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Decision Support Tools Evaluation Report

For

Prediction Of Worldwide Energy Resource (POWER)

Bio-Energy/Bio-Mass Thrust

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# Table of Contents {#table-of-contents .Heading-NoTOC}

-   Executive Summary
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-   Introduction
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-   NASA, ESE, ESA, and Application mission traceability

-   Brief mention/introduction to decision support tool (DST)

-   Initial partnering activities

-   Systems engineering approach

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-   Overview of DST
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-   Background (organizational, programmatic, technical)

-   Detailed description of DST and functional and technical needs, with
    emphasis on remote sensing and modeling requirements (i.e. areas
    where NASA can contribute). Include plans for DST future
    development, if any.

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-   This should include a clear description of how and when the tool is
    used, data inputs and their sources, data outputs, who uses the
    outputs, what actions and decisions are made as a result, and the
    impact of these decisions.

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-   Consideration of NASA inputs
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-   Overview of NASA inputs matched against technical requirements

-   Description of past and current NASA contributions that contribute
    to the DST (e.g. past research, currently funded activities such as
    REASoN, etc.)

-   Detailed description and analysis of NASA inputs to improve DST

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-   Measurements

-   Models

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-   Gaps in meeting DST needs
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-   Description of limitations in NASA inputs that prevent NASA from
    addressing certain DST requirements

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-   Conclusions and Recommendations
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-   Findings - Summarize DST and potential NASA inputs

-   Recommendations

-   Next Steps

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**Evaluation Report for BioEnergy/BioMass POWER Thrust\
**

**Executive Summary:**

A recent report from the Energy Information Administration projects that
net United States (U. S.) energy imports will grow from about one
quarter to just over one-third of total U.S. energy demands by 2025
creating a major strategic and economic vulnerability for the U. S..
Shifting America's energy dependence away from imported petroleum
sources toward alternative, renewable, domestic agricultural sources is
one approach that has been proposed to minimize U.S.
dependency/vulnerability on foreign energy sources. Because biomass
resources in the U. S. are abundant and renewable, biofuels (especially
ethanol and biodiesel) have been viewed as fuel supplements and/or
alternatives to decrease the U. S. reliance on foreign energy sources.
In short, biofuels offer a tremendous potential to supplement the energy
requirements of the United States.

The Multiyear Plan 2004 and Beyond, published by Office of the Biomass
Program (OBP), outlines technical challenges and planned approaches
leading to a more vibrate supply of U. S. produced biofuels. An
important element of the OBP plan is the development of bio-energy
Decision Support Tools (DST) for (1) feedstock supply forecasts; (2)
predicting potential location, price, and quantity of new biomass crops;
and (3) forecast bio-refineries demands for biomass resources. Key
components for many of the bio-energy DST's will be

agricultural crop models that rely upon environmental data (e.g.
precipitation, solar radiation, winds, temperature, etc.) for accurate
forecasts.

The environmental data required by crop models is, in principle, a
product of many programs within NASA's Earth Science Enterprise (ESE).
Accordingly, NASA is evaluating the potential for products from NASA's
ESE missions to add value to the bio-energy DST's. In particular, NASA
is using a systems engineering approach to evaluate the potential
enhancement to agricultural decision support systems (DSS) (i.e. crop
models). The approach is to first understand the components of the
system and its input requirements, then review NASA products that may be
integrated as system inputs to improve the accuracy, quality, or
efficiency of the DSS's output, and finally demonstrate the
applicability of the NASA products as DSS inputs through simulation
studies. This report documents the evaluation phase of the systems
engineering process and includes an examination of the system
architecture, operations, and input requirements, as well as an initial
assessment of specific ESE measurement systems and products that should
be considered for their potential to enhance agricultural DSS and thus
future bio-energy DST's.

Weather data represents critical input parameters to
agricultural/bio-energy crop models. The need for historical, real-time,
or forecast weather data is driven by the way in which the crop model is
used. Historical data is typically used to develop and/or refine
specific crop models, address "what-if" questions, and to evaluate the
potential of future crop sites, while real time and short term forecast
data is typically used for day-to-day crop management. Historical or
climate trends in weather are also used for season-to-season planning of
future crop cycles. NASA's Earth Science Enterprise (ESE) satellite
systems and research programs, which have long supported the study of
the earth's climate and climate processes, have generated global data
archives that include long-term estimates of meteorological quantities
that are important to crop models. The results of this evaluation
demonstrates that NASA's ESE existing historical/climate assets can make
important contributions to current agricultural DSS's and thus
bio-energy DST's. However, the nature of the ESE and of the instruments
that it develops presents some limitations when the need for real time
and forecast data products are required. The lack of system
redundancies, for example, could cause data gaps for operational DSS's
if a system fails. System continuity may also be an issue in instances
where sensor systems are nearing the ends of their scheduled missions.
Finally, the data and products produced for the NASA science community
may not always meet the real-time data delivery requirements of
operational DSS's used for day-to-day crop management.

This evaluation report not only shows the potential value of ESE
products for supporting the development of U. S. bio-energy programs,
but highlights the fact that NASA's past experience in developing
industry focused data archives offers a roadmap for providing the
agricultural/bio-energy communities access to the ESE products in a
user-friendly format. Specifically, the Surface meteorology and Solar
Energy (SSE) archive was developed to foster the use of ESE data
products in the renewable solar energy industry. The ESE satellite-based
products, including solar flux, which is a critical parameter for crop
models, have been shown to be accurate and reliable providing data for
applications such as the design of photovoltaic and renewable energy
systems for regions where surface measurements are sparse or
nonexistent. The ESE solar data, along with over 200 additional
parameters related to solar and wind energy sources, are currently
available through the SSE internet-based data archive at
<http://eosweb.larc.nasa.gov/sse/>.

The SSE data archive and web site were developed in partnership with the
Department of Energy/National Renewable Energy Laboratory (DOE/NREL),
the Canadian Meteorological Service (CANMET) Energy Diversification
Research Laboratory (CEDRL), and several small solar and wind companies.
The Prediction Of Worldwide Energy Resource (POWER) project was recently
initiated to improve the current SSE data archive and to create new
datasets directly applicable to other industries. Based upon over 5
years of experience since the first release of the SSE data archive, the
POWER Project is acutely sensitive to the value of providing datasets in
the native format of each industry to facilitate seamless integration
into their Decision Support Tools' (DST's). It is recommended that
development of data sets for the agricultural/bio-energy community build
upon the SSE experienced gained seeking collaborative inputs directly
from the user community. Moreover, the overall development of
ESE-products supporting bio-energy DST's should continue to be managed
through a systems engineering approach so that the projects and products
selected for implementation will have passed through a rigorous
verification and validation process to ensure that DST requirements are
being met and that the resulting improvements in the system can be
effectively benchmarked.

**Introduction:**

The Energy Information Administration's (EIA) most recent projections
(Annual Energy Outlook 2004 with Projections to 2025;
<http://www.eia.doe.gov/oiaf/aeo/index.html>) paint a clear picture of
future energy challenges in the United States. Despite continued
advances in technology, total U.S. energy consumption is projected to
increase from 98 quadrillion British thermal units (BTUs) in 2002 to 136
quadrillion BTUs in 2025.

Because of slow growth in domestic energy production, net energy imports
are projected to grow from about one quarter to just over one-third of
U.S. demand by 2025 creating a major strategic and economic
vulnerability for the U. S.. Shifting America's energy dependence away
from imported petroleum sources toward alternative, renewable, domestic
agricultural sources could reduce this dependency. Biofuels (especially
ethanol and biodiesel) have the potential to decrease the U. S. reliance
on foreign energy sources.

Biomass resources in the United States are abundant, renewable, and
offer a tremendous potential to supplement our current energy resources.
The current total available domestic biomass, beyond its current use for
food, feed, and forest products, is estimated to be between 500-600
million dry tons per year for the period 2010-2020 (The Office of Energy
Efficiency and Renewable Energy, Office of the Biomass Program (OBP),
Multiyear Plan: 2004 and Beyond; Draft November 6, 2003;
<http://www.eere.energy.gov/biomass/pdfs/multiyear_plan2004.pdf>). These
biomass resources represent about 3-5 quadrillion Btus of delivered
energy or as much as 5-6 percent of the total U.S. energy consumption.
In terms of fuels and power, this translates into 60 billion gallons of
fuel ethanol or 160 gigawatts of electricity. According to OBP, this is
enough energy to meet 30 percent of U.S. demand for gasoline or service
160,000 households with power, and if only 10 percent of the biomass
resource were converted to chemicals, the result would be 25-30 million
tons of products or about five times the current production of
industrial bioproducts. Based on the estimates provided in the OBP
multiyear plan, available domestic biomass represents a potentially
significant energy resource. Moreover, available biomass potential could
be greatly increased through advances in technology and changes in
public policy, infrastructure, and lifestyle.

The OBP Multiyear Plan: 2004 and Beyond discusses technical barriers
facing the bio-energy efforts in the U.S. and planned approaches to
address these barriers. Many of the technical challenges/planned
approaches are associated with fostering improvements in areas ranging
from biomass variability (e.g. local variation in types and amounts of
biomass available, variations in physical composition, and
characteristics of un-treated biomass) to sustainability requirements
(e.g. potential loss in crop productivity, and information to predict
environmental effects of residue removal). Underlying many of the
planned solutions is the development, testing and implementation of
bio-energy Decision Support Tools (DST) for (1) feedstock supply
forecasts to optimize feedstock supply chains; (2) predicting potential
location, price, and quantity of new biomass crops in view of competing
energy sources; and (3) forecast bio-refineries demands for biomass
resources using specific soils, climate and weather information. Core
components within many of the bio-energy DSTs are crop models that rely
on environmental data such as solar radiation, precipitation, air
temperature, winds, soil parameters including nutrient content, and a
mathematical representation of a specific crop and its dependence on the
weather and soil data for accurate forecasts.

Weather data represents critical input parameters to the
agricultural/bio-energy crop models. The need for historical, real-time,
or forecast weather data is driven by the way in which the crop model is
used. Historical data, for example, is typically used to develop and/or
refine specific crop models, address "what-if" questions, and to
evaluate the potential of future crop sites, while real time and short
term forecast data is typically used for day-to-day crop management.
Climate trends, derived from historical data, are also used for
season-to-season planning of future crop cycles. It is of interest to
note that while the U.S. Department of Agriculture (USDA) does not
currently provide specific forecast for bio-energy crops (e.g. barley;
corn; grain sorghum; oats; rice; wheat; soybeans; cotton seed; sunflower
seed; canola; crambe; rapeseed; safflower; sesame seed; flaxseed;
mustard seed; cellulosic crops, such as switchgrass and hybrid poplars),
the forecast of crop yields by the USDA National Agricultural Statistics
Service is based upon historical trends rather than predicted future
weather conditions. "Long-range weather forecasts are not used in any
forecast models. Crop production forecasts are based on conditions as of
the survey reference date and projected assuming normal conditions for
the remainder of the season. For example, the assumption of \"normal
conditions\" is that temperatures and precipitation will be at historic
averages for the remainder of the season." (Frederic A. Vogel**,** and
Gerald A. Bange, Understanding Crop Statistics, U.S. Department of
Agriculture, Miscellaneous Publication No.1554,
<http://www.usda.gov/nass/nassinfo/pub1554.htm>).

NASA's Earth Science Enterprise (ESE) satellite systems and research
programs, which have long supported the study of the earth's climate and
climate processes, have generated global data archives that include
long-term estimates of meteorological quantities. The NASA Surface
meteorology and Solar Energy (SSE) is an illustration of how historical
ESE data products can be used by industry. Under the SSE program, ESE
satellite-based products, including solar flux, a critical parameter for
crop models, have been shown to be sufficiently accurate and reliable to
provide data for applications such as the design of photovoltaic and
renewable energy systems for regions where surface measurements are
sparse or nonexistent (Whitlock et al. 2001; Perez et al. 2002, Chandler
et al 2004). The SSE solar data, along with over 200 additional
parameters related to solar and wind energy sources, are currently
available through the SSE internet-based data archive
(<http://eosweb.larc.nasa.gov/sse/>). These parameters are currently
available on a global grid of nominally 1-degree latitude by 1-degree
longitude from July 1983 through June 1993. Since its inception, the SSE
web site has received over 2.2 million hits with over 430,000 data
document downloads and over 10,000 unique users. The SSE data archive
and web site was developed in partnership with the Department of
Energy/National Renewable Energy Laboratory (DOE/NREL), the Canadian
Meteorological Service (CANMET) Energy Diversification Research
Laboratory (CEDRL), and several small solar and wind companies. NASA has
developed a direct link between the SSE web site and CEDRL's renewable
energy DSS called RETScreen^TM^. A similar prototype web based archive
focused on the building industry is currently under development in
collaboration with ASHRAE and DOE.

Recently NASA established an Energy Management theme within the ESE
Applications Program. Under this program, a follow-on to the
SSE-program, the Prediction Of Worldwide Energy Resource (POWER) project
(Stackhouse et al. 2004), was initiated both to improve the current SSE
dataset in subsequent releases, expand the temporal cover of the data,
and to create new datasets directly applicable to other industries from
new satellite observations and the accompanying results from forecast
modeling. The POWER Project will expand the highly successful SSE data
archive and eventually add near-real time and forecasted datasets to the
historical datasets already provided. Based upon over 5 years of
experience since the first release of the SSE data archive, the POWER
Project is acutely sensitive to the value of providing datasets in the
native format of each targeted industry (e.g. bio-energy) to facilitate
seamless integration into their Decision Support Tools' (DST's). While
the current web based SSE archive was designed specifically to meet the
needs of renewable energy production associated with the solar and wind
communities, it serves as an example for a web-based archive focused on
the bio-energy industry.

