service for hedging against wind power imbalances is introduced as a novel application for EVs. This application enhances the predictability of wind power and reduces the power imbalances between the scheduled output and actual power. An Auto Regressive Moving Average (ARMA) wind speed model is developed to forecast the wind power output. Driving patterns of EVs are stochastically modeled and the EVs are clustered in the fleets of similar daily driving patterns. Monte Carlo Simulation (MCS) simulates the system behavior by generating samples of system states using the wind ARMA model and EVs driving patterns. A Genetic Algorithm (GA) is used in combination with MCS to optimally coordinate the EV fleets for their V2G services and minimize the penalty cost associated with wind power imbalances. The economic characteristics of automotive battery technologies and costs of V2G service are incorporated into a cost-benefit analysis which evaluates the economic justification of the proposed V2G application. Simulation results demonstrate that the developed algorithm enhances wind power utilization and reduces the penalty cost for wind power under-/over-production. This offers potential revenues for the wind producer. Our cost-benefit analysis also demonstrates that the proposed algorithm will provide the EV owners with economic incentives to participate in V2G services. The proposed smart scheduling strategy develops a sustainable integrated electricity and transportation infrastructure.

Verification and implementation of microburst day potential index (MDPI) and wind INDEX (WINDEX) forecasting tools at Cape Canaveral Air Station

NASA Technical Reports Server (NTRS)

Wheeler, Mark

1996-01-01

This report details the research, development, utility, verification and transition on wet microburst forecasting and detection the Applied Meteorology Unit (AMU) did in support of ground and launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The unforecasted wind event on 16 August 1994 of 33.5 ms-1 (65 knots) at the Shuttle Landing Facility raised the issue of wet microburst detection and forecasting. The AMU researched and analyzed the downburst wind event and determined it was a wet microburst event. A program was developed for operational use on the Meteorological Interactive Data Display System (MIDDS) weather system to analyze, compute and display Theta(epsilon) profiles, the microburst day potential index (MDPI), and wind index (WINDEX) maximum wind gust value. Key microburst nowcasting signatures using the WSR-88D data were highlighted. Verification of the data sets indicated that the MDPI has good potential in alerting the duty forecaster to the potential of wet microburst and the WINDEX values computed from the hourly surface data do have potential in showing a trend for the maximum gust potential. WINDEX should help in filling in the temporal hole between the MDPI on the last Cape Canaveral rawinsonde and the nowcasting radar data tools.

Real-data tests of a single-Doppler radar assimilation system

NASA Astrophysics Data System (ADS)

Nehrkorn, Thomas; Hegarty, James; Hamill, Thomas M.

1994-06-01

Real data tests of a single-Doppler radar data assimilation and forecast system have been conducted for a Florida sea breeze case. The system consists of a hydrostatic mesoscale model used for prediction of the preconvective boundary layer, an objective analysis that combines model first guess fields with radar derived horizontal winds, a thermodynamic retrieval scheme that obtains temperature information from the three-dimensional wind field and its temporal evolution, and a Newtonian nudging scheme for forcing the model forecast to closer agreement with the analysis. As was found in earlier experiments with simulated data, assimilation using Newtonian nudging benefits from temperature data in addition to wind data. The thermodynamic retrieval technique was successful in retrieving a horizontal temperature gradient from the radar-derived wind fields that, when assimilated into the model, led to a significantly improved forecast of the seabreeze strength and position.

A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model

DOE PAGES

James, Eric P.; Benjamin, Stanley G.; Marquis, Melinda

2016-10-28

A new gridded dataset for wind and solar resource estimation over the contiguous United States has been derived from hourly updated 1-h forecasts from the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) 3-km model composited over a three-year period (approximately 22 000 forecast model runs). The unique dataset features hourly data assimilation, and provides physically consistent wind and solar estimates for the renewable energy industry. The wind resource dataset shows strong similarity to that previously provided by a Department of Energy-funded study, and it includes estimates in southern Canada and northern Mexico. The solar resource dataset represents anmore » initial step towards application-specific fields such as global horizontal and direct normal irradiance. This combined dataset will continue to be augmented with new forecast data from the advanced HRRR atmospheric/land-surface model.« less

Power fluctuation reduction methodology for the grid-connected renewable power systems

NASA Astrophysics Data System (ADS)

Aula, Fadhil T.; Lee, Samuel C.

2013-04-01

This paper presents a new methodology for eliminating the influence of the power fluctuations of the renewable power systems. The renewable energy, which is to be considered an uncertain and uncontrollable resource, can only provide irregular electrical power to the power grid. This irregularity creates fluctuations of the generated power from the renewable power systems. These fluctuations cause instability to the power system and influence the operation of conventional power plants. Overall, the power system is vulnerable to collapse if necessary actions are not taken to reduce the impact of these fluctuations. This methodology aims at reducing these fluctuations and makes the generated power capability for covering the power consumption. This requires a prediction tool for estimating the generated power in advance to provide the range and the time of occurrence of the fluctuations. Since most of the renewable energies are weather based, as a result a weather forecast technique will be used for predicting the generated power. The reduction of the fluctuation also requires stabilizing facilities to maintain the output power at a desired level. In this study, a wind farm and a photovoltaic array as renewable power systems and a pumped-storage and batteries as stabilizing facilities are used, since they are best suitable for compensating the fluctuations of these types of power suppliers. As an illustrative example, a model of wind and photovoltaic power systems with battery energy and pumped hydro storage facilities for power fluctuation reduction is included, and its power fluctuation reduction is verified through simulation.

Impacts of Potential Aircraft Observations on Forecasts of Tropical Cyclones Over the Western North Pacific

DTIC Science & Technology

2014-12-01

anticyclone. Vertical wind shear was low, while a moderate level of upper level diffluence existed. The minimum sea level pressure ( SLP ) was estimated...pre-Sinlaku disturbance. At this time, JTWC estimated maximum surface level winds to be 15 to 20 kt, with a SLP near 1005 hPa. 17 Figure 11...poleward side of the circulation. Surface winds had increased to near 23 kt as the SLP continued to fall to 1004 hPa. JTWC forecasters upgraded the

Characterizing and analyzing ramping events in wind power, solar power, load, and netload

DOE PAGES

Cui, Mingjian; Zhang, Jie; Feng, Cong; ...

2017-04-07

Here, one of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewable, load, and netload. An optimized swinging door algorithm (OpSDA) is used and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging 'bumps' (that have a different changing direction) into adjacent ramping segments is included to improve the performancemore » of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (9.77%, 15.85%, and 51.38%). Comparison results show that (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even with a low renewable penetration level.« less

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

PubMed

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-06-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

Applied Meteorology Unit (AMU) Quarterly Report - Fourth Quarter FY-10

NASA Technical Reports Server (NTRS)

Bauman, William; Crawford, Winifred; Barrett, Joe; Watson, Leela; Wheeler, Mark

2010-01-01

Three AMU tasks were completed in this Quarter, each resulting in a forecast tool now being used in operations and a final report documenting how the work was done. AMU personnel completed the following tasks (1) Phase II of the Peak Wind Tool for General Forecasting task by delivering an improved wind forecasting tool to operations and providing training on its use; (2) a graphical user interface (GUI) she updated with new scripts to complete the ADAS Update and Maintainability task, and delivered the scripts to the Spaceflight Meteorology Group on Johnson Space Center, Texas and National Weather Service in Melbourne, Fla.; and (3) the Verify MesoNAM Performance task after we created and delivered a GUI that forecasters will use to determine the performance of the operational MesoNAM weather model forecast.

Hosting a Fourier Transform Spectrometer (FTS) on CubeSat Spacecraft Platforms for Global Measurements of Three-Dimensional Winds

NASA Astrophysics Data System (ADS)

Scott, D. K.; Neilsen, T. L.; Weston, C.; Frazier, C.; Smith, T.; Shumway, A.

2015-12-01

Global measurements of vertically-resolved atmospheric wind profiles offer the potential for improved weather forecasts and superior predictions of atmospheric wind patterns. A small-satellite constellation that uses a Fourier Transform Spectrometer (FTS) instrument onboard 12U CubeSats can provide measurements of global tropospheric wind profiles from space at a very low cost. These small satellites are called FTS CubeSats. This presentation will describe a spacecraft concept that provides a stable, robust platform to host the FTS payload. Of importance to the payload are power, data, station keeping, thermal, and accommodations that enable high spectral measurements to be made from a LEO orbit. The spacecraft concept draws on Space Dynamics Laboratory (SDL) heritage and the recent success of the Dynamic Ionosphere Cubesat Experiment (DICE) and HyperAngular Rainbow Polarimeter (HARP) missions. Working with team members, SDL built a prototype observatory (spacecraft and payload) for testing and proof of concept.

A technique for determining cloud free versus cloud contaminated pixels in satellite imagery

NASA Technical Reports Server (NTRS)

Wohlman, Richard A.

1994-01-01

Weather forecasting has been called the second oldest profession. To do so accurately and with some consistency requires an ability to understand the processes which create the clouds, drive the winds, and produce the ever changing atmospheric conditions. Measurement of basic parameters such as temperature, water vapor content, pressure, windspeed and wind direction throughout the three dimensional atmosphere form the foundation upon which a modern forecast is created. Doppler radar, and space borne remote sensing have provided forecasters the new tools with which to ply their trade.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

The eSurge-Venice project: how satellite data can improve the storm surge forecasting in the northern Adriatic Sea

NASA Astrophysics Data System (ADS)

Zecchetto, Stefano; Vignudelli, Stefano; Donlon, Craig; De Biasio, Francesco; Della Valle, Antonio; Umgiesser, Georg; Bajo, Marco

The Data User Element (DUE) program of the European Space Agency (ESA) is funding two projects (eSurge and eSurge-Venice) aimed to demonstrate the improvement of the storm surge forecasting through the use of Earth Observation (EO) data. eSurge-Venice (http://www.esurge-venice.eu/), is specifically focused on the Gulf of Venice, northern Adriatic Sea. The project objectives are: a) Select a number of Storm Surge Events occurred in the Venice lagoon since 1999; b) Provide the available satellite EO data related to the Storm Surge Events, mainly satellite winds and altimeter data, as well as all the available in-situ data and model forecasts; c) Provide a demonstration Near Real Time service (eSurge-Venice live) of EO data products and services in support of operational and experimental forecasting and warning services; d) Run a number of re-analysis cases, both for historical and contemporary storm surge events, to demonstrate the usefulness of EO data. Present storm surge models use atmospheric model wind fields as forcing. These are know to underestimate the wind in small basins like the Adriatic Sea (~1000 km by 300 km), where the orography plays an important role in shaping the winds. Therefore there is the need to verify and tune the atmospheric model wind fields used in the storm surge modeling, an activity which can easily done using satellite scatterometer winds. The project is now in the middle of his life, and promising preliminary results have been achieved using satellite scatterometer wind data to forge the atmospheric model wind fields forcing the storm surge model. This contribution will present the methodology adopted to tune the model wind fields according to the bias with scatterometer winds and the improvements induced in the storm surge model hindcast.

Simulating effects of a wind-turbine array using LES and RANS: Simulating turbines using LES and RANS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vanderwende, Brian J.; Kosović, Branko; Lundquist, Julie K.

2016-08-27

Growth in wind power production has motivated investigation of wind-farm impacts on in situ flow fields and downstream interactions with agriculture and other wind farms. These impacts can be simulated with both large-eddy simulations (LES) and mesoscale wind-farm parameterizations (WFP). The Weather Research and Forecasting (WRF) model offers both approaches. We used the validated generalized actuator disk (GAD) parameterization in WRF-LES to assess WFP performance. A 12-turbine array was simulated using the GAD model and the WFP in WRF. We examined the performance of each scheme in both convective and stable conditions. The GAD model and WFP produced qualitatively similarmore » wind speed deficits and turbulent kinetic energy (TKE) production across the array in both stability regimes, though the magnitudes of velocity deficits and TKE production levels were underestimated and overestimated, respectively. While wake growth slowed in the latter half of the WFP array as expected, wakes did not approach steady state by the end of the array as simulated by the GAD model. A sensitivity test involving the deactivation of explicit TKE production by the WFP resulted in turbulence levels within the array well that were below those produced by the GAD in both stable and unstable conditions. Finally, the WFP overestimated downwind power production deficits in stable conditions because of the lack of wake stabilization in the latter half of the array.« less

High resolution simulations of orographic flow over a complex terrain on the Southeast coast of Brazil

NASA Astrophysics Data System (ADS)

Chou, S. C.; Zolino, M. M.; Gomes, J. L.; Bustamante, J. F.; Lima-e-Silva, P. P.

2012-04-01

The Eta Model is used operationally by CPTEC to produce weather forecasts over South America since 1997. The model has gone through upgrades. In order to prepare the model for operational higher resolution forecasts, the model is configured and tested over a region of complex topography located near the coast of Southeast Brazil. The Eta Model was configured, with 2-km horizontal resolution and 50 layers. The Eta-2km is a second nesting, it is driven by Eta-15km, which in its turn is driven by Era-Interim reanalyses. The model domain includes the two Brazilians cities, Rio de Janeiro and Sao Paulo, urban areas, preserved tropical forest, pasture fields, and complex terrain and coastline. Mountains can rise up to about 700m. The region suffers frequent events of floods and landslides. The objective of this work is to evaluate high resolution simulations of wind and temperature in this complex area. Verification of model runs uses observations taken from the nuclear power plant. Accurate near-surface wind direction and magnitude are needed for the plant emergency plan and winds are highly sensitive to model spatial resolution and atmospheric stability. Verification of two cases during summer shows that model has clear diurnal cycle signal for wind in that region. The area is characterized by weak winds which makes the simulation more difficult. The simulated wind magnitude is about 1.5m/s, which is close to observations of about 2m/s; however, the observed change of wind direction of the sea breeze is fast whereas it is slow in the simulations. Nighttime katabatic flow is captured by the simulations. Comparison against Eta-5km runs show that the valley circulation is better described in the 2-km resolution run. Simulated temperatures follow closely the observed diurnal cycle. Experiments improving some surface conditions such as the surface temperature and land cover show simulation error reduction and improved diurnal cycle.

Optimization of Evaporative Demand Models for Seasonal Drought Forecasting

NASA Astrophysics Data System (ADS)

McEvoy, D.; Huntington, J. L.; Hobbins, M.

2015-12-01

Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are time scales with moderate skill and are more likely to be used in hydro-climatic applications and decision-making.

Impact of using scatterometer and altimeter data on storm surge forecasting

NASA Astrophysics Data System (ADS)

Bajo, Marco; De Biasio, Francesco; Umgiesser, Georg; Vignudelli, Stefano; Zecchetto, Stefano

2017-05-01

Satellite data are rarely used in storm surge models because of the lack of established methodologies. Nevertheless, they can provide useful information on surface wind and sea level, which can potentially improve the forecast. In this paper satellite wind data are used to correct the bias of wind originating from a global atmospheric model, while satellite sea level data are used to improve the initial conditions of the model simulations. In a first step, the capability of global winds (biased and unbiased) to adequately force a storm surge model are assessed against that of a high resolution local wind. Then, the added value of direct assimilation of satellite altimeter data in the storm surge model is tested. Eleven storm surge events, recorded in Venice from 2008 to 2012, are simulated using different configurations of wind forcing and altimeter data assimilation. Focusing on the maximum surge peak, results show that the relative error, averaged over the eleven cases considered, decreases from 13% to 7%, using both the unbiased wind and assimilating the altimeter data, while, if the high resolution local wind is used to force the hydrodynamic model, the altimeter data assimilation reduces the error from 9% to 6%. Yet, the overall capabilities in reproducing the surge in the first day of forecast, measured by the correlation and by the rms error, improve only with the use of the unbiased global wind and not with the use of high resolution local wind and altimeter data assimilation.

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in computational expense. The low computational cost (and by association low computer-power requirement) means that the machine learning algorithms could be installed on a wave-energy converter as a form of "edge computing" where a device could forecast its own 48-hour energy production.

An Oceanographic and Climatological Atlas of Bristol Bay

DTIC Science & Technology

1987-10-01

36 Forecasting Method ................................ 38 SUPERSTRUCTURE ICING.............................. 41 WIND...slicks and risk general advection of oil by large-scale ice move- analysis to coastal regions were computed. ment, and specific advection of oil by the...tide 1) Fetch wind (speed and direction) from tables or other sources. Forecast time of a surface map analysis of pressure highest range based on loss of

Vandenberg Air Force Base Pressure Gradient Wind Study

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn A.

2013-01-01

Warning category winds can adversely impact day-to-day space lift operations at Vandenberg Air Force Base (VAFB) in California. NASA's Launch Services Program and other programs at VAFB use wind forecasts issued by the 30 Operational Support Squadron Weather Flight (30 OSSWF) to determine if they need to limit activities or protect property such as a launch vehicle. The 30 OSSWF tasked the AMU to develop an automated Excel graphical user interface that includes pressure gradient thresholds between specific observing stations under different synoptic regimes to aid forecasters when issuing wind warnings. This required the AMU to determine if relationships between the variables existed.

Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide-Wind-Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Wei-Na; Huang, Hui-ming; Wang, Yi-gang; Chen, Da-ke; Zhang, lin

2018-03-01

Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide-wind-wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5-6; while wind drag contributes mostly at wind scale 2-4.

Large eddy simulations and reduced models of the Unsteady Atmospheric Boundary Layer

NASA Astrophysics Data System (ADS)

Momen, M.; Bou-Zeid, E.

2013-12-01

Most studies of the dynamics of Atmospheric Boundary Layers (ABLs) have focused on steady geostrophic conditions, such as the classic Ekman boundary layer problem. However, real-world ABLs are driven by a time-dependent geostrophic forcing that changes at sub-diurnal scales. Hence, to advance our understanding of the dynamics of atmospheric flows, and to improve their modeling, the unsteady cases have to be analyzed and understood. This is particularly relevant to new applications related to wind energy (e.g. short-term forecast of wind power changes) and pollutant dispersion (forecasting of rapid changes in wind velocity and direction after an accidental spill), as well as to classic weather prediction and hydrometeorological applications. The present study aims to investigate the ABL behavior under variable forcing and to derive a simple model to predict the ABL response under these forcing fluctuations. Simplifications of the governing Navier-Stokes equations, with the Coriolis force, are tested using LES and then applied to derive a physical model of the unsteady ABL. LES is then exploited again to validate the analogy and the output of the simpler model. Results from the analytical model, as well as LES outputs, open the way for inertial oscillations to play an important role in the dynamics. Several simulations with different variable forcing patterns are then conducted to investigate some of the characteristics of the unsteady ABL such as resonant frequency, ABL response time, equilibrium states, etc. The variability of wind velocity profiles and hodographs, turbulent kinetic energy, and vertical profiles of the total stress and potential temperature are also examined. Wind Hodograph of the Unsteady ABL at Different Heights - This figure shows fluctuations in the mean u and v components of the velocity as time passes due to variable geostrophic forcing

Forecasting of Radiation Belts: Results From the PROGRESS Project.

