Tutorials on how to use Argovis

Announcement

Working session on how to import data from Argovis into your programming environment of choice: Friday 26 June at 1 pm MT. The session is free, yet please register here.

Slides

Slides from presentation delivered as part of SOCCOM meeting on 26 May, 2020. There is an emphasis on BGC floats and the Southern Ocean.

Slides from presentation delivered as part of the US CLIVAR POS Panel Webinar series on 2 April, 2020. There is an emphasis on BGC and Deep Argo floats.

Videos

Video tutorial series on YouTube
The latest video posted on 21 November 2019 talks about how to get data from Argovis via the API function and how to make some plots in Python based on the returned data.

Video of above presentation delivered as part of the US CLIVAR POS Panel Webinar series on 2 April, 2020.

Scripts

Global metadata from Argovis

Here are links to the global metadata API call in:

• python
• matlab

The script is designed to query the Argovis database by month and can be looped over all months and years to get statistics for the entire Argovis dataset. There is an additional Matlab script that creates a map with the density of profiles in 1° bins similar to what is shown below.

Queries of profile, platform and region data from Argovis

Here are links to:

August 2019	Profile API call in python and matlab
August 2019	Platform API call in python and matlab
August 2019	Region API call in python
June 2020	Link to Argovis GitHub containing Matlab scripts for a regional API call with option to get only core/Deep data, only BGC data or both and a BGC platform API

There is a hard limit of 15mb that can be downloaded at a time, so if no data are returned for a region and time selection, please try reducing the size of the download. This can be done by reducing the region or the time period and then looping over several regions or time periods.

Other Argovis module API calls

Blog post with examples and instructions for the other Argovis module API calls which co-locate Atmospheric River data and show Argo float trajectory forecasts.

SOSE sea ice coverage API call in Matlab.

API calls for Argovis

The Argovis database is comprised of Argo profiles with good quality data, times and locations. There are also profiles with missing locations (likely under-ice profiles) which will be returned when data from the entire float is requested via the API. Currently, there are four different types of API calls to access the database which are described in the table below. There are links to example codes in both Python and Matlab for each type of call below the table.

Argo API call types	Input	Output
Profile	Platform number and cycle number	lat lon position_qc POSITIONING_SYSTEM date date_qc cycle_number platform_number PLATFORM_TYPE DATA_MODE dac station_parameters PI_NAME	Measurements: Temperature, Pressure, Salinity WMO_INST_TYPE	pres_max_for_TEMP pres_max_for_PSAL pres_min_for_TEMP pres_min_for_PSAL date_added station_parameters_in_nc DIRECTION VERTICAL_SAMPLING_SCHEME
Platform	Platform number
Region	Time range, pressure range, set of latitude and longitude vertices to define shape
Global metadata by month	Month and year		pres_max_for_TEMP pres_max_for_PSAL pres_min_for_TEMP pres_min_for_PSAL date_added VERTICAL_SAMPLING_SCHEME

Argovis module API calls	Input	Output	URL
Atmospheric rivers	Datetime in following format: %YYYY-%mm-%ddT%HH:%MM:%SSZ	Polygon coordinates for 20 atmospheric river shapes on that day	https://argovis.colorado.edu/arShapes/findByDate?date=2010-01-01T00:00:00Z Returns atmospheric river shapes for January 1, 2010
Float trajectory	Location and forecast length in days	Float trajectory object at that location	https://argovis.colorado.edu/covarGrid/60.000001/10/120 Returns trajectory object at 60°E and 10°N with a forecast length of 120 days
Grids	Latitude and longitude range, grid name, year, month and depth level	Grid values in desired ranges	https://argovis.colorado.edu/griddedProducts/grid/window?latRange=[21.834305,31.103117]&lonRange=[-42.128202,-20.32501]&gridName=rgTempAnom&monthYear=01-2012&presLevel=10

Visualization of global metadata from Argovis

Density of profiles in 1° by 1° bins

How many profiles are real time, adjusted and delayed mode by age?

Positioning system used to determine profile position vs. time

Number of profiles from each Data Assembly Center (DAC) vs. time

Argo float types vs. time

Acknowledgments and Support Some of these python tutorials are adapted from a blog written by Tyler Tucker. If you have any questions or problems using the site, please feel free to contact us at argo@ucsd.edu.