getting to large-scale solar integration
pollutants and even surface reflectivity. The
industry needs analyses of existing high-quality data to evaluate current forecasting skill.
The ARM Southern Great Plains program has
deployed a vast suite of meteorological instruments that can help directly address how the
various environmental factors can affect solar
radiation available for electricity production.
Analyzing data from the ARM SGP site will
allow solar forecasters to quantify the quality
of forecasts and identify the parameters that
are contributing most significantly to observed
forecast errors. Such analysis should enable us to
improve the models’ physics and, consequently,
decrease forecast errors of irradiance.
An improved version of LAPS, called Space-Time Multiscale Analysis System (STMAS) will
use variational methods to improve the assimilation of clouds and radar data. That helps to make
the model fields more consistent with the obser-vational data and the atmospheric equations of
motion. This consistency is important so that the
clouds are more accurately forecast beyond the
first hour or so of the forecast period.
The LAPS system as shown in figure 3 (page
55) is an example of how solar forecasts can be
made. Clouds are initialized at time zero of the
model forecast and continue to progress through
the first few hours of the forecast. Forecast solar
radiation varies with the cloud motion and the
time of day. The right panel shows a Weather
Research and Forecast (WRF) model solar radiation forecast that was initialized using the LAPS
cloud analysis. Two hours into the forecast, the
Copyright © 2011 by the American Solar Energy Society Inc. All rights reserved.
Public-Private Partnership Is Essential
Obtaining the meteorological measurements and NWP model improvements needed
for a national, operational solar forecast with
the spectral, spatial and temporal accuracy
needed for efficient large-scale solar power integration will require close collaboration among
multiple federal agencies, the private sector and
academia. The public-sector role is to provide
an adequate foundational weather forecast that
the private sector can then use to provide value-added forecasts and tailored products for the
energy industry, including forecasts of power
production. Because this effort is in a very early
stage, details of what this arrangement should
be are yet to be defined. As solar energy development expands, continual communication
about respective roles will be crucial among
federal agencies, such as the DOE, NOAA, the
National Renewable Energy Laboratory and the
National Science Foundation; private entities,
such as solar power plant developers, owners,
and operators, private forecast vendors, utilities, balancing authorities and independent system operators; and academic institutions. Flexibility to accommodate changes in the energy
industry’s needs and advances in technology
will be essential as these partnerships grow.
The meteorological research community is
well poised to address the issues critical to the
success of solar energy. However, a number
of questions need to be addressed to support
solar energy development. For instance, at what
spatial density are observations of the DNI or
global solar irradiance necessary, and in which
regions of the nation, to obtain the needed
improvements in NWP forecasts of solar irradiance? Specialized studies to examine the quantitative impact of water vapor, aerosols and
clouds will be needed to improve forecasts on
temporal and spatial scales important for grid
balancing. This work will require a combination
of improvements in weather forecast models,
high-quality measurements and radiative transfer models. Because the forecasting requirements depend on the technology, the required
research needs to be flexible and adaptive. The
academic and governmental institutions can
make strong contributions to the fundamental
research, while private industry can apply the
research to specific business needs and focused
forecasts. The work of the two is intertwined;
the success of solar energy development will
require the work of both to succeed. ST
solar radiation field closely matches the analyzed
cloud-cover locations from the same time on the
left panel. This shows a fairly accurate short-term
forecast as a storm moved across California earlier this year.
In addition to forecasting clouds, we can
better analyze and forecast aerosols and water
vapor using models such as the WRF, particularly if atmospheric chemistry components of the
model are activated. NOAA is developing a version of the HRRR model that includes explicit
forecasts of aerosols. Improved networks of solar
radiation observations can be used to help verify
The most pressing need for the solar industry is for advances in the hour-ahead forecast,
which, despite its name, refers to the time two
to four hours ahead. For such short-term prediction needs, NWP forecasts can be supplemented by additional approaches. As Ihnen
(2009) explains, one strategy is to use near-real-time satellite images of the solar plant area with
ground-based observations and other information to derive horizontal wind speeds and
other meteorological variables such as humidity
structure. Then a statistical technique called a
Markov grid is applied to derive cloud fraction
and opacity in the time period of interest: two
to four hours out. For minute-ahead forecasts,
a tailored all-sky camera can be deployed at the
solar plant, and observations from it are used to
provide probabilities of power ramp events, for
instance, from single clouds, in each 10-minute
period for the next hour.
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