.gitignore
Globally ignored files bygit
for the project.environment.yml
conda
environment description needed to run this project.README.md
Description of the project (see suggested headings below)
Each team member has it's own folder under contributors, where they can work on their contribution. Having a dedicated folder for each person helps to prevent conflicts when merging with the main branch.
Notebooks that are considered delivered results for the project should go in here.
Helper utilities that are shared with the team
Project repistory to develop tools and framework to easily integrate available in situ datasets over Grand Mesa for use as calibration/validation for remote sensing datasets and spatially-distributed models. Emphasis on snow depth (and potentially SWE) measurements.
Integration_station: Towards a sandbox for SnowEx datasets
- Max Stevens
- Mitch Creelman
- Seth Vanderwilt
- Shad O'Neel
- Ryan Crumley
- Gifty Attiah
- Tri Datta
- Mansi Joshi
- Jessica Scheick
- Shashank Bhushan (Data science helper)
- Michelle Hu (Project Lead)
The snowex capaign(s) produce a treasure trove of datasets measuring different physical parameters such as snow depth, density, temperatures, ASO LiDAR etc. From a purely exploratory measure, we can use this awesome treasure trove to validate and calibrate satellite remote sensing and model derived outputs. The calibration
List one specific application of this work.
- SnowEx
- Snow pits https://nsidc.org/data/SNEX20_GM_SP/versions/1
- Depth transects https://nsidc.org/data/SNEX20_SD/versions/1, depth spirals
- ASO Lidar, USGS 3DEP
- ?Time lapse camera snow depths (gives high time resolution perhaps)?
- GPR depths
- External
- IS2
- CryoSat
- RS imagery
- Weather station data (SNOTEL, other AWS bits)
Some specifics questions(tasks) which we want to answer(accomplish) during the hackweek:
- Query: run through tutorial, work on extracting via bounding box, work towards developing streamlined functions for SnowEx datasets
- Query and fetch external datasets (IceSat-2/Snotel)
- Preprocessing: Alignment of elevation datasets
- Comparison of datasets, exploring interpolation techniques to create rasters from point datasets.
How would you or others traditionally try to address this problem?
Building from what you learn at this hackweek, what new approaches would you like to try to implement?
Optional: links to manuscripts or technical documents for more in-depth analysis.