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Research for Renewable Energy Development

ESRL’s Role in Wind and Solar Energy

NOAA provides the national-scale meteorological observations and numerical weather prediction forecast models used by the renewable energy (RE) industry. As the nation’s wind and solar industries grow, NOAA faces increased demands for better products and services, including improved meteorological observations and more accurate wind and cloud forecasts over a range of timescales. NOAA’s Earth System Research Laboratory (ESRL) is uniquely qualified to provide the improved weather forecasts, observations, and climate information needed to support the effective planning for and efficient operation of a national renewable energy system.

With highly accurate observations, forecasts, and understanding of how wind and solar resources vary and co-vary across time and space, the electric grid will be better able to accommodate the variable nature of wind and solar energy. This will yield greater production of carbon-free renewable energy while also reducing air pollutant emissions.

ESRL is currently working with the wind and solar energy industries and the Department of Energy to improve existing meteorological observing networks and weather forecast models for RE applications. ESRL is working to improve NOAA’s numerical weather prediction guidance, which is used as input to the private sector’s tailored forecast products. Leveraging our expertise in meteorology, ESRL is also conducting research on how wind and solar power can be optimized to meet energy demand. Further, ESRL is working with other labs and line offices in NOAA maximize our agency’s support of wind and solar power.

Wind Forecast Improvement Project (WFIP)

Project Contacts
Melinda Marquis, 303.497.4487
Jim Wilczak, 303.497.6245

In the Wind Forecast Improvement Project (WFIP), NOAA partnered with private forecasters to develop more accurate methods for wind forecasts. The Department of Energy (DOE) funded this effort.

WFIP had four main goals: 1) to collect new meteorological observations from the public and private sector; 2) to incorporate those observations into NOAA’s hourly-updated 13-km resolution Rapid Refresh (RAP) model and its hourly-updated 3-km High-Resolution Rapid Refresh (HRRR) model; 3) to determine whether using these additional observations led to better wind forecasts; and 4) to determine whether improved model forecasts also improved the efficiency and economics of wind power generation.

NOAA temporarily installed meteorological instruments in the Upper Great Plains and Texas to collect data during the twelve-month project, and wind power providers shared with NOAA their observations from their networks of tall towers and wind plants. When private forecasters used the RAP and HRRR models to make their wind forecasts, their intra-day (within a day) forecasts improved. Specifically, one of WFIP’s major accomplishments was to show improved short-term (0-6 hour) wind power forecasts using the NOAA Earth System Research Laboratory (ESRL) RAP model as compared to forecasts made with the National Weather Service (NWS) Rapid Update Cycle (RUC) model.

At the start of WFIP, the RUC model was the hourly-updated forecast model widely used by the wind energy industry. Over the first six months of the WFIP field study, when the RAP hourly-updated forecast model was used in the Upper Great Plains Study area, there was a 13 percent power improvement at forecast hour 1 as compared to the RUC forecast, decreasing to a 6 percent improvement in later forecast hours. In the Texas study area, there was a 15 percent power improvement at forecast hour 1, decreasing to a 5 percent improvement for a 15-hour forecast.

Private wind forecasters are collaborating with the DOE’s National Renewable Energy Laboratory (NREL) to quantify the financial savings realized from better short-term (0-6 hour) wind forecasts. Results are expected by Spring 2015.

Additional Resources:

Solar Forecast Improvement Project (SFIP)

Project Contact
Kathy Lantz, 303.497.4487

For the Solar Forecast Improvement Project (SFIP), the Earth System Research Laboratory (ESRL) is partnering with the National Center for Atmospheric Research (NCAR) and IBM to develop more accurate methods for solar forecasts using their state-of-the-art weather models. The Department of Energy (DOE) is funding this effort.

SFIP has three main goals: 1) to develop solar forecasting metrics tailored to the utility sector; 2) to improve solar radiation forecasts from minutes to several hours to two days; and 3) to incorporate solar forecasts into utility and Independent System Operator (ISO) system operations and identify economic and reliability benefits.

NOAA is providing numerical weather prediction (NWP) modeling with new information that will help solar forecasts. Specifically, NOAA is modifying forecasts from the 3-km High-Resolution Rapid Refresh (HRRR) model and an advanced version of the 13-km Rapid Refresh (RAP) model to provide information forecasters need to predict power production from photovoltaic (PV) and concentrating solar power (CSP) systems. NOAA is providing these grids to the NCAR and IBM teams.

