Renewable Energy

Background

Since 2009 RAL has collaborated with university researchers, U.S. Department of Energy (DOE) laboratories, international research organizations, commercial partners, and other NCAR laboratories to develop methods to more accurately analyze and predict wind and solar power in support of the renewable energy industry. Projects have focused on resource assessment, analysis of the interaction between the atmosphere and operating wind turbines, real-time wind, solar, and load forecasting to improve operations and economics of incorporating renewable energy into the power mix, and characterization and quantification of variability in wind and solar energy.

NCAR scientists have become recognized as world experts in applying meteorology concepts for enhancing renewable energy production. During 2019, they collaborated on two new book chapters documenting the background of meteorological modeling for renewable energy (Haupt et al. 2019a; Jiménez et al. 2019). Numerous other journal articles related to renewable energy research have been published this year or are in review; many of these are referenced in the sections below.

FY19 Accomplishments

NCAR/RAL is currently expanding capabilities of WRF-Solar through enhancing physics parameterizations, and developing a solar irradiance nowcasting system by combining satellite observations from multiple platforms with the WRF numerical weather prediction model. A large-scale, ongoing collaboration with Kuwait is furthering wind and solar power forecasting research, and the next phase of a project in New York will lead to distributed solar power forecasting statewide. Further research in coupling of mesoscale to microscale models and in flow in complex terrain will enable further improvements in high-resolution wind forecasting.

Wind Power Forecasting for Xcel Energy

As the wind power capacity in the areas served by Xcel Energy grew, Xcel Energy recognized a need for accurate forecasts of this variable resource. In 2009 NCAR started collaboration with Xcel Energy aimed at developing a renewable power forecasting system. The renewable power forecasting system enables more economical utilization of resources and more reliable grid operation while still meeting the needs of the utility’s electricity customers. In collaboration with Xcel Energy, RAL has developed, deployed, and transferred wind and solar power forecasting systems, which allows powering down traditional coal- and natural gas–powered plants when sufficient winds and solar irradiance are predicted (Mahoney et al. 2012; Haupt et al. 2013; Haupt and Mahoney 2015; Haupt and Kosović 2017; Haupt et al. 2017c). Operational implementation of the initial day-ahead forecasting system resulted in significant savings for the utility and the ratepayer, along with substantial reductions in emissions of carbon dioxide and other pollutants to the atmosphere (Pierce 2014). Although the most recent collaboration with Xcel Energy was completed during FY15, during FY19 the team continued publications on the results, including a new paper submitted to Energies (Kosović et al. 2019).

Renewable Energy Forecasting for Kuwait

FY19 was the second full year of NCAR’s Renewable Energy Forecasting for Kuwait project, a 3-year, $5.1M project sponsored by the Kuwait Institute for Scientific Research (KISR) (https://news.ucar.edu/126802/ncar-develop-advanced-wind-and-solar-energy-forecasting-system-kuwait). There are 18 tasks in this large project, spanning areas of numerical weather prediction (NWP), solar and wind nowcasting, general science, software engineering, system assessment, and management. The ultimate goal of this project is to deliver to KISR an operational wind and solar power forecasting system, the Kuwait Renewable Energy Prediction System (KREPS), for both nowcasting and day-ahead time horizons (and beyond), with which they can provide forecasts to their national power grid operators and wind/solar power plant operators.

Figure 1. Shagaya Renewable Energy Park Phase 1 10-MW wind plant. Photo by Jared A. Lee (NCAR/RAL).
Figure 1. Shagaya Renewable Energy Park Phase 1 10-MW wind plant. Photo by Jared A. Lee (NCAR/RAL).
Figure 2. The NCAR project management team (L to R: Gerry Wiener, Branko Kosović, Sue Ellen Haupt, and Jared Lee) visiting the Shagaya Renewable Energy Park for its official Grand Opening ceremony on 20 February 2019. Photo by Jared A. Lee (NCAR/RAL).
Figure 2. The NCAR project management team (L to R: Gerry Wiener, Branko Kosović, Sue Ellen Haupt, and Jared Lee) visiting the Shagaya Renewable Energy Park for its official Grand Opening ceremony on 20 February 2019. Photo by Jared A. Lee (NCAR/RAL).

