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 atmosphere and operating wind turbines, and 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 2018, they collaborated on two more book chapters documenting the background of meteorological modeling for renewable energy that are expected to appear in print in 2019 (Haupt et al. 2019; Jiménez et al. 2019), and have begun writing a review article leveraging all this work. Several scientists from NCAR also contributed to a chapter about the history of applied meteorology over the past 100 years, including renewable energy forecasting, in an American Meteorological Society (AMS) Monograph (Haupt et al. 2018c), in conjunction with the upcoming AMS Centennial Celebration.

FY18 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. Collaborations with Kuwait and New York are furthering wind and solar power forecasting research, while wind power resource assessments for Bangladesh and Alaska’s offshore were completed this year. 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 FY18 the team continued publications on the results, including a new paper submitted to Renewable Energy (Kosović et al. 2018b).

Renewable Energy Forecasting for Kuwait

FY18 was the first 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, 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 Phase 1 10-MW PV solar plant, with the five turbines that comprise the 10-MW wind plant in the background. Photo taken by Jared A. Lee (NCAR/RAL).
Figure 1. Shagaya Phase 1 10-MW PV solar plant, with the five turbines that comprise the 10-MW wind plant in the background. Photo taken by Jared A. Lee (NCAR/RAL).
Figure 2. A few of the parabolic trough mirrors that comprise the Shagaya Phase 1 50-MW CSP plant. Photo taken by Jared A. Lee (NCAR/RAL).
Figure 2. A few of the parabolic trough mirrors that comprise the Shagaya Phase 1 50-MW CSP plant. Photo taken by Jared A. Lee (NCAR/RAL).

Kuwait has a stated national goal of 15% renewable energy generation by 2030, 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 demonstration-scale 10-MW photovoltaic (PV) solar and 10-MW wind plants that were commissioned in May 2017 (Figure 1), and a 50-MW concentrated solar power (CSP) plant that is set to be commissioned early in FY19 (Figure 2). Phase 2 of Shagaya will include a 1500-MW PV solar plant, which will be the second-largest PV plant in the world. Construction of this 1500-MW al-Dibdibah PV plant at Shagaya is hoped to begin late in FY19 or in FY20, with completion expected after the end of NCAR’s initial 3-year project. 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 FY18 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 FY18 include running daily WRF-Solar® forecasts for day-ahead forecasting at 3-km grid spacing over Kuwait; the creation several retrospective WRF-Solar datasets that help guide refinements to the quasi-operational WRF-Solar system and allow for training for various machine learning (ML) algorithms; studying the impact of modeled aerosols and radiation (Gueymard and Jiménez 2018); quantifying the uncertainty in solar irradiance forecasts using the analog ensemble (AnEn) techniques (Alessandrini et al. 2018); understanding the quality of the historical observation data we have received thus far, and identifying good candidates for future case studies; making several improvements to DICast® that allow it to run in a reforecast/playback mode to easily compare the value added by a particular model or observation component (which will benefit numerous other RAL projects that use DICast); building a prototype power grid operator display; and engineering the forecasting system to the extent possible with simulated observational data, in order to enable as seamless a transition as possible when real-time meteorological and power data from Shagaya starts flowing to NCAR. We expect the number of conference presentations and journal papers submitted that stem from research accomplished on this project to increase significantly in FY19 and FY20.

Solar Forecasting for New York

In FY18 NCAR completed Phase 1 and began 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). A proposed Phase 3 would begin in FY19, funded by the New York State Energy Research and Development Authority (NYSERDA), NYPA, and DOE.

Figure 3. WRF monthly average global horizontal irradiance (GHI) ensemble mean, valid at 1700 UTC for the WRF-Solar model runs simulating dates in July 2017 during Phase 1 of the Solar Forecasting for New York project. Locations are marked for Buffalo (BUF), Albany (ALB), Staten Island (STA), and Brookhaven National Laboratory (BNL), which are the locations targeted by BNL for installing networks of sky cameras for solar irradiance nowcasting purposes.
Figure 3. WRF monthly average global horizontal irradiance (GHI) ensemble mean, valid at 1700 UTC for the WRF-Solar model runs simulating dates in July 2017 during Phase 1 of the Solar Forecasting for New York project. Locations are marked for Buffalo (BUF), Albany (ALB), Staten Island (STA), and Brookhaven National Laboratory (BNL), which are the locations targeted by BNL for installing networks of sky cameras for solar irradiance nowcasting purposes.

