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AvalancheModelingResources.md

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+https://www.reddit.com/r/MachineLearning/comments/1u13zz/help_with_predicting_avalanche_risk/
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+https://www.researchgate.net/publication/233598351_Applying_machine_learning_methods_to_avalanche_forecasting
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+http://cs229.stanford.edu/proj2010/Dyer-ForecastingAvalanchesInThePacificNorthwest.pdf
+
+https://www.researchgate.net/publication/235977937_Statistical_evaluation_of_local_to_regional_snowpack_stability_using_simulated_snow-cover_data
+
+http://cs229.stanford.edu/proj2010/Dyer-ForecastingAvalanchesInThePacificNorthwest.pdf
+
+https://books.google.com/books?id=bwekCgAAQBAJ&pg=PA458&lpg=PA458&dq=machine+learning+avalanche&source=bl&ots=Ic44hS63I6&sig=fDD889joHawBRnczgG4RKTPfE1M&hl=en&sa=X&ved=0ahUKEwjEsLeJ04bQAhVoj1QKHTQqDOM4ChDoAQg4MAg#v=onepage&q=machine%20learning%20avalanche&f=false
+
+http://www.nat-hazards-earth-syst-sci.net/11/367/2011/nhess-11-367-2011.pdf
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+http://research.microsoft.com/en-us/um/people/horvitz/weather_hybrid_representation.pdf
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+http://www.meted.ucar.edu/afwa/avalanche/navmenu.php?tab=1&page=3.3.3
+
+Similar problem solving rain estimation using radar data with RNN
+http://simaaron.github.io/Estimating-rainfall-from-weather-radar-readings-using-recurrent-neural-networks/
+https://github.com/simaaron/kaggle-Rain
+
+TOREAD: https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_106B_LSTM_Timeseries_with_IOT_Data.ipynb
+TOREAD:  https://github.com/Microsoft/acceleratoRs/tree/master/SolarPanelForecasting
+TOREAD: https://hal.archives-ouvertes.fr/hal-02318407/document (satellite detection of avy deposits)
+
+
+http://nsidc.org/data/G02158
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+Europe snow: http://www.umr-cnrm.fr/spip.php?article555&lang=en
+
+Modeling: read: https://phys.org/news/2018-08-subtle-mechanics-avalanche-3d.html
+
+ToRead:
+https://www.climatechange.ai/CameraReadySubmissions%202-119/24/CameraReadySubmission/Wildfire-Prediction-Camera-Ready-NeurIPS-workshop.pdf
+https://www.climatechange.ai/CameraReadySubmissions%202-119/30/CameraReadySubmission/neurips_2019_paper_camera_ready.pdf
+https://hal.archives-ouvertes.fr/hal-02318407/document
+
+https://deepmind.com/blog/article/A_new_model_and_dataset_for_long-range_memory
+Graph Cast: https://arxiv.org/pdf/2212.12794.pdf
+	Data: ERA5 hourly data on single levels from 1959 to present (copernicus.eu)
+ClimaX: foundational weather model: [2301.10343] ClimaX: A foundation model for weather and climate (arxiv.org)
+
+
+
+https://arxiv.org/pdf/1809.07394.pdf
+https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019MS001705
+
+Stats:
+https://online.stat.psu.edu/statprogram/stat510
+
+Noisy Labels
+Pervasive Label Errors in ML Benchmark Test Sets, Consequences, and Benefits – corresponding Python package cleanlab.
+Learning with Noisy Labels
+
+Radar: Avalanche Visualisation Using Satellite Radar (diva-portal.org)
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+https://www.nature.com/articles/s41467-021-25801-2
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+Deep Micro Climate: https://www.microsoft.com/en-us/research/uploads/prod/2021/07/MCP_KDD_2021___Camera_Ready-4.pdf
+
+Forecast Accuracy Baseline: https://arc.lib.montana.edu/snow-science/objects/ISSW2018_O17.1.pdf
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+Understanding Clouds: Understanding cirrus clouds using explainable machine learning | Environmental Data Science | Cambridge Core
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+
+https://ai.googleblog.com/2020/01/using-machine-learning-to-nowcast.html?m=1
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+https://github.com/veeral-patel/awesome-risk-quantification/blob/master/README.md
+
+http://algorithmsbook.com/
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+https://news.ucar.edu/132811/gpus-open-potential-forecast-urban-weather-drones-and-air-taxis
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+https://www.technologyreview.com/2021/09/29/1036331/deepminds-ai-predicts-almost-exactly-when-and-where-its-going-to-rain/
+
+https://www.intel.com/content/www/us/en/research/news/probabilistic-computing.html
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+https://www.newyorker.com/magazine/2020/03/23/snow-science-against-the-avalanche
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+https://dionhaefner.github.io/2021/12/supercharged-high-resolution-ocean-simulation-with-jax/#jax-hpc