Imme Ebert-Uphoff

Imme Ebert-Uphoff

Colorado State University

H-index: 37

North America-United States

About Imme Ebert-Uphoff

Imme Ebert-Uphoff, With an exceptional h-index of 37 and a recent h-index of 25 (since 2020), a distinguished researcher at Colorado State University, specializes in the field of Causality, machine learning, earth sciences..

His recent articles reflect a diverse array of research interests and contributions to the field:

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography

Identifying and Categorizing Bias in AI/ML for Earth Sciences

A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models

Using Grouped Features to Improve Explainable AI Results for Atmospheric AI Models that use Gridded Spatial Data and Complex Machine Learning Technique

Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture

Super-Resolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network

GREMLIN: GOES Radar Estimation via Machine Learning to Inform NWP

Machine-learned uncertainty quantification is not magic: Lessons learned from emulating radiative transfer with ML

Imme Ebert-Uphoff Information

University

Position

___

Citations(all)

5257

Citations(since 2020)

2404

Cited By

3487

hIndex(all)

37

hIndex(since 2020)

25

i10Index(all)

74

i10Index(since 2020)

46

Email

University Profile Page

Colorado State University

Google Scholar

View Google Scholar Profile

Imme Ebert-Uphoff Skills & Research Interests

Causality

machine learning

earth sciences.

Top articles of Imme Ebert-Uphoff

Title

Journal

Author(s)

Publication Date

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography

AI Magazine

Amy McGovern

Imme Ebert‐Uphoff

Elizabeth A Barnes

Ann Bostrom

Mariana G Cains

...

2024/2

Identifying and Categorizing Bias in AI/ML for Earth Sciences

Bulletin of the American Meteorological Society

Amy McGovern

Ann Bostrom

Marie McGraw

Randy J Chase

David John Gagne

...

2024/1/22

A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models

Imme Ebert-Uphoff

Jebb Q Stewart

Jacob T Radford

2024/3/7

Using Grouped Features to Improve Explainable AI Results for Atmospheric AI Models that use Gridded Spatial Data and Complex Machine Learning Technique

Authorea Preprints

Evan Krell

Philippe Tissot

Antonios Mamalakis

Waylon Collins

Imme Ebert-Uphoff

...

2024/2/15

Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture

Artificial Intelligence for the Earth Systems

Katherine Haynes

Jason Stock

Jack Dostalek

Charles Anderson

Imme Ebert-Uphoff

2024/1

Super-Resolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network

Artificial Intelligence for the Earth Systems

Charles H White

Imme Ebert-Uphoff

John M Haynes

Yoo-Jeong Noh

2024/2/14

GREMLIN: GOES Radar Estimation via Machine Learning to Inform NWP

Kyle Aaron Hilburn

2023

Machine-learned uncertainty quantification is not magic: Lessons learned from emulating radiative transfer with ML

Authorea Preprints

Ryan Lagerquist

Imme Ebert-Uphoff

David D Turner

Jebb Q Stewart

2023/11/14

Machine learning for clouds and climate

Clouds and their climatic impacts: Radiation, circulation, and precipitation

Tom Beucler

Imme Ebert‐Uphoff

Stephan Rasp

Michael Pritchard

Pierre Gentine

2023/12/19

The Role of Earth System Interactions in Large-Scale Atmospheric Circulation and Climate

Simchan Yook

2023

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences

Ann Bostrom

Julie L Demuth

Christopher D Wirz

Mariana G Cains

Andrea Schumacher

...

2023/11/8

Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks

Environmental Data Science

Akansha S Bansal

Yoonjin Lee

Kyle Hilburn

Imme Ebert-Uphoff

2023/1

Creating and evaluating uncertainty estimates with neural networks for environmental-science applications

Artificial Intelligence for the Earth Systems

Katherine Haynes

Ryan Lagerquist

Marie McGraw

Kate Musgrave

Imme Ebert-Uphoff

2023/4

Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity

Journal of Atmospheric and Oceanic Technology

Ryan Lagerquist

David D Turner

Imme Ebert-Uphoff

Jebb Q Stewart

2023/11

Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience

Artificial Intelligence for the Earth Systems

Antonios Mamalakis

Elizabeth A Barnes

Imme Ebert-Uphoff

2023/1

The outlook for AI weather prediction

Imme Ebert-Uphoff

Kyle Hilburn

2023/7/20

A primer on topological data analysis to support image analysis tasks in environmental science

Artificial Intelligence for the Earth Systems

Lander Ver Hoef

Henry Adams

Emily J King

Imme Ebert-Uphoff

2023/1

Improving NHC’s Operational Intensity Guidance Suite and Situational Awareness with Better Metrics of Ocean-TC Interaction

103rd AMS Annual Meeting

Galina Chirokova

Gregory Foltz

John Kaplan

Debra Molenar

Imme Ebert-Uphoff

...

2023/1/10

Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School

Bulletin of the American Meteorological Society

Amy McGovern

David John Gagne

Christopher D Wirz

Imme Ebert-Uphoff

Ann Bostrom

...

2023/6

Assessing the State-Dependency of Infrared Satellite Precipitation Errors

Eric Goldenstern

2022

See List of Professors in Imme Ebert-Uphoff University(Colorado State University)