About Fire Weather and Impact Theme: This year's AI Workshop is piloting a collaboration development activitu modeled after the hackweek which brings participants with different backgrounds together to form collaboration and develop various use cases that are related to research and applications for fire weather and impacts. The core objective of FireWxHack are two-fold:
1. Foster collaboration building in applying AI/ML for fire weather research: Provide opportunities for fire weather researchers and data scientists to develop project ideas on how to use AI/ML and open environmental data for fire weather research & application development.
2. Collect user feedback on open environmental data for AI/ML in fire weather: Collect user feedback on how to improve open environmental data that can accelerate the adoption of AI/ML in fire weather research.
This event is
NOT a data science competition and there is no fixed target for each individual team.
Session information:
Lightning talks:
11:35: Christina Kumler (NOAA/GSL/CIRES) – Using Machine Learning as a Tool for Improving Wildfire Intensity Forecasting
11:50: Kyle Hillburn (CSU/CIRA) – The Value of Low Latency Satellite Data for Initializing Coupled Fire Models
12:05: Michael Seablim (NASA) – NASA Wildland Fire Program and FireSense
12:20: Use case daily check in and feedback
Each use case team have five minutes to present use case ideas and get feedback from lightning talk presenters and other workshop participants.
Relevant materials:
1.
About FireWxHack: Document summarizing the goal of fire weather theme activities.
2.
Introduction slides: Slides used during meet and greet to describe the timeline of fire weather theme.
3.
Suggested use cases: These are three suggested use cases with relevant environmental data for fire weather research.
4.
JupyterHub Environment: https://oasis.ncics.org (require GitHub user name to sign in).