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Meghan J. Mitchell, Brian Ancell, Jared A. Lee, and Nicholas H. Smith

Abstract

The wind energy industry needs accurate forecasts of wind speeds at turbine hub height and in the rotor layer to accurately predict power output from a wind farm. Current numerical weather prediction (NWP) models struggle to accurately predict low-level winds, partially due to systematic errors within the models due to deficiencies in physics parameterization schemes. These types of errors are addressed in this study with two statistical postprocessing techniques—model output statistics (MOS) and the analog ensemble (AnEn)—to understand the value of each technique in improving rotor-layer wind forecasts. This study is unique in that it compares the techniques using a sonic detection and ranging (SODAR) wind speed dataset that spans the entire turbine rotor layer. This study uses reforecasts from the Weather Research and Forecasting (WRF) Model and observations in west Texas over periods of up to two years to examine the skill added to forecasts when applying both MOS and the AnEn. Different aspects of the techniques are tested, including model horizontal and vertical resolution, number of predictors, and training set length. Both MOS and the AnEn are applied to several levels representing heights in the turbine rotor layer (40, 60, 80, 100, and 120 m). This study demonstrates the degree of improvement that different configurations of each technique provides to raw WRF forecasts, to help guide their use for low-level wind speed forecasts. It was found that both AnEn and MOS show significant improvement over the raw WRF forecasts, but the two methods do not differ significantly from each other.

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Meghan J. Mitchell, Brian Ancell, Jared A. Lee, and Nicholas H. Smith

Abstract

The wind energy industry needs accurate forecasts of wind speeds at turbine hub height and in the rotor layer to accurately predict power output from a wind farm. Current numerical weather prediction (NWP) models struggle to accurately predict low-level winds, partially due to systematic errors within the models due to deficiencies in physics parameterization schemes. These types of errors are addressed in this study with two statistical postprocessing techniques—model output statistics (MOS) and the analog ensemble (AnEn)—to understand the value of each technique in improving rotor-layer wind forecasts. This study is unique in that it compares the techniques using a sonic detection and ranging (SODAR) wind speed dataset that spans the entire turbine rotor layer. This study uses reforecasts from the Weather Research and Forecasting (WRF) Model and observations in west Texas over periods of up to two years to examine the skill added to forecasts when applying both MOS and the AnEn. Different aspects of the techniques are tested, including model horizontal and vertical resolution, number of predictors, and training set length. Both MOS and the AnEn are applied to several levels representing heights in the turbine rotor layer (40, 60, 80, 100, and 120 m). This study demonstrates the degree of improvement that different configurations of each technique provides to raw WRF forecasts, to help guide their use for low-level wind speed forecasts. It was found that both AnEn and MOS show significant improvement over the raw WRF forecasts, but the two methods do not differ significantly from each other.

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Jared A. Lee, Walter C. Kolczynski, Tyler C. McCandless, and Sue Ellen Haupt

Abstract

Ensembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncertainty. Limited computational resources constrain the size of ensembles, so choices must be made about which members to include. No known objective methodology exists to guide users in choosing which combinations of physics parameterizations to include in an NWP ensemble, however. This study presents such a methodology.

The authors build an NWP ensemble using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). This 24-member ensemble varies physics parameterizations for 18 randomly selected 48-h forecast periods in boreal summer 2009. Verification focuses on 2-m temperature and 10-m wind components at forecast lead times from 12 to 48 h. Various statistical guidance methods are employed for down-selection, calibration, and verification of the ensemble forecasts. The ensemble down-selection is accomplished with principal component analysis. The ensemble PDF is then statistically dressed, or calibrated, using Bayesian model averaging. The postprocessing techniques presented here result in a recommended down-selected ensemble that is about half the size of the original ensemble yet produces similar forecast performance, and still includes critical diversity in several types of physics schemes.

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Jared A. Lee, Sue Ellen Haupt, Pedro A. Jiménez, Matthew A. Rogers, Steven D. Miller, and Tyler C. McCandless

Abstract

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

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Pedro A. Jiménez, Stefano Alessandrini, Sue Ellen Haupt, Aijun Deng, Branko Kosovic, Jared A. Lee, and Luca Delle Monache

Abstract

The shortwave radiative impacts of unresolved cumulus clouds are investigated using 6-h ensemble simulations performed with the WRF-Solar Model and high-quality observations over the contiguous United States for a 1-yr period. The ensembles use the stochastic kinetic energy backscatter scheme (SKEBS) to account for implicit model uncertainty. Results indicate that parameterizing the radiative effects of both deep and shallow cumulus clouds is necessary to largely reduce (55%) a systematic overprediction of the global horizontal irradiance. Accounting for the model’s effective resolution is necessary to mitigate the underdispersive nature of the ensemble and provide meaningful quantification of the short-range prediction uncertainties.

