引用次数
引用: 5,719
H-指数: 38
I10-指数 : 80
出版物: 243
Development and interpretation of a neural-network-based synthetic radar reflectivity estimator using GOES-R satellite observations
Kyle A Hilburn , Imme Ebert-Uphoff , Steven D Miller
Journal of Applied Meteorology and Climatology 60 ( 1) 3 -21
64
2020
The outlook for AI weather prediction
Imme Ebert-Uphoff , Kyle Hilburn
Nature 619 ( 7970) 473 -474
14
2023
Evaluation, tuning and interpretation of neural networks for meteorological applications
Imme Ebert-Uphoff , Kyle A Hilburn
arXiv preprint arXiv:2005.03126
6
2020
Using neural network methods to estimate cloud properties from satellite imagery challenges and recent approaches
Imme Ebert-Uphoff , Kyle Hilburn , Ryan Lagerquist , Yoonjin Lee
AGU Fall Meeting Abstracts 2021 H21E -01
1
2021
A research agenda for the evaluation of AI-based weather forecasting models (Core Science Keynote)
Imme Ebert-Uphoff , Jebb Q Stewart , Kyle A Hilburn , Jacob T Radford
104th AMS Annual Meeting
2024
Exploring Tropical Cyclone Structure and Evolution through Machine Learning Applications
Kate D Musgrave , Stephanie Stevenson , Kyle Hilburn , Benjamin Trabing
AGU23
2023
Using optical flow to remove storm motion from GOES-16 image sequences to help machine learning algorithms detect convection
Yoonjin Lee , Jason Apke , Imme Ebert-Uphoff , Kyle Hilburn
한국기상학회 학술대회 논문집 261 -261
2022
How to Develop Custom Loss Functions for Neural Networks in Meteorology
Imme Ebert-Uphoff , Ryan Lagerquist , Kyle A Hilburn , Yoonjin Lee
102nd American Meteorological Society Annual Meeting
2022
Demonstrations of New Dense Optical Flow Applications for Geostationary Satellite Imagery
Jason Apke , Steven D Miller , Matthew A Rogers , Kyle Hilburn
AGU Fall Meeting Abstracts 2020 A008 -0023
2020
Opening the" Black Box": Tools to Improve Understanding of Neural Network Reasoning for Geoscience Applications
Imme Ebert-Uphoff , Kyle A Hilburn , Benjamin A Toms , Elizabeth A Barnes
AGU Fall Meeting Abstracts 2019 A51U -2666
2019
Evaluation, Tuning and Interpretation of Neural Networks for Working
Imme Ebert-Uphoff , Kyle Hilburn
Low cloud detection in multilayer scenes using satellite imagery with machine learning methods
John M Haynes , Yoo-Jeong Noh , Steven D Miller , Katherine D Haynes
Journal of Atmospheric and Oceanic Technology 39 ( 3) 319 -334
11
2022
Recent Advances in Vertical Cloud Structure Determination and Visualization from Passive Satellite Sensors
John M Haynes , Yoo-Jeong Noh , Brandon John Daub , Matthew Niznik
Collective Madison Meeting
2022
Conceptual design of a generalized stereolithography machine
Brad Geving , Alok Kataria , Chad Moore , Imme Ebert-Uphoff
Japan-USA Symposium on Flexbile Automation
16
2000
Aiding tropical cyclone forecasting by simulating 89-GHz imagery from operational geostationary satellites
Katherine Haynes , Christopher Slocum , John Knaff , Kate Musgrave
35th Conference on Hurricanes and Tropical Meteorology
4
2022
What Can Machine Learning Methods Tell Us About the Tropical Cyclone Intensity Forecasting Problem?
Marie McGraw , Kate Musgrave , John Knaff , Christopher Slocum
35th Conference on Hurricanes and Tropical Meteorology
1
2022
Aiding Tropical Cyclone Forecasting by Creating Synthethic 89-and 37-GHz Imagery from Operational Geostationary Satellites
Katherine Haynes , Christopher Slocum , John Knaff , Kate Musgrave
36th Conference on Hurricanes and Tropical Meteorology
2024
GeoCenter: Real-Time Deep Learning with Uncertainty Quantification for Center-Fixing of Tropical Cyclones
Ryan A Lagerquist , Galina Chirokova , Robert DeMaria , Mark DeMaria
36th Conference on Hurricanes and Tropical Meteorology
2024
ProxyVis Geostationary Satellite Imagery: Current product Status and Future Development
Galina Chirokova , Robert T DeMaria , Alan Brammer , Imme Ebert-Uphoff
104th AMS Annual Meeting
2024
Exploring Tropical Cyclone Structure and Evolution with AI-based Synthetic Passive Microwave Data
Marie McGraw , Katherine Haynes , Kate D Musgrave , Imme Ebert-Uphoff
104th AMS Annual Meeting
2024