ABSTRACTLightning strikes to wind turbines (WTs) pose significant hazards and operational costs to the renewable wind industry. These strikes fall into two categories: downward cloud‐to‐ground (CG) strokes and upward discharges, which can be self‐initiated or triggered by a nearby flash. The incidence of each type of strike depends on several factors, including the electrical structure of the thunderstorm and turbine height. The strike rates of CG strokes and triggered upward lightning can be normalized by the amount of local lightning activity, where the constant of proportionality carries units of area and is often termed the collection area. This paper introduces a statistical analysis technique that uses lightning locating system (LLS) data to estimate the collection areas for downward and triggered upward lightning strikes to WTs. The technique includes a normalization method that addresses the confounding factor of neighboring WTs. This analysis method is applied to 7 years of data from the National Lightning Detection Network and the US WT Database to investigate the dependence of collection areas on blade tip height. The results are compared against estimates of collection areas derived from counts of LLS‐detected CG strokes close to WTs.

Model parameters are one of the sources of uncertainties in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the sensitive parameters has the potential to improve solar irradiance predictions. In this work, we adopted a multi-objective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error (NMAE) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California. We selected two cloudy cases, one over less polluted SGP and another over Central California with high aerosol loading associated with wildfire events. The results show that generalized linear model (GLM)based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in NMAE reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of NMAE.

The second Wind Forecast Improvement Project (WFIP2) is a multiagency field campaign held in the Columbia Gorge area (October 2015–March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011–August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and three lidars, and to forecasts from the High-Resolution Rapid Refresh (HRRR), 3-km, and from the High-Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

In 2014 a multi-institution team led by Vaisala, Inc. was selected by the Department of Energy (DOE) to partner with multiple DOE and National Oceanic and Atmospheric Administration (NOAA) laboratories on a project designed to improve the quality of wind power forecasts in areas of complex terrain. This was the second Wind Forecast Improvement Project (WFIP2) funded by DOE and it extended from late 2014 through the middle of 2018. It encompassed an 18-month observational field campaign, numerical weather prediction (NWP) model development, extensive analysis of data and NWP output, and the creation of decision support tool algorithms to convey forecast information to end users in the wind industry. WFIP2 focused on improvements to the representation of near-surface and boundary-layer physics in NOAA’s High-Resolution Rapid Refresh (HRRR) model. Improvements to HRRR, which is run operationally over the continental United States, benefit the wind industry in multiple ways. Forecasts from the operational HRRR are used directly by wind power forecast vendors and the operators of wind plants. In addition, because HRRR is built using the widely-used Weather Research and Forecasting (WRF) model, improvements to its parameterizations become available to commercial and research institutions using WRF for a myriad of purposes. The geographic area studied by WFIP2 was a region of the Columbia River basin located to the east of the Cascade Mountains between Oregon and Washington. Home to over 6 GW of installed capacity for wind energy production, this area also hosts a variety of atmospheric phenomena either unique to or augmented by complex topography. This makes it an attractive test-bed for the analysis of wind forecast in complex terrain, though results found are should be applicable in any area of topographic complexity. WFIP2 succeeded as a collaborative effort, and while this report focuses on the activities of the team led by Vaisala, the work described here is part of a larger whole. The Vaisala team accomplished a number of specific tasks as described in this report, while also contributing to this larger effort. The primary accomplishments of the Vaisala team under WFIP2 were: Creation of an experimental design for the overall project. Logistical arrangements for field study locations. Deployment/maintenance/removal of instruments for the field study. Analysis of field study data. Development of a 3D PBL parameterization for WRF. Creation of a data catalog to enhance the value of the field study observations. Creation and analysis of historical NWP forecast simulations. Generation and validation of wind power forecasts based on NWP model output. Creation of decision support algorithms and development of a prototype display. WFIP2 was conducted in an open and collaborative manner, with data and model improvements shared publicly wherever possible.

In 2015 the U.S. Department of Energy (DOE) initiated a 4-yr study, the Second Wind Forecast Improvement Project (WFIP2), to improve the representation of boundary layer physics and related processes in mesoscale models for better treatment of scales applicable to wind and wind power forecasts. This goal challenges numerical weather prediction (NWP) models in complex terrain in large part because of inherent assumptions underlying their boundary layer parameterizations. The WFIP2 effort involved the wind industry, universities, the National Oceanographic and Atmospheric Administration (NOAA), and the DOE’s national laboratories in an integrated observational and modeling study. Observations spanned 18 months to assure a full annual cycle of continuously recorded observations from remote sensing and in situ measurement systems. The study area comprised the Columbia basin of eastern Washington and Oregon, containing more than 6 GW of installed wind capacity. Nests of observational systems captured important atmospheric scales from mesoscale to NWP subgrid scale. Model improvements targeted NOAA’s High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to National Weather Service (NWS) operational forecast models, and these modifications have already yielded quantitative improvements for the short-term operational forecasts. This paper describes the general WFIP2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP2 observational science, modeling challenges and solutions, incorporation of forecasting uncertainty into decision support tools for the wind industry, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

During the second Wind Forecast Improvement Project (WFIP2; October 2015–March 2017, held in the Columbia River Gorge and Basin area of eastern Washington and Oregon states), several improvements to the parameterizations used in the High Resolution Rapid Refresh (HRRR – 3 km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST – 750 m horizontal grid spacing) numerical weather prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80 m wind speeds (wind turbine hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and three profiling lidars for comparison. Improvements due to the experimental physics (EXP vs. CNT runs) and those due to finer horizontal grid spacing (HRRRNEST vs. HRRR) and the combination of the two are compared, using standard bulk statistics such as mean absolute error (MAE) and mean bias error (bias). On average, the HRRR 80 m wind speed MAE is reduced by 3 %–4 % due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5 % on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80 m wind speed MAE, up to 7 %–8 %. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena. Causes of model weaknesses are identified. Finally, bias correction methods are applied to the 80 m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors.

