From f686451f40fa4dfcfda697a52504c2caa112839a Mon Sep 17 00:00:00 2001 From: David John Gagne Date: Wed, 7 Aug 2024 09:58:59 -0500 Subject: [PATCH] Fixed a buch of things on the website --- config.yml | 21 +-- content/about/_index.md | 11 -- content/current_projects/_index.md | 7 - content/current_projects/ai2es.md | 15 -- content/news/20220624-marshall-fire-vis.md | 22 --- content/news/20220624-vizaccess_prototype.md | 11 -- content/news/20220726-uxarray.md | 30 ---- content/software/bridgescaler.md | 11 ++ content/software/hagelslag.md | 4 +- content/software/milesguess.md | 11 ++ data/presentations.yml | 136 +++++++-------- data/publications.yml | 159 +++++++++++------- data/whatwedo.yml | 4 +- .../images/projects/ai2es-logo-curve-name.png | Bin 0 -> 695183 bytes static/images/projects/hagelslag_info.png | Bin 0 -> 333294 bytes themes/ncar/assets/scss/components/_blog.scss | 107 ++++++++++++ themes/ncar/assets/scss/style.scss | 5 +- .../ncar/layouts/current_projects/list.html | 23 --- themes/ncar/layouts/index.html | 3 + themes/ncar/layouts/publications/list.html | 2 +- 20 files changed, 318 insertions(+), 264 deletions(-) delete mode 100644 content/current_projects/_index.md delete mode 100644 content/current_projects/ai2es.md delete mode 100644 content/news/20220624-marshall-fire-vis.md delete mode 100644 content/news/20220624-vizaccess_prototype.md delete mode 100644 content/news/20220726-uxarray.md create mode 100644 content/software/bridgescaler.md create mode 100644 content/software/milesguess.md create mode 100644 static/images/projects/ai2es-logo-curve-name.png create mode 100644 static/images/projects/hagelslag_info.png create mode 100644 themes/ncar/assets/scss/components/_blog.scss delete mode 100644 themes/ncar/layouts/current_projects/list.html diff --git a/config.yml b/config.yml index 440807a..c856e21 100644 --- a/config.yml +++ b/config.yml @@ -21,8 +21,8 @@ menu: url : education weight : 1 - parent : what - name : Current Projects - url : current_projects + name : Projects + url : projects weight : 2 - parent : what name : Datasets @@ -41,9 +41,6 @@ menu: - name : Collaboration url : collaboration weight : 6 - - name : Real Time Products - url : realtimeproducts - weight : 7 # Site Params params: @@ -51,23 +48,23 @@ params: dateFormat: "26 FEB 1994" description: "MILES is the Machine Integration and Learning for Earth Systems group at the National Center for Atmospheric Research (NCAR)." author: "MILES" - email: "aiml@ucar.edu" + email: "miles@ucar.edu" news: - enable: true + enable: false title: RECENT NEWS subtitle: Some of our most recent news projects: enable: true title: RECENT PROJECTS - subtitle: Some of our most recent datasets + subtitle: Some of our most recent research projects footer: contactInfo: title : Contact Info address: 1850 Table Mesa Dr.
Boulder, CO 80305 - email : miles@ucar.edu + email : milescore@ucar.edu sitemap: title: Sitemap @@ -83,13 +80,11 @@ params: url : presentations - name: Collaboration url : collaboration - - name: Real Time Products - url : realtimeproducts - link: - name: Education url : education - - name: Current Projects - url : current_projects + - name: Projects + url : projects - name: Datasets url : datasets - name: Software diff --git a/content/about/_index.md b/content/about/_index.md index 4d9229e..b3cc778 100644 --- a/content/about/_index.md +++ b/content/about/_index.md @@ -10,14 +10,3 @@ The mission of the Machine Integration and Learning for Earth Systems (MILES) gr * Developing robust machine learning infrastructure and community benchmark evaluations and datasets that can interface with critical ESS modeling and observation systems. * Educating and empowering a diverse group of ESS students, researchers, and stakeholders on how best to integrate machine learning into their domains through tutorials, workshops, internships, collaborations, and co-development. -___ -CISL SPACEHOLDER -___ - -___ -Partners SPACEHOLDER - -AI2ES - -LEAP -___ \ No newline at end of file diff --git a/content/current_projects/_index.md b/content/current_projects/_index.md deleted file mode 100644 index 0a89b75..0000000 --- a/content/current_projects/_index.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -title : "Current Projects" -date : 2022-04-27T00:00:00-06:00 -image : images/banners/cloudSimulation.png ---- - - diff --git a/content/current_projects/ai2es.md b/content/current_projects/ai2es.md deleted file mode 100644 index bb7e1cc..0000000 --- a/content/current_projects/ai2es.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: "AI2ES" -date: 2024-08-06T12:00:00-06:00 -type: projects -image: "images/projects/esds.png" -category: ["RESEARCH"] -project_images: ["images/projects/esds.png"] ---- - -##### Term: 2020 - 2024 -##### Website: [https://ai2es.org](https://ai2es.org/) - -AI2ES is the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography. The vision of AI2ES is to create trustworthy Artificial Intelligence (AI) methods for diverse environmental science (ES) users that will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and create new educational pathways to develop a more diverse AI and environmental science workforce. - -MILES's role in AI2ES is in developing more physics-guided explainable AI methods, developing and evaluating more robust uncertainty quantification methods and deriving deeper physical understanding from analysis of the uncertainties. MILES has hosted multiple SIParCS interns funded by AI2ES and has co-led two Trustworthy AI for Environmental Science Summer Schools. diff --git a/content/news/20220624-marshall-fire-vis.md b/content/news/20220624-marshall-fire-vis.md deleted file mode 100644 index 65ede47..0000000 --- a/content/news/20220624-marshall-fire-vis.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: "Visualizations of the recent, devastating Marshall fire" -author: "John Clyne" -date: 2022-06-24T10:58:23-06:00 -type: news -image: images/blog/marshall-fire-vis1.png -feature_image: images/blog/marshall-fire-vis2.png -layout: staticpage ---- - -The most destructive wildfire in Colorado history was ignited on -December 30, 2021 and spread quickly to neighborhoods in Superior -and Louisville, driven by drought parched vegetation and winds -gusting over 100 miles per hour. Over a thousand homes were lost -and thousands of people were evacuated. Numerical models of wildfires -developed by NCAR are some of the most sophisticated in the world, -and are used by researchers and forecasters to help analyze, -interpret, and predict the impact of wildfires on weather, air -quality, and the economy. A recent simulation with NCAR’s Coupled -Atmosphere-Wildland Fire Environment (CAWFE) model attempts to -recreate the devastating events of the day and was visualized by -the [ViSR]() team [here](https://www.youtube.com/watch?v=hVgvjs20vrc&t=57s). diff --git a/content/news/20220624-vizaccess_prototype.md b/content/news/20220624-vizaccess_prototype.md deleted file mode 100644 index b8e5cfc..0000000 --- a/content/news/20220624-vizaccess_prototype.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: "Tactile representations of arctic sea ice data" -author: "Nihanth Cherukuru" -date: 2022-06-24T10:58:23-06:00 -type: news -image: images/blog/seaIceVideo.png -feature_image: images/blog/seaIce.png -layout: staticpage ---- - -Data visualizations are not the only way to communicate scientific insights; 3D printing & laser cutting offer an inclusive, hands-on way to share data. As part of a larger data visualization accessibility project, VAST researchers & educators are developing tactile data representations of arctic sea ice extant. Here, the September mean sea ice extent data simulation was 3D printed with the assistance of Boulder Public Library's BLDG61, including laser cutouts of changing sea ice at different time stamps. [Visit the project page to learn more about this work](https://ncar.github.io/AccessibleVisualizations/pages/seaice.html). diff --git a/content/news/20220726-uxarray.md b/content/news/20220726-uxarray.md deleted file mode 100644 index fe98aa0..0000000 --- a/content/news/20220726-uxarray.