NASA Astrophysics Data System (ADS)

Balikhin, M. A.; Arber, T. D.; Ganushkina, N. Y.; Walker, S. N.

2017-12-01

Forecasting of Radiation Belts: Results from the PROGRESS Project. The overall goal of the PROGRESS project, funded in frame of EU Horizon2020 programme, is to combine first principles based models with the systems science methodologies to achieve reliable forecasts of the geo-space particle radiation environment.The PROGRESS incorporates three themes : The propagation of the solar wind to L1, Forecast of geomagnetic indices, and forecast of fluxes of energetic electrons within the magnetosphere. One of the important aspects of the PROGRESS project is the development of statistical wave models for magnetospheric waves that affect the dynamics of energetic electrons such as lower band chorus, hiss and equatorial noise. The error reduction ratio (ERR) concept has been used to optimise the set of solar wind and geomagnetic parameters for organisation of statistical wave models for these emissions. The resulting sets of parameters and statistical wave models will be presented and discussed. However the ERR analysis also indicates that the combination of solar wind and geomagnetic parameters accounts for only part of the variance of the emissions under investigation (lower band chorus, hiss and equatorial noise). In addition, advances in the forecast of fluxes of energetic electrons, exploiting empirical models and the first principles IMPTAM model achieved by the PROGRESS project is presented.

Swainson's Thrushes do not show strong wind selectivity prior to crossing the Gulf of Mexico.

PubMed

Bolus, Rachel T; Diehl, Robert H; Moore, Frank R; Deppe, Jill L; Ward, Michael P; Smolinsky, Jaclyn; Zenzal, Theodore J

2017-10-27

During long-distance fall migrations, nocturnally migrating Swainson's Thrushes often stop on the northern Gulf of Mexico coast before flying across the Gulf. To minimize energetic costs, trans-Gulf migrants should stop over when they encounter crosswinds or headwinds, and depart with supportive tailwinds. However, time constrained migrants should be less selective, balancing costs of headwinds with benefits of continuing their migrations. To test the hypotheses that birds select supportive winds and that selectivity is mediated by seasonal time constraints, we examined whether local winds affected Swainson's Thrushes' arrival and departure at Ft. Morgan, Alabama, USA at annual, seasonal, and nightly time scales. Additionally, migrants could benefit from forecasting future wind conditions, crossing on nights when winds are consistently supportive across the Gulf, thereby avoiding the potentially lethal consequences of depleting their energetic reserves over water. To test whether birds forecast, we developed a movement model, calculated to what extent departure winds were predictive of future Gulf winds, and tested whether birds responded to predictability. Swainson's Thrushes were only slightly selective and did not appear to forecast. By following the simple rule of avoiding only the strongest headwinds at departure, Swainson's Thrushes could survive the 1500 km flight between Alabama and Veracruz, Mexico.

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

VERDE Analytic Modules

DOE Office of Scientific and Technical Information (OSTI.GOV)

2008-01-15

The Verde Analytic Modules permit the user to ingest openly available data feeds about phenomenology (storm tracks, wind, precipitation, earthquake, wildfires, and similar natural and manmade power grid disruptions and forecast power outages, restoration times, customers outaged, and key facilities that will lose power. Damage areas are predicted using historic damage criteria of the affected area. The modules use a cellular automata approach to estimating the distribution circuits assigned to geo-located substations. Population estimates served within the service areas are located within 1 km grid cells and converted to customer counts by conversion through demographic estimation of households and commercialmore » firms within the population cells. Restoration times are estimated by agent-based simulation of restoration crews working according to utility published prioritization calibrated by historic performance.« less

Global assimilation of X Project Loon stratospheric balloon observations

NASA Astrophysics Data System (ADS)

Coy, L.; Schoeberl, M. R.; Pawson, S.; Candido, S.; Carver, R. W.

2017-12-01

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agree well and assimilating the Loon winds have only a small impact on short-term forecasting of the Loon winds, however, in the tropics the loon winds and DAS winds often disagree substantially (8 m/s or more in magnitude) and in these cases assimilating the loon winds significantly improves the forecast of the loon winds. By highlighting cases where the Loon and DAS winds differ, these results can lead to improved understanding of stratospheric winds, especially in the tropics.

Global Assimilation of X Project Loon Stratospheric Balloon Observations

NASA Technical Reports Server (NTRS)

Coy, Lawrence; Schoeberl, Mark R.; Pawson, Steven; Candido, Salvatore; Carver, Robert W.

2017-01-01

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agree well and assimilating the Loon winds have only a small impact on short-term forecasting of the Loon winds, however, in the tropics the loon winds and DAS winds often disagree substantially (8 m/s or more in magnitude) and in these cases assimilating the loon winds significantly improves the forecast of the loon winds. By highlighting cases where the Loon and DAS winds differ, these results can lead to improved understanding of stratospheric winds, especially in the tropics.

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

PubMed Central

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-01-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Key Points Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations PMID:26213518

Verification of National Weather Service spot forecasts using surface observations

NASA Astrophysics Data System (ADS)

Lammers, Matthew Robert

Software has been developed to evaluate National Weather Service spot forecasts issued to support prescribed burns and early-stage wildfires. Fire management officials request spot forecasts from National Weather Service Weather Forecast Offices to provide detailed guidance as to atmospheric conditions in the vicinity of planned prescribed burns as well as wildfires that do not have incident meteorologists on site. This open source software with online display capabilities is used to examine an extensive set of spot forecasts of maximum temperature, minimum relative humidity, and maximum wind speed from April 2009 through November 2013 nationwide. The forecast values are compared to the closest available surface observations at stations installed primarily for fire weather and aviation applications. The accuracy of the spot forecasts is compared to those available from the National Digital Forecast Database (NDFD). Spot forecasts for selected prescribed burns and wildfires are used to illustrate issues associated with the verification procedures. Cumulative statistics for National Weather Service County Warning Areas and for the nation are presented. Basic error and accuracy metrics for all available spot forecasts and the entire nation indicate that the skill of the spot forecasts is higher than that available from the NDFD, with the greatest improvement for maximum temperature and the least improvement for maximum wind speed.

The measurement of winds over the ocean from Skylab with application to measuring and forecasting typhoons and hurricanes

NASA Technical Reports Server (NTRS)

Cardone, V. J.; Pierson, W. J.

1975-01-01

On Skylab, a combination microwave radar-radiometer (S193) made measurements in a tropical hurricane (AVA), a tropical storm, and various extratropical wind systems. The winds at each cell scanned by the instrument were determined by objective numerical analysis techniques. The measured radar backscatter is compared to the analyzed winds and shown to provide an accurate method for measuring winds from space. An operational version of the instrument on an orbiting satellite will be able to provide the kind of measurements in tropical cyclones available today only by expensive and dangerous aircraft reconnaissance. Additionally, the specifications of the wind field in the tropical boundary layer should contribute to improved accuracy of tropical cyclone forecasts made with numerical weather predictions models currently being applied to the tropical atmosphere.

Recommendations for a wind profiling network to support Space Shuttle launches

NASA Technical Reports Server (NTRS)

Zamora, R. J.

1992-01-01

The feasibility is examined of a network of clear air radar wind profilers to forecast wind conditions before Space Shuttle launches during winter. Currently, winds are measured only in the vicinity of the shuttle launch site and wind loads on the launch vehicle are estimated using these measurements. Wind conditions upstream of the Cape are not monitored. Since large changes in the wind shear profile can be associated with weather systems moving over the Cape, it may be possible to improve wind forecasts over the launch site if wind measurements are made upstream. A radar wind profiling system is in use at the Space Shuttle launch site. This system can monitor the wind profile continuously. The existing profiler could be combined with a number of radars located upstream of the launch site. Thus, continuous wind measurements would be available upstream and at the Cape. NASA-Marshall representatives have set the requirements for radar wind profiling network. The minimum vertical resolution of the network must be set so that the wind shears over the depths greater than or = 1 km will be detected. The network should allow scientists and engineers to predict the wind profile over the Cape 6 hours before a Space Shuttle launch.

The Impact of British Airways Wind Observations on the Goddard Earth Observing System Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Rukhovets, Leonid; Sienkiewicz, M.; Tenenbaum, J.; Kondratyeva, Y.; Owens, T.; Oztunali, M.; Atlas, Robert (Technical Monitor)

2001-01-01

British Airways flight data recorders can provide valuable meteorological information, but they are not available in real-time on the Global Telecommunication System. Information from the flight recorders was used in the Global Aircraft Data Set (GADS) experiment as independent observations to estimate errors in wind analyses produced by major operational centers. The GADS impact on the Goddard Earth Observing System Data Assimilation System (GEOS DAS) analyses was investigated using GEOS-1 DAS version. Recently, a new Data Assimilation System (fvDAS) has been developed at the Data Assimilation Office, NASA Goddard. Using fvDAS , the, GADS impact on analyses and forecasts was investigated. It was shown the GADS data intensify wind speed analyses of jet streams for some cases. Five-day forecast anomaly correlations and root mean squares were calculated for 300, 500 hPa and SLP for six different areas: Northern and Southern Hemispheres, North America, Europe, Asia, USA These scores were obtained as averages over 21 forecasts from January 1998. Comparisons with scores for control experiments without GADS showed a positive impact of the GADS data on forecasts beyond 2-3 days for all levels at the most areas.

Selection of a Planning Horizon for a Hybrid Microgrid Using Simulated Wind Forecasts

DTIC Science & Technology

2014-12-01

microgrid robustness and efficiency and may provide operators with real-time guidance and control policies for microgrid operation. ACKNOWLEDGMENTS The...A PLANNING HORIZON FOR A HYBRID MICROGRID USING SIMULATED WIND FORECASTS Mumtaz Karatas Turkish Naval Academy Tuzla, Istanbul, 34942, TURKEY Emily M...Craparo Dashi I. Singham Naval Postgraduate School 1411 Cunningham Road Monterey, CA, 93943 USA ABSTRACT Hybrid microgrids containing renewable energy

Wind Forecasting for Yacht Racing at the 1991 Pan American Games.

NASA Astrophysics Data System (ADS)

Powell, Mark D.

1993-01-01

The U.S. Sailing Team competed successfully at the 1991 Pan American Games despite having no previous experience with the sailing conditions off Havana, Cuba. One of the key factors in the team's success was meteorological support in the form of wind climate analysis; application of sea breeze forecasting typical of the south Florida area, modified by tropical weather systems; and effective preregatta briefing.

NOAA HRD's HEDAS Data Assimilation System's performance for the 2010 Atlantic Hurricane Season

NASA Astrophysics Data System (ADS)

Sellwood, K.; Aksoy, A.; Vukicevic, T.; Lorsolo, S.

2010-12-01

The Hurricane Ensemble Data Assimilation System (HEDAS) was developed at the Hurricane Research Division (HRD) of NOAA, in conjunction with an experimental version of the Hurricane Weather and Research Forecast model (HWRFx), in an effort to improve the initial representation of the hurricane vortex by utilizing high resolution in-situ data collected during NOAA’s Hurricane Field Program. HEDAS implements the “ensemble square root “ filter of Whitaker and Hamill (2002) using a 30 member ensemble obtained from NOAA/ESRL’s ensemble Kalman filter (EnKF) system and the assimilation is performed on a 3-km nest centered on the hurricane vortex. As part of NOAA’s Hurricane Forecast Improvement Program (HFIP), HEDAS will be run in a semi-operational mode for the first time during the 2010 Atlantic hurricane season and will assimilate airborne Doppler radar winds, dropwindsonde and flight level wind, temperature, pressure and relative humidity, and Stepped Frequency Microwave Radiometer surface wind observations as they become available. HEDAS has been implemented in an experimental mode for the cases of Hurricane Bill, 2009 and Paloma, 2008 to confirm functionality and determine the optimal configuration of the system. This test case demonstrates the importance of assimilating thermodynamic data in addition to wind observations and the benefit of increasing the quantity and distribution of observations. Applying HEDAS to a larger sample of storm forecasts would provide further insight into the behavior of the model when inner core aircraft observations are assimilated. The main focus of this talk will be to present a summary of HEDAS performance in the HWRFx model for the inaugural season. The HEDAS analyses and the resulting HWRFx forecasts will be compared with HWRFx analyses and forecasts produced concurrently using the HRD modeling group’s vortex initialization which does not employ data assimilation. The initial vortex and subsequent forecasts will be evaluated based on the thermodynamic structure, wind field, track and intensity. Related HEDAS research to be presented by HRD’s data assimilation group include evaluations of the geostrophic wind balance and covariance structures for the Bill experiments, and Observation System Simulation experiments (OSSEs) for the case of hurricane Paloma using both model generated and real observations.

Extreme Wind, Rain, Storm Surge, and Flooding: Why Hurricane Impacts are Difficult to Forecast?

NASA Astrophysics Data System (ADS)

Chen, S. S.

2017-12-01

The 2017 hurricane season is estimated as one of the costliest in the U.S. history. The damage and devastation caused by Hurricane Harvey in Houston, Irma in Florida, and Maria in Puerto Rico are distinctly different in nature. The complexity of hurricane impacts from extreme wind, rain, storm surge, and flooding presents a major challenge in hurricane forecasting. A detailed comparison of the storm impacts from Harvey, Irma, and Maria will be presented using observations and state-of-the-art new generation coupled atmosphere-wave-ocean hurricane forecast model. The author will also provide an overview on what we can expect in terms of advancement in science and technology that can help improve hurricane impact forecast in the near future.

Peak Wind Tool for General Forecasting

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Short, David

2008-01-01

This report describes work done by the Applied Meteorology Unit (AMU) in predicting peak winds at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron requested the AMU develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. Based on observations from the KSC/CCAFS wind tower network , Shuttle Landing Facility (SLF) surface observations, and CCAFS sounding s from the cool season months of October 2002 to February 2007, the AMU created mul tiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence , the temperature inversion depth and strength, wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft.

An Analysis of Peak Wind Speed Data from Collocated Mechanical and Ultrasonic Anemometers

NASA Technical Reports Server (NTRS)

Short, David A.; Wells, Leonard A.; Merceret, Francis J.; Roeder, William P.

2005-01-01

This study focuses on a comparison of peak wind speeds reported by mechanical and ultrasonic anemometers at Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC) on the east central coast of Florida and Vandenberg Air Force Base (VAFB) on the central coast of California. The legacy mechanical wind instruments on CCAFS/KSC and VAFB weather towers are being changed from propeller-and-vane (CCAFS/KSC) and cup-and-vane (VAFB) sensors to ultrasonic sensors under the Range Standardization and Automation (RSA) program. The wind tower networks on KSC/CCAFS and VAFB have 41 and 27 towers, respectively. Launch Weather Officers, forecasters, and Range Safety analysts at both locations need to understand the performance of the new wind sensors for a myriad of reasons that include weather warnings, watches, advisories, special ground processing operations, launch pad exposure forecasts, user Launch Commit Criteria (LCC) forecasts and evaluations, and toxic dispersion support. The Legacy sensors measure wind speed and direction mechanically. The ultrasonic RSA sensors have no moving parts. Ultrasonic sensors were originally developed to measure very light winds (Lewis and Dover 2004). The technology has evolved and now ultrasonic sensors provide reliable wind data over a broad range of wind speeds. However, because ultrasonic sensors respond more quickly than mechanical sensors to rapid fluctuations in speed, characteristic of gusty wind conditions, comparisons of data from the two sensor types have shown differences in the statistics of peak wind speeds (Lewis and Dover 2004). The 45th Weather Squadron (45 WS) and the 30 WS requested the Applied Meteorology Unit (AMU) to compare data from RSA and Legacy sensors to determine if there are significant differences in peak wind speed information from the two systems.

ON THE IMPACT OF SUPER RESOLUTION WSR-88D DOPPLER RADAR DATA ASSIMILATION ON HIGH RESOLUTION NUMERICAL MODEL FORECASTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chiswell, S

2009-01-11

Assimilation of radar velocity and precipitation fields into high-resolution model simulations can improve precipitation forecasts with decreased 'spin-up' time and improve short-term simulation of boundary layer winds (Benjamin, 2004 & 2007; Xiao, 2008) which is critical to improving plume transport forecasts. Accurate description of wind and turbulence fields is essential to useful atmospheric transport and dispersion results, and any improvement in the accuracy of these fields will make consequence assessment more valuable during both routine operation as well as potential emergency situations. During 2008, the United States National Weather Service (NWS) radars implemented a significant upgrade which increased the real-timemore » level II data resolution to 8 times their previous 'legacy' resolution, from 1 km range gate and 1.0 degree azimuthal resolution to 'super resolution' 250 m range gate and 0.5 degree azimuthal resolution (Fig 1). These radar observations provide reflectivity, velocity and returned power spectra measurements at a range of up to 300 km (460 km for reflectivity) at a frequency of 4-5 minutes and yield up to 13.5 million point observations per level in super-resolution mode. The migration of National Weather Service (NWS) WSR-88D radars to super resolution is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current operational mesoscale model domains utilize grid spacing several times larger than the legacy data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of super resolution reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution is investigated here to determine the impact of the improved data resolution on model predictions.« less

The potential impact of scatterometry on oceanography - A wave forecasting case

NASA Technical Reports Server (NTRS)

Cane, M. A.; Cardone, V. J.

1981-01-01

A series of observing system simulation experiments have been performed in order to assess the potential impact of marine surface wind data on numerical weather prediction. In addition to conventional data, the experiments simulated the time-continuous assimilation of remotely sensed marine surface wind or temperature sounding data. The wind data were fabricated directly for model grid points intercepted by a Seasat-1 scatterometer swath and were assimilated into the lowest active level (945 mb) of the model using a localized successive correction method. It is shown that Seasat wind data can greatly improve numerical weather forecasts due to better definition of specific features. The case of the QE II storm is examined.

The Scientific Foundations of Forecasting Magnetospheric Space Weather

NASA Astrophysics Data System (ADS)

Eastwood, J. P.; Nakamura, R.; Turc, L.; Mejnertsen, L.; Hesse, M.

