Project Milestones

[hscannell]

• Figure out how to run pyqg and floater. Figure out how to run pyqg #6
• Reproduce RCLV detection method in Zhang et al. 2020. Learn how to apply the established RCLV method to QG data #7
• Review literature. Read Haller (2015) #1 Read Abernathey & Haller (2018) #2 Read Haller & Beron Vera (2013) #3 Read Zhang et al. (2020) #4 Read Liu et al. (2019) #5 Read Xiong et al. 2020 #10 Read Ashkezari #13
• Create an ensemble of QG model runs to generate a bigger dataset. Figure out how to make an ensemble to parallelize data generation #15
• Save snapshots of the RCLV mask and corresponding LAVD fields for supervised learning. Create test / training dataset for supervised learning problem #14
• Train a NN to label the RCLVs with supervised learning (object segmentation). First ML step: supervised learning #8
• Identify the loss function for unsupervised learning. Exciting ML step: unsupervised learning #9

[rabernat]

This is great Hillary!

If you want to really 🤓 out on github, you could use the "Milestones" feature for this

[hscannell]

We now have milestones!