Kuwait has a stated national goal of 15% renewable energy generation by 2030 (KISR 2019), and to that end has established the Shagaya Renewable Energy Park in the desert about 100 km west of Kuwait City. Phase 1 of Shagaya is now complete, with a demonstration-scale 10-MW photovoltaic (PV) solar plant commissioned in May 2017 (Al-Rasheedi et al. 2019), a 10-MW wind plant commissioned in July 2017 (Figure 1), and a 50-MW concentrated solar power (CSP) plant that was commissioned in November 2018. The NCAR management team for this project traveled to Kuwait in February 2019 for the official Grand Opening of Shagaya, a ceremony that was widely covered by local media and attended by government ministers (Figure 2). Additional wind, PV solar, and CSP solar capacity is planned beyond that in Phases 2 and 3 of Shagaya, with a goal of 3–5 GW of combined wind and solar power installed capacity at Shagaya by 2030.

Throughout FY19 the NCAR team, in collaboration with researchers from Penn State University and Solar Consulting Services, has been building various aspects of the system, leveraging advancements made on several past and current renewable energy forecasting projects in RAL, and developing new and improved techniques and products with pioneering research. The accomplishments in FY19 include securing real-time data feeds from the PV solar and wind farms at Shagaya in spring 2019; hosting a two-month training workshop on research data, machine learning, and high performance computing for a KISR staff person who will be in charge of operating KREPS upon its transfer to KISR; the continued operation of daily WRF-Solar® forecasts for day-ahead forecasting at 3-km grid spacing over Kuwait; studying the correspondence between various forecast models that predict aerosol lofting and advection, to identify the best aerosol forecast product to use with WRF-Solar (Lee et al. 2019a); improving analog ensemble (AnEn) forecasts for rare events (Alessandrini et al. 2019); testing new machine learning models to further improve StatCast-Wind and StatCast-Solar forecasts in the first 6 h (see the Weather Prediction Machine Learning Optimization]  section for more information on StatCast-Wind and StatCast-Solar); researching improved methods for power conversion (McCandless and Haupt 2019; Brummet et al. 2019); beginning to carry out meteorological case studies to better understand forecast performance; making several improvements to DICast® that will also feed back into other RAL projects; and enhancing the power grid operator display with feedback from KISR staff and grid operators in Kuwait. Several papers from this project are currently in preparation and will be submitted during FY20.

Solar Forecasting for New York State

In FY19, NCAR completed Phase 2 of a multi-phase, multi-agency effort to improve solar forecasting in New York State. The work for Phases 1 and 2 was funded by both the New York Power Authority (NYPA) and DOE, and was done in collaboration with partners at the Electric Power Research Institute (EPRI) and Brookhaven National Laboratory (BNL). Phase 3 has been funded by the New York State Energy Research and Development Authority (NYSERDA) and NYPA, also includes the University at Albany as a collaborator, and will begin in FY20 for two and a half years.

Figure 3. WRF-Solar 6-h forecast of GHI, valid on 1 Jun 2018 at 1800 UTC.
Figure 3. WRF-Solar 6-h forecast of GHI, valid on 1 Jun 2018 at 1800 UTC.

NCAR’s work in Phase 1 of this project used a WRF-Solar® 10-member ensemble to assess the modeled variability in solar resource, focusing on various locations around the New York at which BNL proposed to install networks of sky cameras, and comparing it to observed irradiance variability at BNL. In Phase 2 NCAR configured WRF-Solar for a one-year reforecast dataset of nowcasts (0–6 h) at 3-km grid spacing over the entirety of New York State (Figure 3). This WRF-Solar reforecast dataset, combined with meteorological and irradiance observations at BNL, provided the training dataset for machine learning methods (StatCast-Solar) to blend recent observations with WRF-Solar for improved nowcasts of irradiance in the first 1–2 hours (Lee et al. 2019b). Phase 3 will extend this work further, making the WRF-Solar and StatCast-Solar systems quasi-operational at BNL and New York State Mesonet sites, building an open source gridded model blending tool for use with WRF-Solar with select publicly available models for both intra-day and day-ahead time horizons, and building an open source solar power forecasting algorithm for select utility-scale PV plants and distributed PV at ZIP code granularity across New York State.