2017-11-01

The magnetosphere is the lens through which solar space weather phenomena are focused and directed towards the Earth. In particular, the non-linear interaction of the solar wind with the Earth's magnetic field leads to the formation of highly inhomogenous electrical currents in the ionosphere which can ultimately result in damage to and problems with the operation of power distribution networks. Since electric power is the fundamental cornerstone of modern life, the interruption of power is the primary pathway by which space weather has impact on human activity and technology. Consequently, in the context of space weather, it is the ability to predict geomagnetic activity that is of key importance. This is usually stated in terms of geomagnetic storms, but we argue that in fact it is the substorm phenomenon which contains the crucial physics, and therefore prediction of substorm occurrence, severity and duration, either within the context of a longer-lasting geomagnetic storm, but potentially also as an isolated event, is of critical importance. Here we review the physics of the magnetosphere in the frame of space weather forecasting, focusing on recent results, current understanding, and an assessment of probable future developments.

National Weather Service Forecast Office - Honolulu, Hawai`i

Science.gov Websites

Locations - Coastal Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel Coastal Wind Observations Buoy Reports, and current weather conditions for selected locations tides , sunrise and sunset information Coastal Waters Forecast general weather overview Tropical information

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-level Winds For Space Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Flinn, Clay

2012-01-01

Launch directors need to know upper-level wind forecasts. We developed an Excel-based GUI to display upper-level winds: (1) Rawinsonde at CCAFS, (2) Wind profilers at KSC, (3) Model point data at CCAFS.

Impact of Local Sensors

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III

2008-01-01

Forecasters at the 45th Weather Squadron (45 WS) use observations from the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) wind tower network and the CCAFS (XMR) daily rawinsonde observations (RAOB) to issue and verify wind advisories and warnings for operations. These observations are also used by the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) in Houston, Texas and the NWS Melbourne, Florida (NWS MLB) to initialize their locally-run mesoscale models. In addition, SMG uses these observations to support shuttle landings at the Shuttle Landing Facility (SLF). Due to impending budget cuts, some or all of the KSC/CCAFS wind towers on the east-central Florida mainland and the XMR RAOBs may be eliminated. The locations of the mainland towers and XMR RAOB site are shown in Figure I. The loss of these data may impact the forecast capability of the 45 WS, SMG and NWS MLB. The AMU was tasked to conduct an objective independent modeling study to help determine how important these observations are to the accuracy of the model output used by the forecasters. To accomplish this, the Applied Meteorology Unit (AMU) performed a sensitivity study using the Weather Research and Forecasting (WRF) model initialized with and without KSC/CCAFS wind tower and XMR RAOB data.

Operational wind shear detection and warning - The 'CLAWS' experience at Denver and future objectives

NASA Technical Reports Server (NTRS)

Mccarthy, John; Wilson, James W.; Hjelmfelt, Mark R.

1986-01-01

An operational wind shear detection and warning experiment was conducted at Denver's Stapleton International Airport in summer 1984. Based on meteorological interpretation of scope displays from a Doppler weather radar, warnings were transmitted to the air traffic control tower via voice radio. Analyses of results indicated real skill in daily microburst forecasts and very short-term (less than 5-min) warnings. Wind shift advisories with 15-30 min forecasts, permitted more efficient runway reconfigurations. Potential fuel savings were estimated at $875,000/yr at Stapleton. The philosophy of future development toward an automated, operational system is discussed.

Complementarity among climate related energy sources: Sensitivity study to climate characteristics across Europe

NASA Astrophysics Data System (ADS)

Francois, Baptiste; Hingray, Benoit; Creutin, Jean-Dominique; Raynaud, Damien; Borga, Marco; Vautard, Robert

2015-04-01

Climate related energy sources like solar-power, wind-power and hydro-power are important contributors to the transitions to a low-carbon economy. Past studies, mainly based on solar and wind powers, showed that the power from such energy sources fluctuates in time and space following their driving climatic variables. However, when combining different energy sources together, their intermittent feature is smoothed, resulting to lower time variability of the produced power and to lower storage capacity required for balancing. In this study, we consider solar, wind and hydro energy sources in a 100% renewable Europe using a set of 12 regions following two climate transects, the first one going from the Northern regions (Norway, Finland) to the Southern ones (Greece, Andalucía, Tunisia) and the second one going from the oceanic climate (West of France, Galicia) to the continental one (Romania, Belorussia). For each of those regions, we combine wind and solar irradiance data from the Weather Research and Forecasting Model (Vautard et al., 2014), temperature data from the European Climate Assessment & Dataset (Haylock et al., 2008) and runoff from the Global Runoff Data Center (GRDC, 1999) for estimating solar-power, wind-power, run-of-the-river hydro-power and the electricity demand over a time period of 30 years. The use of this set of 12 regions across Europe allows integrating knowledge about time and space variability for each different energy sources. We then assess the optimal share of each energy sources, aiming to decrease the time variability of the regional energy balance at different time scales as well as the energy storage required for balancing within each region. We also evaluate how energy transport among regions contributes for smoothing out both the energy balance and the storage requirement. The strengths of this study are i) to handle with run-of-the-river hydro power in addition to wind and solar energy sources and ii) to carry out this analysis over a long time period while past studies, to our knowledge, have used less than 10 year time period. References: Vautard, R., Thais, F., Tobin, I., Bréon, F.-M., de Lavergne, J.-G.D., Colette, A., Yiou, P., and Ruti, P.M. (2014). Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms. Nat. Commun. 5, 3196. Haylock, M.R., Hofstra, N., Tank, A.M.G.K., Klok, E.J., Jones, P.D., New, M., 2008. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res.-Atmos. 113. doi:10.1029/2008JD010201 GRDC (Global Runoff Data Center), 1999. Long-term mean monthly discharges of selected GRDC stations, Global Runoff Data Centre, Koblenz, Germany.

Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

DTIC Science & Technology

2010-09-30

In part 2 (Bonazzi et al., 2010), the impact of the ensemble forecast methodology based on MFS-Wind-BHM perturbations is documented. Forecast...absence of dt data stage inputs, the forecast impact of MFS-Error-BHM is neutral. Experiments are underway now to introduce dt back into the MFS-Error...BHM and quantify forecast impacts at MFS. MFS-SuperEnsemble-BHM We have assembled all needed datasets and completed algorithmic development

A Hybrid Wind-Farm Parametrization for Mesoscale and Climate Models

NASA Astrophysics Data System (ADS)

Pan, Yang; Archer, Cristina L.

2018-04-01

To better understand the potential impact of wind farms on weather and climate at the regional to global scales, a new hybrid wind-farm parametrization is proposed for mesoscale and climate models. The proposed parametrization is a hybrid model because it is not based on physical processes or conservation laws, but on the multiple linear regression of the results of large-eddy simulations (LES) with the geometric properties of the wind-farm layout (e.g., the blockage ratio and blockage distance). The innovative aspect is that each wind turbine is treated individually based on its position in the farm and on the wind direction by predicting the velocity upstream of each turbine. The turbine-induced forces and added turbulence kinetic energy (TKE) are first derived analytically and then implemented in the Weather Research and Forecasting model. Idealized simulations of the offshore Lillgrund wind farm are conducted. The wind-speed deficit and TKE predicted with the hybrid model are in excellent agreement with those from the LES results, while the wind-power production estimated with the hybrid model is within 10% of that observed. Three additional wind farms with larger inter-turbine spacing than at Lillgrund are also considered, and a similar agreement with LES results is found, proving that the hybrid parametrization works well with any wind farm regardless of the spacing between turbines. These results indicate the wind-turbine position, wind direction, and added TKE are essential in accounting for the wind-farm effects on the surroundings, for which the hybrid wind-farm parametrization is a promising tool.

Online Analysis of Wind and Solar Part II: Transmission Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

2012-01-31

To facilitate wider penetration of renewable resources without compromising system reliability concerns arising from the lack of predictability of intermittent renewable resources, a tool for use by California Independent System Operator (CAISO) power grid operators was developed by Pacific Northwest National Laboratory (PNNL) in conjunction with CAISO with funding from California Energy Commission. The tool analyzes and displays the impacts of uncertainties in forecasts of loads and renewable generation on: (1) congestion, (2)voltage and transient stability margins, and (3)voltage reductions and reactive power margins. The impacts are analyzed in the base case and under user-specified contingencies.A prototype of the toolmore » has been developed and implemented in software.« less

A New Objective Technique for Verifying Mesoscale Numerical Weather Prediction Models

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Manobianco, John; Lane, John E.; Immer, Christopher D.

2003-01-01

This report presents a new objective technique to verify predictions of the sea-breeze phenomenon over east-central Florida by the Regional Atmospheric Modeling System (RAMS) mesoscale numerical weather prediction (NWP) model. The Contour Error Map (CEM) technique identifies sea-breeze transition times in objectively-analyzed grids of observed and forecast wind, verifies the forecast sea-breeze transition times against the observed times, and computes the mean post-sea breeze wind direction and speed to compare the observed and forecast winds behind the sea-breeze front. The CEM technique is superior to traditional objective verification techniques and previously-used subjective verification methodologies because: It is automated, requiring little manual intervention, It accounts for both spatial and temporal scales and variations, It accurately identifies and verifies the sea-breeze transition times, and It provides verification contour maps and simple statistical parameters for easy interpretation. The CEM uses a parallel lowpass boxcar filter and a high-order bandpass filter to identify the sea-breeze transition times in the observed and model grid points. Once the transition times are identified, CEM fits a Gaussian histogram function to the actual histogram of transition time differences between the model and observations. The fitted parameters of the Gaussian function subsequently explain the timing bias and variance of the timing differences across the valid comparison domain. Once the transition times are all identified at each grid point, the CEM computes the mean wind direction and speed during the remainder of the day for all times and grid points after the sea-breeze transition time. The CEM technique performed quite well when compared to independent meteorological assessments of the sea-breeze transition times and results from a previously published subjective evaluation. The algorithm correctly identified a forecast or observed sea-breeze occurrence or absence 93% of the time during the two- month evaluation period from July and August 2000. Nearly all failures in CEM were the result of complex precipitation features (observed or forecast) that contaminated the wind field, resulting in a false identification of a sea-breeze transition. A qualitative comparison between the CEM timing errors and the subjectively determined observed and forecast transition times indicate that the algorithm performed very well overall. Most discrepancies between the CEM results and the subjective analysis were again caused by observed or forecast areas of precipitation that led to complex wind patterns. The CEM also failed on a day when the observed sea- breeze transition affected only a very small portion of the verification domain. Based on the results of CEM, the RAMS tended to predict the onset and movement of the sea-breeze transition too early and/or quickly. The domain-wide timing biases provided by CEM indicated an early bias on 30 out of 37 days when both an observed and forecast sea breeze occurred over the same portions of the analysis domain. These results are consistent with previous subjective verifications of the RAMS sea breeze predictions. A comparison of the mean post-sea breeze winds indicate that RAMS has a positive wind-speed bias for .all days, which is also consistent with the early bias in the sea-breeze transition time since the higher wind speeds resulted in a faster inland penetration of the sea breeze compared to reality.

Introduction on the operational storm surge forecasting system in Korea Operational Oceanographic System (KOOS)

NASA Astrophysics Data System (ADS)

Kwon, Jae-Il; Park, Kwang-Soon; Choi, Jung-Woon; Lee, Jong-Chan; Heo, Ki-Young; Kim, Sang-Ik

2017-04-01

During last more than 50 years, 258 typhoons passed and affected the Korean peninsula in terms of high winds, storm surges and extreme waves. In this study we explored the performance of the operational storm surge forecasting system in the Korea Operational Oceanographic System (KOOS) with 8 typhoons from 2010 to 2016. The operation storm surge forecasting system for the typhoon in KOOS is based on 2D depth averaged model with tides and CE (U.S. Army Corps of Engineers) wind model. Two key parameters of CE wind model, the locations of typhoon center and its central atmospheric pressure are based from Korea Meteorological administrative (KMA)'s typhoon information provided from 1 day to 3 hour intervals with the approach of typhoon through the KMA's web-site. For 8 typhoons cases, the overall errors, other performances and analysis such as peak time and surge duration are presented in each case. The most important factor in the storm surge errors in the operational forecasting system is the accuracy of typhoon passage prediction.

A new method for evaluating impacts of data assimilation with respect to tropical cyclone intensity forecast problem

NASA Astrophysics Data System (ADS)

Vukicevic, T.; Uhlhorn, E.; Reasor, P.; Klotz, B.

2012-12-01

A significant potential for improving numerical model forecast skill of tropical cyclone (TC) intensity by assimilation of airborne inner core observations in high resolution models has been demonstrated in recent studies. Although encouraging , the results so far have not provided clear guidance on the critical information added by the inner core data assimilation with respect to the intensity forecast skill. Better understanding of the relationship between the intensity forecast and the value added by the assimilation is required to further the progress, including the assimilation of satellite observations. One of the major difficulties in evaluating such a relationship is the forecast verification metric of TC intensity: the maximum one-minute sustained wind speed at 10 m above surface. The difficulty results from two issues : 1) the metric refers to a practically unobservable quantity since it is an extreme value in a highly turbulent, and spatially-extensive wind field and 2) model- and observation-based estimates of this measure are not compatible in terms of spatial and temporal scales, even in high-resolution models. Although the need for predicting the extreme value of near surface wind is well justified, and the observation-based estimates that are used in practice are well thought of, a revised metric for the intensity is proposed for the purpose of numerical forecast evaluation and the impacts on the forecast. The metric should enable a robust observation- and model-resolvable and phenomenologically-based evaluation of the impacts. It is shown that the maximum intensity could be represented in terms of decomposition into deterministic and stochastic components of the wind field. Using the vortex-centric cylindrical reference frame, the deterministic component is defined as the sum of amplitudes of azimuthal wave numbers 0 and 1 at the radius of maximum wind, whereas the stochastic component is represented by a non-Gaussian PDF. This decomposition is exact and fully independent of individual TC properties. The decomposition of the maximum wind intensity was first evaluated using several sources of data including Step Frequency Microwave Radiometer surface wind speeds from NOAA and Air Force reconnaissance flights,NOAA P-3 Tail Doppler Radar measurements, and best track maximum intensity estimates as well as the simulations from Hurricane WRF Ensemble Data Assimilation System (HEDAS) experiments for 83 real data cases. The results confirmed validity of the method: the stochastic component of the maximum exibited a non-Gaussian PDF with small mean amplitude and variance that was comparable to the known best track error estimates. The results of the decomposition were then used to evaluate the impact of the improved initial conditions on the forecast. It was shown that the errors in the deterministic component of the intensity had the dominant effect on the forecast skill for the studied cases. This result suggests that the data assimilation of the inner core observations could focus primarily on improving the analysis of wave number 0 and 1 initial structure and on the mechanisms responsible for forcing the evolution of this low-wavenumber structure. For the latter analysis, the assimilation of airborne and satellite remote sensing observations could play significant role.

Integrating Solar PV in Utility System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, A.; Botterud, A.; Wu, J.

2013-10-31

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, andmore » day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.« less

Could Crop Height Affect the Wind Resource at Agriculturally Productive Wind Farm Sites?

NASA Astrophysics Data System (ADS)

Vanderwende, Brian; Lundquist, Julie K.

2016-03-01

The collocation of cropland and wind turbines in the US Midwest region introduces complex meteorological interactions that could influence both agriculture and wind-power production. Crop management practices may affect the wind resource through alterations of land-surface properties. We use the weather research and forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. A hypothetical wind farm consisting of 121 1.8-MW turbines is represented using the WRF model wind-farm parametrization. We represent the impact of selecting soybeans rather than maize by altering the aerodynamic roughness length in a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 0.1 and 0.25 m represent the mature soy crop and a mature maize crop, respectively. In all but the most stable atmospheric conditions, statistically significant hub-height wind-speed increases and rotor-layer wind-shear reductions result from switching from maize to soybeans. Based on simulations for the entire month of August 2013, wind-farm energy output increases by 14 %, which would yield a significant monetary gain. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop-management practices. These considerations must be balanced by other influences on crop choice such as soil requirements and commodity prices.

Could crop height affect the wind resource at agriculturally productive wind farm sites?

DOE PAGES

Vanderwende, Brian; Lundquist, Julie K.

2015-11-07

The collocation of cropland and wind turbines in the US Midwest region introduces complex meteorological interactions that could influence both agriculture and wind-power production. Crop management practices may affect the wind resource through alterations of land-surface properties. We use the weather research and forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. A hypothetical wind farm consisting of 121 1.8-MW turbines is represented using the WRF model wind-farm parametrization. We represent the impact of selecting soybeans rather than maize by altering the aerodynamic roughness length inmore » a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 0.1 and 0.25 m represent the mature soy crop and a mature maize crop, respectively. In all but the most stable atmospheric conditions, statistically significant hub-height wind-speed increases and rotor-layer wind-shear reductions result from switching from maize to soybeans. Based on simulations for the entire month of August 2013, wind-farm energy output increases by 14 %, which would yield a significant monetary gain. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop-management practices. As a result, these considerations must be balanced by other influences on crop choice such as soil requirements and commodity prices.« less

Could crop height affect the wind resource at agriculturally productive wind farm sites?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vanderwende, Brian; Lundquist, Julie K.

The collocation of cropland and wind turbines in the US Midwest region introduces complex meteorological interactions that could influence both agriculture and wind-power production. Crop management practices may affect the wind resource through alterations of land-surface properties. We use the weather research and forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. A hypothetical wind farm consisting of 121 1.8-MW turbines is represented using the WRF model wind-farm parametrization. We represent the impact of selecting soybeans rather than maize by altering the aerodynamic roughness length inmore » a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 0.1 and 0.25 m represent the mature soy crop and a mature maize crop, respectively. In all but the most stable atmospheric conditions, statistically significant hub-height wind-speed increases and rotor-layer wind-shear reductions result from switching from maize to soybeans. Based on simulations for the entire month of August 2013, wind-farm energy output increases by 14 %, which would yield a significant monetary gain. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop-management practices. As a result, these considerations must be balanced by other influences on crop choice such as soil requirements and commodity prices.« less

Long- Range Forecasting Of The Onset Of Southwest Monsoon Winds And Waves Near The Horn Of Africa

DTIC Science & Technology

2017-12-01

SUMMARY OF CLIMATE ANALYSIS AND LONG-RANGE FORECAST METHODOLOGY Prior theses from Heidt (2006) and Lemke (2010) used methods similar to ours and to...6 II. DATA AND METHODS .......................................................................................7 A...9 D. ANALYSIS AND FORECAST METHODS .........................................10 1. Predictand Selection

7 CFR 612.3 - Data collected and forecasts.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.3 Data..., and wind. (b) Water supply forecasts in the western states area are generally made monthly from.... Data sites generally include a snow course where both snow depth and water equivalent of snow are...

