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ML Overview

ML Activities Related to Satellites

CIRA has a long history of developing satellite products.  Machine learning (ML), also often called Artificial Intelligence (AI), has emerged as a powerful tool to process and extract information from satellites.  Thus many of CIRA’s machine learning activities are related to satellites.  Topics include:

  • Detecting convection from satellite imagery (for NWP data assimilation),
  • Nowcasting convection from satellite imagery (for severe weather forecasting)
  • Using satellite imagery to infer cloud properties (for aviation)
  • Using satellite imagery to improve estimates of vertical profiles (for aviation),
  • Image-to-image translation:
    • Generating simulated radar imagery from geostationary satellite imagery (for severe weather forecasting)
    • Generating simulated passive microwave imagery from geostationary satellite imagery (for tropical cyclones)
  • Anticipating rapid intensification of tropical cyclones

To learn more about these activities, visit ML4Clouds and ML4TC

CIRA’s long history of working with satellites

Satellite Earth Station

With NOAA support, CIRA has operated a Geostationary Operational Environmental Satellite (GOES) Earth Station since 1980.  Today our collection capability handles three simultaneous GOES transmissions, NOAA polar data and European MSG.  CIRA also plays an important role in each new GOES GVAR satellite as one of the primary test sites for initial transmissions and sensor verification.

(On right:  Satellite dishes at CIRA headquarters, Fort Collins, providing real-time satellite data feeds.)

Satellite Products

Many of CIRA’s satellite products are publicly available here:
Check out SLIDER at
(For starters, switch “sector” from “Full disk” to “Conus”, then choose a “Product” to view different satellite products, e.g., from GOES-16, in real time.)

Tropical Cyclones

CIRA and the Atmospheric Science Department at CSU also have a long and strong history of research on tropical cyclones which we leverage for some of the satellite activities described above

ML Activities Related to Numerical Weather Prediction (NWP) Models

CIRA’s ML activities related to NWP models fall into two categories:

  1. Extracting information from satellites for assimilation into NWP models.
    For more information, see activities outlined for satellite tasks above.
  2. Using ML to speed up NWP models by emulating those physics equations that are particularly slow, namely radiative transfer, by much faster ML approaches.
    For more information on this topic see ML4RT.
  3. Using ML to combine satellite data and NWP model output to improve vertical profiles of temperature and dewpoint.
    For more information, see ML4Soundings.

Our ML Approaches and Expertise

Using machine learning to inform life-and-death decision making, e.g., to make decisions on which regions to evacuate, is very different from using machine learning for standard computer science applications. In particular, in order for machine learning approaches to be accepted into operational use for weather forecasting, they must be as robust and interpretable as possible.

We believe the best way to achieve robustness and interpretability is to make ML models as simple as possible, using any of the following approaches:

Use of an attribution method, namely Layer-Wise Relevance Propagation (LRP), to analyze a neural network model that seeks to translate imagery from the Geostationary Operational Environmental Satellite into Multi-Radar Multi-Sensor (MRMS) imagery. LRP results (bottom row) show where in the input channels the neural network is focusing when predicting the MRMS value for a single (central) pixel.  See Ebert-Uphoff and Hilburn (2020) for details.

CIRA Resources for the Community

A) Short courses for Atmospheric Scientists to get started in Machine Learning

CIRA Short Course on Machine Learning for Weather and Climate:

CIRA/AI2ES Short Course on Explainable AI:

The Roebber Lectures:

B) Papers / Reports on Foundational Topics: