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
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)
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
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.
- More on WFIP from ESRL's Physical Science Division
- Wilczak, J., L. Bianco, J. Olson, I. Djalalova, J. Carley, S. Benjamin, and M. Marquis, 2014:
Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short
Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations
NOAA Final Technical Report to DOE, award number DE-EE0003080, 162 pp.
- Melinda Marquis, Jim Wilczak, Mark Ahlstrom, Justin Sharp, Andrew Stern,
J. Charles Smith, and Stan Calvert, 2011: Forecasting the Wind to Reach
Significant Penetration Levels of Wind Energy. Bull. Amer. Meteor. Soc.,
Solar Forecast Improvement Project (SFIP)
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
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
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.
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
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
- Wind Profiling Radars
- High Resolution Doppler Lidar (HRDL)
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
Renewable Energy Team Members at ESRL
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.
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
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.
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.
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.
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.
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 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.
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 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
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.