• About
  • Sections
  • Topics
    • Climate
    • Earth Science
    • Oceans
    • Space & Planets
    • Health & Ecosystems
    • Culture & Policy
    • Education & Careers
    • Opinions
  • Projects
    • ENGAGE
    • Editors’ Highlights
    • Editors’ Vox
    • Eos en Español
    • Eos 简体中文版
    • Print Archive
  • Science Policy Tracker
  • Blogs
    • Research & Developments
    • The Landslide Blog
  • Newsletter
  • Submit to Eos
  • AGU.org
  • Career Center
  • Join AGU
  • Give to AGU
  • About
  • Sections
  • Topics
    • Climate
    • Earth Science
    • Oceans
    • Space & Planets
    • Health & Ecosystems
    • Culture & Policy
    • Education & Careers
    • Opinions
  • Projects
    • ENGAGE
    • Editors’ Highlights
    • Editors’ Vox
    • Eos en Español
    • Eos 简体中文版
    • Print Archive
  • Science Policy Tracker
  • Blogs
    • Research & Developments
    • The Landslide Blog
  • Newsletter
  • Submit to Eos
Skip to content
  • AGU.org
  • Career Center
  • Join AGU
  • Give to AGU
Eos

Eos

Science News by AGU

Support Eos
Sign Up for Newsletter
  • About
  • Sections
  • Topics
    • Climate
    • Earth Science
    • Oceans
    • Space & Planets
    • Health & Ecosystems
    • Culture & Policy
    • Education & Careers
    • Opinions
  • Projects
    • ENGAGE
    • Editors’ Highlights
    • Editors’ Vox
    • Eos en Español
    • Eos 简体中文版
    • Print Archive
  • Science Policy Tracker
  • Blogs
    • Research & Developments
    • The Landslide Blog
  • Newsletter
  • Submit to Eos

machine learning & AI

An illustration of rainfall estimates from ground-based radar and spaceborne Tropical Rainfall Measuring Mission (TRMM) radar
Posted inEditors' Highlights

Machine Learning Improves Satellite Rainfall Estimates

by Valeriy Ivanov 31 October 201925 July 2022

A new deep learning approach bridges ground rain gauge and radar data with spaceborne radar observations of Tropical Rainfall Measuring Mission to improve precipitation estimation.

Before choosing an appropriate artificial intelligence approach for an Earth science application, key questions must be considered
Posted inOpinions

Thoughtfully Using Artificial Intelligence in Earth Science

by I. Ebert-Uphoff, S. M. Samarasinghe and E. A. Barnes 11 October 201915 October 2019

Deriving scientific insights from artificial intelligence methods requires adhering to best practices and moving beyond off-the-shelf approaches.

A global map of ocean temperature during the 2016 El Niño event
Posted inNews

Artificial Intelligence May Help Predict El Niño

Jenessa Duncombe, Staff Writer by Jenessa Duncombe 25 September 20195 July 2022

Deep learning techniques give scientists the longest–lead time forecasts yet.

An ominous dark cloud gathers above a dirt road
Posted inNews

Finding Faces in Hailstorms

Mary Caperton Morton, Science Writer by Mary Caperton Morton 13 September 20198 March 2022

Machine learning technology helps scientists recognize severe weather patterns.

Phytoplankton under a scanning electron microscope
Posted inNews

Artificial Intelligence Can Spot Plankton from Space

Jenessa Duncombe, Staff Writer by Jenessa Duncombe 6 September 20191 February 2023

Training an algorithm with satellite images of ocean color reveals the blooms and busts of phytoplankton communities.

Hurricane Michael approaches the coastline of the Florida Panhandle on 10 October 2018.
Posted inOpinions

Artificial Intelligence May Be Key to Better Weather Forecasts

by S.-A. Boukabara, V. Krasnopolsky, J. Q. Stewart, S. G. Penny, R. N. Hoffman and E. Maddy 1 August 20195 October 2021

Recent advances in machine learning hold great potential for converting a deluge of data into weather forecasts that are fast, accurate, and detailed.

Ship tracks (linear cloud features) seen over the Pacific Ocean.
Posted inNews

Algorithm Spots Climate-Altering Ship Tracks in Satellite Data

Katherine Kornei, Science Writer by Katherine Kornei 23 July 201918 October 2022

Tens of thousands of ship tracks—cloud structures created when ships’ exhaust plumes interact with the atmosphere—are pinpointed automatically, furthering study of these climate-altering features.

Futuristic interface concept. - Stock image
Posted inScience Updates

Machine Learning in Geoscience: Riding a Wave of Progress

by Daniel T. Trugman, G. C. Beroza and P. A. Johnson 3 May 201913 January 2022

2nd Annual Machine Learning in Solid Earth Geoscience Conference; Santa Fe, New Mexico, 18–22 March 2019

Satellite image of a fire in Northern California
Posted inNews

New Eyes on Wildfires

Jon Kelvey, Science Writer by Jon Kelvey 30 April 20192 July 2025

Onboard machine learning and compact thermal imaging could turn satellites into real-time fire management tools to help officials on the ground.

Global map of the dominant cycles in surface partial pressure of carbon dioxide
Posted inEditors' Highlights

Sea-Surface Carbon Patterns Linked to Large-scale Climate Modes

by J. Sprintall 2 April 201927 September 2022

A new 34-year global time series of observed sea surface partial pressure of CO2 links regional variation to major climate modes.

Posts pagination

Newer posts 1 … 16 17 18 19 20 Older posts
A view of a Washington, D.C., skyline from the Potomac River at night. The Lincoln Memorial (at left) and the Washington Monument (at right) are lit against a purple sky. Over the water of the Potomac appear the text “#AGU24 coverage from Eos.”

Features from AGU Publications

Research Spotlights

Droughts Sync Up as the Climate Changes

18 September 202518 September 2025
Editors' Highlights

Unexpected Carbonate Phase Revealed by Advanced Simulations

25 September 2025
Editors' Vox

How Glacial Forebulges Shape the Seas and Shake the Earth

23 September 202519 September 2025
Eos logo at left; AGU logo at right

About Eos
ENGAGE
Awards
Contact

Advertise
Submit
Career Center
Sitemap

© 2025 American Geophysical Union. All rights reserved Powered by Newspack