This evaluation report is focused on the potential application and use
of NASA data products contained in the SSE/POWER archive for biomass
fuel development. In particular, the applicability of the solar data
contained in the SSE/POWER archive has been evaluated through the use of
several crop models. The minimum weather data set typically required by
crop models is site-specific, daily averaged solar radiation and daily
averaged precipitation, and the minimum and maximum daily air
temperature. Surface temperature, winds, and soil moisture content are
also useful parameters evaluation of the evapotranspiration associated
with a crop/environment. Solar radiance data, however, is often the more
problematical of these parameters in that accurate measurements of soar
radiation requires more sophisticated equipment than for precipitation
or temperature measurements and is much more labor intensive requiring
frequent calibrations by a qualified technician. Because if its
importance to crop models, the scarcity of ground based measurements,
and the fact that the SSE/POWER archive has a proven set of solar data,
our evaluation has focused on this parameter.

Overview of Decision Support Tools:

Crop models and DSS's have become important tools in modern agricultural
research. Most agricultural DSSs contain an explicit crop model-module,
which synthesizes the current insights into the physiological and
ecological processes that govern a given crop's growth through
mathematical equations. Comparing forecasts/simulation results with
experimental observations tests the understanding of a crop model's
validity/performance. Once a model is validated, it can be used to help
analyze and interpret field experiments, and perhaps equally important,
it can also be used in application-oriented research such as the design
of crop ideotypes, the analysis of yield gaps, the optimization of crop
management, the ex ante analysis of the effects of climate change on
crop growth, and agro-ecological zonation.

Table 1 provides a list of crop models with the characteristics of the
weather data required as input by each model. This Table is not intended
to be an exhaustive list of crop models, but does provide an indication
of the number of models available, and, more importantly, emphases the
fact that all of the models require meteorological data. A casual review
of the table makes it clear that most of the crop models are based upon
daily averaged meteorological parameters, and that the two primary
meteorological parameters are the air temperature and rainfall followed
by solar irradiance. While it's not obvious from the entries in Table 1,
the air temperature for most models is the daily maximum and minimum
values, and the rainfall and solar irradiance or radiation required are
daily averaged values. For some of models, the maximum and minimum daily
temperatures are used to generate an estimate of the daily averaged
solar estimate when a measurement is solar radiation is not available.

The various crop models are typically modules contained within DSS that
supports various types of analysis ranging from climate-change impact
studies to "real" time agricultural-crop management to of hydrological
and soil erosion analysis. The temporal and special resolution of input
data, and in particular meteorological data, ranges from regional
monthly or seasonal averages to site-specific daily averages. We have
selected two prominent DSS for use in this evaluation. One, the Decision
Support System for Agrotechnology Transfer (DSSAT), has been in use for
more than 15 years by researchers in over 100 countries worldwide. DSSAT
was developed and is maintained through collaboration between scientists
at the University of Florida; University of Georgia; University of
Guelph; University of Hawaii; the International Center for Soil
Fertility and Agricultural Development, Iowa State University; and other
scientists associated with the International Consortium for Agricultural
Systems Applications (ICASA - <http://www.icasa.net/index.html>). The
crop models currently accessible under the DSSAT shell include; the
CERES family of models -CERES-maize, CERES-wheat, CERES-rice,
CERES-barley, CERES-sorghum, and CERES-millet; the CROPGRO series of
models for legumes -CROPGRO-soybean, CROPGRO-peanut, CROPGRO-dry bean;
the CROPSIM model series for root crops - CROPSIM-cassava and
SUBSTOR-potato; and for other crops - CROPGRO-Tomato, CROPGRO-Chickpea,
sugarcane, and sunflower.

The second crop "model" is a series of software modules contained in
Tools for Agro-meteorology and Agricultural Modeling (TAAM)
(<http://www.sipeaa.it/ASP/ASP2/index_tools.asp>), and like, DSSAT,
modules in TAAM have been developed, and are maintained, through a
collaborative effort of scientists at the Department of Biological
Systems Engineering, Washington State University; the European
Biotechnology and GMOs; the European Institute for Health and Consumer
Protection; the European Joint Research Centre; the European Sustainable
Development Department; the Food and Agriculture Organization of the
United Nations; the Department of Agronomy and Agro-Ecosystem
Management, University of Pisa; and scientists associated with the
Research Institute for Industrial Crops and the Agriculture Research
Council (ISCI-CRA).

DSSAT is a software program combining crop soil and weather databases
and programs to manage them, with crop models and application programs,
to simulate multi-year outcomes of crop management strategies. The
application of DSSAT is often focused on "what-if" simulations requiring
historical data, and while not explicitly a bio-energy DST, the weather
data requirements of DSSAT are anticipated to be identical to those of
future bio-energy DSTs. The minimum weather data required by DSSAT is
site-specific daily averaged solar radiation and precipitation, and the
maximum and minimum daily temperatures. Dry and wet bulb temperatures
and wind speed are optional data inputs. The length of weather records
for validation must, at minimum, cover the duration of the experiment
and preferably should begin a few weeks before planting and continue a
few weeks after harvest so that \"what-if\" type analyses may be
performed. Similar inputs are required by the TAAM crop model, however
several of the modules within the TAAM's deal with developing/formatting
meteorological data. In the absence of solar radiation, one of the
modules, RadEst3, provides estimates of the daily averaged solar
radiation based upon measurements of the daily maximum and minimum air
temperature, and site/region specific "parameters". These parameters are
generated by iteratively comparing daily averaged solar measurements
obtained over at least one year (and preferably several years) to
calculated values until "agreement" between the measured and estimated
solar radiation is obtained. These parameters are then applied to the
estimation of solar radiation for that given site/region when solar
measurements are not available.

**Consideration of NASA inputs:**

The minimum set of weather related parameters required by virtually all
crop models are solar radiation, precipitation, and maximum and minimum
air temperature (Note that while not part of the required data, some
models also ingest wet and dry bulb temperature, and surface winds.),
along with soil parameters (e.g. moisture, nutrients, and soil type) and
parameters related to the crop species and planting conditions.
Currently weather data for crop models are from ground based
measurements, which as noted above, can be particularly problematical
for solar radiation measurements; and when solar measurements are
available, they are, at times, of questionable value because of the lack
of documentation/calibration. Moreover, historical ground based
meteorological data for an arbitrary site is more likely to be
unavailable than available, and when available the data is often in a
format that is incompatible with the format requirements of models. ESE
data products, and in particular, the solar/meteorological data in the
SSE/POWER archive, offers the potential for meeting the weather related
needs of agricultural/bio-energy DSTs. While the current SSE/POWER
archive was developed specifically to meet the needs of the solar
voltaic energy community, the success of the SSE/POWER web-based
distribution site clearly demonstrates that NASA data products can
successfully be formatted to meet a user community's needs. This
approach can also be applied to the development of a meteorological data
archive for the bio-energy community.

