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Nicholas H. Smith and Brian C. Ancell

Abstract

This work investigates the sensitivity of wind speed forecasts during wind ramp events to parameters within a numerical weather prediction model boundary layer physics scheme. In a novel way, it explores how these sensitivities vary across 1) ensemble members with different initial conditions, 2) different times during the events, 3) different types of ramp-causing events, and 4) different horizontal grid spacing. Previous research finds that a small number of parameters in the surface layer and boundary layer schemes are responsible for the majority of the forecast uncertainty. In this study, the values of parameters within the Mellor–Yamada–Nakahishi–Niino (MYNN) boundary layer scheme and the MM5 surface layer scheme of the Weather Research and Forecasting (WRF) Model are perturbed in a systematic way to evaluate parametric sensitivity for two types of specific ramp-causing phenomena: marine pushes and stable mix-out events. This work is part of the Department of Energy’s Second Wind Forecast Improvement Project (WFIP2). A major finding of this study is that there are large differences in parametric sensitivity between members of the same initial condition ensemble for all cases. These variations in sensitivity are the result of differences in the atmospheric state within the initial condition ensemble, and the parametric sensitivity changes over the course of each forecast. Finally, parametric sensitivity changes between event type and with model resolution. These conclusions are particularly relevant for future sensitivity studies and efforts at model tuning.

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Nicholas H. Smith and Brian C. Ancell

Abstract

Wind ramps present a significant challenge to the wind energy industry and are a source of inefficiency for wind farm owners and power grid operators. One approach to investigating wind ramp predictability is ensemble sensitivity analysis (ESA), which relates a scalar response function to an atmospheric variable at an earlier time. Applying ESA to wind ramps is challenging because the transient nature of the events makes it difficult to capture the ramp with a traditional response function that is fixed in space and time. This study introduces four response functions that are allowed to vary in space and time in order to identify key features of the wind ramp, such as the timing of the ramp and the largest horizontal extent of the ramp. Comparing these event-based response functions to a traditional response function reveals key differences in the sensitivity, which indicates that different aspects of the wind ramp event are sensitive to different atmospheric features. The use of multiple response functions is shown to provide a more complete understanding of the ramp event when compared to using only a traditional response function. Observation targeting is addressed by manipulating the ESA fields of six synoptically driven wind ramp events, with results showing that the horizontal location of the optimal target region varies widely between cases and a single observation location likely would not provide benefit to each case. These results indicate that a dynamic observing system would be preferable to a fixed observation for improving wind ramp forecasts.

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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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William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia, Arun Kumar, Wolfgang A. Müller, Michel Rixen, Andrew W. Robertson, Doug M. Smith, Yuhei Takaya, Matthias Tuma, Frederic Vitart, Christopher J. White, Mariano S. Alvarez, Constantin Ardilouze, Hannah Attard, Cory Baggett, Magdalena A. Balmaseda, Asmerom F. Beraki, Partha S. Bhattacharjee, Roberto Bilbao, Felipe M. de Andrade, Michael J. DeFlorio, Leandro B. Díaz, Muhammad Azhar Ehsan, Georgios Fragkoulidis, Sam Grainger, Benjamin W. Green, Momme C. Hell, Johnna M. Infanti, Katharina Isensee, Takahito Kataoka, Ben P. Kirtman, Nicholas P. Klingaman, June-Yi Lee, Kirsten Mayer, Roseanna McKay, Jennifer V. Mecking, Douglas E. Miller, Nele Neddermann, Ching Ho Justin Ng, Albert Ossó, Klaus Pankatz, Simon Peatman, Kathy Pegion, Judith Perlwitz, G. Cristina Recalde-Coronel, Annika Reintges, Christoph Renkl, Balakrishnan Solaraju-Murali, Aaron Spring, Cristiana Stan, Y. Qiang Sun, Carly R. Tozer, Nicolas Vigaud, Steven Woolnough, and Stephen Yeager

Abstract

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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