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -title: "UXarray ready for unstructured grids data analysis!" -author: "Orhan Eroglu" -date: 2022-07-26T09:03:23-06:00 -type: news -image: images/blog/uxarray/uxarray.png -feature_image: images/blog/uxarray/uxarray-feature.png -layout: staticpage ---- - -[Project Raijin](https://vast.ucar.edu/projects/raijin/) realizes -the unstructured grids data recognition and analysis through our -recently created, open-source, Python-based package, -[UXarray](https://github.com/UXARRAY/uxarray). - -We are thrilled to -announce that UXarray has made its official debut in June, 2022 via -[Conda](https://anaconda.org/conda-forge/uxarray) and -[PyPI](https://pypi.org/project/uxarray/) releases. So far, it has -had June and July versions, and is planned to be released monthly. - -UXarray, with its current versions, makes it possible to read in -unstructured meshes in UGRID, SCRIP, and Exodus formats, as well as -to execute analysis operators such as calculating individual/total -face areas and integrating over all the faces of the mesh. It can -even help visualize unstructured meshes if combined with a Python -visualization tool, as can be seen in the images of this blog post -that are created through our SIParCS 2022 internship by Philip -Chmielowiec. - diff --git a/content/software/bridgescaler.md b/content/software/bridgescaler.md new file mode 100644 index 0000000..8771c81 --- /dev/null +++ b/content/software/bridgescaler.md @@ -0,0 +1,11 @@ +--- +title : "Bridgescaler" +date : 2023-04-05T12:00:00-06:00 +type : software +tagline: "Robust, distributed data re-scaling at scale" +docs : "https://bridgescaler.readthedocs.io/en/latest/" +repo : "https://github.com/NCAR/bridgescaler" +image : "images/backgrounds/geocat-curly.png" +--- + +Bridgescaler is a package to support re-scaling of 2D and multi-dimensional data in parallel. \ No newline at end of file diff --git a/content/software/hagelslag.md b/content/software/hagelslag.md index 51b0278..2c84236 100644 --- a/content/software/hagelslag.md +++ b/content/software/hagelslag.md @@ -5,11 +5,11 @@ type : software tagline: "Storm Tracking and Verification" docs : "https://hagelslag.readthedocs.io/en/latest/" repo : "https://github.com/djgagne/hagelslag" -image : "images/backgrounds/geocat-curly.png" +image : "images/projects/hagelslag_info.png" --- Hagelslag is a Python package for tracking storms in numerical model output, performing machine learning post-processing for severe weather, and performing probabilistic verification. -Hagelslag development has been funded by the National Science Foundation and NOAA. \ No newline at end of file +Hagelslag development has been funded by the National Science Foundation and NOAA. diff --git a/content/software/milesguess.md b/content/software/milesguess.md new file mode 100644 index 0000000..ff04fa9 --- /dev/null +++ b/content/software/milesguess.md @@ -0,0 +1,11 @@ +--- +title : "MILES GUESS" +date : 2023-04-05T12:00:00-06:00 +type : software +tagline: "Generalized Uncertainty for Earth System Science (GUESS)" +docs : "https://pypi.org/project/miles-guess/" +repo : "https://github.com/NCAR/miles-guess" +image : "images/backgrounds/geocat-curly.png" +--- + +MILES GUESS provides Keras and Pytorch implementations of evidential deep learning models and supports analysis of aleatoric and epistemic uncertainties. \ No newline at end of file diff --git a/data/presentations.yml b/data/presentations.yml index 438804c..d37610e 100644 --- a/data/presentations.yml +++ b/data/presentations.yml @@ -5,395 +5,395 @@ items : - title: Uncertainty Quantification Decomposition through the Lens of Explainable AI authors: - - Becker, C. - - J. Schreck. - - D. J. Gagne. + - Becker, C. + - J. Schreck. + - D. J. Gagne. - M. Molina - - G. Gantos + - G. Gantos - Y. Sha conference: AMS Annual Meeting 2023 year: 2023 - title: Machine Learning Data Challenges across the Pipeline authors: - - D. J. Gagne. + - D. J. Gagne. conference: "Innovations in Open Science (IOS) Planning Workshop: Community Expectations for a Geoscience Data Commons" year: 2024 - title: Machine Learning Uncertainty Quantification and Numerical Weather Prediction authors: - - D. J. Gagne. + - D. J. Gagne. conference: "St. Andrews Vortex Dynamics Research Group Seminar Series" year: 2024 - title: From Machine Learning Parameterization to Full Model Emulation authors: - - D. J. Gagne. + - D. J. Gagne. conference: "Cambridge ICCS Seminar Series" year: 2024 - title: Machine Learning Uncertainty Quantification and Numerical Weather Prediction authors: - - D. J. Gagne. + - D. J. Gagne. conference: ECMWF AIFS Group Seminar year: 2024 - title: AI Numerical Weather Prediction and Uncertainty Quantification at NSF NCAR authors: - - D. J. Gagne. + - D. J. Gagne. conference: Met Office Seminar Series year: 2024 - title: Machine-Learning in climate modelling at NCAR authors: - - D. J. Gagne. + - D. J. Gagne. conference: Oxford AOPP Seminar Series year: 2024 - title: How We are Building Trustworthy AI for Weather and Climate in AI2ES authors: - - D. J. Gagne. + - D. J. Gagne. conference: Climate Informatics 2024 year: 2024 - title: Lessons Learned from Building Real-Time Machine Learning Testbeds for AI2ES authors: - - D. J. Gagne. + - D. J. Gagne. conference: 14th Conference on Transition of Research to Operations year: 2024 - title: "Machine Integration and Learning for Earth Systems (MILES): Bridging Key Gaps in Machine Learning for Earth System Science" authors: - - D. J. Gagne. + - D. J. Gagne. conference: 23rd Conference on Artificial Intelligence for Environmental Science year: 2024 - title: Machine Learning Uncertainty Quantification for Predicting Hazards and Impacts from Convection-Allowing LAMs authors: - - D. J. Gagne. + - D. J. Gagne. conference: 45th European Working Group on Limited-Area Modelling (EWGLAM) Meeting year: 2023 - title: Combining Uncertainty Quantification and XAI to Understand the Sensitivities of Deep Learning Winter Precipitation Type Predictions authors: - - D. J. Gagne. + - D. J. Gagne. conference: DoD Cloud Post-Processing and Verification Workshop year: 2023 - title: How NCAR/UCAR Teams Have Used Artificial Intelligence to Advance Earth System Science authors: - - D. J. Gagne. + - D. J. Gagne. conference: NCAR/UCAR Day of Science and Discovery year: 2023 - title: Understanding the Context of Machine Learning Uncertainty Quantification authors: - - D. J. Gagne. + - D. J. Gagne. conference: National Institute for Meteorological Studies Seminar year: 2023 - title: Bringing Meteorological AI to the People authors: - - D. J. Gagne. + - D. J. Gagne. conference: WMO Nowcasting and Mesoscale Research Working Group year: 2023 - title: AI/ML Data Challenges in the Earth Sciences authors: - - C. Becker. + - C. Becker. conference: Unidata Users Workshop - 2023 year: 2023 - title: Machine Learning Uncertainty Quantification for Assessing Weather Impacts authors: - - D. J. Gagne. + - D. J. Gagne. conference: World Meteorological Congress year: 2023 - title: Machine Learning for Real-time Prediction of Freezing Precipitation authors: - - C. Becker. + - C. Becker. conference: WINTRE-MIX Workshop year: 2023 - title: "Harnessing the Potential of Satellite Imagery: Machine Learning for Hazardous Weather Prediction" authors: - - J. Schreck. + - J. Schreck. conference: UCAR Remote Sensing Initiative Workshop 2023 year: 2023 - title: Semi-supervised and Transfer Learning - Applications of a Hybrid Approach for Prediction of Convective Storm Mode authors: - - C. Becker. + - C. Becker. conference: NOAA ML Working Group Meeting year: 2023 - title: Mimicking non-ideal instrument behavior for hologram processing using neural style translation authors: - - G. Gantos. + - G. Gantos. conference: NCAR - CSU Planning Meetup year: 2023 - title: Machine learning opportunities and challenges from the ncar analytics and integrative machine learning group authors: - - D. J. Gagne. + - D. J. Gagne. conference: 22nd Conference on Artificial Intelligence for Environmental Science year: 2023 - title: "Machine Learning Analysis of Convective Mode in Convection-Allowing Models: Updated Models and Real-Time Multi-Model Intercomparison" authors: - - C. Becker. + - C. Becker. conference: AMS Annual Meeting year: 2023 - title: Visualization and Trustworthiness of Machine Learning for Tornadic Storm Prediction authors: - - G. Gantos. + - G. Gantos. conference: AMS Annual Meeting year: 2023 - title: Uncertainty