WRF-Solar v2

In collaboration with the Pacific Northwest National Laboratory (PNNL) and Vaisala, NCAR is developing the WRF-Solar® version 2 model (Jiménez et al. 2016a), supported by the DOE Solar Energy Technologies Office. While the first version of WRF-Solar resulted in significant improvements in short-range and day-ahead forecasts for both clear sky and cloudy conditions there are several areas where further improvement could result in significant error reduction in predicted solar irradiance. The goal of this project is to reduce forecast errors of global horizontal irradiance and direct normal irradiance and to yield better forecasts of irradiance ramps, improvements in estimates of sub-grid scale variability, and more accurate estimates of forecast uncertainty. WRF-Solar v2 will include a number of enhancements including:

  • New representation of boundary-layer clouds (both shallow cumulus and the breakup of stratocumulus) including the impact of entrainment on cloud fraction in a grid cell,
  • Improved treatment of cloud microphysics, and impacts of aerosol (including black carbon),
  • New parameterizations to account for the sub-grid temporal variability of solar irradiance during periods with broken clouds, and
  • Detailed analysis to better quantify model uncertainty and improved calibration of WRF-Solar v2 using uncertainty quantification (UQ) techniques.

Figure 4. Sketch representing the physical processes that WRF-Solar® improves. The different components of the radiation are also indicated.
Figure 4. Sketch representing the physical processes that WRF-Solar® improves. The different components of the radiation are also indicated.

With these improvements, WRF-Solar v2 will be a new tool that will lead to improved intra-day and day ahead forecasts. The new version of WRF-Solar (Figure 4) will be a community model that will become the new standard in irradiance forecasts.

During 2019 we made progress in the following areas:

  • Improved the representation of boundary layer clouds and examined the potential of a better representation of horizontal cloud entrainment,
  • Identified a parameterization to account for sub-grid temporal variability of solar irradiance,
  • Compared reference simulations performed with WRF-Solar v1 against an improved representation of the unresolved clouds, and
  • Developed the strategy to include in WRF-Solar the impacts of black carbon on radiation as well as the methodology to assess the developments.

WRF-Solar Ensembles

In collaboration with the National Renewable Energy Laboratory (NREL), NCAR is enhancing the WRF-Solar model (Jiménez et al. 2016a) to incorporate a probabilistic framework specifically tailored for solar energy applications.

Figure 5. GHI observations and simulations from a WRF-Solar ensemble for a day-ahead forecast at the Table Mountain (TBL) SURFRAD site near Boulder, CO. The ensemble introduces stochastic perturbations in the radiation parameterization. The top panel is for the observed and simulated irradiance, while the bottom panel shows the difference between the observed irradiance and the various ensemble member simulations (simulated minus observed).
Figure 5. GHI observations and simulations from a WRF-Solar ensemble for a day-ahead forecast at the Table Mountain (TBL) SURFRAD site near Boulder, CO. The ensemble introduces stochastic perturbations in the radiation parameterization. The top panel is for the observed and simulated irradiance, while the bottom panel shows the difference between the observed irradiance and the various ensemble member simulations (simulated minus observed).

During a first task of the project, we have identified the variables from six physical packages that produce the larges impact on the shortwave irradiance. These packages include a shortwave radiation parameterization, a microphysics parameterization, a planetary boundary layer parameterization, a land surface model, and two parameterizations of the radiative effects of unresolved clouds. The relevant variables have been identified using a sensitivity analysis based on adjoint modeling (Yang et al. 2019).

During the second task, we are developing WRF-Solar to introduce stochastic perturbations of the variables identified in the previous task. We have incorporated a user-friendly interface wherein the user can select which variables to perturb and the characteristics of the perturbations. We are in the process of coupling these perturbations to the physical packages. So far we have coupled the perturbations to the radiation parameterization. Figure 5 shows an example of an ensemble simulation introducing stochastic perturbations in the radiation parameterization. We are in the process of incorporating the perturbations in a parameterization that accounts for the radiative effects of unresolved clouds.