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 (Figure 3). In Phase 2 NCAR built on what was learned in Phase 1 to configure WRF-Solar for a one-year reforecast dataset of WRF-Solar nowcasts (0–6 h) at 3-km grid spacing over the entirety of New York State. This WRF-Solar reforecast dataset, combined with meteorological and irradiance observations at BNL, will provide the training dataset for machine learning methods to blend recent observations with WRF-Solar for improved nowcasts of irradiance in the first 30–60 minutes. Phase 3, if funded, would extend this work further, making the WRF-Solar and machine learning blended forecasting systems quasi-operational for demonstration purposes at multiple sites in NY, and serve as the foundation for introducing a photovoltaic (PV) solar power forecasting system.

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.

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.

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.

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).

 

Figure 5. 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 5. 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).

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 5 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). 

Mesoscale to Microscale Coupling for Renewable Energy

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 6). NCAR is leading a collaboration of five DOE national laboratories: Argonne National Laboratory (ANL), Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), National Renewable Energy Laboratory (NREL), and Pacific Northwest National Laboratory (PNNL). This collaboration’s goal is to accomplish mesoscale and microscale coupled simulations of carefully selected cases that are representative of wind farm conditions.

During FY18 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. 2018). 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.

Figure 6. Process for coupling mesoscale to microscale simulations for wind plant management.
Figure 6. Process for coupling mesoscale to microscale simulations for wind plant management.

The team worked on near-surface physics improvements through the use of a pseudo-canopy model that applies drag terms to the momentum equations over a specified depth in place of Monin-Obukhov Similarity Theory (MOST). Three different PCM shape functions are explored, each improving on the standard MOST approach.

Furthermore, the team continued to develop, test, and evaluate several techniques to couple the mesoscale to the microscale.  A first basic technique is nesting from WRF run in mesoscale mode into the WRF-LES mode, which we call the concurrent online approach. This technique was used for the studies of the terra incognita region of spatio-temporal scales as well the in the development and analysis of perturbation method approaches. This online approach was also used to study a frontal passage case for the WFIP2 site on 15 November 2016. Using rather large grid refinement ratios of 9 between the nested and parent domains to skip over the terra incognita, the WRF simulation was able to capture the changes in the flow due to the frontal passage in terms of changes in wind speed, temperature, and total kinetic energy (TKE), but the timing of the passage was incorrect.

Reports from previous years of the MMC project are available (Haupt et al. 2015, 2017a,b). This year’s report will soon be made available by PNNL (Haupt et al. 2018b).

Wind Forecast Improvement Project 2 (WFIP2) – Forecast Improvement in Complex Terrain Near the Columbia River Gorge

The project “Forecast Improvement in Complex Terrain Near the Columbia River Gorge” was a component of the Wind Forecast Improvement Project 2 (WFIP2) led by Vaisala, Inc. The goal of the project was to develop and validate new modeling capabilities for high-resolution mesoscale flow simulations over complex terrain with the WRF-ARW model (Olson et al. 2018). The WFIP2 project was funded by the DOE Energy Efficiency and Renewable Energy (EERE) Office. The NCAR team conducted research in collaboration with the National Atmospheric and Oceanic Administration (NOAA) and Vaisala on development of a new three-dimensional planetary boundary layer (3D PBL) parameterization. The new 3D PBL model was implemented and tested as a component of the WRF-ARW model to enhance wind forecasting skill in complex terrain. High-resolution LES was used to guide model development and evaluation. The performance of the new 3D PBL parameterization was also compared to the existing one-dimensional (1D) PBL parameterization using WRF-ARW. The WRF-ARW mesoscale simulations with the 3D PBL scheme were validated by comparing to data collected the observational field campaign in the Columbia River Gorge. The data were collected by the Vaisala team and collaborators from NOAA and DOE laboratories.

At present most NWP models include parameterizations of turbulent stresses and fluxes based on the assumption of horizontal homogeneity over a grid cell and therefore reduced to 1D PBL parameterizations. As the horizontal grid cell sizes decrease, the assumption of horizontal homogeneity is violated and the effects of neglected terms must be accounted for. Under convective atmospheric conditions the homogeneity assumption is violated even in flows over flat, homogeneous surface due to the presence of large convectively induced secondary circulations (Ching et al. 2014). We have therefore developed a 3D PBL parameterization following the work of Mellor and Yamada (1974, 1982) and implemented it in the WRF model. The new 3D PBL parameterization was first assessed by carrying out idealized mesoscale simulations over heterogeneous terrain characterized by sharp differences in surface heat fluxes. These simulations demonstrated the deficiency of a 1D PBL parameterization when grid cell size is in the so called “terra incognita” range, between 100 m and 1 km. We used the MYNN 1D PBL parameterization in this study and it resulted in unphysical secondary circulations. In contrast, the simulation with the 3D PBL parameterization correctly maintained homogeneity in one horizontally-homogeneous direction while capturing the dynamical effects of the heterogeneity in the other horizontal direction. We have demonstrated that the results obtained using the 3D PBL parameterization are consistent with the averages from an ensemble LES (Kosović et al. 2017, 2018a).