Quantification Decomposition through the lens of XAI authors: - - C. Becker. + - C. Becker. conference: AMS Annual Meeting year: 2023 - title: "Crisis: Convergent risk intelligence for severe impacts to society" authors: - - D. J. Gagne. + - D. J. Gagne. conference: 22nd Conference on Artificial Intelligence for Environmental Science year: 2023 - title: Using Machine Learning to Monitor the Fuel Moisture Content over CONUS and Alaska based on VIIRS authors: - - J. Schreck. + - J. Schreck. conference: AMS Annual Meeting year: 2023 - title: Mimicking non-ideal instrument behavior for hologram processing using neural style translation authors: - - J. Schreck. + - J. Schreck. conference: AMS Annual Meeting year: 2023 - title: Towards trustworthy artificial intelligence for high impact weather prediction authors: - - D. J. Gagne. + - D. J. Gagne. conference: University of North Dakota Department of Atmospheric Sciences Seminar Series year: 2022 - title: Explainable uncertainty in machine learning weather prediction authors: - - D. J. Gagne. + - D. J. Gagne. conference: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction year: 2022 - title: The challenges of developing trustworthy ai systems for high impact weather prediction authors: - - D. J. Gagne. + - D. J. Gagne. conference: 1st KMA-NOAA Workshop on AI for Weather and Climate year: 2022 - title: Evaluating the Uncertainty of Tornadic Population Exposure with Machine Learning authors: - - C. Becker. + - C. Becker. conference: NCAR CISL WIP Talk year: 2022 - title: An intercomparison of real-time machine learning hail guidance authors: - - D. J. Gagne. + - D. J. Gagne. conference: North American Workshop on Hail and Hailstorms year: 2022 - title: Machine Learning Techniques for Improving Prediction of Winter Weather Precipitation Types authors: - - C. Becker. + - C. Becker. conference: New York State Mesonet Symposium year: 2022 - title: Machine Learning Techniques for Improving Prediction of Winter Precipitation Types authors: - - C. Becker. + - C. Becker. conference: University at Albany NY State Mesonet Symposium year: 2022 - title: Neural Network Processing of Holographic Images authors: - - D. J. Gagne. + - D. J. Gagne. conference: Joint CISL/EOL seminar year: 2022 - title: Machine Learning Analysis of Severe Storm Mode in Convection-Allowing Models authors: - - C. Becker. + - C. Becker. conference: American Meteorological Society Annual Meeting 2022 year: 2022 - title: Accelerating Mechanistic Simulation of Organic Chemistry in Weather and Climate Models with Machine Learning authors: - - J. Schreck. + - J. Schreck. conference: American Meteorological Society Annual Meeting 2022 year: 2022 - title: Machine Learning Challenges and Opportunities on NCAR HPC authors: - - J. Schreck. + - J. Schreck. conference: CISL GPU TIGER meeting year: 2021 - title: Machine Learning and Hyperparameter Optimization on the GPUs at NCAR authors: - - J. Schreck. + - J. Schreck. conference: CISL Work In Progress (WIP) discussion year: 2021 - title: Pathways to Trustworthy AI in Weather and Climate authors: - - D. J. Gagne. + - D. J. Gagne. conference: 2nd Conference on the relation between AI, social Sciences, and Humanities year: 2021 - title: Accelerating Mechanistic Simulation of Organic Chemistry in Weather and Climate Models with Machine Learning authors: - - J. Schreck. + - J. Schreck. conference: NCAR AIAG Roundtable Discussion year: 2021 - title: Evaluation of Two ML Approaches for the Objective Identification of Convective Storm Mode Using Convection-allowing Model Output authors: - - C. Becker. + - C. Becker. conference: NOAA Workshop on Leveraging AI in Environmental Sciences year: 2021 - title: 4D Post-Processing of Holographic Images authors: - - G. Gantos. + - G. Gantos. conference: NOAA Workshop on Leveraging AI in Environmental Sciences year: 2021 - title: Accelerating Mechanistic Simulation of Organic Chemistry in Weather and Climate Models with Machine Learning authors: - - J. Schreck. + - J. Schreck. conference: "NOAA 21: Leveraging AI in Environmental Sciences" year: 2021 - title: "The NCAR Analytics and Integrative Machine Learning