2010 CORM Conference

Dr. James HallJHtech

Nature’s Solar Power Plants

Agriculture Needs Better Solar Data for Modeling

Solar Radiation Measurements

Satellite-based of solar radiation data:

• 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.

However:

• Near-Real time

• Provides Global Coverage

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

Satellite Measurements

Average Error of Satellite ObservationsRMS error of 35W/m2 = 0.84 kWh/m2/day

20 % Error

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

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

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.

Solar Data Warehouse

SDW data shows much greater discrimination of solar variations

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

Relative Accuracy

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

Under Development

• Improved Solar ForecastsImproved Solar Forecasts

• In-season Crop Growth ModelsIn-season Crop Growth Models

• Unique Solar AtlasUnique Solar Atlas

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

Crop Physiological Growth Model

Accurate Yield

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

Detailed Climate & Soil Data

Crop Growth Models

Crop Physiological Growth Model

Accurate Yield

Forecast

• 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.

Detailed Climate & Soil Data

Regional Adjustments

Grower Adjustments

Crop Growth Models

Solar-Atlas.blogspot.com

Unique Solar Atlas

Near real time using measured data from 3000 stations

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