Sea surface teleconnections with coastal southern California wind and cloud patterns linked to climate, land, ocean, anomalies

About This Project:

• This Project Focuses on Assembling multi-decadal time series datasets of RS weather, land, and ocean data to assess spatial and temporal tele-connections linked to cloud cover, high temperature and wind patterns
• Analyze annual and intra-annual variations for ocean variables linked to atmospheric conditions producing severe weather events using Python, R, and GIS tools
• Identify mechanisms for PO.DAAC to facilitate access, manipulation, and processing of data for external users, communities, and the larger public
• Develop visualizations, ArcGIS StoryMaps, lab exercises, tutorials, and videos for the public and future students

Goals:

• Provide a report outlining the approach, results for a research paper, and presentations at APCG or AGU
• Provide data access feedback to PO.DAAC
• Publish ArcGIS StoryMaps for community partners to share and visualize results
• Create demos and class exercises to manipulate data and generate products for the larger public, including visualizations for underserved communities to better understand the benefits of RS data science
• Give presentations to ARCS colloquium and JPL student program

Getting Started

Datasets:

• Ocean Link

Prerequisites

Some of the modules used include:

• pandas
• xarray
• zarr
• cartopy

Installing

Installation steps are necessary so go through each step to do so

git clone https://github.com/sea-surface-teleconnections/jupyter-examples.git

License

Most like MIT License because we like open sourcing but it can be whatever license you like - Again we want to put the license in a different file to not make the readme too big LICENSE.md

Acknowledgments

• Do we need to give credit to other people or groups? May be fun to share the love here.