Could Crop Roughness Impact the Wind Resource at Agriculturally Productive Wind Farm Sites?

NASA Astrophysics Data System (ADS)

Vanderwende, B. J.; Lundquist, J. K.

2014-12-01

The high concentration of both large-scale agriculture and wind power production in the United States Midwest region raises new questions concerning the interaction of the two activities. For instance, it is known from internal boundary layer theory that changes in the roughness of the land-surface resulting from crop choices could modify the momentum field aloft. Upward propagation of such an effect might impact the properties of the winds encountered by modern turbines, which typically span a layer from about 40 to 120 meters above the surface. As direct observation of such interaction would require impractical interference in the planting schedules of farmers, we use numerical modeling to quantify the magnitude of crop-roughness effects. To simulate a collocated farm and turbine array, we use version 3.4.1 of the Weather Research and Forecasting model (WRF). The hypothetical farm is inserted near the real location of the 2013 Crop Wind Energy Experiment (CWEX). Reanalyses provide representative initial and boundary conditions. A month-long period spanning August 2013 is used to evaluate the differences in flows above corn (maize) and soybean crops at the mature, reproductive stage. Simulations are performed comparing the flow above each surface regime, both in the absence and presence of a wind farm, which consists of a parameterized 11x11 array of 1.8 MW Vestas V90 turbines. Appreciable differences in rotor-layer wind speeds emerge. The use of soybeans results in an increase in wind speeds and a corresponding reduction in rotor-layer shear when compared to corn. Despite the turbulent nature of flow within a wind farm, high stability reduces the impact of crop roughness on the flow aloft, particularly in the upper portion of the rotor disk. We use these results to estimate the economic impact of crop selection on wind power producers.

An Improved Ocean Observing System for Coastal Louisiana: WAVCIS (WAVE-CURRENT-SURGE Information System )

NASA Astrophysics Data System (ADS)

Zhang, X.; Stone, G. W.; Gibson, W. J.; Braud, D.

2005-05-01

WAVCIS is a regional ocean observing and forecasting system. It was designed to measure, process, forecast, and distribute oceanographic and meteorological information. WAVCIS was developed and is maintained by the Coastal Studies Institute at Louisiana State University. The in-situ observing stations are distributed along the central Louisiana and Mississippi coast. The forecast region covers the entire Gulf of Mexico with emphasis on offshore Louisiana. By using state-of-the-art instrumentation, WAVCIS measures directional waves, currents, temperature, water level, conductivity, turbidity, salinity, dissolved oxygen, chlorophyll, Meteorological parameters include wind speed and direction, air pressure and temperature visibility and humidity. Through satellite communication links, the measured data are transmitted to the WAVCIS laboratory. After processing, they are available to the public via the internet on a near real-time basis. WAVCIS also includes a forecasting capability. Waves, tides, currents, and winds are forecast daily for up to 80 hours in advance. There are a number of numerical wave and surge models that can be used for forecasts. WAM and SWAN are used for operational purposes to forecast sea state. Tides at each station are predicted based on the harmonic constants calculated from past in-situ observations at respective sites. Interpolated winds from the ETA model are used as input forcing for waves. Both in-situ and forecast information are available online to the users through WWW. Interactive GIS web mapping is implemented on the WAVCIS webpage to visualize the model output and in-situ observational data. WAVCIS data can be queried, retrieved, downloaded, and analyzed through the web page. Near real-time numerical model skill assessment can also be performed by using the data from in-situ observing stations.

Near-term Forecasting of Solar Total and Direct Irradiance for Solar Energy Applications

NASA Astrophysics Data System (ADS)

Long, C. N.; Riihimaki, L. D.; Berg, L. K.

2012-12-01

Integration of solar renewable energy into the power grid, like wind energy, is hindered by the variable nature of the solar resource. One challenge of the integration problem for shorter time periods is the phenomenon of "ramping events" where the electrical output of the solar power system increases or decreases significantly and rapidly over periods of minutes or less. Advance warning, of even just a few minutes, allows power system operators to compensate for the ramping. However, the ability for short-term prediction on such local "point" scales is beyond the abilities of typical model-based weather forecasting. Use of surface-based solar radiation measurements has been recognized as a likely solution for providing input for near-term (5 to 30 minute) forecasts of solar energy availability and variability. However, it must be noted that while fixed-orientation photovoltaic panel systems use the total (global) downwelling solar radiation, tracking photovoltaic and solar concentrator systems use only the direct normal component of the solar radiation. Thus even accurate near-term forecasts of total solar radiation will under many circumstances include inherent inaccuracies with respect to tracking systems due to lack of information of the direct component of the solar radiation. We will present examples and statistical analyses of solar radiation partitioning showing the differences in the behavior of the total/direct radiation with respect to the near-term forecast issue. We will present an overview of the possibility of using a network of unique new commercially available total/diffuse radiometers in conjunction with a near-real-time adaptation of the Shortwave Radiative Flux Analysis methodology (Long and Ackerman, 2000; Long et al., 2006). The results are used, in conjunction with persistence and tendency forecast techniques, to provide more accurate near-term forecasts of cloudiness, and both total and direct normal solar irradiance availability and variability. This new system could be a long term economical solution for solar energy applications.xample of SW Flux Analysis global hemispheric (light blue) and direct (yellow) clear-sky shortwave (SW) along with corresponding actual global hemispheric (blue) and direct (red) SW, and the corresponding fractional sky cover (black, right Y-axis). Note in afternoon about 40-50% of the global SW is available, yet most times there is no direct SW.

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2012 CFR

2012-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2013 CFR

2013-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2014 CFR

2014-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2011 CFR

2011-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

Impact of geostationary satellite water vapor channel data on weather analysis and forecasting

NASA Technical Reports Server (NTRS)

Velden, Christopher S.

1995-01-01

Preliminary results from NWP impact studies are indicating that upper-tropospheric wind information provided by tracking motions in sequences of geostationary satellite water vapor imagery can positively influence forecasts on regional scales, and possibly on global scales as well. The data are complimentary to cloud-tracked winds by providing data in cloud-free regions, as well as comparable in quality. First results from GOES-8 winds are encouraging, and further efforts and model impacts will be directed towards optimizing these data in numerical weather prediction (NWP). Assuming successful launches of GOES-J and GMS-5 satellites in 1995, high quality and resolution water vapor imagers will be available to provide nearly complete global upper-tropospheric wind coverage.

Assimilation of Tropical Cyclone Track and Wind Radius Data with an Ensemble Kalman Filter

NASA Astrophysics Data System (ADS)

Kunii, M.

2014-12-01

Improving tropical cyclone (TC) forecasts is one of the most important issues in meteorology, but TC intensity forecasts are a challenging task. Because the lack of observations near TCs usually results in degraded accuracy of initial fields, utilizing TC advisory data in data assimilation typically has started with an ensemble Kalman filtering (EnKF). In this study, TC intensity and position information was directly assimilated using the EnKF, and the impact of these observations was investigated by comparing different assimilation strategies. Another experiment with TC wind radius data was carried out to examine the influence of TC shape parameters. Sensitivity experiments indicated that the assimilation of TC intensity and position data yielded results that were superior to those based on conventional assimilation of TC minimum sea level pressure as a standard surface pressure observation. Assimilation of TC radius data modified TC outer circulations closer to observations. The impacts of these TC parameters were also evaluated using the case of Typhoon Talas in 2011. The TC intensity, position, and wind radius data led to improved TC track forecasts and thence to improved precipitation forecasts. These results imply that initialization with these TC-related observations benefits TC forecasts, offering promise for the prevention and mitigation of natural disasters caused by TCs.

Validation of Model Forecasts of the Ambient Solar Wind

NASA Technical Reports Server (NTRS)

Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

2009-01-01

Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

Applied Meteorology Unit Quarterly Report, Second Quarter FY-13

NASA Technical Reports Server (NTRS)

Bauman, William; Crawford, Winifred; Watson, Leela; Shafer, Jaclyn; Huddleston, Lisa

2013-01-01

The AMU team worked on six tasks for their customers: (1) Ms. Crawford continued work on the objective lightning forecast task for airports in east-central Florida, and began work on developing a dual-Doppler analysis with local Doppler radars, (2) Ms. Shafer continued work for Vandenberg Air Force Base on an automated tool to relate pressure gradients to peak winds, (3) Dr. Huddleston continued work to develop a lightning timing forecast tool for the Kennedy Space Center/Cape Canaveral Air Force Station area, (4) Dr. Bauman continued work on a severe weather forecast tool focused on east-central Florida, (5) Mr. Decker began developing a wind pairs database for the Launch Services Program to use when evaluating upper-level winds for launch vehicles, and (6) Dr. Watson began work to assimilate observational data into the high-resolution model configurations, she created for Wallops Flight Facility and the Eastern Range.

Parameter identification of JONSWAP spectrum acquired by airborne LIDAR

NASA Astrophysics Data System (ADS)

Yu, Yang; Pei, Hailong; Xu, Chengzhong

2017-12-01

In this study, we developed the first linear Joint North Sea Wave Project (JONSWAP) spectrum (JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging (LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin (LH) random-phase method to generate the time series of wave records and used the fast Fourier transform (FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors (wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.

Online Analysis of Wind and Solar Part I: Ramping Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel V.; Ma, Jian; Makarov, Yuri V.

2012-01-31

To facilitate wider penetration of renewable resources without compromising system reliability concerns arising from the lack of predictability of intermittent renewable resources, a tool for use by California Independent System Operator (CAISO) power grid operators was developed by Pacific Northwest National Laboratory (PNNL) in conjunction with CAISO with funding from California Energy Commission. This tool predicts and displays additional capacity and ramping requirements caused by uncertainties in forecasts of loads and renewable generation. The tool is currently operational in the CAISO operations center. This is one of two final reports on the project.

ISS Pass Over Hurricane Jose and Hurricane Irma 9/8/17

NASA Image and Video Library

2017-09-08

The International Space Station passed over two major Atlantic hurricanes on Friday, Sept. 8. First, the station flew approximately 250 miles over Hurricane Jose at approximately 10:10 a.m. EDT while the Category 3 storm was in the Atlantic just east of the Caribbean. One orbit of the Earth later, the station flew over Hurricane Irma at approximately 11:40 a.m. EDT. The powerful Category 4 storm had already brought destructive wind and rain to islands across the Caribbean and is forecast to impact the Florida peninsula.

Analysis of the World Experience of Smart Grid Deployment: Economic Effectiveness Issues

NASA Astrophysics Data System (ADS)

Ratner, S. V.; Nizhegorodtsev, R. M.

2018-06-01

Despite the positive dynamics in the growth of RES-based power production in electric power systems of many countries, the further development of commercially mature technologies of wind and solar generation is often constrained by the existing grid infrastructure and conventional energy supply practices. The integration of large wind and solar power plants into a single power grid and the development of microgeneration require the widespread introduction of a new smart grid technology cluster (smart power grids), whose technical advantages over the conventional ones have been fairly well studied, while issues of their economic effectiveness remain open. Estimation and forecasting potential economic effects from the introduction of innovative technologies in the power sector during the stage preceding commercial development is a methodologically difficult task that requires the use of knowledge from different sciences. This paper contains the analysis of smart grid project implementation in Europe and the United States. Interval estimates are obtained for their basic economic parameters. It was revealed that the majority of smart grid implemented projects are not yet commercially effective, since their positive externalities are usually not recognized on the revenue side due to the lack of universal methods for public benefits monetization. The results of the research can be used in modernization and development planning for the existing grid infrastructure both at the federal level and at the level of certain regions and territories.

Potential impact of remote sensing data on sea-state analysis and prediction

NASA Technical Reports Server (NTRS)

Cardone, V. J.

1983-01-01

The severe North Atlantic storm which damaged the ocean liner Queen Elizabeth 2 (QE2) was studied to assess the impact of remotely sensed marine surface wind data obtained by SEASAT-A, on sea state specifications and forecasts. Alternate representations of the surface wind field in the QE2 storm were produced from the SEASAT enhanced data base, and from operational analyses based upon conventional data. The wind fields were used to drive a high resolution spectral ocean surface wave prediction model. Results show that sea state analyses would have been vastly improved during the period of storm formation and explosive development had remote sensing wind data been available in real time. A modest improvement in operational 12 to 24 hour wave forecasts would have followed automatically from the improved initial state specification made possible by the remote sensing data in both numerical and sea state prediction models. Significantly improved 24 to 48 hour wave forecasts require in addition to remote sensing data, refinement in the numerical and physical aspects of weather prediction models.

University of Arizona Compressed Air Energy Storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simmons, Joseph; Muralidharan, Krishna

2012-12-31

Boiled down to its essentials, the grant’s purpose was to develop and demonstrate the viability of compressed air energy storage (CAES) for use in renewable energy development. While everyone agrees that energy storage is the key component to enable widespread adoption of renewable energy sources, the development of a viable scalable technology has been missing. The Department of Energy has focused on expanded battery research and improved forecasting, and the utilities have deployed renewable energy resources only to the extent of satisfying Renewable Portfolio Standards. The lack of dispatchability of solar and wind-based electricity generation has drastically increased the costmore » of operation with these components. It is now clear that energy storage coupled with accurate solar and wind forecasting make up the only combination that can succeed in dispatchable renewable energy resources. Conventional batteries scale linearly in size, so the price becomes a barrier for large systems. Flow batteries scale sub-linearly and promise to be useful if their performance can be shown to provide sufficient support for solar and wind-base electricity generation resources. Compressed air energy storage provides the most desirable answer in terms of scalability and performance in all areas except efficiency. With the support of the DOE, Tucson Electric Power and Science Foundation Arizona, the Arizona Research Institute for Solar Energy (AzRISE) at the University of Arizona has had the opportunity to investigate CAES as a potential energy storage resource.« less

Modelling a reliable wind/PV/storage power system for remote radio base station sites without utility power

NASA Astrophysics Data System (ADS)

Bitterlin, Ian F.

The development of photovoltaic (PV) cells has made steady progress from the early days, when only the USA space program could afford to deploy them, to now, seeing them applied to roadside applications even in our Northern European climes. The manufacturing cost per watt has fallen and the daylight-to-power conversion efficiency increased. At the same time, the perception that the sun has to be directly shining on it for a PV array to work has faded. On some of those roadside applications, particularly for remote emergency telephones or for temporary roadwork signage where a utility electrical power connection is not practical, the keen observer will spot, usually in addition to a PV array, a small wind-turbine and an electrical cabinet quite obviously (by virtue of its volume) containing a storage battery. In the UK, we have the lions share (>40%) of Europe's entire wind power resource although, despite press coverage of the "anti-wind" lobby to the contrary, we have hardly started to harvest this clean and free energy source. Taking this (established and proven) roadside solution one step further, we will consider higher power applications. A cellular phone system is one where a multitude of remote radio base stations (RBS) are required to provide geographical coverage. With networks developing into the so called "3G" technologies the need for base stations has tripled, as each 3G cell covers only 1/3 the geographical area of its "2G" counterpart. To cover >90% of the UK's topology (>97% population coverage) with 3G cellular technology will requires in excess of 12,000 radio base stations per operator network. In 2001, there were around 25,000 established sites and, with an anticipated degree of collocation by necessity, that figure is forecast to rise to >47,000. Of course, the vast majority of these sites have a convenient grid connection. However, it is easy to see that the combination of wind and PV power generation and an energy storage system may be an interesting solution for the more rural and remote applications - particularly those where an electrical supply is not available or practical - and this paper attempts to explore the current practicalities of such a power generation solution for those cellular phone base stations.

Climatology and Predictability of Cool-Season High Wind Events in the New York City Metropolitan and Surrounding Area

NASA Astrophysics Data System (ADS)

Layer, Michael

Damaging wind events not associated with severe convective storms or tropical cyclones can occur over the Northeast U.S. during the cool season and can cause significant problems with transportation, infrastructure, and public safety. These non-convective wind events (NCWEs) events are difficult for operational forecasters to predict in the NYC region as revealed by relatively poor verification statistics in recent years. This study investigates the climatology of NCWEs occurring between 15 September and 15 May over 13 seasons from 2000-2001 through 2012-2013. The events are broken down into three distinct types commonly observed in the region: pre-cold frontal (PRF), post-cold frontal (POF), and nor'easter/coastal storm (NEC) cases. Relationships between observed winds and some atmospheric parameters such as 900 hPa height gradient, 3-hour MSLP tendency, low-level wind profile, and stability are also studied. Overall, PRF and NEC events exhibit stronger height gradients, stronger low-level winds, and stronger low-level stability than POF events. Model verification is also conducted over the 2009-2014 time period using the Short Range Ensemble Forecast system (SREF) from the National Centers for Environmental Prediction (NCEP). Both deterministic and probabilistic verification metrics are used to evaluate the performance of the ensemble during NCWEs. Although the SREF has better forecast skill than most of the deterministic SREF control members, it is rather poorly calibrated, and exhibits a significant overforecasting, or positive wind speed bias in the lower atmosphere.

Thirty-four years of Hawaii wave hindcast from downscaling of climate forecast system reanalysis

NASA Astrophysics Data System (ADS)

Li, Ning; Cheung, Kwok Fai; Stopa, Justin E.; Hsiao, Feng; Chen, Yi-Leng; Vega, Luis; Cross, Patrick

2016-04-01

The complex wave climate of Hawaii includes a mix of seasonal swells and wind waves from all directions across the Pacific. Numerical hindcasting from surface winds provides essential space-time information to complement buoy and satellite observations for studies of the marine environment. We utilize WAVEWATCH III and SWAN (Simulating WAves Nearshore) in a nested grid system to model basin-wide processes as well as high-resolution wave conditions around the Hawaiian Islands from 1979 to 2013. The wind forcing includes the Climate Forecast System Reanalysis (CFSR) for the globe and downscaled regional winds from the Weather Research and Forecasting (WRF) model. Long-term in-situ buoy measurements and remotely-sensed wind speeds and wave heights allow thorough assessment of the modeling approach and data products for practical application. The high-resolution WRF winds, which include orographic and land-surface effects, are validated with QuickSCAT observations from 2000 to 2009. The wave hindcast reproduces the spatial patterns of swell and wind wave events detected by altimeters on multiple platforms between 1991 and 2009 as well as the seasonal variations recorded at 16 offshore and nearshore buoys around the Hawaiian Islands from 1979 to 2013. The hindcast captures heightened seas in interisland channels and around prominent headlands, but tends to overestimate the heights of approaching northwest swells and give lower estimates in sheltered areas. The validated high-resolution hindcast sets a baseline for future improvement of spectral wave models.