Figure 7. Large-eddy simulation (LES) of a topographic wake and mountain waves observed on 7–8 March 2016 during the WFIP2 field study in the Columbia River Gorge area. A contour plot of vertical velocity is shown at 1200 m above ground level. The wake of Mt. Hood is well resolved as well as the mountain waves resulting from the westerly flow over the Cascade Mountains.
Figure 7. Large-eddy simulation (LES) of a topographic wake and mountain waves observed on 7–8 March 2016 during the WFIP2 field study in the Columbia River Gorge area. A contour plot of vertical velocity is shown at 1200 m above ground level. The wake of Mt. Hood is well resolved as well as the mountain waves resulting from the westerly flow over the Cascade Mountains.

Finally, we have assessed the performance of the 3D PBL parameterization, which utilizes the boundary-layer approximation in order to directly solve a system of linear algebraic equations for all the turbulent stresses and fluxes. For that purpose, we have used WFIP2 observations during the “ten-day” retrospective period from 13– 24 August 2016.

At present the 3D PBL parameterization implemented in WRF is Level 2 according to Mellor and Yamada (1982) classification. This means that the TKE is estimated using a diagnostic equation. Future developments include implementation of a prognostic equation for TKE. Following implementation of the prognostic equation for TKE large-eddy simulation results will be used to guide further development of the 3D PBL parameterization including calibration of different model parameters.

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 8. 100-m average wind speed from the 2002–2016 WRF simulation within the technical resource area. [From Fig. 14 in Doubrawa et al. (2017).]
Figure 8. 100-m average wind speed from the 2002–2016 WRF simulation within the technical resource area. [From Fig. 14 in Doubrawa et al. (2017).]

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 8), 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 (Lee et al. 2018). The WRF modeled wind speed was found to have near-zero average bias and positive skill, which provided confidence of the wind resource  assessment.

Wind Resource Assessment for Bangladesh

In 2014, NCAR embarked on a project with NREL and Harness Energy, funded by the U.S. Agency for International Development (USAID) and in partnership with the Government of Bangladesh (GOB), to assess and quantify the wind resource in Bangladesh. GOB has pledged that 10% of its national electricity generation will be from renewable sources by 2021, but no utility-scale wind farms yet exist in Bangladesh.

NREL deployed one sodar (Figure 9) at two sites as well as seven meteorological observation towers (six to 80-m height, one to 60-m height) at locations throughout Bangladesh. Each site had at least 12 months of observations, and some sites had up to 43 months. The original project plan called for sodars and met towers to be deployed for two years, but poor weather, civil unrest, and other local challenges led to delays in removal of the observation equipment, which incidentally led to a longer, more robust wind resource assessment.

Figure 9. Transporting the sodar instrument via ox cart through a field to the platform near Rajshahi, Bangladesh. Photo credit: Harness Energy, and Fig. 20 in Jacobson et al. (2018).
Figure 9. Transporting the sodar instrument via ox cart through a field to the platform near Rajshahi, Bangladesh. Photo credit: Harness Energy, and Fig. 20 in Jacobson et al. (2018).

 

 
Figure 10. A detailed wind resource map for Bangladesh, with transmission lines and met tower locations overlaid. From Fig. ES-1 of Jacobson et al. (2018).
Figure 10. A detailed wind resource map for Bangladesh, with transmission lines and met tower locations overlaid. From Fig. ES-1 of Jacobson et al. (2018).

