The Users’ Perspective: How are Energy Utilities and Developers Using Earth Observation Data to Address Climate Resilience and Support Renewable Energy?


Energy utilities and power project developers are increasingly recognizing the climate-sensitivity of their day-to-day operations and planning on the 20-30 year timescale. Solar and wind power project developers require resource data and forecasts for project design, operations, and financing. Increasingly sophisticated software and models from value-added providers and in-house meteorology groups at some utilities rely on Earth observations from NASA, NOAA, and other sources to support their decisions.  Hear from energy sector end users on their current and emerging decision-support needs focused on a climate resilient infrastructure and renewable energy development. A summary of a recent NASA-sponsored workshop on this topic will provide context for the panel discussion.


Presentation: Challenges and opportunities in the growing solar energy industry

Motivation: 1. Solar has a huge potential, such as energy for home, reducing carbon; 2. Clouds are the greatest challenges;
Solar Resource Assessment: a function of irradiance values at the location; Loass assumptions; Temperature, wind and other met variables are also important; wind speed;
Challenges: Radiative transfer in clouds (high spatial and temporal variability); Clouds are among the main modulators of radiation;Cloud shadow; Aerosols vary in time and in space; Precipitable water varies in space and time
NASA products

  •   Modis products:
    • data quality: a lot of missing data leads to the use of interpolation; no data over desert areas -> MISR better at desert areas but too high aerosol values in snow covered and bright desert area
  • Clean Power Research:
    • Solar prediction: different spatial and temporal resolution
    • NWP models primarily used for a day ahead solar forecast
      • Model biases, lack of computational capability, difficult to accurately simulate
    • Satellite Cloud Motion Vector(CMV) forecasting methodology
    • Why Bother w/Forecasting Behind-The-Meter PV?

Opportunities for NASA:

  • A better radiative transfer model
  • High spatial and temporal resolution of inputs
  • Algorithm to get AOD at a channel suitable for Solar Energy Application

Presentation: Weather and Climate Risk in the Electric Power Sector
Energy and Climate
: how weather and climate impact energy?

  • Electricity demand: heating, cooling (temperature, humidity, wind speeds)
    • Natural gas, coal, nuclear, …
  • Electricity supply
    • Transmission lines (temperature, humidity, wind speeds)
    • Thermal power plants (temperature, hydrology)
    • Variable renewable energy: Energy storage capability of hydroelectric dams degrades at coarser time resolution;
  • PV solar production depends on irradiation, but also depends on temperatures

Integrating weather data in power system modeling

  • Synthetic time series generation (temperature, ) -> supervised machine learning : demand renewable generation system outages -> optimization: system operations, long term planning
  • Weather -> system operations and risk management
    • Financial risk management via index insurance -> payout determined by value of agreed-upon “index”

Climate -> long term infrastructure planning and performance

  • Non-stationarity
  • The role of climate in choice of generation to build

Presentation: Source meteorology and solar energy: a renewable energy resource web site
Data access: monthly and daily data table
Atmospheric science data center: demo 1) how to access the data 2) how to publish data to ArcGIS online;


Eckman, R.; Zell, E.; The Users’ Perspective: How are Energy Utilities and Developers Using Earth Observation Data to Address Climate Resilience and Support Renewable Energy?; 2016 ESIP Summer Meeting. ESIP Commons , April 2016