The Wind Forecast Improvement Project 2 (WFIP2), in the complex terrain of the Columbia Gorge, focuses on a set of weather phenomena that poses particular challenges for wind and wind power forecasting. It further aims to understand and improve the skill of weather forecast models, particularly the National Oceanic and Atmospheric Administration's (NOAA's) High Resolution Rapid Refresh (HRRR), in complex terrain. The project also includes an extensive field campaign in the Columbia River Gorge, during which data from many different instruments were collected. WFIP2 is funded by the U.S. Department of Energy (DOE). Scientists from four U.S. national laboratories, NOAA, Vaisala, and universities contribute to the project with wide and varied interests and skill sets. Therefore, coordinated verification & validation (V&V) efforts across these members allows for the development of a clear picture of model improvements and scientific findings within WFIP2. The WFIP2 V&V team is tasked with providing tools, methods, and guidance to enable repeatable, metrics-based assessment of the Weather Research and Forecasting (WRF) model and associated modeling suites for analysis and forecasting of mesoscale weather phenomena that are important for wind energy in the Columbia River Gorge and other parts of the continental United States. This report summarizes what has been worked on and accomplished within the duration of the WFIP2 project, 4 years, by the V&V team.

Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and especially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer profilers, six radar wind profilers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical profiles of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the field project. Characteristics of these events are described, including statistics of the wind speed distributions and profiles, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.

The deployment of solar-based electricity generation, especially in the form of photovoltaics (PVs), has increased markedly in recent years due to a wide range of factors including concerns over greenhouse gas emissions, supportive government policies, and lower equipment costs. Still, a number of challenges remain for reliable, efficient integration of solar energy. Chief among them will be developing new tools and practices that manage the variability and uncertainty of solar power.

Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical weather prediction models, which is based on a state-of-the-art circular-circular regression approach. To calibrate forecast ensembles, a Bayesian model averaging scheme for directional variables is introduced, where the component distributions are von Mises densities centered at the individually bias-corrected ensemble member forecasts. These techniques are applied to 48-h forecasts of surface wind direction over the Pacific Northwest, using the University of Washington mesoscale ensemble, where they yield consistent improvements in forecast performance.

Wind power continues its rapid worldwide growth. In some places wind energy penetration is so high that the variation in wind energy production is the dominant driving force behind variation in the generation-load balance. To ensure a reliable supply of power, system operators must be able to schedule sufficient operating reserves. However, it is impractical to always supply 100% back-up for all generation units. Typically the N-1 criterion is applied, establishing the effect of the loss of the single largest generation unit. Wind energy compels different operating requirements, because failure of an entire wind energy project is improbable, but rapid variation in energy output is common. In such an environment, wind energy forecasting has significant value, especially during times of rapid change, and the use of a probabilistic forecast tool can minimize reserve requirements.

Virtually all numerical forecast models possess systematic biases. Although attempts to reduce such biases at individual stations using simple statistical corrections have met with some success, there is an acute need for bias reduction on the entire model grid. Such a method should be viable in complex terrain, for locations where gridded high-resolution analyses are not available, and where long climatological records or long-term model forecast grid archives do not exist. This paper describes a systematic bias removal scheme for forecast grids at the surface that is applicable to a wide range of regions and parameters. Using observational data and model forecasts over the Pacific Northwest, a method was developed to reduce the biases in gridded 2-m temperature, 2-m dewpoint temperature, and 12-h precipitation forecasts. The method first estimates bias at observing locations using errors from forecasts that are similar to the current forecast. These observed biases are then used to estimate bias on the model grid by pairing model grid points with stations that have similar elevation and/or land-use characteristics. Results show that this approach reduces bias substantially, particularly for periods when biases are large. Adaptations to weather regime changes are made within a short period, and the method essentially "shuts off" when model biases are small. With modest modifications, this approach can be extended to additional variables.

The Western Wind and Solar Integration Study (WWSIS) is one of the world's largest regional integration studies to date. This paper discusses the creation of the wind dataset that will be the basis for assessing the operating impacts and mitigation options due to the variability and uncertainty of wind power on the utility grids. The dataset is based on output from a mesoscale numerical weather prediction (NWP) model, covering over 4 million square kilometers with a spatial resolution of approximately two-kilometers over a period of three years with a temporal resolution of 10 minutes. The mesoscale model dataset includes all the meteorological variables necessary to calculate wind energy production. Individual time series were produced for over 30 thousand locations representing more than 900 GW of potential wind power generation.

Abstract One widely accepted measure of the utility of ensemble prediction systems is the relationship between ensemble spread and deterministic forecast accuracy. Unfortunately, this relationship is often characterized by spread–error linear correlations, which oversimplify the true spread–error relationship and ignore the possibility that some end users have categorical sensitivities to forecast error. In the present paper, a simulation study is undertaken to estimate the idealized spread–error statistics for stochastic ensemble prediction systems of a finite size. Under a variety of spread–error metrics, the stochastic ensemble spread–error joint distributions are characterized by increasing scatter as the ensemble spread grows larger. A new method is introduced that recognizes the inherent nonlinearity of spread–error joint distributions and capitalizes on the fact that the probability of large forecast errors increases with ensemble spread. The ensemble spread–error relationship is measured by the sk...