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:
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. |