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Brian P. Reen, Kerrie J. Schmehl, George S. Young, Jared A. Lee, Sue Ellen Haupt, and David R. Stauffer

Abstract

The relationship between atmospheric boundary layer (ABL) depth uncertainty and uncertainty in atmospheric transport and dispersion (ATD) simulations is investigated by examining profiles of predicted concentrations of a contaminant. Because ensembles are an important method for quantifying uncertainty in ATD simulations, this work focuses on the utilization and analysis of ensemble members’ ABL structures for ATD simulations. A 12-member physics ensemble of meteorological model simulations drives a 12-member explicit ensemble of ATD simulations. The relationship between ABL depth and plume depth is investigated using ensemble members, which vary both the relevant model physics and the numerical methods used to diagnose ABL depth. New analysis methods are used to analyze ensemble output within an ABL-depth relative framework. Uncertainty due to ABL depth calculation methodology is investigated via a four-member mini-ensemble. When subjected to a continuous tracer release, concentration variability among the ensemble members is largest near the ABL top during the daytime, apparently because of uncertainty in ABL depth. This persists to the second day of the simulation for the 4-member diagnosis mini-ensemble, which varies only the ABL depth, but for the 12-member physics ensemble the concentration variability is large throughout the daytime ABL. This suggests that the increased within-ABL concentration variability on the second day is due to larger differences among the ensemble members’ predicted meteorological conditions rather than being solely due to differences in the ABL depth diagnosis methods. This work demonstrates new analysis methods for the relationship between ABL depth and plume depth within an ensemble framework and provides motivation for directly including ABL depth uncertainty from a meteorological model into an ATD model.

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Jared A. Lee, L. Joel Peltier, Sue Ellen Haupt, John C. Wyngaard, David R. Stauffer, and Aijun Deng

Abstract

The relationships between atmospheric transport and dispersion (AT&D) plume uncertainty and uncertainties in the transporting wind fields are investigated using the Second-Order Closure, Integrated Puff (SCIPUFF) AT&D model driven by numerical weather prediction (NWP) meteorological fields. Modeled contaminant concentrations for episode 1 of the 1983 Cross-Appalachian Tracer Experiment (CAPTEX-83) are compared with recorded ground-level concentrations of the inert tracer gas C7F14. This study evaluates a Taylor-diffusion-based parameterization of dispersion uncertainty for SCIPUFF that uses Eulerian meteorological ensemble velocity statistics and a Lagrangian integral time scale as input. These values are diagnosed from NWP ensemble data. Individual simulations of the tracer release fail to reproduce some of the monitored surface concentrations of the tracer. The plumes that are predicted using the uncertainty model in SCIPUFF are broader, improving the overlap between the predicted and observed results. Augmenting the meteorological input to SCIPUFF with meteorological ensemble-uncertainty parameters therefore provides both a better estimate of the expected plume location and the relative uncertainties in the predicted concentrations than single deterministic forecasts. These results suggest that this new parameterization of NWP wind field uncertainty for dispersion may provide more sophisticated information that may benefit emergency response and decision making.

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Jared A. Lee, Joshua P. Hacker, Luca Delle Monache, Branko Kosović, Andrew Clifton, Francois Vandenberghe, and Javier Sanz Rodrigo

Abstract

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 because of 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 study the WRF single-column model (SCM) is 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 , the time-varying sea surface roughness length, four WRF-SCM/DART experiments are conducted during the October–December 2006 period. The two methods for determining are the default Fairall-adjusted Charnock formulation in WRF and use of the parameter estimation techniques to estimate in DART. Using DART to estimate is found to reduce 1-h forecast errors of wind speed over the Charnock–Fairall ensembles by 4%–22%. However, parameter estimation of does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.

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Leonard J. Peltier, Sue Ellen Haupt, John C. Wyngaard, David R. Stauffer, Aijun Deng, Jared A. Lee, Kerrie J. Long, and Andrew J. Annunzio

Abstract

A parameterization of numerical weather prediction uncertainty is presented for use by atmospheric transport and dispersion models. The theoretical development applies Taylor dispersion concepts to diagnose dispersion metrics from numerical wind field ensembles, where the ensemble variability approximates the wind field uncertainty. This analysis identifies persistent wind direction differences in the wind field ensemble as a leading source of enhanced “virtual” dispersion, and thus enhanced uncertainty for the ensemble-mean contaminant plume. This dispersion is characterized by the Lagrangian integral time scale for the grid-resolved, large-scale, “outer” flow that is imposed through the initial and boundary conditions and by the ensemble deviation-velocity variance. Excellent agreement is demonstrated between an explicit ensemble-mean contaminant plume generated from a Gaussian plume model applied to the individual wind field ensemble members and the modeled ensemble-mean plume formed from the one Gaussian plume simulation enhanced with the new ensemble dispersion metrics.

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Sue Ellen Haupt, Branko Kosović, Tara Jensen, Jeffrey K. Lazo, Jared A. Lee, Pedro A. Jiménez, James Cowie, Gerry Wiener, Tyler C. McCandless, Matthew Rogers, Steven Miller, Manajit Sengupta, Yu Xie, Laura Hinkelman, Paul Kalb, and John Heiser

Abstract

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.

Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed.

This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

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