As noted above, the systems analysis described herein focuses primarily
on evaluating the potential of the current SSE/POWER archive as a viable
source of solar data for crop models This is due to the importance of
solar radiation to crop models and because the solar data in the
SSE/POWER archive has already proven its value to other energy related
industries. We note here that daily averaged maximum and minimum air
temperature, daily averaged dry and wet bulb temperatures, and wind
speed, as well as estimates of the daily averaged precipitation are
other important meteorological parameters that, in principle, are
available through the SSE/POWER archive. Thus the current data holding
of the SSE/POWER archive offer the potential for providing a complete
suite of meteorological data required by agricultural/bio-energy DSTs on
a 1-degree latitude by 1-degree longitude global grid. While the current
SSE/POWER data only covers the 10 year period from July 1983 to June
1993, work is under way to extend the temporal coverage to near real
time, and eventually to include forecasted data products of interest to
crop model. We note that the solar data used in this evaluation was
obtained from the SRB archive in the Atmospheric Sciences Data Center at
NASA's Langley Research Center. The SRB data covers the time period from
July 1983 to October 1995. The SSE solar data is a subset of the SRB
data.

Up to this point the systems analysis presented here has established the
data requirements of the agricultural DST's and has identified ESE data
products (i.e. SSE/POWER data archive) that have the potential for
meeting these data requirements. In the remainder of this section we
show that the SSE/POWER archive can provide viable solar data for crop
models. In particular we (1) compare solar data available through the
SSE/POWER archive to the ground based solar measurements reported at
various crop sites around the world and to estimates of solar radiation
generated by the RasEst3 module contained in the TAAM crop model; and
(2) compare DSSAT model forecasts using daily averaged solar radiation
based upon ground based measurements to forecast using daily averaged
solar radiation from the SSE/POWER archive. The results of these
comparisons demonstrate the compatibility of the SSE/POWER daily
averaged solar data with agricultural/bio-energy crop models.

Comparison of the daily averaged solar radiation obtained from ground
observations, the RadEst3 module based upon the daily maximum and
minimum temperatures, and the SSE/POWER archive is illustrated in
Figures 1 and 2 for two sites. The RadEst3 module provides estimates of
the daily averaged solar radiation using any one of four models: (1)
\'Bristow and Campbell\' (Bristow and Campbell, 1984); (2) Campbell and
Donatelli\' (Campbell and Donatelli, 1998); (3) Donatelli and Bellocchi
(Donatelli and Bellocchi, 2002); (4) the Modular DCBB, which includes
features of the previous three models which can be switched on or off.
In the results presented in Figures 1 and 2 we show the solar radiation
estimate from the Campbell and Donatelli (CD) model, where the daily
maximum and minimum temperature input to RadEst3 for each site is taken
from the ground site measurements.

Figure 1 shows data for a site in Hermiston, OR and Figure 2 is for a
site in Stuttgart, GE. The (a) and (b) panels of each figure show,
respectively, time series plots for a full year and the time period from
April 1 to August 31 from the same year. The ground site observations at
the Hermiston site are from automated agricultural-meteorological (e.g.
AgriMet stations) weather stations operated and maintained by the Bureau
of Reclamation (http://www.usbr.gov/pn/agrimet/index.html). These
stations are located in selected irrigated agricultural areas in the
Pacific Northwest and represent the "typical" data currently available
for use by crop models. The ground site observations at the Stuttgart
site are from the World Radiation Data Center. Figures 1c and 2c show
the scatter plot of the SSE/POWER daily averaged solar radiation versus
the daily averaged ground observations for multiple years and the
estimated radiation from RadEst3.

Recall that the SSE/POWER solar data is given (e.g. resolution) on a
1-degree latitude by 1-degree longitude grid, while the ground-based
measurements are essentially point measurements. It is therefore
somewhat surprising to see such a high degree of correlation between the
satellite and ground based values, but such agreement clearly suggest
that the SSE/POWER solar data can be used in crop models particularly in
data sparse regions. Table 2 lists additional sites for which similar
comparisons have been conducted during this evaluation. The source of
the ground-based observations is listed along with the geographical
location. The slope, intercept, and correlation coefficient (R\^2) are
for the linear fit between the SSE/POWER versus ground-based values (see
figures 1c and 2c), and the root mean square error (RMSE) between the
SSE/POWER and the ground-based values are given for each site. With the
exception of a few sites, the agreement between the ground observations
and the SSE/POWER data is very good.

Note that the solar radiation estimated by RadEst3 for Hermiston, OR
(Figure 1a and b), is generally in agreement with the ground
observations, except for the time period beginning early July through
the end of October. During this period the RadEst3 module tends to over
estimate the radiation. For the 1988 year shown, the over estimate for
this time period is about 15%. For the sites studies so far (see Table
2), systematic differences between the RadEst3 estimates and
ground-based measurements are not "typical", although they are also not
uncommon. The reason for these differences is thought to be associated
with the RadEst3 site-specific model parameters, which are "tuned" for
the atmospheric conditions existing at the time the solar "calibration"
data were obtained. Re-tuning these model parameters using, for example,
SSE/POWER data may bring the RadEst3 estimates more in line with the
ground observations.

Based upon the agreement observed between the SSE/POWER solar radiation
and the ground-based observations, one would anticipate little
difference in the forecast output of a crop model whether using ground
or satellite based observations. To explore this expectation, DSSAT
forecasts using ground based solar observations, SSE/POWER data, and
modeled solar data from the National Climate Data Center (NCDC) were
compared in a study conducted at the Hampton University (HU), Hampton,
VA (Santos, 2004). This study involved using ground based meteorological
data obtained during field studies initially conducted at three U.S.
sites to compare DSSAT forecasted parameters to field measurements of
the forecasted parameters. Table 3 list the three sites used in this
study and the coordinates for the three solar data sets were use -- the
daily averaged based upon ground based measurements at each site, the
SSE/POWER daily averaged values for each site, and modeled solar data
taken from the NCDC. Recall that the SSE/POWER data has a resolution of
1-degree latitude by 1-degree longitude, so the center of the 1-degree
grid is listed. The NCDC solar data are modeled values using information
from reporting weather stations. The coordinates of the station
reporting observations used to generate the NCDC modeled data are
listed. Notice that the NCDC data is not coincident with the ground site
measurements, but may be within the 1 by 1 degree grind of the SSE/POWER
values.

Figure 3a, b and c illustrate the agreement between the SSE/POWER and
NCDC solar data and the ground site measurements for the Kellogg
Biological Station, MI. in 1993. As with the data illustrated in figures
1 and 2, the three solar data sets used in the HU study exhibit a high
degree of correlation at the Kellogg stations and similar agreement was
observed at the other two sites. Included at the bottom of Table 2 are
the coefficients (i.e. slope, intercept, and square of the correlation
coefficient) for a linear fit through the scatter plot of SSE/POWER vs.
ground site solar data, and the root mean square error between the SSE
and ground site observations.

