no code implementations • 22 Oct 2022 • Akansha Singh Bansal, Yoonjin Lee, Kyle Hilburn, Imme Ebert-Uphoff
Then we provide an overview of many different concepts and techniques that are helpful for the interpretation of meteorological image sequences, such as (1) feature engineering methods to strengthen the desired signal in the input, using meteorological knowledge, classic image processing, harmonic analysis and topological data analysis (2) explain how different convolution filters (2D/3D/LSTM-convolution) can be utilized strategically in convolutional neural network architectures to find patterns in both space and time (3) discuss the powerful new concept of 'attention' in neural networks and the powerful abilities it brings to the interpretation of image sequences (4) briefly survey strategies from unsupervised, self-supervised and transfer learning to reduce the need for large labeled datasets.
no code implementations • 19 Aug 2022 • Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly.
1 code implementation • 21 Jul 2022 • Lander Ver Hoef, Henry Adams, Emily J. King, Imme Ebert-Uphoff
One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find, but why those results are to be expected given what we know about the theory of persistent homology.
no code implementations • 21 Mar 2022 • Ryan Lagerquist, Imme Ebert-Uphoff
We provide a general guide to using SELFs, including technical challenges and the final Python code, as well as demonstrating their use for the convection problem.
1 code implementation • 7 Feb 2022 • Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiotemporal patterns.
no code implementations • 15 Dec 2021 • Amy McGovern, Imme Ebert-Uphoff, David John Gagne II, Ann Bostrom
In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system.
no code implementations • 17 Jun 2021 • Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn, Yoonjin Lee, Katherine Haynes, Jason Stock, Christina Kumler, Jebb Q. Stewart
Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification.
1 code implementation • 18 Mar 2021 • Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes
Here, we provide a framework, based on the use of additively separable functions, to generate attribution benchmark datasets for regression problems for which the ground truth of the attribution is known a priori.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
no code implementations • 6 May 2020 • Imme Ebert-Uphoff, Kyle A. Hilburn
Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology.
no code implementations • 16 Apr 2020 • Kyle A. Hilburn, Imme Ebert-Uphoff, Steven D. Miller
Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques.
no code implementations • 4 Dec 2019 • Benjamin A. Toms, Elizabeth A. Barnes, Imme Ebert-Uphoff
As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason.
no code implementations • 13 Nov 2017 • Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, Vipin Kumar
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet.
no code implementations • 12 Sep 2017 • Jamal Golmohammadi, Imme Ebert-Uphoff, Sijie He, Yi Deng, Arindam Banerjee
We compare ACLIME-ADMM with baselines on both synthetic data simulated by partial differential equations (PDEs) that model advection-diffusion processes, and real data (50 years) of daily global geopotential heights to study information flow in the atmosphere.
no code implementations • 27 Dec 2015 • Imme Ebert-Uphoff, Yi Deng
Causal discovery algorithms based on probabilistic graphical models have emerged in geoscience applications for the identification and visualization of dynamical processes.