MAD-WRF: A solar irradiance nowcasting system to support the Group on Earth Observations (GEO) Vision for Energy

A GEO Vision for Energy (GEO-VENER) goal includes “the availability and long-term acquisition of data from satellite and in-situ instruments and models to make possible the effective deployment, operation, and maintenance of renewable energy systems and their integration in the grid.” The challenge in renewable energy systems is managing the high spatial and temporal variability of renewable resources. The adverse effects of this high variability in energy production can be mitigated via accurate predictions of the renewable resources (e.g., solar irradiance).

To overcome the limitations of current solar irradiance nowcasting systems we are blending a satellite- based initialization system with an NWP-based nowcasting approach to create an improved end-to-end solar irradiance forecast system, called MAD-WRF. The development of MAD-WRF forms part of the National Aeronautics and Space Administration (NASA) Earth Science Division (ESD) efforts to advance specific elements of the GEO Work Programme 2017–2019 through the Applied Science Program (ASP).

MAD-WRF will blend two nowcasting systems. The Multi-sensor Advection Diffusion nowCast (MADCast) version 2 (Xu et al. 2016) assimilates infrared profiles from instruments on board different satellites (GOES, MODIS, AIRS, etc.) using a particle filter to infer the presence of clouds. This three-dimensional (3D) cloud field is subsequently advected and diffused by a modified version of the WRF model that produces the solar irradiance forecast. The second nowcasting system is based on WRF with extensions for solar energy applications (WRF-Solar, Jimenez et al. 2016a,b; Haupt et al. 2018a). Our results running both models indicate that MADCast typically produces superior performance than WRF- Solar at the beginning of the simulated period (Lee et al. 2017; Haupt et al. 2018a). This is due to the improved satellite-based cloud analysis. However, after an hour or two into the forecast, the improved physics of WRF-Solar provides the superior solar irradiance forecast. Blending the two systems in MAD-WRF will provide a prediction system with the strengths of both nowcasting methods. Figure 6 shows a conceptual diagram illustrating the performance of MADCast, WRF-Solar, and the expected performance of MAD-WRF. An initial exploration of the viability of MAD-WRF showed promising results (Haupt et al. 2018a).

Figure 6. Schematic diagram illustrating the current performance of WRF-Solar and MADCast in the first six hours of forecast (blue and red solid lines), their expected performance after the improvements introduced in this project (dashed lines), and the expected performance of MAD-WRF (green solid line).
Figure 6. Schematic diagram illustrating the current performance of WRF-Solar and MADCast in the first six hours of forecast (blue and red solid lines), their expected performance after the improvements introduced in this project (dashed lines), and the expected performance of MAD-WRF (green solid line).

During 2019 we blended MADCast and WRF-Solar into a first version of MAD-WRF. In this version, the hydrometeors are nudged toward the hydrometeors that are advected and diffused by the WRF model without any microphysics. This strategy allows us to take advantage of the strengths of the two blended models illustrated in the diagram shown in Figure 6. We are in the process of comparing MAD-WRF with WRF-Solar to quantify the improvements.

We also made progress in the cloud analysis. We completed a first version of the cloud initialization system wherein we can impose the cloud mask and the cloud top height retrieved from NOAA’s GOES-16 satellite and the cloud base height from METAR stations. We are also training a statistical model to calculate the cloud mask directly from GOES-16 radiances in order to evaluate if we can improve the cloud mask level-2 product from GOES-16.

We are in the process of setting up MAD-WRF for a demonstration that will take place during the last year of the project (1 Feb 2020–31 Jan 2021).

Assessing the Value of WRF-Solar with Enhanced Cloud Initialization for Solar Irradiance Nowcasting

Iberdrola has approached NCAR to improve their solar irradiance nowcasting system. Iberdrola is interested in assessing the potential of using GOES-16/GOES-17 and METAR observations to improve solar irradiance nowcasting. This is well aligned with our research and development efforts with the MAD-WRF model. The goal of the project is to compare Iberdrola in-house forecasts with MAD-WRF simulations initializing the clouds with GOES-16/GOES-17 and METAR observations at two solar farms within CONUS.

We have started to process the data Iberdrola has sent, both observations and in-house forecasts, in preparation for the assessment. We have also performed preliminary WRF simulations at the target locations.

Mesoscale to Microscale Coupling for Renewable Energy

Figure 7. Process for coupling mesoscale to microscale simulations for wind plant management.
Figure 7. Process for coupling mesoscale to microscale simulations for wind plant management.