In the HU study, DSSAT forecasted crop parameters were generated using
the weather data file containing the daily averaged solar data based
upon the ground site measurements. DSSAT was then re-run for the same
conditions except the ground based solar data were replaced with the
SSE/POWER data and then the NCDC data. For each solar data set DSSAT was
run to obtain the modeled output of leaf index and grain weight. Figures
4, and 5 are from the HU study and show the DSSAT modeled predictions
for leaf area index and grain weight maximums using the three solar data
sets. The predicted leaf area indexes for the three set of solar data
are virtually indistinguishable, and although some differences between
the grain weight predictions for the three solar data sets are apparent,
they are considered minor.

Our analysis demonstrates a high degree of correlation between the
SSE/POWER solar data and ground based observations for sites around the
world; the HU study demonstrates little difference between forecasted
parameters using ground vs SSE solar data. Taken together these results
clearly indicates that the SSE/POWER archive represents a viable source
for the daily averaged solar data compatible with
agricultural/bio-energy DST's. As noted above, precipitation and minimum
and maximum air temperature are additional parameters required by crop
models. The SSE/POWER archive also offers the potential for providing
these additional parameters thereby giving a complete suite of
meteorological parameters for agricultural/bio-energy DST's.

**Gaps in meeting DST needs:**

As demonstrated in the preceding section, the historical solar radiation
data available in the SSE/POWER archive can be used as input for
agricultural/bio-energy crop models. Other parameters in the SSE/POWER
archive, such as precipitation, air temperature, and winds, also have
the potential for meeting crop model requirements. However when
reviewing the entire scope of applications envisioned for
agricultural/bio-energy DST's, there are clear "gaps" or shortcomings
associated with these ESE data products. The three major applications in
which agricultural/bio-energy DST's are either currently used or
envisioned to be used include (1) research for development/validation of
new crop models and crop management strategies, (2) crop management
practices in the agro/bio-energy industries, and (3) operational
bio-energy/bio-crop forecasts such as the crop forecast currently
produced by the USDA Production Estimates and Crop Assessment Division
(PECAD).

Historical meteorological data, such as that in the SSE/POWER archive,
satisfies a significant part of the needs of research and crop model
development activities in applications area (1), and to the extend that
historical data is used in the remaining two application areas, the
SSE/POWER archive will also satisfy needs of these applications area.
The gap that does exit with respect to historical data is the need to
expand its' temporal coverage. The SSE/POWER archive covers a 10-year
time period from July 1983 through June 1993, and solar data through
October 1995, is available in the SRB archive at the Atmospheric Science
Data Center at NASA's Langley Research Center. One of the objectives of
the POWER project is to extend the temporal cover of the SSE/POWER
archive into the 2000's. The underlying satellite data for the solar
radiation is in hand to extend the time period to 2001. Meteorological
data required to calculate the solar radiation will be obtained from the
GEOS-4 (<http://dao.gsfc.nasa.gov/>) 1-degree latitude by 1-degree
longitude reanalysis meteorology for the 20-year period from 1984
through 2004. The GEOS- reanalysis is anticipated to be available by
January 2006. The subsequent updates to the SSE/POWER archive will
significantly reduce the "gap" in the historical database.

As the application of DST's moves from the research to the operational
arena, the requirement for real time data, global coverage, increased
spatial resolution, and the continuity/reliability of the data source
becomes increasingly important. These requirements represent areas for
which there are currently gaps in meeting the agro-/bio-energy DST's
needs. The reason for this is for the most part related to the fact that
NASA's satellite program is, by design, focused on research issues
rather than operational issues. Rapid turn-around of satellite data, for
example, while important in the NASA program, is not a prime driver. The
basic research nature of NASA's satellite program can also compromise
the continuity of data products, often as a result of funding
issues/priorities or unforeseen delays in development of new measurement
capabilities. Many of the ESE data products are from non-geo-stationary
satellites that provide global coverage over time periods ranging from a
1 to 3 days to up to 16 days (i.e. revisit cycle). As the revisit time
increases the usefulness of the data products for operation application
diminishes. We note here that the SSE/POWER data products are on a
global 1-degree latitude by 1-degree longitude grid because the
underlying data source is an amalgamation of satellite radiance
measurements from multiple satellites. ( The World Climate Research
Program\'s International Satellite and Cloud Climatology Program
sponsored by NASA - <http://isccp.giss.nasa.gov/>). However this process
of generating global coverage does not lend itself to producing
"real-time " data products.

**Conclusions:**

This systems analysis has focused on the weather data requirements of
agricultural/bio-energy DSTs, and in particular on the data products
that are available through the NASA SSE/POWER archive. Based upon the
systems analysis presented herein, we conclude that NASA through the
SSE/POWER archive currently has the ability to provide solar radiation
data for many agricultural/bio-energy applications, particularly those
requiring a historical database. The current SSE/POWER data is on a
global 1-degree latitude by 1-degree longitude grid for the 10-year time
period from July 1983 to June 1993. It is anticipated that by 2006 the
temporal coverage will be extended to the year 2004, there by greatly
enhancing its' value to the agricultural/bio-energy communities.
Moreover, the SSE/POWER archive also offers the potential for providing
precipitation and air temperature, which, along with the solar data,
would constitute the complete minimum weather data set required by most
agricultural/bio-energy DST's. Application areas requiring real time
and/or high spatial resolution data, and operational type applications
requiring continuity and reliable data sources tend to be more
problematical and the gap between data requirements and what currently
can be provided is large.

**Recommendations:**

1.  The SSE/POWER archive offers a viable data source for many
    agricultural/bio-energy crop modes. This systems analysis has
    demonstrated the applicability of the solar to crop models.
    Additional analysis is recommended to verify that additional weather
    parameters, such as precipitation , air temperature, and winds are
    equally applicable as input to crop models.

2.  The SSE/POWER archive was developed through collaboration with the
    partners in renewable solar power industry. The experience of
    developing the SSE/POWER archive and it's associated Internet web
    site offers a road map for the development of an
    agricultural/bio-energy archive/web site. Development of a prototype
    agricultural/bio-energy archive using the same design methodology as
    that employed for the SSE/POWER archive is strongly recommended.

3.  The current SSE/POWER data spans the 10-year time period from July
    1983 to June 1993. We recognize that through the newly initiated
    POWER program plans are underway to extend coverage through the year
    2004; however, since we view that this as crucial to the success a
    future agricultural/bio-energy archive, we include it here as a
    specific recommendation to highlight its' importance.

**References:**

Chandler, S. Williams, Charles H. Whitlock, Paul W. Stackhouse, Jr.,
NASA Climatological Data for Renewable Energy Assessment, Journal of
Solar Energy Engineering, 126, 945-949, 2004.

Bristow, R.L., and G.S. Campbell. 1984. On the relationship between
incoming solar radiation and daily maximum and minimum temperature. Agr.
For. Meteorol., 31:159-166

Donatelli, M., and G. Bellocchi, 2002. Estimating daily global solar
radiation in tropical and temperate sites. Agr. For. Meteorol.
(submitted)

Donatelli, M., and G.S. Campbell. 1998. A simple model to estimate
global solar radiation. In: Proc. 5th ESA Congress, Nitra, Slovak
Republic, 2:133-134.

