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Development and Interpretation of a Neural Network-Based Synthetic Radar
  Reflectivity Estimator Using GOES-R Satellite Observations

Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations

Journal of Applied Meteorology and Climatology (JAMC), 2020
16 April 2020
Kyle Hilburn
I. Ebert‐Uphoff
S. Miller
ArXiv (abs)PDFHTML

Papers citing "Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations"

15 / 15 papers shown
RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts
RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts
Xuming He
Zhiyuan You
Junchao Gong
Couhua Liu
Xiaoyu Yue
Peiqin Zhuang
Wenlong Zhang
Wenlong Zhang
130
9
0
17 Aug 2025
DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from
  Satellite Observations
DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations
Xuming He
Zhiwang Zhou
Wenlong Zhang
Xiangyu Zhao
Hao Chen
Shiqi Chen
Junlin Wu
150
8
0
11 Nov 2024
Prototype-Based Methods in Explainable AI and Emerging Opportunities in
  the Geosciences
Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences
Anushka Narayanan
Karianne J. Bergen
363
9
0
22 Oct 2024
SRViT: Vision Transformers for Estimating Radar Reflectivity from
  Satellite Observations at Scale
SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
Jason Stock
Kyle Hilburn
Imme Ebert-Uphoff
Charles Anderson
377
5
0
20 Jun 2024
The Transformation Risk-Benefit Model of Artificial Intelligence:
  Balancing Risks and Benefits Through Practical Solutions and Use Cases
The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing Risks and Benefits Through Practical Solutions and Use Cases
Richard Fulton
Diane Fulton
Nate Hayes
Susan Kaplan
135
7
0
11 Apr 2024
Interpretable Machine Learning for Weather and Climate Prediction: A
  Survey
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Ruyi Yang
Jingyu Hu
Zihao Li
Jianli Mu
Tingzhao Yu
Jiangjiang Xia
Xuhong Li
Aritra Dasgupta
Haoyi Xiong
AI4CE
359
9
0
24 Mar 2024
Transformer-based nowcasting of radar composites from satellite images
  for severe weather
Transformer-based nowcasting of radar composites from satellite images for severe weatherArtificial Intelligence for the Earth Systems (AIES), 2023
Çaglar Küçük
Apostolos Giannakos
Stefan Schneider
Alexander Jann
378
9
0
30 Oct 2023
Finding the right XAI method -- A Guide for the Evaluation and Ranking
  of Explainable AI Methods in Climate Science
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate ScienceArtificial Intelligence for the Earth Systems (AI4ES), 2023
P. Bommer
M. Kretschmer
Anna Hedström
Dilyara Bareeva
Marina M.-C. Höhne
409
63
0
01 Mar 2023
A Machine Learning Tutorial for Operational Meteorology, Part II: Neural
  Networks and Deep Learning
A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep LearningWeather and forecasting (WAF), 2022
Randy J. Chase
David R. Harrison
G. Lackmann
A. McGovern
253
19
0
31 Oct 2022
Developing a Series of AI Challenges for the United States Department of
  the Air Force
Developing a Series of AI Challenges for the United States Department of the Air ForceIEEE Conference on High Performance Extreme Computing (HPEC), 2022
V. Gadepally
Greg Angelides
Andrei Barbu
Andrew Bowne
L. Brattain
...
Mark S. Veillette
Matthew L. Weiss
Allan B. Wollaber
S. Yuditskaya
J. Kepner
AI4CE
216
6
0
14 Jul 2022
A Machine Learning Tutorial for Operational Meteorology, Part I:
  Traditional Machine Learning
A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning
Randy J. Chase
David R. Harrison
Amanda L. Burke
G. Lackmann
A. McGovern
AI4CE
156
53
0
15 Apr 2022
Investigating the fidelity of explainable artificial intelligence
  methods for applications of convolutional neural networks in geoscience
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscienceArtificial Intelligence for the Earth Systems (AI4ES), 2022
Antonios Mamalakis
E. Barnes
I. Ebert‐Uphoff
296
94
0
07 Feb 2022
The Need for Ethical, Responsible, and Trustworthy Artificial
  Intelligence for Environmental Sciences
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences
A. McGovern
I. Ebert‐Uphoff
D. Gagne
A. Bostrom
304
83
0
15 Dec 2021
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental
  Sciences -- Version 1
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1
I. Ebert‐Uphoff
Ryan Lagerquist
Kyle Hilburn
Yoonjin Lee
Katherine Haynes
Jason Stock
C. Kumler
J. Stewart
204
25
0
17 Jun 2021
Evaluation, Tuning and Interpretation of Neural Networks for
  Meteorological Applications
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological ApplicationsBulletin of The American Meteorological Society - (BAMS) (BAMS), 2020
I. Ebert‐Uphoff
Kyle Hilburn
281
31
0
06 May 2020
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