A collaboration with DOE began in FY15 that focuses on blending information from mesoscale model simulations into microscale simulations in order to provide a capability to more accurately model details of flow that impacts a wind plant (Figure 7). NCAR is leading a collaboration of DOE national laboratories: Pacific Northwest National Laboratory (PNNL), Lawrence Livermore National Laboratory (LLNL), National Renewable Energy Laboratory (NREL), and Los Alamos National Laboratory (LANL). This collaboration’s goal is to accomplish mesoscale and microscale coupled simulations of carefully selected cases that are representative of wind farm conditions.

During FY19 the Mesoscale to Microscale Coupling (MMC) project team conducted a rigorous analysis of the impact of modeling in the terra incognita on the microscale simulations (Rai et al. 2019). We found that 1) the upper range of the terra incognita is roughly the current depth of the boundary layer, 2) using higher resolution for the mesoscale model will produce a smaller fetch distance in a microscale simulation that will thus contain more turbulence kinetic energy, 3) use of the Lilly turbulence model on the microscale domain results in a higher level of turbulence than using the MYNN or YSU mesoscale planetary boundary layer (PBL) parameterization schemes, and 4) the microscale results do not vary with the type of turbulence model (PBL schemes or large eddy simulation [LES] closure) used by its parent domain whose grid spacing falls within the terra incognita.

In FY19, two coordinating initiatives position the team for making important contributions that can be easily transitioned to industry use. The first of these initiatives is building an MMC-specific Phenomena Identification and Ranking Table (PIRT) that allows us to identify the most important areas for research. The PIRT table identified that offshore wind issues are important, yet not modeled nor validated well at this time. Specific phenomena to pursue include low level jets, land-sea breezes, weather fronts, tropical cyclones, Nor’easters, thermal pooling and terrain-gap flows, icing and precipitation, surface energy and momentum exchange, air-water-wave interactions, and roughness and canopy effects. The second major initiative was to compile and archive MMC code in a GitHub repository that form the basis of the code and toolset that is being transitioned to industry. This repository includes assessment tools in the form of Jupyter notebooks written in Python that enable reproducible comparison of multiple techniques. It also includes a common base of the WRF model that is our mesoscale solver. This MMC version is based on WRF v4.1, includes MMC-specific upgrades and additions, and is accompanied by a “setups” repository.

NCAR oversaw and participated in two major intercomparisons: in the actual coupling and in designing and assessing best practices for initializing turbulence at the microscale that are subgrid to the mesoscale. Additionally, NCAR worked on near-surface physics improvements through the use of a machine-learning model in place of Monin-Obkhov Similarity Theory (MOST). Both random forests and artificial neural networks improved on the standard MOST approach.

Figure 8. Twice the turbulent kinetic energy at a location from the WFIP2 region. Both the observations and different numerical simulations are shown (see legend).
Figure 8. Twice the turbulent kinetic energy at a location from the WFIP2 region. Both the observations and different numerical simulations are shown (see legend).

We continued with our developments of a planetary boundary layer (PBL) parameterization that represents the turbulent mixing in three dimensions (see the 2018 RAL LAR entry on WFIP2 for more information on the 3D PBL development). The standard practice is only to account for turbulent mixing in the vertical direction (one-dimensional). We identified the source of model instabilities. To overcome this issue, we run our PBL parameterization in sub-steps smaller than the model time step. The scheme is now stable. Figure 8 demonstrates how the time series of twice the TKE at an observational site from the Second Wind Forecast Improvement Project (WFIP2; Olson et al. 2019). Our parameterization (labeled as PBL3, blue line) clearly shows the best comparison with observations (purple line). In particular, it shows superior performance compared to a standard one-dimensional PBL parameterization (red line).

To ensure that the MMC efforts remain relevant to the wind industry, the team held three webinars with industry, both to present our most recent advances and to solicit feedback from industrial partners on their needs and where they see the most useful advances. In addition, MMC formed an Industrial Advisory Panel including six members that represent wind plant developers, turbine manufacturers, and wind power forecasters. This Panel is helping to plan an Industry Workshop to be held in June 2020.