NOAA is also providing high-quality, ground-based solar measurements from its Integrated Surface Irradiation Study (ISIS) and SURFace RADiation (SURFRAD) networks. The ISIS and SURFRAD instruments at sites across the U.S. measure incoming direct beam, total and diffuse solar radiation with the high accuracy required to calibrate satellites and verify model output. NOAA also will provide advanced satellite products.

NCAR and IBM began work on SFIP in January 2013, and NOAA began work in May 2014. The project will continue through December 2016.

National Energy with Weather System (NEWS) Simulator

Christopher Clack, 303.497.4296

Researchers at the Earth System Research Laboratory (ESRL) have developed a tool—National Energy with Weather System (NEWS) Simulator —to simulate the electric (and energy) sector. Specifically, they are investigating what happens within the system as large amounts of variable generation (wind and solar PV) are integrated as power sources. The aim is to produce a simulator that can be leveraged for decision making on a variety of scales and incorporate a broad range of technologies.

The NEWS simulator designs new systems based on the inputs provided, and the system is cost optimized. NEWS can find additional solutions that produce the least amount of carbon dioxide, waste the smallest percentage of the electric load, build the least amount of new generation, or even create the smallest amount of new transmission.

One important requirement of the new system is that it must meet the electricity demand each hour for the entire year, without fail. NEWS will select the type of energy and the locations for generation that best meet the specific needs of the system.

The simulator uses linear programming to find optimal solutions that consider simultaneously generation, transmission, losses, variability, and electric load. The current version has a built-in dataset for the weather over the US for 2006 to 2008 with concurrent electric load for 256 regions. Additional datasets include the power estimates from the weather, siting constraints on variable (and current conventional) generators and the HVDC transmission line paths that can be constructed by the simulator. There is also a global weather and power dataset for 2008 that allows NEWS to be used around the world.

NEWS is currently undergoing further development to expand its capabilities. For a summary of the incorporated mathematics, see Clack et al., below. Additional information on the optimization and accompanying data can be found in recent presentations.

Additional Resources:

Renewable Energy Challenges

The U.S. has agreed to cut its greenhouse gas emissions by 26-28 percent by 2025 and by 80 percent by 2050, compared to 2005 levels. To meet these goals, a large proportion of electricity that otherwise would have been produced from fossil fuels will need to be generated instead by low-carbon sources—most likely wind and solar power.

Because wind and solar power production depend on the weather, they are variable. This variability of wind and solar power introduces unique challenges to those who must maintain the constant balance between energy supply and demand required for a stable electric power grid. Unless and until energy storage is economical, “flexibility” in the power grid is key to its efficient operation. Improved forecasting across a range of time scales for wind and solar resources will provide critical flexibility and facilitate integration of weather-dependent renewable energy.

There are several ways forecast skill can be improved. One way is to better model atmospheric phenomena, by improving various parts of the weather models known as “schemes” and the mathematical coupling of these “schemes” to other schemes. Another way of improving forecast skill is to improve the data assimilation methods, and another approach is to improve our observations of relevant phenomena. The Earth System Research Laboratory (ESRL) is working on all of these.

ESRL has recently begun to optimize two of its numerical weather prediction (NWP) models—the Rapid Refresh and the High Resolution Rapid Refresh—for wind and solar applications. Specifically, research being done to improve forecast skill is targeting the intersection of wind and power with the atmosphere, including processes such as: turbulence, low level jets, shear, and formation and movement of clouds and aerosols.

ESRL is also performing research to determine the optimal suite of sensors in a national observation network to support integration of wind and solar into the power system. More vertical profiles of winds and more and higher-quality observations of solar irradiance (both total and direct) would support improvements in forecast skill. In the first Wind Forecast Improvement Project, ESRL and its partners were able to collect additional vertical profiles of winds with instruments that were available for the duration of the twelve-month field campaign. Several utility companies and Independent System Operators (ISOs) are helping fill the gap in observations by sharing with NOAA the meteorological measurements they collect at wind and solar plants.

Further research needs include an improved understanding of the co-variability of wind and solar resources, together with energy demand, on broader spatial and temporal scales; research to identify whether and how large-scale climate drivers, such as the El Nino Southern Oscillation and the Pacific Decadal Oscillation, affect wind and solar resources; and improved predictions at two-week, seasonal, annual, and decadal time scales.