School Science Inspired by Improving Weather Forecasts

ERIC Educational Resources Information Center

Reid, Heather; Renfrew, Ian A.; Vaughan, Geraint

2014-01-01

High winds and heavy rain are regular features of the British weather, and forecasting these events accurately is a major priority for the Met Office and other forecast providers. This is the challenge facing DIAMET, a project involving university groups from Manchester, Leeds, Reading, and East Anglia, together with the Met Office. DIAMET is part…

Reducing Probabilistic Weather Forecasts to the Worst-Case Scenario: Anchoring Effects

ERIC Educational Resources Information Center

Joslyn, Susan; Savelli, Sonia; Nadav-Greenberg, Limor

2011-01-01

Many weather forecast providers believe that forecast uncertainty in the form of the worst-case scenario would be useful for general public end users. We tested this suggestion in 4 studies using realistic weather-related decision tasks involving high winds and low temperatures. College undergraduates, given the statistical equivalent of the…

North American Meso Model Forecast Meteograms

Science.gov Websites

BUFR unpacking is also available. New RUC FORECAST METEOGRAMS are now available. Forecasts of surface variables and vertical profiles of cloud and wind are available for over 1300 stations within the North American Meso model domain. A complete list of the available stations can be found here . Select a region

A GEOS-Based OSSE for the "MISTiC Winds" Concept

NASA Technical Reports Server (NTRS)

McCarty, W.; Blaisdell, J.; Fuentes, M.; Carvalho, D.; Errico, R.; Gelaro, R.; Kouvaris, L.; Moradi, I.; Pawson, S.; Prive, N.;

The Impact of Discontinuity Front Orientation on the Accuracy of L1 Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Szabo, A.

2013-12-01

Current space weather forecasting from the Sun-Earth first Lagrange (L1) point assumes that all observed solar wind discontinuity fronts (interplanetary shocks, ICME boundaries) are perpendicular to the Sun-Earth line and are propagating radially out from eh Sun. In reality, these weather fronts can have significantly tilted orientation. Combined ACE, Wind and Soho observations allow the quantification of this effect. With the launch of the DSCOVR spacecraft in early 2015, dual real-time solar wind measurements will become available (at least at some time). Algorithms and their impact exploiting this unique scenario will be discussed.

7 CFR 1710.302 - Financial forecasts-power supply borrowers.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 11 2011-01-01 2011-01-01 false Financial forecasts-power supply borrowers. 1710.302... AND GUARANTEES Long-Range Financial Forecasts § 1710.302 Financial forecasts—power supply borrowers. (a) The requirements of this section apply only to financial forecasts submitted by power supply...

Simulation studies of the application of SEASAT data in weather and state of sea forecasting models

NASA Technical Reports Server (NTRS)

Cardone, V. J.; Greenwood, J. A.

1979-01-01

The design and analysis of SEASAT simulation studies in which the error structure of conventional analyses and forecasts is modeled realistically are presented. The development and computer implementation of a global spectral ocean wave model is described. The design of algorithms for the assimilation of theoretical wind data into computers and for the utilization of real wind data and wave height data in a coupled computer system are presented.

HAKOU v3: SWIMS Hurricane Inundation Fast Forecasting Tool for Hawaii

DTIC Science & Technology

2012-02-01

SUBTITLE HAKOU v3: SWIMS Hurricane Inundation Fast Forecasting Tool For Hawaii 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...Coupled SWAN+ADCIRC were driven with wind and pressure fields generated by the planetary boundary layer model TC96 (Thompson and Cardone 1996...F., and V. J. Cardone . 1996. Practical modeling of hurricane surface wind fields. J. Waterw. Port C-ASCE. 122(4): 195-205. Zijlema, M. 2010

A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

NASA Astrophysics Data System (ADS)

Huang, Laura X.; Isaac, George A.; Sheng, Grant

2014-01-01

This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0-6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

An Assessment of Wind Plant Complex Flows Using Advanced Doppler Radar Measurements

NASA Astrophysics Data System (ADS)

Gunter, W. S.; Schroeder, J.; Hirth, B.; Duncan, J.; Guynes, J.

2015-12-01

As installed wind energy capacity continues to steadily increase, the need for comprehensive measurements of wind plant complex flows to further reduce the cost of wind energy has been well advertised by the industry as a whole. Such measurements serve diverse perspectives including resource assessment, turbine inflow and power curve validation, wake and wind plant layout model verification, operations and maintenance, and the development of future advanced wind plant control schemes. While various measurement devices have been matured for wind energy applications (e.g. meteorological towers, LIDAR, SODAR), this presentation will focus on the use of advanced Doppler radar systems to observe the complex wind flows within and surrounding wind plants. Advanced Doppler radars can provide the combined advantage of a large analysis footprint (tens of square kilometers) with rapid data analysis updates (a few seconds to one minute) using both single- and dual-Doppler data collection methods. This presentation demonstrates the utility of measurements collected by the Texas Tech University Ka-band (TTUKa) radars to identify complex wind flows occurring within and nearby operational wind plants, and provide reliable forecasts of wind speeds and directions at given locations (i.e. turbine or instrumented tower sites) 45+ seconds in advance. Radar-derived wind maps reveal commonly observed features such as turbine wakes and turbine-to-turbine interaction, high momentum wind speed channels between turbine wakes, turbine array edge effects, transient boundary layer flow structures (such as wind streaks, frontal boundaries, etc.), and the impact of local terrain. Operational turbine or instrumented tower data are merged with the radar analysis to link the observed complex flow features to turbine and wind plant performance.

Baseline predictability of daily east Asian summer monsoon circulation indices

NASA Astrophysics Data System (ADS)

Ai, Shucong; Chen, Quanliang; Li, Jianping; Ding, Ruiqiang; Zhong, Quanjia

2017-05-01

The nonlinear local Lyapunov exponent (NLLE) method is adopted to quantitatively determine the predictability limit of East Asian summer monsoon (EASM) intensity indices on a synoptic timescale. The predictability limit of EASM indices varies widely according to the definitions of indices. EASM indices defined by zonal shear have a limit of around 7 days, which is higher than the predictability limit of EASM indices defined by sea level pressure (SLP) difference and meridional wind shear (about 5 days). The initial error of EASM indices defined by SLP difference and meridional wind shear shows a faster growth than indices defined by zonal wind shear. Furthermore, the indices defined by zonal wind shear appear to fluctuate at lower frequencies, whereas the indices defined by SLP difference and meridional wind shear generally fluctuate at higher frequencies. This result may explain why the daily variability of the EASM indices defined by zonal wind shear tends be more predictable than those defined by SLP difference and meridional wind shear. Analysis of the temporal correlation coefficient (TCC) skill for EASM indices obtained from observations and from NCEP's Global Ensemble Forecasting System (GEFS) historical weather forecast dataset shows that GEFS has a higher forecast skill for the EASM indices defined by zonal wind shear than for indices defined by SLP difference and meridional wind shear. The predictability limit estimated by the NLLE method is shorter than that in GEFS. In addition, the June-September average TCC skill for different daily EASM indices shows significant interannual variations from 1985 to 2015 in GEFS. However, the TCC for different types of EASM indices does not show coherent interannual fluctuations.

Probabilistic forecasting of extreme weather events based on extreme value theory

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert

2016-04-01

Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance. We consider the combined events when an observation variable Y (for instance wind speed) exceeds a high threshold y and its corresponding deterministic forecasts X also exceeds a high forecast threshold y. More specifically two problems are addressed:} We consider pairs (X,Y) of extreme events where X represents a deterministic forecast, and Y the observation variable (for instance wind speed). More specifically two problems are addressed: Given a high forecast X=x_0, what is the probability that Y>y? In other words: provide inference on the conditional probability: [ Pr{Y>y|X=x_0}. ] Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events. These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007). We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair (X,Y). The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008). Coles, S. (2001) An Introduction to Statistical modelling of Extreme Values. Springer-Verlag.Ferro, C.A.T. (2007) A probability model for verifying deterministic forecasts of extreme events. Wea. Forecasting {22}, 1089-1100.Hagedorn, R. (2008) Using the ECMWF reforecast dataset to calibrate EPS forecasts. ECMWF Newsletter, {117}, 8-13.Ramos, A., Ledford, A. (2009) A new class of models for bivariate joint tails. J.R. Statist. Soc. B {71}, 219-241.

Flexible operation of batteries in power system scheduling with renewable energy

DOE PAGES

Li, Nan; Uckun, Canan; Constantinescu, Emil M.; ...

2015-12-17

The fast growing expansion of renewable energy increases the complexities in balancing generation and demand in the power system. The energy-shifting and fast-ramping capability of energy storage has led to increasing interests in batteries to facilitate the integration of renewable resources. In this paper, we present a two-step framework to evaluate the potential value of energy storage in power systems with renewable generation. First, we formulate a stochastic unit commitment approach with wind power forecast uncertainty and energy storage. Second, the solution from the stochastic unit commitment is used to derive a flexible schedule for energy storage in economic dispatchmore » where the look-ahead horizon is limited. Here, analysis is conducted on the IEEE 24-bus system to demonstrate the benefits of battery storage in systems with renewable resources and the effectiveness of the proposed battery operation strategy.« less

Analysis and Forecast of Two Storms Characterized by Extreme Deepening Rates

NASA Technical Reports Server (NTRS)

Reale, Oreste; Riishojgaard, Lars Peter

2003-01-01

Between 25 and 27 December 1999 two very intense cyclones, named Lothar and Martin, swept across northern and western France causing substantial life and property loss. In this work, the finite volume general circulation model and data assimilation system (fvDAS) developed at the Data Assimilation Office of the NASA Goddard Space and Flight Center is being used to investigate these storms. In the first part of this article the dynamics of the storms is analyzed, and some important mechanisms are unveiled. The second part describes a set of eleven data assimilation experiments to study the impact of different data types on the automated analyses. Cloud-track winds provided by EUMETSAT and surface winds from QuikSCAT are being used. These data are assimilated with a range of different parameter settings of the forecast error covariance model. The results show that generally the additional wind data set have positive impacts on the analyses: particularly, the analysis of Lothar can be slightly improved by using the Eumetsat winds, and the analysis of Martin can be strongly improved by using the full-resolution QuikSCAT winds with a more localized influence. The third part of this article is focused on the forecast of Lothar which is very well predicted in the 1-5 day range by the fvDAS system.

Simulation of the Atmospheric Boundary Layer for Wind Energy Applications

NASA Astrophysics Data System (ADS)

Marjanovic, Nikola

Energy production from wind is an increasingly important component of overall global power generation, and will likely continue to gain an even greater share of electricity production as world governments attempt to mitigate climate change and wind energy production costs decrease. Wind energy generation depends on wind speed, which is greatly influenced by local and synoptic environmental forcings. Synoptic forcing, such as a cold frontal passage, exists on a large spatial scale while local forcing manifests itself on a much smaller scale and could result from topographic effects or land-surface heat fluxes. Synoptic forcing, if strong enough, may suppress the effects of generally weaker local forcing. At the even smaller scale of a wind farm, upstream turbines generate wakes that decrease the wind speed and increase the atmospheric turbulence at the downwind turbines, thereby reducing power production and increasing fatigue loading that may damage turbine components, respectively. Simulation of atmospheric processes that span a considerable range of spatial and temporal scales is essential to improve wind energy forecasting, wind turbine siting, turbine maintenance scheduling, and wind turbine design. Mesoscale atmospheric models predict atmospheric conditions using observed data, for a wide range of meteorological applications across scales from thousands of kilometers to hundreds of meters. Mesoscale models include parameterizations for the major atmospheric physical processes that modulate wind speed and turbulence dynamics, such as cloud evolution and surface-atmosphere interactions. The Weather Research and Forecasting (WRF) model is used in this dissertation to investigate the effects of model parameters on wind energy forecasting. WRF is used for case study simulations at two West Coast North American wind farms, one with simple and one with complex terrain, during both synoptically and locally-driven weather events. The model's performance with different grid nesting configurations, turbulence closures, and grid resolutions is evaluated by comparison to observation data. Improvement to simulation results from the use of more computationally expensive high resolution simulations is only found for the complex terrain simulation during the locally-driven event. Physical parameters, such as soil moisture, have a large effect on locally-forced events, and prognostic turbulence kinetic energy (TKE) schemes are found to perform better than non-local eddy viscosity turbulence closure schemes. Mesoscale models, however, do not resolve turbulence directly, which is important at finer grid resolutions capable of resolving wind turbine components and their interactions with atmospheric turbulence. Large-eddy simulation (LES) is a numerical approach that resolves the largest scales of turbulence directly by separating large-scale, energetically important eddies from smaller scales with the application of a spatial filter. LES allows higher fidelity representation of the wind speed and turbulence intensity at the scale of a wind turbine which parameterizations have difficulty representing. Use of high-resolution LES enables the implementation of more sophisticated wind turbine parameterizations to create a robust model for wind energy applications using grid spacing small enough to resolve individual elements of a turbine such as its rotor blades or rotation area. Generalized actuator disk (GAD) and line (GAL) parameterizations are integrated into WRF to complement its real-world weather modeling capabilities and better represent wind turbine airflow interactions, including wake effects. The GAD parameterization represents the wind turbine as a two-dimensional disk resulting from the rotation of the turbine blades. Forces on the atmosphere are computed along each blade and distributed over rotating, annular rings intersecting the disk. While typical LES resolution (10-20 m) is normally sufficient to resolve the GAD, the GAL parameterization requires significantly higher resolution (1-3 m) as it does not distribute the forces from the blades over annular elements, but applies them along lines representing individual blades. In this dissertation, the GAL is implemented into WRF and evaluated against the GAD parameterization from two field campaigns that measured the inflow and near-wake regions of a single turbine. The data-sets are chosen to allow validation under the weakly convective and weakly stable conditions characterizing most turbine operations. The parameterizations are evaluated with respect to their ability to represent wake wind speed, variance, and vorticity by comparing fine-resolution GAD and GAL simulations along with coarse-resolution GAD simulations. Coarse-resolution GAD simulations produce aggregated wake characteristics similar to both GAD and GAL simulations (saving on computational cost), while the GAL parameterization enables resolution of near wake physics (such as vorticity shedding and wake expansion) for high fidelity applications. (Abstract shortened by ProQuest.).

High Resolution Wind Direction and Speed Information for Support of Fire Operations

Treesearch

B.W. Butler; J.M. Forthofer; M.A. Finney; L.S. Bradshaw; R. Stratton

2006-01-01

Computational Fluid Dynamics (CFD) technology has been used to model wind speed and direction in mountainous terrain at a relatively high resolution compared to other readily available technologies. The process termed “gridded wind” is not a forecast, but rather represents a method for calculating the influence of terrain on general wind flows. Gridded wind simulations...

National Assessment of Energy Storage for Grid Balancing and Arbitrage: Phase 1, WECC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kintner-Meyer, Michael CW; Balducci, Patrick J.; Colella, Whitney G.

2012-06-01

To examine the role that energy storage could play in mitigating the impacts of the stochastic variability of wind generation on regional grid operation, the Pacific Northwest National Laboratory (PNNL) examined a hypothetical 2020 grid scenario in which additional wind generation capacity is built to meet renewable portfolio standard targets in the Western Interconnection. PNNL developed a stochastic model for estimating the balancing requirements using historical wind statistics and forecasting error, a detailed engineering model to analyze the dispatch of energy storage and fast-ramping generation devices for estimating size requirements of energy storage and generation systems for meeting new balancingmore » requirements, and financial models for estimating the life-cycle cost of storage and generation systems in addressing the future balancing requirements for sub-regions in the Western Interconnection. Evaluated technologies include combustion turbines, sodium sulfur (Na-S) batteries, lithium ion batteries, pumped-hydro energy storage, compressed air energy storage, flywheels, redox flow batteries, and demand response. Distinct power and energy capacity requirements were estimated for each technology option, and battery size was optimized to minimize costs. Modeling results indicate that in a future power grid with high-penetration of renewables, the most cost competitive technologies for meeting balancing requirements include Na-S batteries and flywheels.« less

New Model of a Solar Wind Airplane for Geomatic Operations

NASA Astrophysics Data System (ADS)

Achachi, A.; Benatia, D.

2015-08-01

The ability for an aircraft to fly during a much extended period of time has become a key issue and a target of research, both in the domain of civilian aviation and unmanned aerial vehicles. This paper describes a new design and evaluating of solar wind aircraft with the objective to assess the impact of a new system design on overall flight crew performance. The required endurance is in the range of some hours in the case of law enforcement, border surveillance, forest fire fighting or power line inspection. However, other applications at high altitudes, such as geomatic operations for delivering geographic information, weather research and forecast, environmental monitoring, would require remaining airborne during days, weeks or even months. The design of GNSS non precision approach procedure for different airports is based on geomatic data.

The diagnosis and forecast system of hydrometeorological characteristics for the White, Barents, Kara and Pechora Seas

NASA Astrophysics Data System (ADS)

Fomin, Vladimir; Diansky, Nikolay; Gusev, Anatoly; Kabatchenko, Ilia; Panasenkova, Irina

2017-04-01

The diagnosis and forecast system for simulating hydrometeorological characteristics of the Russian Western Arctic seas is presented. It performs atmospheric forcing computation with the regional non-hydrostatic atmosphere model Weather Research and Forecasting model (WRF) with spatial resolution 15 km, as well as computation of circulation, sea level, temperature, salinity and sea ice with the marine circulation model INMOM (Institute of Numerical Mathematics Ocean Model) with spatial resolution 2.7 km, and the computation of wind wave parameters using the Russian wind-wave model (RWWM) with spatial resolution 5 km. Verification of the meteorological characteristics is done for air temperature, air pressure, wind velocity, water temperature, currents, sea level anomaly, wave characteristics such as wave height and wave period. The results of the hydrometeorological characteristic verification are presented for both retrospective and forecast computations. The retrospective simulation of the hydrometeorological characteristics for the White, Barents, Kara and Pechora Seas was performed with the diagnosis and forecast system for the period 1986-2015. The important features of the Kara Sea circulation are presented. Water exchange between Pechora and Kara Seas is described. The importance is shown of using non-hydrostatic atmospheric circulation model for the atmospheric forcing computation in coastal areas. According to the computation results, extreme values of hydrometeorological characteristics were obtained for the Russian Western Arctic seas.

