OpportunityImproved weather prediction and precise spatial analysis of small-scale weather events are crucial for energy management, as is the need to further develop and implement advanced technologies. The National Center for Atmospheric Research (NCAR), a leader in atmospheric research, development and technology transfer for the past 50 years, is uniquely qualified to support the renewable energy industry in these endeavors. NCAR scientists are already actively engaged with industry decision makers on how best to foresee and respond to short- and long-term changes in atmospheric conditions to mitigate risks associated with weather, particularly wind and solar energy prediction.

NCAR scientists are already actively engaged with industry decision makers on how best to foresee and respond to short- and long-term changes in atmospheric conditions to mitigate risks associated with weather, particularly wind and solar energy prediction.

Industry RequirementsThe renewable energy industry expressed requirements for:

• Improved wind and solar energy forecast accuracy

• Improved resolution of forecast information and diagnostic data in time and space (e.g., hourly and shorter term wind and solar predictions)

• Better understanding of the uncertainty that is inherent in the forecasts

• Additional weather observations at strategic locations for energy nowcasting

• Improved decision support products focused on:• Load forecasting• Increased efficiency in pricing for hourly

and bulk markets• Precision management of demand in the local

distribution system• Analysis of potential environmental and societal

impacts related to both short- and long-term supply strategies

• Improved anticipatory response management planning for high impact weather

• Improved air quality and greenhouse gas management strategies

• Renewable energy planning, development, and operations

Wind and Solar Energy Prediction

BackgroundRenewable energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions.

Wind and solar energy are among the most difficult weather variables to forecast. Topography, surface roughness, ground cover, temperature inversions, foliage, gravity waves, low-level jets, clouds, and aerosols, all affect wind and solar energy prediction skill.

As wind and solar energy portfolios expand, this forecast problem is taking on new urgency because wind and solar energy forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy.

ClOud and SOlaR RadIatIOn PRedICtIOnAlthough there has been a substantial, long-term effort by the weather research community to improve precipitation prediction, little attention has been paid to the prediction of clouds and insolation. The need for accurate insolation prediction is growing as the energy industry increases the percentage of distributed and concentrated solar energy. In addition, smart grid initiatives are expanding and the need for accurate forecasts of insolation (and temperature) is growing in parallel. NCAR’s Joint Numerical Testbed (JNT) has a mission to test and verify the accuracy of weather models for NOAA and the research community. It is well positioned to assess the accuracy of operational and research models in predicting clouds and solar radiation. The results would highlight model skill and would provide valuable feedback to modelers on accuracy deficiencies. The results would be used to improve the models providing better information in the future to solar energy decision makers.

tuRBIne ICIngFor more than 20 years, NCAR, with funding from the FAA and NASA, has led the nation in aircraft icing analysis and prediction. NCAR’s aircraft icing products are disseminated to the aviation industry through NOAA’s Aviation Weather Center. There are many similarities between aircraft icing and structural (wind turbine blade) icing because icing on wind turbines can dramatically affect their efficiency. Improved understanding of turbine icing is critical for the accurate prediction of wind energy. NCAR would welcome an opportunity to apply its icing expertise to the wind turbine-icing problem.

nCaR Collaborations NCAR has a long history of developing and maintaining strong collaborations with universities; the private sector; professional associations including the American Meteorological Society, American Wind Energy Association, Utility Wind Integration Group, American Geophysical Union, National Business Aviation Association, and the Intelligent Transportation Society of America; and with a host of federal agencies such as DOE, NOAA, DOD, DHS, NASA, USDOT, and NTSB. NCAR’s ability to perform research, develop real-world solutions, and work with industry to bring the solutions to the marketplace to benefit the public make it an ideally suited to contribute to the advancement of renewable energy both nationally and internationally.

For More Information, Contact:Sue Haupt, PhDNational Center for Atmospheric Research (NCAR)Research Applications LaboratoryPO Box 3000 Boulder CO 80307-3000303-497-2763 303-497-8401 faxhaupt@ucar.edu www.ral.ucar.edu

National Center for Atmospheric Research

Icing on wind turbines can dramatically affect their efficiency.

the need for accurate insolation prediction is growing as the energy industry increases the percentage of distributed

and concentrated solar energy.

Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for energy management.

research applications laboratory

evaluatIOn Of glOBal WIndand SOlaR ReSOuRCeSNCAR’s climate modeling and downscaling technologies are well positioned to support the development of wind and solar resource assessment datasets. In particular, the Climate Four Dimensional Data Assimilation System (CFDDA) has been used to create 25 years of global hourly climatologies with a 60 km grid size increment. This unique and large dataset was generated by NCAR with support by the Defense Threat Reduction Agenda (DTRA) and can be downscaled further to generate high-resolution physically balanced datasets that would provide critical information on the historical characteristics of the wind field at various wind generator hub heights. The global nature of these data makes them particularly useful for assessing the character of onshore and offshore wind resources.

futuRe ClImate ImPaCtS On WIndand SOlaR ReSOuRCeSNCAR is a world leader in climate research and modeling and has been intimately involved in the work of the Intergovernmental Panel on Climate Change (IPCC). NCAR is also a key contributor to the North American Regional Climate Change Assessment Program (NARCCAP), which produces high-resolution climate change simulations in order to investigate uncertainties in regional scale projections of future climate and generate climate change scenarios for use in impacts research. This program has generated a full suite of regional climate projections at multiple scales that have been used to create climate change scenarios by end-users. These future climate data sets can be refined and reapplied to investigate future wind and solar energy resources across the nation.

UCAR has spent more than 15 years developing and operationally deploying a robust very short-term nowcasting system called the Auto-Nowcaster. These capabilities are uniquely suited for wind ramp and solar energy nowcasting.

BOundaRy layeR ReSeaRChNCAR has extensive expertise in planetary boundary layer (PBL) meteorology and modeling. NCAR research includes three-dimensional, time-dependent, PBL turbulence using turbulence-resolving numerical simulations and in particular Large-Eddy Simulation (LES) for a wide variety of geophysical flows. LES explicitly captures large turbulent eddies, which contain most of the turbulent kinetic energy and carry most of turbulent transport, and approximates the effects of small turbulent eddies in its subgrid-scale model. This technique was first developed at NCAR in the late 1960s, and is now widely used as a major tool for investigations of turbulence in engineering and geophysical fields. LES techniques are well suited for investigations of wind flow characterization, which is critical for the proper design and operation of wind turbines.

Relevant experienceNCAR is a world-renowned atmospheric scientific research, development and technology transfer center which works to advance weather capabilities for mission agencies and the public and private sectors. NCAR is operated by the University Corporation for Atmospheric Research (UCAR), a non-profit organization established in 1960 to oversee a wide range of programs and facilities that support its 100+ university affiliates, as well as the national and international scientific community. As a national center, NCAR is able to utilize advancements developed not only at NCAR, but at research centers, institutes, universities, and national laboratories worldwide. NCAR is well-positioned to apply its expertise and technologies to support the renewable energy community and, through its technology transfer programs, to ensure that advancements are quickly adopted and utilized by industry. Below we highlight specific contributions NCAR can make to improving wind and solar energy prediction.

WeatheR PRedICtIOn mOdelIngand data aSSImIlatIOnNCAR is one of the lead organizations developing the Weather Research and Forecasting (WRF) model, as well as testing and distributing the code to the weather research and operational communities. WRF features multiple dynamical cores, a growing suite of data assimilation systems, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. NCAR’s Joint Numerical Testbed (JNT) tests and evaluates numerical weather prediction systems to provide meaningful information about forecast performance to operational decision makers and to provide the research and development community (including NOAA and AFWA) with support in their development of these systems.

data aSSImIlatIOnNCAR leads a large effort on data assimilation and has developed the WRF 3-Dimensional Variational Data Assimilation (3DVAR) system and a continuous Real-Time Four-Dimensional Data Assimilation (RTFDDA) system. NCAR has ported its RTFDDA assimilation technique to the WRF model, which is considered the best method of incorporating local observations into high-resolution models, such as those required for wind energy nowcasting and forecasting. The WRF RTFDDA allows for smooth and uninterrupted assimilation of new observations between forecast cycles (i.e., with no cold-starts that incur model spin-up effects in the first 6-12 hours of each forecast). The RTFDDA technology is part of the operational suite of weather systems NCAR has implemented for the U.S. Army’s Test and Evaluation Command.

WInd eneRgy nOWCaStIngUCAR has spent more than 15 years developing and operationally deploying a robust very short-term nowcasting system called the Auto-Nowcaster (image below). This system ingests available observational datasets (radar, surface station, satellite, lidar, met tower, and rawinsonde) in real-time, analyzes the atmosphere using a physical model, combines observational data with weather model output, and generates nowcasts out to 2 hours every 6-10 minutes. Although initially designed to predict thunderstorm initiation, growth and decay, the methods and techniques used within this system are focused on the analysis and prediction of the lower boundary layer, and in particular, the local wind fields that are critical for wind energy nowcasting. These capabilities are uniquely suited for wind ramp and solar energy nowcasting.

Quantifying interannual wind variability between el niño and la niña years - January winds at 0600 utC (2300 mSt)

a 300-megawatt wind farm in Colorado will make enough elec-tricity to power about 90,000 homes.

nCaR's auto-nowcaster display showing approaching wind surge.

modeled fine-scale wind flow over a wooded area.