Finally, the team began the pivot toward studying MMC processes for the offshore environment during FY19. As stated above, the PIRT analysis identified the offshore environment as ripe for advance. As the team winds up the onshore efforts, the members are also beginning the process of identifying appropriate data, constructing ML models of the offshore surface layer, testing fully coupled simulations for an offshore case, and using actuator disk codes to simulate turbines in both WRF-LES and Nalu-Wind. This planned initial case study will further inform where to focus resources to make the most important progress.

Reports from previous years of the MMC project are available (Haupt et al. 2015, 2017a,b, 2019b). A summary journal article has also been accepted by BAMS and will be in print soon (Haupt et al. 2019c).

The MMC team continues to work collaboratively and has determined strategies to work through the remaining issues required to optimally provide coupled model simulations, including for the offshore environment. These simulations and advances in technologies will provide the wind industry new tools that can be used in the planning, design, layout, and optimization of wind plants, thus facilitating deploying higher capacities of wind generation.

Offshore Wind Resource Assessment for Alaska

In FY17 and FY18 NCAR partnered with NREL to perform a wind resource assessment for Alaska’s offshore regions. NREL had previously developed wind resource assessments for offshore regions of the conterminous U.S. (CONUS) and Hawai’i. This project performed the first known rigorous assessment of the offshore wind resource for Alaska, extending to 200 nautical miles from the coastline.

Figure 9. 100-m average wind speed from the 2002–2016 WRF simulation within the technical resource area. [From Fig. 14 in Doubrawa et al. (2017) and Fig. 4 in Lee et al. (2019c).]
Figure 9. 100-m average wind speed from the 2002–2016 WRF simulation within the technical resource area. (From Fig. 14 in Doubrawa et al. [2017] and Fig. 4 in Lee et al. [2019c].)

This resource assessment was based on a 14-year WRF simulation covering Alaska and its surrounding waters at 4-km grid spacing. The WRF dataset had previously been developed by RAL for regional climate and hydrologic applications in Alaska, and is publicly available for download (Monaghan et al. 2016, 2018). NREL team members performed the wind resource assessment, which found a large technical wind resource at hub height (Figure 9), including in favorable areas with shallow waters close to population centers and existing transmission grids (Doubrawa et al. 2017). NCAR team members performed the validation of this WRF dataset against available surface-based and radiosonde observations, in a study that was published this year (Lee et al. 2019c). The WRF modeled wind speed was found to have near-zero average bias and positive skill, which provided confidence of the wind resource assessment.

FY2020 will continue to be an exciting time for renewable energy research at RAL. New and continuing collaborations with national laboratories, university scientists, private companies, and foreign research institutions and companies will advance the state-of-the-science necessary to make a large penetration of renewable energy capacity feasible. In FY2020 significant efforts will include advancing comprehensive renewable power forecasting capabilities. An emphasis will be on expanding collaborations with industry partners and porting the research to other regions of the world, especially the Middle East and Asia.

Other plans include:

  • Technology transfer of the Kuwait Renewable Energy Prediction System (KREPS) to the Kuwait Institute for Scientific Research (KISR), and training of KISR staff on the system.
  • Developing a new proposal to KISR for a second phase of research tasks.
  • Initial development of open source gridded model blending (and model/obs blending) tools, and distributed PV power forecasting algorithms for New York.
  • Expansion of wind and solar forecasting capability into new areas.
  • Continued collaboration with DOE laboratories to develop best practices for coupling mesoscale with microscale simulations, focusing on complex terrain and nonstationary conditions, and further development of the WRF 3D PBL scheme.
  • Continued collaboration with NREL and PNNL focused on improving and expanding solar forecasting capabilities through development of an ensemble forecasting system and WRF-Solar v2.
  • Continued development of MAD-WRF to develop a more robust and accurate version of MAD-WRF to improve nowcasts of solar irradiance.

References

Alessandrini, S., S. Sperati, and L. Delle Monache, 2019: Improving the analog ensemble wind forecast for rare events. Mon. Wea. Rev., 147, 2677–2692, https://doi.org/10.1175/MWR-D-19-0006.1.

Al-Rasheedi, M. A., C. A. Gueymard, M. H. Al-Khayat, A. H. Ismail, J. A. Lee, and H. J. Al-Duaij, 2019: Performance evaluation of a utility-scale dual-technology photovoltaic power plant at the Shagaya Renewable Energy Park in Kuwait. Renew. Sustain. Energy Rev., submitted.

