ohw23_proj_argo_ml

1. Get argo data

Argopy: https://argopy.readthedocs.io/en/latest/

• Rob: I'll help work on this

2. Define which data we are going to use

Temperature and salinity

3. Define limits for Argo data: locations, time

North Atlantic Last year of data

4. Prepare data for the Machine Learning Model

Prepare the data vertically to be equal gridded. Use of numpy,interp?

The input data needs to have a format similar to each:

• a dataframe or a csv file
• each row represents a profile
• each column represents a data value in a specific depth
• you can add two more columns on the data related to the position of the profiles

5. Define some configurations on the ML model

09AUG - talk about that

6. Apply the ML model

09AUG - talk about that

7. Generate spikes on the data

Add some random noise to the GDAC data

8. Prepare the final result

Jupyter notebook with all the steps

(Optional) Apply IOOS QC on the "fake" data

If we have time, can use the ioos_qc module's qartod.spike_test function.

(Optional) Prepare the data for the Dense Neural Network (DNN) model

(Optional) Apply the DNN model