Instruments and Observations

Coming Soon...


  • Wind Profiling Radars
  • SODARs
  • Lidar
  • High Resolution Doppler Lidar (HRDL)


  • ISIS


Weather Models

Rapid Refresh (RAP)
The Rapid Refresh (RAP) is an hourly updated weather forecast model/assimilation system, which went into operation on May 1, 2012, at the National Centers for Environmental Prediction (NCEP) as NOAA's hourly updated model. RAP version 2, a major upgrade, was implemented at NCEP on February 25, 2014.

Scientists from the Earth System Research Laboratory's, Global Sciences Division work with colleagues from NCEP, the National Center for Atmospheric Research, and other labs on RAP development.

The High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving atmospheric model. The NCEP HRRR has been operational since September 30, 2014.

Ramp Tool and Metric

One of the challenges of integrating large amounts of wind and solar power onto the electric grid is the high temporal variability of these power sources. That is, one gusty day can be followed by a calm day. That means wind power generation can change by large amounts very rapidly, an occurrence called a wind ramp event. NOAA ESRL researchers have developed a Ramp Tool and Metric to identify these wind ramps, and quantify model skill at forecasting them.

This ramp tool has three components: the first is a process to identify ramp events in the time series of power. The second component is a method for matching in time each forecast ramp event with the most appropriate observed ramp event. The third and last component of the ramp tool is a process through which a skill score of the forecast model is determined.

Additional Resources:

Renewable Energy Team Members at ESRL

Melinda Marquis Melinda Marquis is the Renewable Energy Program Manager for the NOAA Earth System Research Laboratory. Marquis' work at ESRL involves leading efforts to improve foundational weather forecast skill for wind and solar power applications. She represents NOAA’s Oceanic and Atmospheric Research line office on the NOAA Energy Team, and is the Chair of the American Meteorological Society's Board on Global Strategies. Marquis joined ESRL in 2007, after serving as Deputy Director for the Intergovernmental Panel on Climate Change (IPCC) Working Group I.
Robert Banta Robert Banta is a senior research meteorologist at the Earth System Research Laboratory's Chemical Sciences Division, is a lidar meteorologist specializing in the structure and dynamics of the atmospheric boundary layer, mesoscale processes, and complex-terrain flows. Dr. Banta, a Fellow of the American Meteorological Society, began his career in meteorology as a Forecaster and Weather Officer in the U.S. Air Force in Texas, Montana, and in the Aleutian Islands of Alaska. He earned his Ph.D. in Atmospheric Science from Colorado State University in 1982, with his dissertation entitled “An Observation and Numerical Study of Mountain Boundary-Layer Flow.” He worked as a research scientist at the Air Force Geophysics Laboratory as a civilian from 1982-1988, focusing on numerical weather prediction modeling of mesoscale flows, including studies on convective cloud initiation in mountainous terrain and “nuclear winter.” Since coming to NOAA in 1988, he has specialized in lidar studies of the boundary layer and lower atmosphere, including mountainous and other complex-terrain flows, air pollution transport studies, atmospheric turbulence, low-level jets, and the stable boundary layer. During the past decade, these studies have emphasized the use of Doppler lidar and other measurement system to assess wind energy applications.
Laura Bianco Laura Bianco is a Research Scientist at the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado. She works in Jim Wilczak’s group. She hold a BSc in Physics and earned her Ph.D. in Atmospheric Science from the University of L’Aquila in Italy, in 2002. She is also an Associate Editor for the Atmospheric Measurement Techniques Journal. Her research focuses at improving remote sensing observations in the boundary layer and study atmospheric processes in this layer of the atmosphere. She was involved in the WFIP campaign planning and data analysis and will be involved in WFIP2 as well.
Jacob Carley Jacob Carley is a support scientist for I.M. Systems Group and works at the National Weather Service’s Environmental Modeling Center (EMC). Jacob earned his Ph.D. in Atmospheric Sciences from Purdue University in 2012 as a National Science Foundation Graduate Research Fellow. While at Purdue his research focused on high-resolution hybrid ensemble-3DVar radar data assimilation for the short-term prediction of convective storms. At the EMC Jacob has worked on both the Wind Forecast Improvement Project and the Position of Offshore Wind Energy Resources project. His current work focuses on the development of the Real Time Mesoscale Analysis system as well as the development of the next-generation of the North American Mesoscale forecast system (NAM), which is known as the NAM-Rapid Refresh (NAMRR). The NAMRR is an hourly updated version of the NAM and will become an important piece of the EMC model suite as development trends toward a rapidly updating and convection-allowing multi-model ensemble prediction system.
Aditya Choukulkar Aditya Choukulkar is a Research Scientist at the Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder. He received his PhD from Arizona State University where his research centered on developing a vector retrieval technique based on optimal interpolation for retrieving two-dimensional wind fields from a coherent Doppler lidar. His current research focus is on characterizing uncertainties associated with Doppler lidar measurements. This work will lead to improved understanding of wind and turbulence retrievals and ensure proper interpretation of observations when comparing to forecast models.
Christopher Clack Christopher Clack is a research scientist at the Cooperative Institute for Research in Environmental Sciences (CIRES) University of Colorado. He is the technical lead on the National Energy with Weather System (NEWS) simulator and works on developing the core optimization model as well as the various inputs necessary for NEWS to run appropriately. Dr Clack received his Ph.D. in applied mathematics and plasma physics from the University of Sheffield in the UK; he also holds a BSc in mathematics and statistics from the University of Manchester in the UK. Christopher is currently developing simulations on the future energy system for the US (and abroad) with a variety of different methods. His interests cover, to name a few: resource assessment, electric power modeling, electric power systems, weather modeling and forecasting, data assimilation, statistical analysis, and mathematical optimization.
Eric James is a research associate at the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado. He works in Stan Benjamin’s group in the Earth System Research Laboratory's Global Systems Division, helping with the development and testing of next-generation Numerical Weather Prediction (NWP) models, including the 13-km Rapid Refresh and the 3-km High-Resolution Rapid Refresh (HRRR). In particular, Eric maintains a long-term archive of HRRR forecasts, which can be used to estimate renewable energy resources at a high resolution throughout the continental United States. Eric is also currently participating in the Solar Forecast Improvement Project (SFIP).
Jaymes Kenyon Jaymes Kenyon is a scientist at the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado. As a member of the Earth Modeling Branch at the Earth System Research Laboratory, he works on developing and evaluating the physical paramaterizations used in the RAP and HRRR models, especially the turbulence (boundary layer) and subgrid-scale cloud parameterizations. Jaymes is also involved with the high-resolution numerical modeling aspects of WFIP2. Previously, he was a U.S. Air Force weather officer.
Katherine McCaffrey Katherine McCaffrey is a postdoctoral research scientist in Jim Wilczak’s group at the Earth System Research Laboratory's Physical Sciences Division. She earned her Ph.D. in Atmospheric and Oceanic Sciences from the University of Colorado Boulder in 2014. McCaffrey’s research has focused on characterizing ocean turbulence with an emphasis on observing and modeling turbulence at tidal energy sites. At NOAA, she’s currently working on improving methods of measuring turbulence dissipation rates from wind profiling radars. she will also be will be working on WFIP2.
Yelena Pichugina Yelena Pichugina Yelena Pichugina is a Research Scientist in the Cooperative Institute for Research in Environmental Sciences (CIRES), affiliated with Atmospheric Remote Sensing Group of the Earth System Research Laboratory. In this capacity she defines and executes field projects involving remote sensing instruments, and performs scientific research in atmospheric science and wind energy. Her expertise includes studies of boundary layer and mesoscale processes, applications of Doppler lidar measurements inland and offshore to quantify wind and turbulence at the height of turbine rotors, including Low Level Jets and turbine wake effects. She has also used ship-borne lidar data to validate forecast models. She is a member of the AMS Renewable Energy Committee.
Jim Wilczak Jim Wilczak is a senior scientist at NOAA’s Earth System Research Laboratory, where he leads a boundary layer research team. His research includes remote sensing of the atmosphere, turbulence, ensemble forecasting, air-sea interaction, and forecasting for wind energy. He has received several NOAA distinguished authorship awards and has been an Associate Editor of the journal Boundary Layer Meteorology for the past 15 years. Most recently he was the technical lead for the DOE-sponsored Wind Forecast Improvement Project.