MicroFun

Introduction

Provide a brief introduction describing the proposed work. Be sure to also decribe what skills team members will get to learn and practice as part of this project.

The proposed work for this project involves trying to extract useful metrics from microstructure data collected during the SnowEx campagignas to tempt modelers and retrieval algorithim specialists with. In NASA's own words, "SnowEx is a multi-year field experiment, which includes extensive surface-based observations to evaluate how to best combine different remote sensing technologies to accurately observe snow throughout the season in various landscapes.", in this mission statement I see this project as one of the bridges in between ground based observations and remote sensing. Many retrieval algorithims have been developed to collect SWE, depth, and other physical property estimates, all of which have uncertainties due to the complex relationship between between microwave range interactions with snow grains. Interpreting microstructure data-- can also be less than ideal.

Collaborators

List all participants on the project. Here is a good space to share your personal goals for the hackweek and things you can help with.

Name	Personal goals	Can help with	Role
Anna Valentine	deepen understanding of auto-correlation and covariance functions, be an excellend team coordinator	Familiar with microCT data, pandas data wrangling, methods for computing SSA	Project Lead
Sydney Baratta	EX. Practice leading a software project	EX. machine learning and python (scipy, scikit-learn)	Team Member
...	...	...	...
...	...	...	...

The problem

Provide a few sentences describing the problem are you going to explore. If this is a technical exploration of software or data science methods, explain why this work is important in a broader context and specific applications of this work.

Data and Methods

Data

Briefly describe and provide citations for the data that will be used (size, format, how to access).

Existing methods

How would you or others traditionally try to address this problem? Provide any relevant citations to prior work.

Proposed methods/tools

What new approaches would you like to implement for addressing your specific question(s) or application(s)?

Will your project use machine learning methods? If so, we invite you to create a model card!

Additional resources or background reading

Optional: links to manuscripts or technical documents providing background information, context, or other relevant information.

Project goals and tasks

Project goals

List the specific project goals or research questions you want to answer. Think about what outcomes or deliverables you'd like to create (e.g. a series of tutorial notebooks demonstrating how to work with a dataset, results of an anaysis to answer a science question, an example of applying a new analysis method, or a new python package).

• Goal 1
• Goal 2
• ...

Tasks

What are the individual tasks or steps that need to be taken to achieve each of the project goals identified above? What are the skills that participants will need or will learn and practice to complete each of these tasks? Think about which tasks are dependent on prior tasks, or which tasks can be performed in parallel.

• Task 1 (all team members will learn to use GitHub)
• Task 2 (team members will use the scikit-learn python library)
  • Task 2a (assigned to team member A)
  • Task 2b (assigned to team member B)
• Task 3
• ...

Project Results

Use this section to briefly summarize your project results. This could take the form of describing the progress your team made to answering a research question, developing a tool or tutorial, interesting things found in exploring a new dataset, lessons learned for applying a new method, personal accomplishments of each team member, or anything else the team wants to share.

You could include figures or images here, links to notebooks or code elsewhere in the repository (such as in the notebooks folder), and information on how others can run your notebooks or code.