Argovis Events

Date Description of event
2 - 4 May 2022

Argovis Hackathon (supported by EarthCube TAC)

Participants:


Suzy Anil, Steve Diggs, Ivana Escobar, Hartmut Frenzel, Shreyas Suni Gaikwad, Donata Giglio, Christopher Gordon, Weiqing Han, Ratnaksha Lele, Will Kamp, Lydi Keppler, Mikael Kuusela, Shannon McClish, Lynne Merchant, Bill Mills, Samuel Mogen, Monica Nelson, Sarah Purkey, Jacopo Sala, Megan Scanderbeg, Giovanni Seijo, Jon Sharp, Myranda Uselton Shirk

Agenda and documentation:

Slides with Agenda and FAIR data principles information
Argovis documentation

Notebook Descriptions:

Argo & GO-SHIP Comparison:
Authors: Ratnaksha Lele, Jon Sharp, Christopher Gordon, Myranda Uselton Shirk
GitHub repository: https://github.com/argovis/hackathon22-argo-goship
In this notebook, metadata from a selected GO-SHIP cruise line is obtained, along with data from nearby Argo profiles, that fall within user-provided time and space constraints from the GO-SHIP profiles. Those Argo profile data are converted to the more user-friendly xarray format, then locations of GO-SHIP stations and Argo profiles are plotted.

Intro to Argovis' Grid API - EOF analysis example:
Authors: Jacopo Sala, Will Kamp, Mikael Kuusela, Weiqing Han
GitHub repository: https://github.com/argovis/hackathon22-grid-slices-1
In this notebook, gridded Argo data from the Roemmich-Gilson Argo climatology is downloaded from a specified region and time, converted to xarray format and plotted, including an area-weighted mean over the region. An EOF analysis is also done and plotted.

Applications of Argovis gridded products
Authors: Ivana Escobar (Oden Institute, U. of Texas), Samuel Mogen (ATOC, CU Boulder), Monica Nelson (Scripps Institution of Oceanography, UC San Diego), and Giovanni Seijo (ATOC, CU Boulder)
GitHub repository: https://github.com/argovis/hackathon22-grid-slices-2
Argovis offers a growing list of gridded products, indexed and downloadable through its API. In this notebook we use the Roemmich-Gilson Argo climatology and GO-SHIP WOCE lines to examine temperature variability for a user-defined region and time window. The preset parameters in the notebook focus on the North Pacific blob as an example. In this notebook we:
  • Query gridded fields of temperature from Argovis for a user-defined polygon and time window.
  • Calculate gridded field anomalies.
  • Obtain vertical slices for user-defined latitudes and/or longitudes.
  • Create hovmoller plots.
  • Interpolate along WOCE lines (if available) for comparisson.

Binning Argo profile data
Authors: Suzy Anil, Shreyas Suni Gaikwad, Lynne Merchant, and Shannon McClish
GitHub repository: https://github.com/argovis/hackathon22-profile-grid
In this notebook, Argo profile data is downloaded, quality control flags are applied and the data are put into xarray for binning by pressure levels.