Whitlock, Charles H., Donald E. Brown, William S. Chandler, Roberta C.
DiPasquale, Shashi K. Gupta, Anne C. Wilber, Nancy A. Ritchey, David P.
Kratz, and Paul W. Stackhouse, 2001: Global Solar Energy Anomalies
Including El Nino and La Nina Years. *J of Solar Energy Engineering*,
**23**, 211-215, Amer. Soc. Mech. Engr.

Stackhouse, P. W., Jr., C. H. Whitlock, W. S. Chandler, J. M. Hoell, and
T. Zhang, 2004: The NASA Prediction of Worldwide Energy Resource (POWER)
Project. Invited presentation *The Value of Improved Weather and Climate
Information from Satellites in the Electric Power Industry*, Washington,
D.C., March 28-29.

Perez, Richard, K. Moore, M. Kmiecik, C. Chain, P. Ineichen, R. George,
and F. Vignola, 2002: A New Operational Satellite-to-Irradiance Model
Description and Validation. Proc. *SOLAR 2002, Sunrise on the Reliable
Energy Economy*, Amer. Solar Energy Soc.

Vogel **,**Frederic A. and Gerald A. Bange, **Understanding Crop
Statistics**, U.S. Department of Agriculture. Miscellaneous Publication
No. 1554. (<http://www.usda.gov/nass/nassinfo/pub1554.htm>)

USDA press release (Release N0. 0323.04 \--
<http://www.usda.gov/Newsroom/0323.04.html>) identified the following
bio-mass commodities under the USDA fiscal year (FY) 2005 Bioenergy
Program: barley; corn; grain sorghum; oats; rice; wheat; soybeans;
cotton seed; sunflower seed; canola; crambe; rapeseed; safflower; sesame
seed; flaxseed; mustard seed; cellulosic crops, such as switchgrass and
hybrid poplars. (Additionally fats, oils, and greases were also
identified as being eligible bio-energy commodities.).

SENSITIVITY OF 4M MAIZE MODEL TO THE INACCURACY OF WEATHER AND SOIL
INPUT DATA, APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 1(1--2): 75--85.

<http://www.ecology.kee.hu/pdf/01075085.pdf>

Santos, Gabriela A. Boscio, Ayman Suleiman Radiation: Ground-Measured
Data Vs. Satellite-Driven And Modeled Data, Atmospheric Sciences
department, Hampton University July 28, 2004

![](media/image2.jpeg){width="5.990972222222222in"
height="4.898611111111111in"}

Table 1. Comparison of crop models and the temporal resolution of the
weather data used required for each model.