Data Analysis, Modeling, and Ensemble Forecasting to Support NOWCAST and Forecast Activities at the Fallon Naval Station

DTIC Science & Technology

2011-09-30

forecasting and use of satellite data assimilation for model evaluation (Jiang et al, 2011a). He is a task leader on another NSF EPSCoR project...K. Horvath, R. Belu, 2011a: Application of variational data assimilation to dynamical downscaling of regional wind energy resources in the western...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Data Analysis, Modeling, and Ensemble Forecasting to

Further Exploring the Potential for Assimilation of Unmanned Aircraft Observations to Benefit Hurricane Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Sippel, Jason A.; Zhang, Fuqing; Weng, Yonghui; Braun, Scott A.; Cecil, Daniel J.

2015-01-01

This study explores the potential of assimilating data from multiple instruments onboard high-altitude, long-endurance unmanned aircraft to improve hurricane analyses and forecasts. A recent study found a significant positive impact on analyses and forecasts of Hurricane Karl when an ensemble Kalman filter was used to assimilate data from the High-altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), a new Doppler radar onboard the NASA Global Hawk (GH) unmanned airborne system. The GH can also carry other useful instruments, including dropsondes and the Hurricane Imaging Radiometer (HIRAD), which is a new radiometer that estimates large swaths of wind speeds and rainfall at the ocean surface. The primary finding is that simultaneously assimilating data from HIWRAP and the other GH-compatible instruments results in further analysis and forecast improvement for Karl. The greatest improvement comes when HIWRAP, HIRAD, and dropsonde data are simultaneously assimilated.

A preliminary intercomparison between numerical upper wind forecasts and research aircraft measurements of jet streams

NASA Technical Reports Server (NTRS)

Shapiro, M. A.

1982-01-01

During the past several years, research on the structure of extra-tropical jet streams has been carried out with direct measurements with instrumented research aircraft from the National Center for Atmospheric Research (NCAR). These measurements have been used to describe the wind, temperature, turbulence and chemical characteristics of jet streams. A fundamental question is one of assessing the potential value of existing operational numerical forecast models for forecasting the meteorological conditions along commercial aviation flight routes so as to execute Minimum Flight Time tracks and thus obtain the maximum efficiency in aviation fuel consumption. As an initial attempt at resolving this question, the 12 hour forecast output from two models was expressed in terms of a common output format to ease their intercomparison. The chosen models were: (1) the Fine-Mesh Spectral hemispheric and (2) the Limited Area Fine Mesh (LFM) model.

Typhoon air-sea drag coefficient in coastal regions

NASA Astrophysics Data System (ADS)

Zhao, Zhong-Kuo; Liu, Chun-Xia; Li, Qi; Dai, Guang-Feng; Song, Qing-Tao; Lv, Wei-Hua

2015-02-01

The air-sea drag during typhoon landfalls is investigated for a 10 m wind speed as high as U10 ≈ 42 m s-1, based on multilevel wind measurements from a coastal tower located in the South China Sea. The drag coefficient (CD) plotted against the typhoon wind speed is similar to that of open ocean conditions; however, the CD curve shifts toward a regime of lower winds, and CD increases by a factor of approximately 0.5 relative to the open ocean. Our results indicate that the critical wind speed at which CD peaks is approximately 24 m s-1, which is 5-15 m s-1 lower than that from deep water. Shoaling effects are invoked to explain the findings. Based on our results, the proposed CD formulation, which depends on both water depth and wind speed, is applied to a typhoon forecast model. The forecasts of typhoon track and surface wind speed are improved. Therefore, a water-depth-dependence formulation of CD may be particularly pertinent for parameterizing air-sea momentum exchanges over shallow water.

Estimating the Wind Resource in Uttarakhand: Comparison of Dynamic Downscaling with Doppler Lidar Wind Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lundquist, J. K.; Pukayastha, A.; Martin, C.

Previous estimates of the wind resources in Uttarakhand, India, suggest minimal wind resources in this region. To explore whether or not the complex terrain in fact provides localized regions of wind resource, the authors of this study employed a dynamic down scaling method with the Weather Research and Forecasting model, providing detailed estimates of winds at approximately 1 km resolution in the finest nested simulation.

AI techniques in geomagnetic storm forecasting

NASA Astrophysics Data System (ADS)

Lundstedt, Henrik

This review deals with how geomagnetic storms can be predicted with the use of Artificial Intelligence (AI) techniques. Today many different Al techniques have been developed, such as symbolic systems (expert and fuzzy systems) and connectionism systems (neural networks). Even integrations of AI techniques exist, so called Intelligent Hybrid Systems (IHS). These systems are capable of learning the mathematical functions underlying the operation of non-linear dynamic systems and also to explain the knowledge they have learned. Very few such powerful systems exist at present. Two such examples are the Magnetospheric Specification Forecast Model of Rice University and the Lund Space Weather Model of Lund University. Various attempts to predict geomagnetic storms on long to short-term are reviewed in this article. Predictions of a month to days ahead most often use solar data as input. The first SOHO data are now available. Due to the high temporal and spatial resolution new solar physics have been revealed. These SOHO data might lead to a breakthrough in these predictions. Predictions hours ahead and shorter rely on real-time solar wind data. WIND gives us real-time data for only part of the day. However, with the launch of the ACE spacecraft in 1997, real-time data during 24 hours will be available. That might lead to the second breakthrough for predictions of geomagnetic storms.

GUMICS-4 Year Run: Ground Magnetic Field Predictions

NASA Astrophysics Data System (ADS)

Honkonen, I. J.; Viljanen, A.; Juusola, L.; Facsko, G.; Vanhamäki, H.

2013-12-01

Space weather can have severe effects even at ground level when Geomagnetically Induced Currents (GIC) disrupt power transmission networks, the worst case being a complete blackout affecting millions of people. The importance of space weather forecasting as well as the need for model improvement and validation has been recognized internationally. The recently concluded GUMICS-4 one year run, in which solar wind observations obtained from OMNIWeb for the period 2002-01-29 to 2003-02-02 were given as input to the model, will allow GUMICS to be validated against observations on an unprecedented scale. The performance of GUMICS can be quantified statistically, as a function of, for example, the solar wind driver, various geomagnetic indices, magnetic local time and other parameters. Here we concentrate on the ability of GUMICS to predict ground magnetic field observations for one year of simulated results. The ground magnetic field predictions are compared to observations of the mainland IMAGE magnetometer stations located at CGM latitudes 54-68 N. Furthermore the GIC derived from ground magnetic field predictions are compared to observations along the natural gas pipeline at Mäntsälä, South Finland. Various metrics are used to objectively evaluate the performance of GUMICS as a function of different parameters, thereby providing significant insight into the space weather forecasting ability of models based on first principles.

Impact of Scatterometer Ocean Wind Vector Data on NOAA Operations

NASA Astrophysics Data System (ADS)

Jelenak, Z.; Chang, P.; Brennan, M. J.; Sienkiewicz, J. M.

2015-12-01

Near real-time measurements of ocean surface vector winds (OSVW), including both wind speed and direction from non-NOAA satellites, are being widely used in critical operational NOAA forecasting and warning activities. The scatterometer wind data data have had major operational impact in: a) determining wind warning areas for mid-latitude systems (gale, storm,hurricane force); b) determining tropical cyclone 34-knot and 50-knot wind radii. c) tracking the center location of tropical cyclones, including the initial identification of their formation. d) identifying and warning of extreme gap and jet wind events at all latitudes. e) identifying the current location of frontal systems and high and low pressure centers. f) improving coastal surf and swell forecasts Much has been learned about the importance and utility of satellite OSVW data in operational weather forecasting and warning by exploiting OSVW research satellites in near real-time. Since December 1999 when first data from QuikSCAT scatterometer became available in near real time NOAA operations have been benefiting from ASCAT scatterometer observations on MetOp-A and B, Indian OSCAT scatterometer on OceanSat-3 and lately NASA's RapidScat mission on International Space Station. With oceans comprising over 70 percent of the earth's surface, the impacts of these data have been tremendous in serving society's needs for weather and water information and in supporting the nation's commerce with information for safe, efficient, and environmentally sound transportation and coastal preparedness. The satellite OSVW experience that has been gained over the past decade by users in the operational weather community allows for realistic operational OSVW requirements to be properly stated for future missions. Successful model of transitioning research data into operation implemented by Ocean Winds Team in NOAA's NESDIS/STAR office and subsequent data impacts will be presented and discussed.

Role of Meteorology in Flights of a Solar-Powered Airplane

NASA Technical Reports Server (NTRS)

Donohue, Casey

2004-01-01

In the summer of 2001, the Helios prototype solar-powered uninhabited aerial vehicle (UAV) [a lightweight, remotely piloted airplane] was deployed to the Pacific Missile Range Facility (PMRF), at Kauai, Hawaii, in an attempt to fly to altitudes above 100,000 ft (30.48 km). The goal of flying a UAV to such high altitudes has been designated a level-I milestone of the NASA Environmental Research Aircraft and Sensor Technology (ERAST) program. In support of this goal, meteorologists from NASA Dryden Flight Research Center were sent to PMRF, as part of the flight crew, to provide current and forecast weather information to the pilots, mission directors, and planners. Information of this kind is needed to optimize flight conditions for peak aircraft performance and to enable avoidance of weather conditions that could adversely affect safety. In general, the primary weather data of concern for ground and flight operations are wind speeds (see Figure 1). Because of its long wing span [247 ft (.75 m)] and low weight [1,500 to 1,600 lb (about 680 to 726 kg)], the Helios airplane is sensitive to wind speeds exceeding 7 kn (3.6 m/s) at the surface. Also, clouds are of concern because they can block sunlight needed to energize an array of solar photovoltaic cells that provide power to the airplane. Vertical wind shear is very closely monitored in order to prevent damage or loss of control due to turbulence.

Effects of the uncertainty of energy price and water availability forecasts on the operation of Alpine hydropower reservoir systems

NASA Astrophysics Data System (ADS)

Anghileri, D.; Castelletti, A.; Burlando, P.

2016-12-01

European energy markets have experienced dramatic changes in the last years because of the massive introduction of Variable Renewable Sources (VRSs), such as wind and solar power sources, in the generation portfolios in many countries. VRSs i) are intermittent, i.e., their production is highly variable and only partially predictable, ii) are characterized by no correlation between production and demand, iii) have negligible costs of production, and iv) have been largely subsidized. These features result in lower energy prices, but, at the same time, in increased price volatility, and in network stability issues, which pose a threat to traditional power sources because of smaller incomes and higher maintenance costs associated to a more flexible operation of power systems. Storage hydropower systems play an important role in compensating production peaks, both in term of excess and shortage of energy. Traditionally, most of the research effort in hydropower reservoir operation has focused on modeling and forecasting reservoir inflow as well as designing reservoir operation accordingly. Nowadays, price variability may be the largest source of uncertainty in the context of hydropower systems, especially when considering medium-to-large reservoirs, whose storage can easily buffer small inflow fluctuations. In this work, we compare the effects of uncertain inflow and energy price forecasts on hydropower production and profitability. By adding noise to historic inflow and price trajectories, we build a set of synthetic forecasts corresponding to different levels of predictability and assess their impact on reservoir operating policies and performances. The study is conducted on different hydropower systems, including storage systems and pumped-storage systems, with different characteristics, e.g., different inflow-capacity ratios. The analysis focuses on Alpine hydropower systems where the hydrological regime ranges from purely ice and snow-melt dominated to mixed snow-melt and rain-dominated regimes.

A New Eddy Dissipation Rate Formulation for the Terminal Area PBL Prediction System(TAPPS)

NASA Technical Reports Server (NTRS)

Charney, Joseph J.; Kaplan, Michael L.; Lin, Yuh-Lang; Pfeiffer, Karl D.

2000-01-01

The TAPPS employs the MASS model to produce mesoscale atmospheric simulations in support of the Wake Vortex project at Dallas Fort-Worth International Airport (DFW). A post-processing scheme uses the simulated three-dimensional atmospheric characteristics in the planetary boundary layer (PBL) to calculate the turbulence quantities most important to the dissipation of vortices: turbulent kinetic energy and eddy dissipation rate. TAPPS will ultimately be employed to enhance terminal area productivity by providing weather forecasts for the Aircraft Vortex Spacing System (AVOSS). The post-processing scheme utilizes experimental data and similarity theory to determine the turbulence quantities from the simulated horizontal wind field and stability characteristics of the atmosphere. Characteristic PBL quantities important to these calculations are determined based on formulations from the Blackadar PBL parameterization, which is regularly employed in the MASS model to account for PBL processes in mesoscale simulations. The TAPPS forecasts are verified against high-resolution observations of the horizontal winds at DFW. Statistical assessments of the error in the wind forecasts suggest that TAPPS captures the essential features of the horizontal winds with considerable skill. Additionally, the turbulence quantities produced by the post-processor are shown to compare favorably with corresponding tower observations.

Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

2015-04-01

The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful between the 5th and the 8th day of the prediction. The information obtained is then included in an early warning system, designed in the framework of the European project iCoast (ECHO/SUB/2013/661009) with the aim of set alarms in coastal areas depending on the wave conditions, the sea level, the flooding and the run up in the coast.

Renewable energy alternatives to mega hydropower: a case study of Inga 3 for Southern Africa

NASA Astrophysics Data System (ADS)

Deshmukh, R.; Mileva, A.; Wu, G. C.

2018-06-01

We assess the feasibility and cost-effectiveness of renewable energy alternatives to Inga 3, a 4.8-GW hydropower project on the Congo River, to serve the energy needs of the host country, the Democratic Republic of Congo (DRC), and the main buyer, South Africa. To account for a key uncertainty in the literature regarding the additional economic impacts of managing variable wind and solar electricity, we built a spatially and temporally detailed power system investment model for South Africa. We find that a mix of wind, solar photovoltaics, and some natural gas is more cost-effective than Inga 3 to meet future demand except in scenarios with pessimistic assumptions about wind technology performance. If a low load growth forecast is used, including Inga 3 in the power mix results in higher system cost across all sensitivities. In our scenarios, the effect of Inga 3 deployment on South African power system cost ranges from an increase of ZAR 4300 (US 330) million annually to savings of ZAR 1600 (US 120) million annually by 2035. A cost overrun as low as 20% makes the Inga 3 scenarios more expensive in all sensitivity cases. Including time and cost overruns and losses in transmission from DRC to South Africa make Inga 3 an even less attractive investment. For DRC, through analysis of spatial datasets representing technical, physical, and environmental constraints, we find abundant renewable energy potential: 60 GW of solar photovoltaic and 0.6–2.3 GW of wind located close to transmission infrastructure have levelized costs less than US 0.07 per kWh, or the anticipated cost of Inga 3 to residential consumers.

Final Report on the Creation of the Wind Integration National Dataset (WIND) Toolkit and API: October 1, 2013 - September 30, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Bri-Mathias

2016-04-08

The primary objective of this work was to create a state-of-the-art national wind resource data set and to provide detailed wind plant output data for specific sites based on that data set. Corresponding retrospective wind forecasts were also included at all selected locations. The combined information from these activities was used to create the Wind Integration National Dataset (WIND), and an extraction tool was developed to allow web-based data access.

A Meso-Climatology Study of the High-Resolution Tower Network Over the Florida Spaceport

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Bauman, William H., III

2004-01-01

Forecasters at the US Air Force 45th Weather Squadron (45 WS) use wind and temperature data from the tower network over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to evaluate Launch Commit Criteria and to issue and verify temperature and wind advisories, watches, and warnings for ground operations. The Spaceflight Meteorology Group at the Johnson Space Center in Houston, TX also uses these data when issuing forecasts for shuttle landings at the KSC Shuttle Landing Facility. Systematic biases in these parameters at any of the towers could adversely affect an analysis, forecast, or verification for all of these operations. In addition, substantial geographical variations in temperature and wind speed can occur under specific wind directions. Therefore, the Applied Meteorology Unit (AMU), operated by ENSCO Inc., was tasked to develop a monthly and hourly climatology of temperatures and winds from the tower network, and identify the geographical variation, tower biases, and the magnitude of those biases. This paper presents a sub-set of results from a nine-year climatology of the KSC/CCAFS tower network, highlighting the geographical variations based on location, month, times of day, and specific wind direction regime. Section 2 provides a description of the tower mesonetwork and instrumentation characteristics. Section 3 presents the methodology used to construct the tower climatology including QC methods and data processing. The results of the tower climatology are presented in Section 4 and Section 5 summarizes the paper.

Observations of the structure and evolution of surface and flight-level wind asymmetries in Hurricane Rita (2005)

NASA Astrophysics Data System (ADS)

Rogers, Robert; Uhlhorn, Eric

2008-11-01

Knowledge of the magnitude and distribution of surface winds, including the structure of azimuthal asymmetries in the wind field, are important factors for tropical cyclone forecasting. With its ability to remotely measure surface wind speeds, the stepped frequency microwave radiometer (SFMR) has assumed a prominent role for the operational tropical cyclone forecasting community. An example of this instrument's utility is presented here, where concurrent measurements of aircraft flight-level and SFMR surface winds are used to document the wind field evolution over three days in Hurricane Rita (2005). The amplitude and azimuthal location (phase) of the wavenumber-1 asymmetry in the storm-relative winds varied at both levels over time. The peak was found to the right of storm track at both levels on the first day. By the third day, the peak in flight-level storm-relative winds remained to the right of storm track, but it shifted to left of storm track at the surface, resulting in a 60-degree shift between the surface and flight-level and azimuthal variations in the ratio of surface to flight-level winds. The asymmetric differences between the surface and flight-level maximum wind radii also varied, indicating a vortex whose tilt was increasing.

Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model

DOE PAGES

Lee, Jared A.; Hacker, Joshua P.; Monache, Luca Delle; ...

2016-08-03

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this paper we usemore » the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining z 0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z 0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques to estimate z 0 in DART. Using DART to estimate z 0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z 0 ensembles by 4%–22%. Finally, however, parameter estimation of z 0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.« less

Application of SeaWinds Scatterometer and TMI-SSM/I Rain Rates to Hurricane Analysis and Forecasting

NASA Technical Reports Server (NTRS)

Atlas, Robert; Hou, Arthur; Reale, Oreste

2004-01-01

Results provided by two different assimilation methodologies involving data from passive and active space-borne microwave instruments are presented. The impact of the precipitation estimates produced by the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I) in a previously developed 1D variational continuous assimilation algorithm for assimilating tropical rainfall is shown on two hurricane cases. Results on the impact of the SeaWinds scatterometer on the intensity and track forecast of a mid-Atlantic hurricane are also presented. This work is the outcome of a collaborative effort between NASA and NOAA and indicates the substantial improvement in tropical cyclone forecasting that can result from the assimilation of space-based data in global atmospheric models.