Brummet, T., J. A. Lee, and G. Wiener, 2019: The relationship between GHI and power in Kuwait. 10th Conf. on Weather, Climate, and the New Energy Economy/18th Conf. on Artificial and Computational Intelligence and its Applications to the Environmental Sciences. Phoenix, AZ, Amer. Meteor. Soc., J3.4, https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/350578.

Doubrawa, P., G. Scott, W. Musial, L. Kilcher, C. Draxl, and E. Lantz, 2017: Offshore wind energy resource assessment for Alaska. NREL Tech. Report NREL/TP-5000-70553, 29 pp., https://www.nrel.gov/docs/fy18osti/70553.pdf.

Haupt, S. E., and B. Kosović, 2017: Variable generation power forecasting as a big data problem. IEEE Trans. Sustain. Energy, 8, 725–732, https://doi.org/10.1109/TSTE.2016.2604679.

Haupt, S. E., and W. P. Mahoney, 2015: Taming wind power with better forecasts. IEEE Spectrum, 52, 47–52, https://doi.org/10.1109/MSPEC.2015.7335902.

Haupt, S. E., W. P. Mahoney, and K. Parks, 2013: Wind power forecasting. In: Troccoli, A., L. Dubus, and S. E. Haupt (Eds.), Weather matters for energy, Springer, 528 pp., https://doi.org/10.1007/978-1-4614-9221-4.

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Haupt, S. E., A. Anderson, L. Berg, B. Brown, M. Churchfield, C. Draxl, C. Kalb, E. Koo, B. Kosovic, R. Kotamarthi, L. Mazzaro, J. Mirocha, E. Quon, R. Rai, and G. Sever, 2017b: Third Year Report of the Atmosphere to Electrons Mesoscale to Microscale Coupling Project. Pacific Northwest National Laboratory Report PNNL-xxxxx, 137 pp.

Haupt, S. E., P. A. Jiménez, J. A. Lee, and B. Kosović, 2017c: Principles of meteorology and numerical weather prediction. In: Kariniotakis, G. (Ed.), Renewable energy forecasting: From models to applications. Woodhead Publishing (Elsevier), Cambridge, MA, 373 pp., https://doi.org/10.1016/B978-0-08-100504-0.00001-9.

Haupt, S. E., B. Kosovic, T. Jensen, J. K. Lazo, J. A. Lee, P. A. Jiménez, J. Cowie, G. Wiener, T. C. McCandless, M. Rogers, S. Miller, M. Sengupta, Y. Xie, L. Hinkelman, P. Kalb, and J. Heiser, 2018a: Building the Sun4Cast system: Improvements in solar power forecasting. Bull. Amer. Meteor. Soc. 99, 121-135, https://doi.org/10.1175/BAMS-D-16-0221.1.

Haupt, S. E., B. Kosović, J. A. Lee, and P. A. Jiménez, 2019a: Mesoscale modeling of the atmosphere. In: Veers, P. (Ed.), Wind power modelling: Atmosphere and wind plant flow. IET Publishing, Stevenage, UK, in press (expected release 27 Dec 2019).

Haupt, S. E., D. Allaerts, L. Berg, M. Churchfield, A. DeCastro, C. Draxl, E. Koo, B. Kosović, R. Kotamarthi, B. Kravitz, L. Mazzaro, J. Mirochoa, E. Q uon, R. Raj, J. Sauer, and G. Sever, 2019b: FY 2018 Report of the Atmosphere to Electrons Mesoscale-to-Microscale Coupling Project. Pacific Northwest Laboratory Report PNNL-28259, 124 pp.

Haupt, S. E., B. Kosović, W. Shaw, L. K. Berg, M. Churchfield, J. Cline, C. Draxl, B. Ennis, E. Koo, R. Kotamarthi, L. Mazzaro, J. Mirocha, P. Moriarty, D. Muñoz-Esparza, E. Quon, R. K. Rai, M. Robinson, and G. Sever, 2019c: On bridging a modeling scale gap: Mesoscale to microscale coupling for wind energy. Bull. Amer. Meteor. Soc., in press, https://doi.org/10.1175/BAMS-D-0033.1.

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