skagit-met

Exploring meteorology data for use in hydrologic modeling of the Skagit River basin

This repository hosts code to construct analysis ready data cubes for the Skagit River basin from a variety of model and observational datasets. There are also Jupyter Notebooks with detailed analysis and figures.

Datasets

Dataset	Spatial Resolution	Range/Availability	Data Granularity	Temperature	Precipitation	Wind Speed	Relative Humidity	Long Wave Radiation	Short Wave Radiation
HRRRv4	3 km	2014 - present	Hourly	✔️	✔️	✔️	✔️	✔️	✔️
PRISM	4 km	1981 - present	Daily	✔️	✔️	X	Via Vapor Pressure Deficit	X	X
UCLA CMIP-6 (WRF)	9 km	1980 - 2100	Hourly	✔️	✔️	Via U and V components	Via Specific Humidity	✔️	✔️
ORNL	4 km	1980-2040	Daily	✔️	✔️	✔️	✔️	✔️	✔️
SNOTEL	Point Data (9 stations)	2010 - present	Hourly	✔️	✔️	X	X	X	X
PNNL*	6 km	1981 - 2020	Hourly	✔️	✔️	Via U and V components	Via Specific Humidity	✔️	✔️

*This is a private experimental dataset hosted at the Pacific Northwest National Laboratory

Environment Setup

We recommend using pixi, an alternate to conda environments that uses a "per-project" rather than "global" environment paradigm. Pixi helps isolate software environments specific to a repository through the use of configuration and lock files to manage dependencies all in one, but backed by the conda-forge repos and pypi.

To use pixi, install it on your machine, clone this repo, then run pixi install in the root of the repo. You may get an error big and long that mentions 'clang' or something like that. If that's the case run export CFLAGS="-Wno-incompatible-function-pointer-types -Wno-implicit-function-declaration" and try again. This is pre-run as a part of the setup script for the conda environment.

To play around with the jupyter notebooks, just run pixi run nb, and a local instance of jupyter will be launched for you with all the necessary packages and dependencies. It's quite magic.

DSHydro JupyterHub

The DSHydro JupyterHub is a limited-access JupyterHub running on servers at the University of Washington.

1. Run curl -fsSL https://pixi.sh/install.sh | bash to install pixi for your user
2. Install the data-download environment or analysis environment using pixi install -e download-data and pixi install -e analysis
3. To look at the analysis notebook, install the analysis kernel with the following command: ./skagit-met/.pixi/envs/analysis/bin/python3 -m ipykernel install --user --name=skagit_analysis
4. Once installed, open the Viz.ipynb file, and select the skagit_analyis kernel.

Cloud-hosted JupyterHub:

Cryocloud is a NASA-supported JupyterHub running in AWS us-west-2. The environment setup is the same as above!

Conda environment:

If you prefer to use conda, there is also a script to install necessary packages in a global conda environment

1. Clone this repo
2. In the root of the repo, run ./setup.sh - this creates the conda environment, installs whats needed, etc.
3. Enter the environment using conda activate skagit-met
4. If you're done, don't forget to conda deactivate.

Additional Resources

• https://rapidrefresh.noaa.gov/Diag-vars-NOAA-TechMemo.pdf
• https://rapidrefresh.noaa.gov/hrrr/HRRR/Welcome.cgi?dsKey=hrrr_ncep_jet
• https://www.nco.ncep.noaa.gov/pmb/products/hrrr/hrrr.t00z.wrfsfcf00.grib2.shtml
• https://www.nco.ncep.noaa.gov/pmb/docs/on388/table2.html
• https://www.nco.ncep.noaa.gov/pmb/docs/grib2/grib2_doc/grib2_table4-2.shtml