Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

Research output: Contribution to conferencePaperpeer-review


With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

Original languageAmerican English
Number of pages4
StatePublished - 20 Jun 2021
Event48th IEEE Photovoltaic Specialists Conference, PVSC 2021 - Fort Lauderdale, United States
Duration: 20 Jun 202125 Jun 2021


Conference48th IEEE Photovoltaic Specialists Conference, PVSC 2021
Country/TerritoryUnited States
CityFort Lauderdale

Bibliographical note

See NREL/CP-6A20-79705 for preprint

NREL Publication Number

  • NREL/CP-6A20-81255


  • cloud properties
  • machine learning
  • physics-guided neural networks
  • remote sensing
  • satellite-derived irradiance
  • solar resource data


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