Group: Research Overview" authors: - - D. J. Gagne. + - D. J. Gagne. conference: 3rd NOAA Workshop on Leveraging AI in Environmental Sciences year: 2021 - title: Machine Learning Roles in Earth System Modeling authors: - - D. J. Gagne. + - D. J. Gagne. conference: Georgia Tech EAS Seminar Series year: 2021 - title: Machine Learning Emulation across the Earth System authors: - - D. J. Gagne. + - D. J. Gagne. conference: Carnegie Mellon University STAMPS Webinars year: 2021 - title: Improving HOLODEC post-processing via Deep Learning authors: - - G. Gantos. + - G. Gantos. conference: American Meteorological Society Annual Meeting year: 2021 - title: Optimization of Neural Emulators in Earth Science Applications authors: - - J. Schreck. + - J. Schreck. conference: American Meteorological Society 2020 conference year: 2021 - title: A machine learning odyssey through the earth system authors: - - D. J. Gagne. + - D. J. Gagne. conference: AMS 20th Conference on Artificial Intelligence for Environmental Science year: 2021 - title: Emulator Benchmarking with an Open GECKO-A Dataset authors: - - C. Becker. + - C. Becker. conference: American Meteorological Society Annual Meeting 2021 year: 2021 - title: Earth system science machine learning challenge problems authors: - - D. J. Gagne. + - D. J. Gagne. conference: AGU Fall Meeting year: 2020 - title: Accelerating and explaining earth system process models with machine learning authors: - - D. J. Gagne. + - D. J. Gagne. conference: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction year: 2020 - title: "The AI for Earth System Science Hackathon: Challenge Problems and Lessons Learned" authors: - - D. J. Gagne. + - D. J. Gagne. conference: 2nd NOAA Workshop on Leveraging AI in Environmental Science year: 2020 - title: Seeing the Atmosphere through Machine Learning authors: - - D. J. Gagne. + - D. J. Gagne. conference: NCAR Explorer Series year: 2020 - title: Interpretable deep learning for analysis of extreme weather risk authors: - - D. J. Gagne. + - D. J. Gagne. conference: Reinsurance Association of America Cat Risk Management Conference year: 2020 - title: "Machine Learning Parameterization of the Surface Layer: Integration with WRF" authors: - - D. J. Gagne. + - D. J. Gagne. conference: AMS Annual Meeting year: 2020 - title: Probabilistic Rapid Intensification Prediction with Convolutional Neural Networks and HWRF authors: - - D. J. Gagne. + - D. J. Gagne. conference: AMS Annual Meeting year: 2020 - title: Emulation of Bin Microphysical Processes with Machine Learning authors: - - D. J. Gagne. + - D. J. Gagne. conference: AMS Annual Meeting year: 2020 - title: authors: - - D. J. Gagne. + - D. J. Gagne. conference: year: 2020 - title: authors: - - D. J. Gagne. + - D. J. Gagne. conference: year: 2020 - title: authors: - - D. J. Gagne. + - D. J. Gagne. conference: year: 2020 - title: authors: - - D. J. Gagne. + - D. J. Gagne. conference: year: 2020 - title: authors: - - D. J. Gagne. + - D. J. Gagne. conference: year: 2020 \ No newline at end of file diff --git a/data/publications.yml b/data/publications.yml index 2d37745..fcd6a1c 100644 --- a/data/publications.yml +++ b/data/publications.yml @@ -2,27 +2,70 @@ enable : true title : PUBLICATIONS image : images/banners/aurora.jpg items : + - title: Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations + authors: + - Da Fan + - Steven J. Greybush + - Eugene E. Clothiaux + - David John Gagne II + journal: Artificial Intelligence for the Earth Systems + year: 2024 + volume: -1 + pages: In Press + url: https://doi.org/10.1175/AIES-D-23-0098.1 + - title: "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications" + authors: + - John Schreck + - David John Gagne + - Charlie Becker + - William Chapman + - Kimberly Elmore + - Da Fan + - Gabrielle Gantos + - Eliot Kim + - Dhamma Kimpara + - Thomas Martin + - Maria J. Molina + - Vanessa M. Pryzbylo + - Jacob Radford + - Belen Saavedra + - Justin Willson + - Christopher Wirz + journal: Artificial Intelligence for the Earth Systems + year: 2024 + pages: Under Review + url: https://arxiv.org/abs/2309.13207 + - title: A Machine Learning-Based Approach