1. 2010 CORM Conference
Dr. James Hall
JHtech

2. Nature’s Solar Power Plants
Agriculture Needs Better Solar Data for Modeling

3. Solar Radiation Measurements
Satellite-based of solar radiation data:
• Near-Real time
• Provides Global Coverage
However:
• Cannot distinguish between clouds and snow cover.
• Measurements are less accurate near mountains,
oceans or other large bodies of water.
• Measurements are made at the top of the atmosphere
and require models to estimate the solar radiation at
the ground.

4. Solar Radiation Measurements
Accuracy of solar radiation data:
• Highest quality research sites: 3-6% error
• Routine operational ground sites: 6-12% error
• Satellite observations: 20% error based on NASA
estimates (35 W/m2 RMS)
• Satellite observations: 19% error based on third party
estimates
Ground-based measurements are clearly
more accurate than satellite data

5. Satellite Measurements
20 % Error
Average Error of Satellite Observations
RMS error of 35W/m2 = 0.84 kWh/m2/day

6. Solar Radiation Measurements
National Solar Radiation Database:
• Measurements from only 40 high-quality stations,
remaining 1414 locations were modeled.
• Not real-time, latest series is 2003-2005.
• Accuracy not published by NREL
• Intended to be statistically representative, not
historically accurate!*
* User Manual for National Solar Radiation Database

7. Solar Data Warehouse
Largest agro-climate database:
• Hourly & Daily data for last 5-20 years at 3000+ US locations
• Soils, Weather, Evapotranspiration, Solar, Soil Temp
• Multiple Layers of Quality Control
• Near real-time
• Lowest error of any national solar radiation source

8. Solar Data Warehouse
Hourly & Daily data on temperature, precipitation, humidity, wind speed, solar
radiation, evapo-transpiration for 3000 US locations. Soil temperature is also
available for many locations.

9. Solar Data Warehouse
SDW data shows much greater discrimination of solar variations

10. Solar Data Warehouse
Our Data Sources:
• Over 30 different networks across the US.
• Run by federal agencies, states and
universities for their own specific purposes
• Many different formats & no bulk access
• Medium-quality sensors
• Little or no quality control

11. Relative Accuracy

12. Relative Accuracy
Using measured data from 10 locations from the Solar Data Warehouse as the
baseline, we calculated the Average Daily Error in the National Solar Radiation
Database. We also calculated the average error for a second, nearby station from
the Solar Data Warehouse

13. Under Development
• Improved Solar Forecasts
• In-season Crop Growth Models
• Unique Solar Atlas

14. Self-Improving Forecasts
• Combine published
techniques for cloud &
climate modeling
• Compare forecast to
actual solar radiation
• Feed back error based
on near-real time station
data

15. Crop Growth Models
Accurate
Detailed Climate & Crop Physiological
Yield
Soil Data Growth Model
Forecast
• 15 years ago, researchers demonstrated accurate yield
forecasts by modeling day-to day crop growth.
• Past models have only proven accurate for a specific
region.
• Accurate, near-real time data on Solar Radiation has
always been a limiting factor

16. Crop Growth Models
Accurate
Detailed Climate & Crop Physiological
Yield
Soil Data Growth Model
Forecast
Regional Grower
Adjustments Adjustments
• Artificial Intelligence technology is used to discover the
correct parameters for different regions and grower
practices.
• Currently developing calibrated corn growth models
for all counties in the US.

17. Unique Solar Atlas
Solar-Atlas.blogspot.com
Near real time using measured data from 3000 stations

18. References and additional information on the material in this presentation can
be found at: http://www.solardatawarehouse.com/WhitePaper.pdf