A Python Workshop to Efficiently Access, Deliver, Visualize, and Analyze CMIP6-like Big Climate Data in the Cloud
Join us for an immersive, full-day workshop designed to equip attendees with practical skills in accessing, processing, analyzing, and visualizing large-scale climate datasets stored in cloud repositories. This full-day Python programming workshop provides attendees with the applied experience necessary to programmatically access and efficiently process, analyze, and visualize massive climate datasets inside of cloud storage providers.
105th AMS Annual Meeting
New Orleans Ernest N. Morial Convention Center
January 11, 2025 at 8:00 AM - 5:00 PM Central Time (Hybrid)
REGISTRATION RATES
| AMS Member Early Rate | Non-Member Early Rate | AMS Student Member Early Rate | AMS Member Late Rate | Non-Member Late Rate | AMS Student Member Late Rate | |
| Through Dec 4 | Through Dec 4 | Through Dec 4 | Dec 5-Jan 10 | Dec 5-Jan 10 | Dec 5-Jan 10 | |
| A Python Workshop to Efficiently Access, Deliver, Visualize, and Analyze CMIP6-like Big Climate Data in the Cloud | $140.00 | $190.00 | $80.00 | $180.00 | $230.00 | $120.0 |
Course Description:
Attendees will learn to navigate and leverage CMIP6-like climate data repositories stored in Google Cloud Storage and Amazon S3. We’ll develop efficient cloud pipelines using Python that process, load, and prepare climate data from the ECMWF (European Centre for Medium-Range Weather Forecasts) and NOAA-RTMA (Real-Time Mesoscale Analysis) repositories to generate stunning visualizations and conduct data-driven analysis.
Through hands-on training, attendees will leverage Python libraries capable of processing large-scale gridded climate data from cloud storage repositories. The skills gained from this workshop will allow attendees to filter, extract, slice, manipulate, analyze, and visualize different climate datasets, climate attributes, and temporal resolutions from any CMIP6-like climate data repository.
At the end of this Python workshop, attendees will be able to:
- Navigate high-resolution CMIP6 cloud buckets with ease using Python,
- Build high-speed climate data pipelines that connect to cloud-object storage providers,
- Analyze ECMWF and NOAA-RTMA high resolution climate datasets,
- Access, slice, query, and generate time series from gridded data using Python’s Xarray library,
- Generate a virtual ZARR store from raw gridded data for quick access in the cloud
- Efficiently manage and process big “out-of-memory” climate records using Python libraries,
- Create stunning static, interactive, and animated visualizations of big climate data.
If you have questions regarding the course, please contact Ryan Paul Lafler.
Instructors:
Ryan Paul Lafler
Premier Analytics Consulting, LLC and San Diego State University
Dr. Samuel Shen
San Diego State University
Dr. Mitch Goldberg
City College of New York/NOAA-CESSRST Program