CANCELED: CaSITA: Real-Time Access to GOES-R Weather Satellite Data

Description: Learn how to access the CaSITA real-time data stream for analyzing the GOES-R 16 Band Advanced Baseline Imager (ABI) (https://www.goes-r.gov/mission/ABI-bands-quick-info.html) as well as some additional products CaSITA is generating from the ABI imagery. The CaSITA data stream, which consists of push messages (messages are sent to your machine) when a new image arrives from the satellite and a storage server where the new images can be downloaded from. A demo web application, leveraging this data stream, can be found here: https://casita.library.ucdavis.edu/. A brief overview of how CaSITA processes GIS data in the cloud will be provided along with CaSITA applications that are in development, including early fire detection.    

Learning Objectives: Attendees will learn about the data offered by the UC Davis CaSITA data stream and see examples of projects that leverage this data.      

Prerequisites: Introductory knowledge of GIS, Python and how to interact with web based APIs (think Twitter API). This workshop will cover how to use web based push notifications, specifically Socket.IO (if you want to read ahead: https://socket.io/) so prior knowledge is not required.   

Software: Python and/or Python in a Jupyter notebook. This course will run examples using Google's hosted Jupyter notebooks, Colaboratory. https://colab.research.google.com/

Instructor: Justin Merz 

Instructor Bio: Justin Merz provides technical consulting to data management issues and develops applications both on focused research projects and components that align with the University of California, Davis Library's strengths and services vision. Justin has 15+ years of experience as a full stack application developer specializing in bringing GIS data, library resources and scientific research tools to the web. Currently Justin is focused on scalable cloud service architectures leveraging cloud services to both parallelize applications for faster run time and break apart traditional monolithic applications into smaller components.