<table style="width:100%;">
<colgroup>
<col style="width: 9%" />
<col style="width: 17%" />
<col style="width: 10%" />
<col style="width: 0%" />
<col style="width: 10%" />
<col style="width: 0%" />
<col style="width: 10%" />
<col style="width: 9%" />
<col style="width: 10%" />
<col style="width: 13%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="11"><strong>Table 2. Comparisons of SSEr5 Solar Radiation
Data with Ground Based observations</strong></td>
</tr>
<tr class="even">
<td><p><strong>Data</strong></p>
<p><strong>Source</strong></p></td>
<td><strong>Location</strong></td>
<td colspan="2"><blockquote>
<p><strong>Lat</strong></p>
</blockquote></td>
<td colspan="2"><blockquote>
<p><strong>Long</strong></p>
</blockquote></td>
<td><strong>Year(s)</strong></td>
<td><strong>Slope</strong></td>
<td><strong>Intercept</strong></td>
<td><strong>R^2</strong></td>
<td><strong>RMSE</strong></td>
</tr>
<tr class="odd">
<td>WRDC</td>
<td><p>Nagpur/</p>
<p>Sonegaon,India</p></td>
<td colspan="2">21 06N</td>
<td colspan="2">79 03E</td>
<td>1988</td>
<td><blockquote>
<p>0.86</p>
</blockquote></td>
<td>0.68</td>
<td>0.74</td>
<td></td>
</tr>
<tr class="even">
<td><p>ICASA/</p>
<p>DSSAT</p></td>
<td>Haderabad, India</td>
<td colspan="2">17.53N</td>
<td colspan="2">78.27E</td>
<td>1988</td>
<td>0.98</td>
<td>0.87</td>
<td>0.8</td>
<td></td>
</tr>
<tr class="odd">
<td>WRDC</td>
<td>Shanghai, China</td>
<td colspan="2">32 10N</td>
<td colspan="2">121 26E</td>
<td>1988</td>
<td>0.73</td>
<td>1.96</td>
<td>0.49</td>
<td></td>
</tr>
<tr class="even">
<td>WRDC</td>
<td>Beijing, China</td>
<td colspan="2">39 56N</td>
<td colspan="2">116 17E</td>
<td>1988</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>WRDC</td>
<td>Chengdu, China</td>
<td colspan="2">30 40N</td>
<td colspan="2">104 01E</td>
<td>1988</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>WRDC</td>
<td>Gungzhou, China</td>
<td colspan="2"><blockquote>
<p>23 08N</p>
</blockquote></td>
<td colspan="2"><blockquote>
<p>113 19E</p>
</blockquote></td>
<td>1988</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>RadEst</td>
<td>Tel-Hayda_SY</td>
<td colspan="2">36 01N</td>
<td colspan="2">36 55E</td>
<td>1988</td>
<td>0.86</td>
<td>0.26</td>
<td>0.88</td>
<td></td>
</tr>
<tr class="even">
<td>WRDC</td>
<td>Boise, ID</td>
<td colspan="2">43 34N</td>
<td colspan="2">-116 3W</td>
<td>1988</td>
<td>0.81</td>
<td>0.48</td>
<td>0.93</td>
<td></td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Lind, WA</td>
<td colspan="2">46.87N</td>
<td colspan="2">-118.73W</td>
<td><p>1984-</p>
<p>1995</p></td>
<td>0.91</td>
<td>1.34</td>
<td>0.93</td>
<td>2.63</td>
</tr>
<tr class="even">
<td>AgriMet</td>
<td>Lind, WA</td>
<td colspan="2">46.87N</td>
<td colspan="2">-118.73W</td>
<td><p>1984-</p>
<p>1995</p></td>
<td>0.91</td>
<td>1.34</td>
<td>0.</td>
<td>2.63</td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Lind, WA</td>
<td colspan="2">46.87N</td>
<td colspan="2">-118.73W</td>
<td><p>1983 -</p>
<blockquote>
<p>1993</p>
</blockquote></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>AgriMet</td>
<td>Odessa, WA</td>
<td colspan="2">47 18N</td>
<td colspan="2">-118 2W</td>
<td><p>1984-</p>
<p>199</p></td>
<td>0.88</td>
<td>1.35</td>
<td>0.92</td>
<td>2.49</td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Odessa, WA</td>
<td colspan="2"><blockquote>
<p>47 18N</p>
</blockquote></td>
<td colspan="2">-118 2W</td>
<td><p>1983-</p>
<p>1993</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>AgriMet</td>
<td>Odessa, WA</td>
<td colspan="2"><blockquote>
<p>47 18N</p>
</blockquote></td>
<td colspan="2">-118 2W</td>
<td>1988</td>
<td>0.86</td>
<td>0.26</td>
<td>0.95</td>
<td></td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Hermiston, OR</td>
<td colspan="2">45.5N</td>
<td colspan="2">-119 7W</td>
<td>1984-1995</td>
<td>0.85</td>
<td>1.66</td>
<td>0.91</td>
<td>2.98</td>
</tr>
<tr class="even">
<td>AgriMet</td>
<td>Hermiston, OR</td>
<td colspan="2">45 49N</td>
<td colspan="2">-119 7W</td>
<td>1983-</td>
<td>0.85</td>
<td>1.73</td>
<td><blockquote>
<p>0.9</p>
</blockquote></td>
<td>2.88</td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Hermiston, OR</td>
<td colspan="2">45 49N</td>
<td colspan="2">-119 7W</td>
<td>1988</td>
<td>0.91</td>
<td>0.28</td>
<td>0.93</td>
<td></td>
</tr>
<tr class="even">
<td>SRML</td>
<td>Hermiston, OR</td>
<td colspan="2">45 49N</td>
<td colspan="2">-119 7W</td>
<td>1988</td>
<td>0.85</td>
<td>0.49</td>
<td><blockquote>
<p>0.96</p>
</blockquote></td>
<td></td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Parma, ID</td>
<td colspan="2">43 48N</td>
<td colspan="2">-116 6W</td>
<td>1988</td>
<td>0.84</td>
<td>0.6</td>
<td>0.94</td>
<td></td>
</tr>
<tr class="even">
<td>AgriMet</td>
<td>Brandon, OR</td>
<td colspan="2">43 05N</td>
<td colspan="2">-124 25W</td>
<td>1988</td>
<td>0.96</td>
<td>0.03</td>
<td>0.9</td>
<td></td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Ceddarville, CA</td>
<td colspan="2">41 35N</td>
<td colspan="2">-120 10W</td>
<td>1988</td>
<td>0.98</td>
<td>0.2</td>
<td>0.92</td>
<td></td>
</tr>
<tr class="even">
<td>AgriMet</td>
<td>Legrow, OR</td>
<td colspan="2">46 12N</td>
<td colspan="2">-118 56W</td>
<td>1988</td>
<td>0.84</td>
<td>0.4</td>
<td>0.96</td>
<td></td>
</tr>
<tr class="odd">
<td>AgriMet</td>
<td>Lind, WA</td>
<td colspan="2">46 52N</td>
<td colspan="2">-118 44W</td>
<td><p>1988-</p>
<p>1993</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>AgriMet</td>
<td>Malta</td>
<td>42 26N</td>
<td colspan="2">-113 24W</td>
<td colspan="2">1988</td>
<td>0.97</td>
<td>0.14</td>
<td>0.91</td>
<td></td>
</tr>
<tr class="odd">
<td>SRML</td>
<td>Burns, OR</td>
<td>43.52N</td>
<td colspan="2"><blockquote>
<p>-119.02W</p>
</blockquote></td>
<td colspan="2">1988</td>
<td>0.93</td>
<td>0.02</td>
<td>0.95</td>
<td></td>
</tr>
<tr class="even">
<td>SRML</td>
<td>Eugene, OR</td>
<td>44.05N</td>
<td colspan="2">-123.07W</td>
<td colspan="2">1988</td>
<td>0.87</td>
<td>0.31</td>
<td>0.91</td>
<td></td>
</tr>
<tr class="odd">
<td>WRDC</td>
<td>Auckland, NZ</td>
<td>-37 01S</td>
<td colspan="2">174 48E</td>
<td colspan="2"><p>1985-</p>
<p>199</p></td>
<td>0.88</td>
<td>1.76</td>
<td>0.86</td>
<td>3.2</td>
</tr>
<tr class="even">
<td>WRDC</td>
<td>Christchurch, NZ</td>
<td>-43 29NS</td>
<td colspan="2">172 33E</td>
<td colspan="2"><p>1985-</p>
<p>199</p></td>
<td>0.66</td>
<td>2.96</td>
<td>0.5</td>
<td></td>
</tr>
<tr class="odd">
<td>WRDC</td>
<td>Invercargill, NZ</td>
<td>-46 25S</td>
<td colspan="2">168 20E</td>
<td colspan="2"><p>1985-</p>
<p>199</p></td>
<td>0.86</td>
<td>0.99</td>
<td>0.89</td>
<td></td>
</tr>
<tr class="even">
<td>WRDC</td>
<td>Mount John, NZ</td>
<td><blockquote>
<p>-43 59S</p>
</blockquote></td>
<td colspan="2">170 28E</td>
<td colspan="2"><p>1985-</p>
<p>199</p></td>
<td>0.66</td>
<td>2.01</td>
<td>0.82</td>
<td></td>
</tr>
<tr class="odd">
<td>WRDC</td>
<td>Okakea, NZ</td>
<td><blockquote>
<p>-40 12S</p>
</blockquote></td>
<td colspan="2">175 22E</td>
<td colspan="2"><p>1985-</p>
<p>199</p></td>
<td>0.82</td>
<td>1.08</td>
<td>0.87</td>
<td></td>
</tr>
<tr class="even">
<td>WRDC</td>
<td><p>Wellington/</p>
<p>Kelburn NZ</p></td>
<td>-41 17S</td>
<td colspan="2">174 46E</td>
<td colspan="2"><p>1984-</p>
<p>199</p></td>
<td>0.83</td>
<td>3.25</td>
<td>0.81</td>
<td></td>
</tr>
<tr class="odd">
<td>AWON</td>
<td>Arlington,WI</td>
<td>43.31N</td>
<td colspan="2"><blockquote>
<p>-89.38W</p>
</blockquote></td>
<td colspan="2"><p>1985-</p>
<p>199</p></td>
<td>0.97</td>
<td>0.99</td>
<td>0.89</td>
<td>2.61</td>
</tr>
<tr class="even">
<td>AWON</td>
<td>Chetek, WI</td>
<td>45.3N</td>
<td colspan="2">-91.53W</td>
<td colspan="2"><p>1984-</p>
<p>199</p></td>
<td>0.87</td>
<td>0.88</td>
<td>0.93</td>
<td>2.56</td>
</tr>
<tr class="odd">
<td>AWON</td>
<td>Antigo, WI</td>
<td>45.17N</td>
<td colspan="2">-89.11W</td>
<td colspan="2"><blockquote>
<p>1985-</p>
<p>199</p>
</blockquote></td>
<td>0.88</td>
<td>0.84</td>
<td>0.92</td>
<td>2.41</td>
</tr>
<tr class="even">
<td>AWON</td>
<td><blockquote>
<p>Lancaster, WI</p>
</blockquote></td>
<td>42.83N</td>
<td colspan="2"><blockquote>
<p>-90.76W</p>
</blockquote></td>
<td colspan="2"><p>1986-</p>
<p>199</p></td>
<td>0.87</td>
<td>0.76</td>
<td>0.93</td>
<td>2.55</td>
</tr>
<tr class="odd">
<td>AZMET</td>
<td>Aquila, AZ</td>
<td>33.93N</td>
<td colspan="2">-113.18W</td>
<td colspan="2"><p>1987 -</p>
<p>1987-</p></td>
<td><p>0.79</p>
<p>0.79</p></td>
<td><p>2.42</p>
<p>2.3</p></td>
<td><p>0.91</p>
<p>0.91</p></td>
<td><p>3.15</p>
<p>3.11</p></td>
</tr>
<tr class="even">
<td>AZMET</td>
<td>YumaMesa, AZ</td>
<td>32.6N -</td>
<td colspan="2">-114.63W</td>
<td colspan="2"><p>1987 -</p>
<p>1987-</p></td>
<td><p>0.87</p>
<p>0.88</p></td>
<td><p>0.31</p>
<p>0.37</p></td>
<td><p>0.92</p>
<p>0.93</p></td>
<td><p>3.09</p>
<p>2.93</p></td>
</tr>
<tr class="odd">
<td>AZMET</td>
<td>Safford, AZ</td>
<td>32.8 N</td>
<td colspan="2">-109.67W</td>
<td colspan="2"><p>1987-</p>
<p>1987 -</p></td>
<td><p>0.83</p>
<p>0.81</p></td>
<td><p>1.79</p>
<p>1.93</p></td>
<td><p>0.93</p>
<p>0.93</p></td>
<td><p>2.67</p>
<p>2.88</p></td>
</tr>
</tbody>
</table>