to Quantify ENSO Sources of Predictability + authors: + - Colfescu, Ioana + - Christensen, Hannah + - Gagne, David John + journal: Geophysical Research Letters + year: 2024 + volume: 51 + pages: e2023GL105194 + url: https://doi.org/10.1029/2023GL105194 - title: A review of machine learning for convective weather authors: - McGovern, A. - R. J. Chase - M. Flora - - D. J. Gagne + - D. J. Gagne - R. Lagerquist - C. Potvin - N. Snook - E. Loken journal: Artificial Intelligence for the Earth Systems - year: 2023a - volume: -1 - pages: In Press - url: -1 + year: 2023 + volume: 2 + pages: e220077 + url: https://doi.org/10.1175/AIES-D-22-0077.1 - title: "Trustworthy artificial intelligence for environmental sciences: An innovative approach for summer school" authors: - McGovern, A. - - D. J. Gagne + - D. J. Gagne - C. D. Wirz - I. Ebert-Uphoff - A. Bostrom @@ -36,36 +79,36 @@ items : - R. Redmon - T. Peterson journal: Bull. Amer. Meteor. Soc - year: 2023b - volume: -1 - pages: In Press + year: 2023 + volume: 104 + pages: E1222–E1231 url: https://doi.org/10.1175/BAMS-D-22-0225.1 - title: Mimicking non-ideal instrument behavior for hologram processing using neural style translation authors: - - Schreck, J. S. + - Schreck, J. S. - M. Hayman - - G. Gantos + - G. Gantos - A. Bansemer - - D. J. Gagne + - D. J. Gagne journal: Optics Express year: 2023 - volume: -1 - pages: In Press - url: https://arxiv.org/abs/2301.02757 + volume: 31 + pages: 20049-20067 + url: https://doi.org/10.1364/OE.486741 - title: Diagnosing storm mode with deep learning in convection-allowing models authors: - Sobash, R. A. - - D. J. Gagne - - C. L. Becker + - D. J. Gagne + - C. L. Becker - D. Ahijevych - - G. Gantos + - G. Gantos - C. S. Schwartz journal: Mon. Wea. Rev. year: 2023 - volume: -1 - pages: In Press + volume: 151 + pages: 2009–2027 url: https://doi.org/10.1175/MWR-D-22-0342.1 - title: Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy @@ -82,7 +125,7 @@ items : - A. DeCastro - S. Dettling - C. Draxl - - D. J. Gagne + - D. J. Gagne - P. Hawbecker - P. Jha - T. Juliano @@ -93,17 +136,17 @@ items : - W. Shaw - R. Thedin journal: Wind Energ. Sci. Discuss - year: 2022 - volume: in review - pages: [preprint] - url: https://doi.org/10.5194/wes-2022-113 + year: 2023 + volume: 8 + pages: 1251–1275 + url: https://doi.org/10.5194/wes-8-1251-2023 - title: "Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status and outlook" authors: - Dueben, P. - M. G. Schultz - M. Chantry - - D. J. Gagne + - D. J. Gagne - D. M. Hall - A. McGovern journal: Artificial Intelligence for the Earth Systems @@ -114,11 +157,11 @@ items : - title: Neural network processing of holographic images authors: - - Schreck, J. S. - - G. Gantos + - Schreck, J. S. + - G. Gantos - M. Hayman - A. Bansemer - - D. J. Gagne + - D. J. Gagne journal: Atmospheric Measurement Technologies year: 2022 volume: 2022 @@ -127,9 +170,9 @@ items : - title: Neural network emulation of the formation of organic aerosols based on the explicit gecko-a chemistry model authors: - - Schreck, J. S. - - C. Becker - - D. J. Gagne + - Schreck, J. S. + - C. Becker + - D. J. Gagne - K. Lawrence - S. Wang - C. Mouchel-Vallon @@ -143,12 +186,12 @@ items : - title: Machine Learning for Improving Surface Layer Flux Estimates authors: - T. McCandless - - D. J. Gagne + - D. J. Gagne - B. Kosovic - S. E. Haupt - B. Yang - - C. Becker - - J. Schreck + - C. Becker + - J. Schreck journal: Boundary Layer Meteorology volume: 185 year: 2022 @@ -158,10 +201,10 @@ items : - title: On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model authors: - Muñoz-Esparza, D. - - C. Becker + - C. Becker - J. Sauer - - D. J. Gagne - - J. Schreck + - D. J. Gagne + - J. Schreck - B. Kosovic journal: JGR Atmospheres year: 2022 @@ -173,7 +216,7 @@ items : authors: - McGovern, A. - I. Ebert-Uphoff - - D. J. Gagne + - D. J. Gagne - A. Bostrom journal: Environmental Data Science year: 2022 @@ -190,7 +233,7 @@ items : - I. Ebert-Uphoff - R. He - J. Hickey - - D. J. Gagne + - D. J. Gagne - N. Snook - J. Q. Stewart - C. Thorncroft @@ -205,7 +248,7 @@ items : - title: A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts authors: - Y. Sha - - D. J. Gagne + - D. J. Gagne - G. West - R. Stull journal: Monthly Weather Review @@ -217,7 +260,7 @@ items : - title: The History and Practice of AI in the Environmental Sciences authors: - Haupt, S. E. - - D. J. Gagne + - D. J. Gagne - W. Hsieh - V. Krasnopolsky - V. Lakshmanan @@ -235,7 +278,7 @@ items : - title: Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations authors: - Foster, D. - - D. J. Gagne + - D. J. Gagne - D. B. Whitt journal: Journal of Advances in Modeling Earth Systems year: 2021 @@ -246,7 +289,7 @@ items : - title: Deep learning classification of potentially severe convective storms in a changing climate authors: - Molina, M. - - D. J. Gagne + - D. J. Gagne - A. Prein journal: Earth and Space Science year: 2021 @@ -257,7 +300,7 @@ items : - title: Deep-learning-based precipitation observation quality control. Journal of Atmospheric and Oceanic Technology authors: - Sha, Y. - - D. J. Gagne + - D. J. Gagne - G. West - R. Stull journal: Earth and Space Science @@ -269,12 +312,12 @@ items : - title: Machine Learning the Warm Rain Process authors: - Gettelman, A. - - D. J. Gagne + - D. J. Gagne - C.-C. Chen - M. Christensen - Z. Lebo - H. Morrison - - G. Gantos + - G. Gantos journal: Journal of Advances in Modeling Earth Systems year: 2021 volume: 13 @@ -284,11 +327,11 @@ items : - title: "Deep-learning-based gridded downscaling of surface meteorological variables in complex terrain. part i: daily maximum and minimum 2-m temperature" authors: - Sha, Y. - - D. J. Gagne + - D. J. Gagne - G. West - R. Stull journal: Journal of Applied Meteorology and Climatology - year: 2020b + year: 2020 volume: 59 pages: 2057–-2073 url: https://doi.org/10.1175/JAMC-D-20-0057.1 @@ -296,23 +339,23 @@ items : - title: "Deep-learning-based gridded downscaling of surface meteorological variables in complex terrain - part ii: daily precipitation" authors: - Sha, Y. - - D. J. Gagne + - D. J. Gagne - G. West - R. Stull journal: Journal of Applied Meteorology and Climatology - year: 2020a + year: 2020 volume: 59 pages: 2075–-2092 url: https://doi.org/10.1175/JAMC-D-20-0058.1 - title: "Machine learning for stochastic parameterization: Generative adversarial networks in the lorenz ’96 model" authors: - - Gagne, D. J. + - Gagne, D. J. - H. Christensen - A. Subramanian - A. Monahan journal: Journal of Advances in Modeling Earth Systems - year: 2020a + year: 2020 volume: 12 pages: 2075–-2092 url: https://doi.org/10.1029/2019MS001896 @@ -322,7 +365,7 @@ items : - Lagerquist, R. - A. McGovern - C. Homeyer - - D. J. Gagne + - D. J. Gagne - T. Smith journal: Mon. Wea. Rev. year: 2020 @@ -334,11 +377,11 @@ items : authors: - Burke, A. - N. Snook - - D. J. Gagne + - D. J. Gagne - S. McCorkle - A. McGovern journal: Wea. Forecasting - year: 2020b + year: 2020 volume: 35 pages: 149–-168 url: https://doi.org/10.1175/WAF-D-19-0105.1 @@ -347,7 +390,7 @@ items : authors: - McGovern, A. - R. Lagerquist - - D. J. Gagne + - D. J. Gagne - G. E. Jergensen - K. Elmore - C. Homeyer @@ -362,7 +405,7 @@ items : authors: - Lagerquist, R. - A. McGovern - - D. J. Gagne + - D. J. Gagne journal: Wea. Forecasting year: 2019 volume: 34 diff --git a/data/whatwedo.yml b/data/whatwedo.yml index eca2831..68996df 100644 --- a/data/whatwedo.yml +++ b/data/whatwedo.yml @@ -6,9 +6,9 @@ items : quote: We host both undergraduate and graduate interns so that they can gain experience in machine learning applied to challenges in Earth system science. url : education - - name : CURRENT PROJECTS + - name : PROJECTS quote: We conduct research aimed at improving our ability to apply rigorous, explainable, and quantifiable modeling to Earth system science challenges. - url : current_projects + url : projects - name : DATASETS quote: We help the scientific community learn about and use available datasets responsibly. diff --git a/static/images/projects/ai2es-logo-curve-name.png b/static/images/projects/ai2es-logo-curve-name.png new file mode 100644 index 0000000000000000000000000000000000000000..33fece3b0de3ecd5d3bc2249212c1a0e3d26aebb GIT binary patch literal 695183 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