coldfusion

Thanks to Lindsey Heagey and Joachim Meyer for the README template!

Project summary of coldfusion

coldfusion is a team effort to merge and validate overlapping GPR and LiDAR datasets using Python, Git/Github and JupyterLab during SnowEx Hackweek 2022. The 2020 NASA SnowEx campaign collected a variety of high-spatial resolution snow depth information using GPR, magnaprobe depth measurements, snow pit densities and LiDAR point clouds between January 29th and February 6th 2020 in the Grand Mesa study area.

Depth from LiDAR and density from GPR are compiled and then validated against snowpit density/SWE values to 'close the loop'.

.jpe

Collaborators on this project

• Curtis Beutler CBeutler
• Peter Marshall pmarshall24
• Tate Meehan tatemeehan
• Kori Mooney korimooney
• Alicia Pouw aliciapouw21
• Thomas Van Der Weide tvanderweide
• Megan Verfaillie Megan-Verf
• Seth Vanderwilt sethv

Database information

SiteData : snowpit information and metadata

PointData : GPR data, snow probes [('depth',),('swe'),('two_way_travel',)]

ImageData : LiDAR ASO data

LayerData : snow pit data (density, depth)

Tasks

• Find LiDAR area with GPR validation data in western and central Grand Mesa study area and create code to query data from database. Validate using:
  • Snow pits (Snow depths & Density)
  • Magnaprobe (snow depth)
  • SNOTEL (snow depth) Mesa Lakes (622)
• Ensure GPR & LiDAR are on like coordinate systems and co-locate GPR points to LiDAR points
• Calculate snow density using dielectric permittivity and wave speed using LiDAR snow depths.
• Validate by co-locating in-situ snow density and snow depth
• Machine Learning
• Look at other sites

Density calcs

$\rho = \frac{z}{SWE}$ eq. 1 density based on ASO snow depth and SWE

Next steps

Application of Machine Learning to begin looking at other study sites

Application example

List one specific application of this work.

Sample data

If you already have some data to explore, briefly describe it here (size, format, how to access).

Existing methods

How would you or others traditionally try to address this problem?

Proposed methods/tools

Building from what you learn at this hackweek, what new approaches would you like to try to implement?

Background reading

Optional: links to manuscripts or technical documents for more in-depth analysis.

Files

• .gitignore
  Globally ignored files by git for the project.
• environment.yml
conda environment description needed to run this project.
• README.md
  Description of the project (see suggested headings below)

Folders

contributors

Each team member has their own folder under contributors, where they can work on their contribution to the project. Having a dedicated folder for each person prevents conflicts when merging with the main branch.

notebooks

Notebooks that are considered delivered results for the project should go in here.

scripts

Helper utilities that are shared with the team

Table of Variables

SiteData Columns	PointData Columns	ImageData Columns	LayerData Columns
air_temp	date	date	bottom_depth
aspect	date_accessed	date accessed	comments
date	doi	description	date
date_accessed	easting	doi	date_accessed
doi	elevation	instrument	depth
easting	equipment	metadata	doi
elevation	geom	observers	easting
geom	instrument	raster	elevation
ground_condition	latitude	registry	flags
ground_roughness	longitude	site_name	geom
ground_vegetation	metadata	time_created	instrument
latitude	northing	time_updated	latitude
longitude	observers	type	longitude
metadata	registry	units	metadata
northing	site_id	-----------	northing
pit_id	site_name	-----------	observers
precip	time	-----------	pit_id
registry	time	-----------	registry
site_id	time_updated	-----------	sample_a
site_name	type	-----------	sample_b
site_notes	units	-----------	sample_c
sky_cover	utm_zone	-----------	site_id
slope_angle	value	-----------	site_name
time	version_number	-----------	time
time_created	-----------	-----------	time_created
time_updated	-----------	-----------	time_updated
total_depth	-----------	-----------	type
tree_canopy	-----------	-----------	units
utm_zone	-----------	-----------	utm_zone
vegetation_height	-----------	-----------	value
weather_description	-----------	-----------	-----------
wind	-----------	-----------	-----------

Fenced Code Block

{
  "firstName": "John",
  "lastName": "Smith",
  "age": 25
}