+------------------+-----------------+----------------+----------------+
| **Table 3.       |                 |                |                |
| Ground sites     |                 |                |                |
| used in DSSAT    |                 |                |                |
| model forecast   |                 |                |                |
| comparisons.**   |                 |                |                |
+------------------+-----------------+----------------+----------------+
| **Site**         | **Coordinates   | **Coordinates  | **Center       |
|                  | of Ground Site  | for NCDC Model | Coordinates of |
|                  | Observations**  | Calculations** | SSE/POWER      |
|                  |                 |                | Grid**         |
+------------------+-----------------+----------------+----------------+
| Gainesville,     | 29.63N Latitude | Jacksonville   | 29.5N Latitude |
| Florida          |                 |                |                |
|                  | -82.37W         |                | -82.5W         |
|                  | Longitude       |                | Longitude      |
+------------------+-----------------+----------------+----------------+
| Williamson,      | 33.17N Latitude | Atlanta        | 33.5N Latitude |
| Georgia          |                 |                |                |
|                  | -84.4W          |                | -84.5W         |
|                  | Longitude       |                | Longitude      |
+------------------+-----------------+----------------+----------------+
| Kellogg          | 41.7N Latitude  | South Bend,    | 41.5N Latitude |
| Biological       |                 | Indiana        |                |
| Station,         | -85.5W          |                | -85.5W         |
| Michigan State   | Longitude       |                | Longitude      |
| University       |                 |                |                |
+------------------+-----------------+----------------+----------------+

![](media/image3.png){width="6.054166666666666in"
height="4.1402777777777775in"}

Figure 1a. Time history of the daily averaged solar radiation from the
SSE/POWER archive, the daily averaged solar radiation based upon ground
based measurements by the AgriMet station at Hermiston, OR, and the
daily averaged solar radiation estimated by the RadEst3 module for the
year 1988..

.![](media/image4.png){width="5.997222222222222in"
height="4.101388888888889in"}

Figure 1b. Time history of the daily averaged solar radiation from the
SSE/POWER archive, the daily averaged solar radiation based upon ground
based measurements by the AgriMet station at Hermiston, OR, and the
daily averaged solar radiation estimated by the RadEst3 module for the
time period 4/1/1988 to 8/30/1988.

![](media/image5.png){width="5.997222222222222in"
height="4.101388888888889in"}

Figure 1c. Scatter Plot of the daily averaged solar radiation from
AgriMet Ground Observations at Hermiston, OR and daily averaged solar
radiation from the SSE/POWER archive for the time period between January
1, 1984 and October 31, 1995.

![](media/image6.jpeg){width="7.488888888888889in"
height="5.120833333333334in"}

Figure 2a. Time history of the daily averaged solar radiation from the
SSE/POWER archive, the daily averaged solar radiation based upon ground
based measurements for Stuttgart, GE from the World Radiation Data
Center, and the daily averaged solar radiation estimated by the RadEst3
module for the year 1989.

![](media/image7.jpeg){width="7.488888888888889in"
height="5.120833333333334in"}

Figure 2b. Time history of the daily averaged solar radiation from the
SSE/POWER archive, the daily averaged solar radiation based upon ground
based measurements for Stuttgart, GE from the World Radiation Data
Center, and the daily averaged solar radiation estimated by the RadEst3
module for the time period from 4/1/1989 to 8/30/1989.

![](media/image8.jpeg){width="7.488888888888889in"
height="5.120833333333334in"}

Figure 2c. Scatter Plot of the daily averaged solar radiation from
AgriMet ground observations at Stuttgart, GE and daily averaged solar
radiation from the SSE/POWER archive for the time period between January
1, 1984 and October 31, 1991

![](media/image9.png){width="7.496527777777778in"
height="5.126388888888889in"}

Figure 3a. Time history of the daily averaged solar radiation from the
SSE/POWER archive and the daily averaged solar radiation based upon
ground based measurements at the Kelloge Biological Station, MI , ~~and
the daily averaged solar radiation from the National Climate Data
Center~~ for the year 1994.

![](media/image10.png){width="7.496527777777778in"
height="5.126388888888889in"}

Figure 3b. Time history of the daily averaged solar radiation from the
SSE/POWER archive, the daily averaged solar radiation based upon ground
based measurements at the Kelloge Biological Station, MI for the time
period from 4/1/1994 to 8/32/1994.

![](media/image11.png){width="7.496527777777778in"
height="5.126388888888889in"}

Figure 3c. Scatter Plot of the daily averaged solar radiation from
ground observations at Kelloge Biological Station, MI and daily averaged
solar radiation from the SSE/POWER archive for the time period between
January 1, 1984 and October 31, 1995

Figure 4. The leaf area index predicted by DSSAT using the measured
solar radiation at the Kelloge Biological Station, MI, the SSE/POWER and
the NCDC modeled solar radiation for the years 1984 -- 1995.

1984

![](media/image12.pct)

1985 1986

![](media/image13.pct)![](media/image14.pct)

1987 1988

![](media/image15.pct)![](media/image16.pct)

1989 1990

![](media/image17.pct)![](media/image18.pct)

1991 1992

![](media/image19.pct)![](media/image20.pct)

1993 1994 ![](media/image21.pct)![](media/image22.pct)

1995

> ![](media/image23.pct)

![](media/image24.pct)

Figure 5. Grain weight maximums predicted by DSSAT for the ground based
solar observations, SSE/POWER solar data and NCDC modeled results.