NCAR’s role was to use WRF to assess the resource and to assimilate data from the new observational network to calibrate the models. The first step was to compare the wind resource from three separate historical reanalyses, which blend information from historical observations with models. The next step involved working closely with NREL to downscale and assimilate the observations using Four-Dimensional Data Assimilation (FDDA). NCAR assimilated the observations from the deployed sodars and met towers to compute, at 3.3-km grid spacing, 3.5 years of modeled wind speeds throughout the country to determine the best locations to deploy wind energy. In FY18 the NCAR team completed both the WRF-FDDA runs and the validation of the WRF dataset. NCAR also demonstrated how self-organizing maps could be used to extend the 3.5-year, high-resolution WRF dataset to 15 years via dynamic downscaling of a coarse-resolution reanalysis. The NREL team used NCAR’s WRF dataset as the basis for their wind resource assessment, publicly viewable on NREL’s Renewable Energy Data Explorer (https://maps.nrel.gov/gst-bangladesh/). The model-driven wind resource at 120 m above ground level is shown in Figure 10.

Representatives from NREL, NCAR, and Harness Energy presented the final results at a workshop in Dhaka, Bangladesh, in May 2018 to the Minister of Power, Energy, and Mineral Resources and several other government officials. The workshop and its findings also generated local media attention (e.g., http://www.newagebd.net/print/article/43570). The project’s results are summarized in an NREL Technical Report (Jacobson et al. 2018), and a journal article is also planned for submission in FY19. 

Plans for FY2019

FY2019 will continue to be an exciting time for renewable energy research at RAL. New 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 FY2019 significant efforts will include advancing comprehensive renewable power forecasting and resource assessment 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:

  • Expansion of wind and solar forecasting capability into new areas, including international, complex terrain, and desert sites.
  • Continued collaboration with DOE laboratories to develop best practices for coupling mesoscale with microscale simulations, focusing on complex terrain and nonstationary conditions.
  • Continued collaboration with NREL and PNNL focused on improving and expanding solar forecasting capabilities through development of an ensemble forecasting system and WRF-Solar version 2.
  • Continued development of MAD-WRF, short-term solar forecasting capability based on blending the satellite data assimilation MADCast facility with WRF-Solar to develop a more robust and accurate version of MAD-WRF to improve nowcasts of solar irradiance. 


References

Alessandrini, S., L. Delle Monache, and S. E. Haupt, 2018: Improving the analog ensemble wind and solar power forecasts for rare events. European Conf. for Applied Meteorology and Climatology 2018, Budapest, Hungary, Euro. Meteor. Soc., EMS2018-647, https://meetingorganizer.copernicus.org/EMS2018/EMS2018-647.pdf.

Ching, J., R. Rotunno, M. LeMone, A. Martilli, B. Kosovic, P. A. Jimenez, and J. Dudhia, 2014: Convectively induced secondary circulations in fine-grid mesoscale numerical weather prediction models. Mon. Wea. Rev., 142, 3284–3302, https://doi.org/10.1175/MWR-D-13-00318.1.

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.

Gueymard, C. A., and P. A. Jiménez, 2018: Validation of real-time solar irradiance simulations over Kuwait using WRF-Solar. 12th International Conf. on Solar Energy for Buildings and Industry (EuroSun 2018), Rapperswill, Switzerland, https://www.researchgate.net/publication/328265383_Validation_of_Real-Time_Solar_Irradiance_Simulations_over_Kuwait_Using_WRF-Solar.

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.

Haupt, S. E., A. Anderson, L. Berg, B. Brown, M. J. Churchfield, C Draxl, B. L. Ennis, Y. Fang, B. Kosovic, R. Kotamarthi, R. Linn, J. D. Mirocha, P. Moriarty, D. Munoz-Esparaza, R. Rai, and W. J. Shaw, 2015: First Year Report of the A2e Mesoscale to Microscale Coupling Project. Pacific Northwest National Laboratory Report PNNL-25108, 124 pp.

Haupt, S. E., A. Anderson, R. Kotamarthi, J. J. Churchfield, Y. Feng, C. Draxl, J. D. Mirocha, E. Quon, E. Koo, W. Shaw, R. Linn, L. Berg, B. Kosovic, R. Rai, B. Brown, and B. L. Ennis, 2017a: Second Year Report of the Atmosphere to Electrons Mesoscale to Microscale Coupling Project: Nonstationary Modeling Techniques and Assessment. Pacific Northwest National Laboratory Report PNNL-26267, 156 pp., https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-26267.pdf.

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

Haupt, S. E., S. McIntosh, B. Kosović, K. Miller, D. Yates, F. Chen, M. Shepherd, M. Williams, and S. Drobot, 2018c: 100 years of progress in applied meteorology. Part 3: Modern applications. In: 100 years of scientific research at AMS, AMS Monograph Series, Boston, MA, in press.

Haupt, S. E., B. Kosović, J. A. Lee, and P. A. Jiménez, 2019: 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 26 Feb 2019).

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