Choice of Control Variables in Variational Data Assimilation and Its Analysis and Forecast Impact

NASA Astrophysics Data System (ADS)

Xie, Yuanfu; Sun, Jenny; Fang, Wei-ting

2014-05-01

Choice of control variables directly impacts the analysis qualify of a variational data assimilation and its forecasts. A theory on selecting control variables for wind and moisture field is introduced for 3DVAR or 4DVAR. For a good control variable selection, Parseval's theory is applied to 3-4DVAR and the behavior of different control variables is illustrated in physical and Fourier space in terms of minimization condition, meteorological dynamic scales and practical implementation. The computational and meteorological benefits will be discussed. Numerical experiments have been performed using WRF-DA for wind control variables and CRTM for moisture control variables. It is evident of the WRF forecast improvement and faster convergence of CRTM satellite data assimilation.

The influence of the North-Atlantic Oscillation on Variable Renewable Energy penetration rate in Europe

NASA Astrophysics Data System (ADS)

Francois, Baptiste

2016-04-01

The on-going transition to low-carbon economy promotes the development of Variable Renewable Energies (VRE) such as wind-power, solar-power and hydro-power. The European Climate Foundation now typically dates for 2050 optimistic scenarios with close to 100 % renewable energy in Europe. When considering 100 % renewable scenarios, backup generation is needed for stabilizing the network when variable renewable energy sources such as wind, solar or run-of-the river hydropower are not sufficient for supplying the load. Several studies show that backup generation needs are reduced by dissipating power densities either in space through grids and time through storage. To our knowledge, most of these published studies were carried out using field measurements collected at meteorological and hydrological stations and over relatively short time period (less than 10 years). By using short period of times, such studies somehow disregarded the space and temporal variability of VRE power generation that could be induced by larger-scale climate variability patterns. This study investigates the influence of the North Atlantic Oscillation (NAO) on the VRE penetration for a set of 11 regions in Europe and Tunisia, and over 1980-2012 time period. These regions are located along two climate transects, the first one going from the Northern regions (Norway, Finland) to the Southern ones (Greece, Andalucía, Tunisia) and the second one going from the oceanic climate (West of France, Galicia) to the continental one (Romania, Belorussia). For each of those regions, we combine data from the Weather Research and Forecasting Model (wind speed, solar radiation; Vautard et al., 2014) and the European Climate Assessment & Dataset (temperature, precipitation; Haylock et al,. 2008) for estimating solar-power, wind-power, run-of-the-river hydro-power and the electricity demand over a time period of 33 years. For each region, we analyze seasonal differences in penetration rates of wind-, solar- and hydro-power and between positive and negative phases of the NAO index. We then discuss about opportunities of combining different VRE within the same region and among neighbor regions for limiting backup generation needs during both positive and negative NAO phases. References: Vautard, R., Thais, F., Tobin, I., Bréon, F.-M., de Lavergne, J.-G.D., Colette, A., Yiou, P., and Ruti, P.M. (2014).Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms. Nat. Commun. 5, 3196. Haylock, M.R., Hofstra, N., Tank, A.M.G.K., Klok, E.J., Jones, P.D., New, M., 2008. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res.-Atmos. 113. doi:10.1029/2008JD010201

Tropical cyclone induced asymmetry of sea level surge and fall and its presentation in a storm surge model with parametric wind fields

NASA Astrophysics Data System (ADS)

Peng, Machuan; Xie, Lian; Pietrafesa, Leonard J.

The asymmetry of tropical cyclone induced maximum coastal sea level rise (positive surge) and fall (negative surge) is studied using a three-dimensional storm surge model. It is found that the negative surge induced by offshore winds is more sensitive to wind speed and direction changes than the positive surge by onshore winds. As a result, negative surge is inherently more difficult to forecast than positive surge since there is uncertainty in tropical storm wind forecasts. The asymmetry of negative and positive surge under parametric wind forcing is more apparent in shallow water regions. For tropical cyclones with fixed central pressure, the surge asymmetry increases with decreasing storm translation speed. For those with the same translation speed, a weaker tropical cyclone is expected to gain a higher AI (asymmetry index) value though its induced maximum surge and fall are smaller. With fixed RMW (radius of maximum wind), the relationship between central pressure and AI is heterogeneous and depends on the value of RMW. Tropical cyclone's wind inflow angle can also affect surge asymmetry. A set of idealized cases as well as two historic tropical cyclones are used to illustrate the surge asymmetry.

Using fire-weather forecasts and local weather observations in predicting burning index for individual fire-danger stations.

Treesearch

Owen P. Cramer

1958-01-01

Any agency engaged in forest-fire control needs accurate weather forecasts and systematic procedures for making the best use of predicted and reported weather information. This study explores the practicability of using several tabular and graphical aids for converting area forecasts and local observations of relative humidity and wind speed into predicted values for...

Forecasting cyanobacteria dominance in Canadian temperate lakes.

PubMed

Persaud, Anurani D; Paterson, Andrew M; Dillon, Peter J; Winter, Jennifer G; Palmer, Michelle; Somers, Keith M

2015-03-15

Predictive models based on broad scale, spatial surveys typically identify nutrients and climate as the most important predictors of cyanobacteria abundance; however these models generally have low predictive power because at smaller geographic scales numerous other factors may be equally or more important. At the lake level, for example, the ability to forecast cyanobacteria dominance is of tremendous value to lake managers as they can use such models to communicate exposure risks associated with recreational and drinking water use, and possible exposure to algal toxins, in advance of bloom occurrence. We used detailed algal, limnological and meteorological data from two temperate lakes in south-central Ontario, Canada to determine the factors that are closely linked to cyanobacteria dominance, and to develop easy to use models to forecast cyanobacteria biovolume. For Brandy Lake (BL), the strongest and most parsimonious model for forecasting % cyanobacteria biovolume (% CB) included water column stability, hypolimnetic TP, and % cyanobacteria biovolume two weeks prior. For Three Mile Lake (TML), the best model for forecasting % CB included water column stability, hypolimnetic TP concentration, and 7-d mean wind speed. The models for forecasting % CB in BL and TML are fundamentally different in their lag periods (BL = lag 1 model and TML = lag 2 model) and in some predictor variables despite the close proximity of the study lakes. We speculate that three main factors (nutrient concentrations, water transparency and lake morphometry) may have contributed to differences in the models developed, and may account for variation observed in models derived from large spatial surveys. Our results illustrate that while forecast models can be developed to determine when cyanobacteria will dominate within two temperate lakes, the models require detailed, lake-specific calibration to be effective as risk-management tools. Copyright © 2015 Elsevier Ltd. All rights reserved.

Weather forecasting based on hybrid neural model

NASA Astrophysics Data System (ADS)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Near Real Time MISR Wind Observations for Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Mueller, K. J.; Protack, S.; Rheingans, B. E.; Hansen, E. G.; Jovanovic, V. M.; Baker, N.; Liu, J.; Val, S.

2014-12-01

The Multi-angle Imaging SpectroRadiometer (MISR) project, in association with the NASA Langley Atmospheric Science Data Center (ASDC), has this year adapted its original production software to generate near-real time (NRT) cloud-motion winds as well as radiance imagery from all nine MISR cameras. These products are made publicly available at the ASDC with a latency of less than 3 hours. Launched aboard the sun-synchronous Terra platform in 1999, the MISR instrument continues to acquire near-global, 275 m resolution, multi-angle imagery. During a single 7 minute overpass of any given area, MISR retrieves the stereoscopic height and horizontal motion of clouds from the multi-angle data, yielding meso-scale near-instantaneous wind vectors. The ongoing 15-year record of MISR height-resolved winds at 17.6 km resolution has been validated against independent data sources. Low-level winds dominate the sampling, and agree to within ±3 ms-1 of collocated GOES and other observations. Low-level wind observations are of particular interest to weather forecasting, where there is a dearth of observations suitable for assimilation, in part due to reliability concerns associated with winds whose heights are assigned by the infrared brightness temperature technique. MISR cloud heights, on the other hand, are generated from stereophotogrammetric pattern matching of visible radiances. MISR winds also address data gaps in the latitude bands between geostationary satellite coverage and polar orbiting instruments that obtain winds from multiple overpasses (e.g. MODIS). Observational impact studies conducted by the Naval Research Laboratory (NRL) and by the German Weather Service (Deutscher Wetterdienst) have both demonstrated forecast improvements when assimilating MISR winds. An impact assessment using the GEOS-5 system is currently in progress. To benefit air quality forecasts, the MISR project is currently investigating the feasibility of generating near-real time aerosol products.

Investigation of water vapor motion winds from geostationary satellites

NASA Technical Reports Server (NTRS)

Velden, Christopher S.; Nieman, Steven J.; Wanzong, Steven

1994-01-01

Water vapor imagery from geostationary satellites has been available for over a decade. These data are used extensively by operational analysts and forecasters, mainly in a qualitative mode (Weldon and Holmes 1991). In addition to qualitative applications, motions deduced in animated water vapor imagery can be used to infer wind fields in cloudless regimes, thereby augmenting the information provided by cloud-drift wind vectors. Early attempts at quantifying the data by tracking features in water vapor imagery met with modest success (Stewart et al. 1985; Hayden and Stewart 1987). More recently, automated techniques have been developed and refined, and have resulted in upper-level wind observations comparable in quality to current operational cloud-tracked winds (Laurent 1993). In a recent study by Velden et al. (1993) it was demonstrated that wind sets derived from Meteosat-3 (M-3) water vapor imagery can provide important environmental wind information in data void areas surrounding tropical cyclones, and can positively impact objective track forecasts. M-3 was repositioned to 75W by the European Space Agency in 1992 in order to provide complete coverage of the Atlantic Ocean. Data from this satellite are being transmitted to the U.S. for operational use. Compared with the current GOES-7 (G-7) satellite (positioned near 112W), the M-3 water vapor channel contains a superior horizontal resolution (5 km vs. 16 km ). In this paper, we examine wind sets derived using automated procedures from both GOES-7 and Meteosat-3 full disk water vapor imagery in order to assess this data as a potentially important source of large-scale wind information. As part of a product demonstration wind sets were produced twice a day at CIMSS during a six-week period in March and April (1994). These data sets are assessed in terms of geographic coverage, statistical accuracy, and meteorological impact through preliminary results of numerical model forecast studies.

Thirty Years of Improving the NCEP Global Forecast System

NASA Astrophysics Data System (ADS)

White, G. H.; Manikin, G.; Yang, F.

2014-12-01

Current eight day forecasts by the NCEP Global Forecast System are as accurate as five day forecasts 30 years ago. This revolution in weather forecasting reflects increases in computer power, improvements in the assimilation of observations, especially satellite data, improvements in model physics, improvements in observations and international cooperation and competition. One important component has been and is the diagnosis, evaluation and reduction of systematic errors. The effect of proposed improvements in the GFS on systematic errors is one component of the thorough testing of such improvements by the Global Climate and Weather Modeling Branch. Examples of reductions in systematic errors in zonal mean temperatures and winds and other fields will be presented. One challenge in evaluating systematic errors is uncertainty in what reality is. Model initial states can be regarded as the best overall depiction of the atmosphere, but can be misleading in areas of few observations or for fields not well observed such as humidity or precipitation over the oceans. Verification of model physics is particularly difficult. The Environmental Modeling Center emphasizes the evaluation of systematic biases against observations. Recently EMC has placed greater emphasis on synoptic evaluation and on precipitation, 2-meter temperatures and dew points and 10 meter winds. A weekly EMC map discussion reviews the performance of many models over the United States and has helped diagnose and alleviate significant systematic errors in the GFS, including a near surface summertime evening cold wet bias over the eastern US and a multi-week period when the GFS persistently developed bogus tropical storms off Central America. The GFS exhibits a wet bias for light rain and a dry bias for moderate to heavy rain over the continental United States. Significant changes to the GFS are scheduled to be implemented in the fall of 2014. These include higher resolution, improved physics and improvements to the assimilation. These changes significantly improve the tropospheric flow and reduce a tropical upper tropospheric warm bias. One important error remaining is the failure of the GFS to maintain deep convection over Indonesia and in the tropical west Pacific. This and other current systematic errors will be presented.

The Nature and Variability of Ensemble Sensitivity Fields that Diagnose Severe Convection

NASA Astrophysics Data System (ADS)

Ancell, B. C.

2017-12-01

Ensemble sensitivity analysis (ESA) is a statistical technique that uses information from an ensemble of forecasts to reveal relationships between chosen forecast metrics and the larger atmospheric state at various forecast times. A number of studies have employed ESA from the perspectives of dynamical interpretation, observation targeting, and ensemble subsetting toward improved probabilistic prediction of high-impact events, mostly at synoptic scales. We tested ESA using convective forecast metrics at the 2016 HWT Spring Forecast Experiment to understand the utility of convective ensemble sensitivity fields in improving forecasts of severe convection and its individual hazards. The main purpose of this evaluation was to understand the temporal coherence and general characteristics of convective sensitivity fields toward future use in improving ensemble predictability within an operational framework.The magnitude and coverage of simulated reflectivity, updraft helicity, and surface wind speed were used as response functions, and the sensitivity of these functions to winds, temperatures, geopotential heights, and dew points at different atmospheric levels and at different forecast times were evaluated on a daily basis throughout the HWT Spring Forecast experiment. These sensitivities were calculated within the Texas Tech real-time ensemble system, which possesses 42 members that run twice daily to 48-hr forecast time. Here we summarize both the findings regarding the nature of the sensitivity fields and the evaluation of the participants that reflects their opinions of the utility of operational ESA. The future direction of ESA for operational use will also be discussed.

Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

NASA Astrophysics Data System (ADS)

BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

2013-09-01

The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

Energy transition in transport sector from energy substitution perspective

NASA Astrophysics Data System (ADS)

Sun, Wangmin; Yang, Xiaoguang; Han, Song; Sun, Xiaoyang

2017-10-01

Power and heating generation sector and transport sector contribute a highest GHG emissions and even air pollutions. This paper seeks to investigate life cycle costs and emissions in both the power sector and transport sector, and evaluate the cost-emission efficient (costs for one unit GHG emissions) of the substitution between new energy vehicles and conventional gasoline based vehicles under two electricity mix scenarios. In power sector, wind power and PV power will be cost comparative in 2030 forecasted with learning curve method. With high subsidies, new energy cars could be comparative now, but it still has high costs to lower GHG emissions. When the government subsidy policy is reversible, the emission reduction cost for new energy vehicle consumer will be 900/ton. According to the sensitive analysis, the paper suggests that the government implement policies that allocate the cost to the whole life cycle of energy production and consumption related to transport sector energy transition and policies that are in favor of new energy vehicle consumers but not the new energy car producers.

Early Calibration Results of CYGNSS Mission

NASA Astrophysics Data System (ADS)

Balasubramaniam, R.; Ruf, C. S.; McKague, D. S.; Clarizia, M. P.; Gleason, S.

2017-12-01

The first of its kind, GNSS-R complete orbital mission, CYGNSS was successfully launched on Dec 15 2016. The goal of this mission is to accurately forecast the intensification of tropical cyclones by modelling its inner core. The 8 micro observatories of CYGNSS carry a passive instrument called Delay Doppler Mapping Instrument (DDMI). The DDMIs form a 2D representation called the Delay-Doppler Map (DDM) of the forward scattered power signal. Each DDMI outputs 4 DDMs per second which are compressed and sent to the ground resulting in a total of 32 sea-surface measurements produced by the CYGNSS constellation per second. These are subsequently used in the Level-2 wind retrieval algorithm to extract wind speed information. In this paper, we perform calibration and validation of CYGNSS measurements for accurate extraction of wind speed information. The calibration stage involves identification and correction for dependence of the CYGNSS observables namely Normalised Bistatic Radar Cross Section and Leading Edge Slope of the Integrated Delay Waveform over instrument parameters, geometry etc. The validation stage involves training of the Geophysical Model Function over a multitude of ground truth sources during the Atlantic hurricane season and also refined validation of high wind speed data products.

Turbulent kinetics of a large wind farm and their impact in the neutral boundary layer

DOE PAGES

Na, Ji Sung; Koo, Eunmo; Munoz-Esparza, Domingo; ...

2015-12-28

High-resolution large-eddy simulation of the flow over a large wind farm (64 wind turbines) is performed using the HIGRAD/FIRETEC-WindBlade model, which is a high-performance computing wind turbine–atmosphere interaction model that uses the Lagrangian actuator line method to represent rotating turbine blades. These high-resolution large-eddy simulation results are used to parameterize the thrust and power coefficients that contain information about turbine interference effects within the wind farm. Those coefficients are then incorporated into the WRF (Weather Research and Forecasting) model in order to evaluate interference effects in larger-scale models. In the high-resolution WindBlade wind farm simulation, insufficient distance between turbines createsmore » the interference between turbines, including significant vertical variations in momentum and turbulent intensity. The characteristics of the wake are further investigated by analyzing the distribution of the vorticity and turbulent intensity. Quadrant analysis in the turbine and post-turbine areas reveals that the ejection motion induced by the presence of the wind turbines is dominant compared to that in the other quadrants, indicating that the sweep motion is increased at the location where strong wake recovery occurs. Regional-scale WRF simulations reveal that although the turbulent mixing induced by the wind farm is partly diffused to the upper region, there is no significant change in the boundary layer depth. The velocity deficit does not appear to be very sensitive to the local distribution of turbine coefficients. However, differences of about 5% on parameterized turbulent kinetic energy were found depending on the turbine coefficient distribution. Furthermore, turbine coefficients that consider interference in the wind farm should be used in wind farm parameterization for larger-scale models to better describe sub-grid scale turbulent processes.« less

An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Bauman, William H., III

2010-01-01

The 45th Weather Squadron (45 WS) Launch Weather Officers use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature and dew point, as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network. Objective statistics will give the forecasters knowledge of the model's strength and weaknesses, which will result in improved forecasts for operations.

Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Jain, Rahul; Vaughan, Joseph; Heitkamp, Kyle; Ramos, Charleston; Claiborn, Candis; Schreuder, Maarten; Schaaf, Mark; Lamb, Brian

The post-harvest burning of agricultural fields is commonly used to dispose of crop residue and provide other desired services such as pest control. Despite careful regulation of burning, smoke plumes from field burning in the Pacific Northwest commonly degrade air quality, particularly for rural populations. In this paper, ClearSky, a numerical smoke dispersion forecast system for agricultural field burning that was developed to support smoke management in the Inland Pacific Northwest, is described. ClearSky began operation during the summer through fall burn season of 2002 and continues to the present. ClearSky utilizes Mesoscale Meteorological Model version 5 (MM5v3) forecasts from the University of Washington, data on agricultural fields, a web-based user interface for defining burn scenarios, the Lagrangian CALPUFF dispersion model and web-served animations of plume forecasts. The ClearSky system employs a unique hybrid source configuration, which treats the flaming portion of a field as a buoyant line source and the smoldering portion of the field as a buoyant area source. Limited field observations show that this hybrid approach yields reasonable plume rise estimates using source parameters derived from recent field burning emission field studies. The performance of this modeling system was evaluated for 2003 by comparing forecast meteorology against meteorological observations, and comparing model-predicted hourly averaged PM 2.5 concentrations against observations. Examples from this evaluation illustrate that while the ClearSky system can accurately predict PM 2.5 surface concentrations due to field burning, the overall model performance depends strongly on meteorological forecast error. Statistical evaluation of the meteorological forecast at seven surface stations indicates a strong relationship between topographical complexity near the station and absolute wind direction error with wind direction errors increasing from approximately 20° for sites in open areas to 70° or more for sites in very complex terrain. The analysis also showed some days with good forecast meteorology with absolute mean error in wind direction less than 30° when ClearSky correctly predicted PM 2.5 surface concentrations at receptors affected by field burns. On several other days with similar levels of wind direction error the model did not predict apparent plume impacts. In most of these cases, there were no reported burns in the vicinity of the monitor and, thus, it appeared that other, non-reported burns were responsible for the apparent plume impact at the monitoring site. These cases do not provide information on the performance of the model, but rather indicate that further work is needed to identify all burns and to improve burn reports in an accurate and timely manner. There were also a number of days with wind direction errors exceeding 70° when the forecast system did not correctly predict plume behavior.

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

NASA Astrophysics Data System (ADS)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

Enabling Characteristics Of Optical Autocovariance Lidar For Global Wind And Aerosol Profiling

NASA Astrophysics Data System (ADS)

Grund, C. J.; Stephens, M.; Lieber, M.; Weimer, C.

2008-12-01

Systematic global wind measurements with 70 km horizontal resolution and, depending on altitude from the PBL to stratosphere, 250m-2km vertical resolution and 0.5m/s - 2 m/s velocity precision are recognized as key to the understanding and monitoring of complex climate modulations, validation of models, and improved precision and range for weather forecasts. Optical Autocovariance Wind Lidar (OAWL) is a relatively new interferometric direct detection Doppler lidar approach that promises to meet the required wind profile resolution at substantial mass, cost, and power savings, and at reduced technical risk for a space-based system meeting the most demanding velocity precision and spatial and temporal resolution requirements. A proof of concept Optical Autocovariance Wind Lidar (OAWL) has been demonstrated, and a robust multi- wavelength, field-widened (more than 100 microR) lidar system suitable for high altitude (over 16km) aircraft demonstration is under construction. Other advantages of the OAWL technique include insensitivity to aerosol/molecular backscatter mixing ratio, freedom from complex receiver/transmitter optical frequency lock loops, prospects for practical continuous large-area coverage wind profiling from GEO, and the availability of simultaneous multiple wavelength High Spectral Resolution Lidar (OA-HSRL) for aerosol identification and optical property measurements. We will discuss theory, development and demonstration status, advantages, limitations, and space-based performance of OAWL and OA-HSRL, as well as the potential for combined mission synergies.

Determining the Probability of Violating Upper-Level Wind Constraints for the Launch of Minuteman Ill Ballistic Missiles At Vandenberg Air Force Base

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn A.; Brock, Tyler M.

2013-01-01

The 30th Operational Support Squadron Weather Flight (30 OSSWF) provides comprehensive weather services to the space program at Vandenberg Air Force Base (VAFB) in California. One of their responsibilities is to monitor upper-level winds to ensure safe launch operations of the Minuteman Ill ballistic missile. The 30 OSSWF requested the Applied Meteorology Unit (AMU) analyze VAFB sounding data to determine the probability of violating (PoV) upper-level thresholds for wind speed and shear constraints specific to this launch vehicle, and to develop a graphical user interface (GUI) that will calculate the PoV of each constraint on the day of launch. The AMU suggested also including forecast sounding data from the Rapid Refresh (RAP) model. This would provide further insight for the launch weather officers (LWOs) when determining if a wind constraint violation will occur over the next few hours, and help to improve the overall upper winds forecast on launch day.

Applied Meteorology Unit (AMU) Quarterly Report Fourth Quarter FY-13

NASA Technical Reports Server (NTRS)

Bauman, William; Crawford, Winifred; Watson, Leela; Shafer, Jaclyn; Huddleston, Lisa

2013-01-01

Ms. Shafer completed the task to determine relationships between pressure gradients and peak winds at Vandenberg Air Force Base (VAFB), and began developing a climatology for the VAFB wind towers; Dr. Huddleston completed the task to develop a tool to help forecast the time of the first lightning strike of the day in the Kennedy Space Center (KSC)/Cape Canaveral Air Force Station (CCAFS) area; Dr. Bauman completed work on a severe weather forecast tool focused on the Eastern Range (ER), and also developed upper-winds analysis tools for VAFB and Wallops Fl ight Facility (WFF); Ms. Crawford processed and displayed radar data in the software she will use to create a dual-Doppler analysis over the east-central Florida and KSC/CCAFS areas; Mr. Decker completed developing a wind pairs database for the Launch Services Program to use when evaluating upper-level winds for launch vehicles; Dr. Watson continued work to assimilate observational data into the high-resolution model configurations she created for WFF and the ER.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

14 CFR 121.646 - En-route fuel supply: flag and supplemental operations.

Code of Federal Regulations, 2012 CFR

2012-01-01

... supply requirements of § 121.333; and (iii) Considering expected wind and other weather conditions. (3..., considering wind and other weather conditions expected, it has the fuel otherwise required by this part and... errors in wind forecasting. In calculating the amount of fuel required by paragraph (b)(1)(i) of this...

14 CFR 121.646 - En-route fuel supply: flag and supplemental operations.

Code of Federal Regulations, 2014 CFR

2014-01-01

... supply requirements of § 121.333; and (iii) Considering expected wind and other weather conditions. (3..., considering wind and other weather conditions expected, it has the fuel otherwise required by this part and... errors in wind forecasting. In calculating the amount of fuel required by paragraph (b)(1)(i) of this...

14 CFR 121.646 - En-route fuel supply: flag and supplemental operations.

Code of Federal Regulations, 2013 CFR

2013-01-01

... supply requirements of § 121.333; and (iii) Considering expected wind and other weather conditions. (3..., considering wind and other weather conditions expected, it has the fuel otherwise required by this part and... errors in wind forecasting. In calculating the amount of fuel required by paragraph (b)(1)(i) of this...

The WindStar project

NASA Astrophysics Data System (ADS)

McCandless, Samuel W.; Jones, W. Linwood; Huxtable, Barton D.; Jones, Lawrence P.

1996-03-01

The ``WindStar'' project is a cooperative, cost-sharing venture between NASA's Earth Observations Commercial Applications Program (EOCAP), directed by the Stennis Space Center (SSC), and User Systems, Incorporated (USI), a Virginia-based remote sensing technology development company. The project seeks to establish the commercial viability of using twice-a-day satellite scatterometer data to produce marine wind forecasts for commercial television weather broadcasts. The WindStar product will be an animated, two dimensional map of wind speed and direction that evolves in time from the observed ``nowcast'' every 12 hours to a projected ``forecast''. Commercial television stations in coastal areas will incorporate this video into the weather segment of their news broadcasts to advise viewers, with both commercial and recreational interests, of coastal and off-shore conditions. While contributing to improved near shore marine operations for both recreational and commercial boaters, the proposed product would also be of use to commercial fishermen, coastal shipping operations, search and rescue operations, state and local governments, the Coast Guard, and the Navy. Projected new business plans include establishing and maintaining a ``Global Wind History'' archive that can be accessed on Internet.

Toward the Probabilistic Forecasting of High-latitude GPS Phase Scintillation

NASA Technical Reports Server (NTRS)

Prikryl, P.; Jayachandran, P.T.; Mushini, S. C.; Richardson, I. G.

2012-01-01

The phase scintillation index was obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network (CHAIN) during years of extended solar minimum 2008-2010. Phase scintillation occurs predominantly on the dayside in the cusp and in the nightside auroral oval. We set forth a probabilistic forecast method of phase scintillation in the cusp based on the arrival time of either solar wind corotating interaction regions (CIRs) or interplanetary coronal mass ejections (ICMEs). CIRs on the leading edge of high-speed streams (HSS) from coronal holes are known to cause recurrent geomagnetic and ionospheric disturbances that can be forecast one or several solar rotations in advance. Superposed epoch analysis of phase scintillation occurrence showed a sharp increase in scintillation occurrence just after the arrival of high-speed solar wind and a peak associated with weak to moderate CMEs during the solar minimum. Cumulative probability distribution functions for the phase scintillation occurrence in the cusp are obtained from statistical data for days before and after CIR and ICME arrivals. The probability curves are also specified for low and high (below and above median) values of various solar wind plasma parameters. The initial results are used to demonstrate a forecasting technique on two example periods of CIRs and ICMEs.

Space-based Doppler lidar sampling strategies: Algorithm development and simulated observation experiments

NASA Technical Reports Server (NTRS)

Emmitt, G. D.; Wood, S. A.; Morris, M.

1990-01-01

Lidar Atmospheric Wind Sounder (LAWS) Simulation Models (LSM) were developed to evaluate the potential impact of global wind observations on the basic understanding of the Earth's atmosphere and on the predictive skills of current forecast models (GCM and regional scale). Fully integrated top to bottom LAWS Simulation Models for global and regional scale simulations were developed. The algorithm development incorporated the effects of aerosols, water vapor, clouds, terrain, and atmospheric turbulence into the models. Other additions include a new satellite orbiter, signal processor, line of sight uncertainty model, new Multi-Paired Algorithm and wind error analysis code. An atmospheric wind field library containing control fields, meteorological fields, phenomena fields, and new European Center for Medium Range Weather Forecasting (ECMWF) data was also added. The LSM was used to address some key LAWS issues and trades such as accuracy and interpretation of LAWS information, data density, signal strength, cloud obscuration, and temporal data resolution.

Identifying Stratospheric Air Intrusions and Associated Hurricane-Force Wind Events over the North Pacific Ocean

NASA Technical Reports Server (NTRS)

Malloy, Kelsey; Folmer, Michael J.; Phillips, Joseph; Sienkiewicz, Joseph M.; Berndt, Emily

2017-01-01

Motivation: Ocean data is sparse: reliance on satellite imagery for marine forecasting; Ocean Prediction Center (OPC) –“mariner’s weather lifeline”. Responsible for: Pacific, Atlantic, Pacific Alaska surface analyses –24, 48, 96 hrs.; Wind & wave analyses –24, 48, 96 hrs.; Issue warnings, make decisions, Geostationary Operational Environmental Satellite –R Series (now GOES-16), Compared to the old GOES: 3 times spectral resolution, 4 times spatial resolution, 5 times faster coverage; Comparable to Japanese Meteorological Agency’s Himawari-8, used a lot throughout this research. Research Question: How can integrating satellite data imagery and derived products help forecasters improve prognosis of rapid cyclogenesis and hurricane-force wind events? Phase I –Identifying stratospheric air intrusions: Water Vapor –6.2, 6.9, 7.3 micron channels; Airmass RGB Product; AIRS, IASI, NUCAPS total column ozone and ozone anomaly; ASCAT (A/B) and AMSR-2 wind data.

Market-Based Indian Grid Integration Study Options: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stoltenberg, B.; Clark, K.; Negi, S. K.

2012-03-01

The Indian state of Gujarat is forecasting solar and wind generation expansion from 16% to 32% of installed generation capacity by 2015. Some states in India are already experiencing heavy wind power curtailment. Understanding how to integrate variable generation (VG) into the grid is of great interest to local transmission companies and India's Ministry of New and Renewable Energy. This paper describes the nature of a market-based integration study and how this approach, while new to Indian grid operation and planning, is necessary to understand how to operate and expand the grid to best accommodate the expansion of VG. Second,more » it discusses options in defining a study's scope, such as data granularity, generation modeling, and geographic scope. The paper also explores how Gujarat's method of grid operation and current system reliability will affect how an integration study can be performed.« less

A Non-Incompressible Non-Boussinesq (NINB) framework for studying atmospheric turbulence

NASA Astrophysics Data System (ADS)

Yan, C.; Archer, C. L.; Xie, S.; Ghaisas, N.

2015-12-01

The incompressible assumption is widely used for studying the turbulent atmospheric boundary layer (ABL) and is generally accepted when the Mach number < ~0.3 (velocity < ~100 m/s). Since the tips of modern wind turbine blades can reach and exceed this threshold, neglecting air compressibility will introduce errors. In addition, if air incompressibility does not hold, then the Boussinesq approximation, by which air density is treated as a constant except in the gravity term of the Navier-Stokes equation, is also invalidated. Here, we propose a new theoretical framework, called NINB for Non-Incompressible Non-Boussinesq, in which air is not considered incompressible and air density is treated as a non-turbulent 4D variable. First, the NINB mass, momentum, and energy conservation equations are developed using Reynolds averaging. Second, numerical simulations of the NINB equations, coupled with a k-epsilon turbulence model, are performed with the finite-volume method. Wind turbines are modeled with the actuator-line model using SOWFA (Software for Offshore/onshore Wind Farm Applications). Third, NINB results are compared with the traditional incompressible buoyant simulations performed by SOWFA with the same set up. The results show differences between NINB and traditional simulations in the neutral atmosphere with a wind turbine. The largest differences in wind speed (up to 1 m/s), turbulent kinetic energy (~10%), dissipation rate (~5%), and shear stress (~10%) occur near the turbine tip region. The power generation differences are 5-15% (depending on setup). These preliminary results suggest that compressibility effects are non-negligible around wind turbines and should be taken into account when forecasting wind power. Since only a few extra terms are introduced, the NINB framework may be an alternative to the traditional incompressible Boussinesq framework for studying the turbulent ABL in general (i.e., without turbines) in the absence of shock waves.

Variability of Wind Speeds and Power over Europe

NASA Astrophysics Data System (ADS)

Tambke, J.; von Bremen, L.; de Decker, J.; Schmidt, M.; Steinfeld, G.; Wolff, J.-O.

2010-09-01

This study comprises two parts: First, we describe the vertical wind speed and turbulence profiles that result from our improved PBL scheme and compare it to observations and 1-dimensional approaches (Monin-Obukhov etc.). Second, we analyse the spatio-temporal correlations in our meso-scale simulations for the years 2004 to 2007 over entire Europe, with special focus on the Irish, North and Baltic Sea. 1.) Vertical Wind Speed Profiles The vertical wind profile above the sea has to be modelled with high accuracy for tip heights up to 160m in order to achieve precise wind resource assessments, to calculate loads and wakes of wind turbines as well as for reliable short-term wind power forecasts. We present an assessment of different models for wind profiles in unstable, neutral and stable thermal stratification. The meso-scale models comprise MM5, WRF and COSMO-EU (LME). Both COSMO-EU from the German Weather Service DWD and WRF use a turbulence closure of 2.5th order - and lead to similar results. Especially the limiting effect of low boundary layer heights on the wind shear in very stable stratification is well captured. In our new WRF-formulation for the mixing length in the Mellor-Yamada-Janjic (MYJ) parameterisation of the Planetary Boundary Layer (PBL-scheme), the master length scale itself depends on the Monin-Obukhov-Length as a parameter for the heat flux effects on the turbulent mixing. This new PBL-scheme shows a better performance for all weather conditions than the original MYJ-scheme. Apart from the low-boundary-layer-effect in very stable situations (which are seldom), standard Monin-Obukhov formulations in combination with the Charnock relation for the sea surface roughness show good agreement with the FINO1-data (German Bight). Interesting results were achieved with two more detailed micro-scale approaches: - the parameterization proposed by Pena, Gryning and Hasager [BLM 2008] that depends on the boundary layer height - our ICWP-model, were the flux of momentum through the air-sea interface is described by a common wave boundary layer with enhanced Charnock dynamics. 2.) Wind Field Variability Time series of wind speed and power from 400 potential offshore locations and 16,000 onshore sites in the 2020 and 2030 scenarios are part of the design basis of the EU-project www.OffshoreGrid.eu. This project investigates the grid integration of all planned offshore farms in Northern Europe and will serve as the basis for the "Blueprint for Offshore Grids" by the European Commission. The synchronous wind time series were calculated with the WRF-model. The simulation comprises four years and was validated with a number of wind measurements. We present detailed statistics of local, clustered and regional power production. The analysis quantifies spatial and temporal correlations, extreme events and ramps. Important results are the smoothing effects in a pan-European offshore grid. Key words: Offshore Wind Resource Assessment; Marine Meteorology; Wind Speed Profile; Marine Atmospheric Boundary Layer; Wind Variability, Spatio-temporal Correlation; Electricity Grid Integration

How El Niño can be used to improve wind speed seasonal skill?

NASA Astrophysics Data System (ADS)

Gonzalez-Reviriego, Nube; Marcos, Raül; Doblas-Reyes, Francisco J.; Torralba, Verónica; Cortesi, Nicola; Lee, Doo Young; Soret, Albert

2017-04-01

The potential benefit of seasonal wind speed forecasts for the energy sector has been recently discussed (Torralba et al. 2016, Buontempo et al. 2016). Nevertheless, the lack of skill over several inland areas and especially at high lead times, can limit the application of these seasonal probabilistic forecasts. By using a simple methodology approach, this study aims to illustrate how the scientific user-driven research, conducted in a context of climate services, should play a role in the improvement of the wind speed seasonal forecast skill. In this framework the results obtained from the correlation coefficients between the ensemble mean prediction of the ECMWF System 4 and the observed wind speeds are compared with the results from the correlations between the wind speed constructed from the seasonal predicted El Niño index and the observations. An improvement of the skill at lead times ranging from 1 up to 5 months is measured over several regions such as Northern United States, Canada, Uruguay and Argentina. The added value of this constructed wind speed predictions is found in those areas over the world where the seasonal prediction system is not able to reproduce correctly the teleconnections of El Niño. Buontempo C, Hanlon H.M., Bruno Soares M., Christel I., Soubeyroux J-M., Viel C., Calmanti S, Bosi L., Falloon P., Palin E.J., Vanvyve E., Torralba V., Gonzalez-Reviriego N., Doblas-Reyes F.J., Pope E.C.D., Newton P. and Liggins F., 2016: What have we learnt from EUPORIAS climate service prototypes? Climate Services (Submitted) Torralba V., Doblas-Reyes F.J., Macleod D., Christel I. and Davis M., 2016: Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology (Submitted)