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OHW 2023

Predicting SST spatial distribution using satellite SST data and deep learning models

One-line Description

SST spatial distribution prediction using deep learning

Collaborators and Roles

Name Location Role
Joseph Gum Virtual Project Facilitator
Jiarui Yu Seattle Participant
Paula Birocchi Seattle Participant
Boris Shapkin Seattle Participant
Hao Tang Australia Participant
Danyang Li Australia Participant
Chandrama Sarker Australia Participant
Zhengxi Zhou Australia Participant
Dafrosa Kataraihya Virtual Participant
Alex Slonimer Virtual Participant

Background

We have a SST time series from 2000 to 2020 from ERA5 reanalysis to start to work with the model, but we are also interested in obtaining and using sattelite data from MUR (https://urs.earthdata.nasa.gov/). The satellite data is available in the S3 bucket. You can easily access this dataset using this Python code: https://github.com/oceanhackweek/ohw23_proj_sst/blob/main/access_MUR_satellite_data_through_python_S3bucket.py

Goals

Pitch + Ideation: Predict SST anomalies (upwelling, other interesting SST anomalies), generate SST spatial distribution forecast. SST prediction is very important to understand the hydrodynamics and thermodynamics processes in the ocean and also near surface atmosphere-ocean interactions.

Datasets

MUR Satellite Data (2002-present): S3 bucket and NASA website: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1

Workflow/Roadmap

  1. Get data, and define our bounding box/ area of interest (desired approach: time [2000-2020], lat [-5,32], lon[45,90]);

  2. Split data on training, validation and testing datasets;

  3. Model Architecture: a) ConvLSTM: this is going to be our first (and main) approach! b) 3D CNN c) Transformers d) Hybrid: CNN + Transformer + LSTM ( we won't have enough time to apply this model)

  4. Complie and fit a) Early-stop b) * Loss function: MSE, MAE, SSIM (until now, we found better results using MSE) c) * metric

  5. Visualization of result and Interpretation!

References

Project idea

The deep learning model developed here can also be used with other type of data as input! The idea is to use this model with other parameters in the future. For now, we are interested in forecasting the next day of SST spatial distribution.

Ideation board

https://jamboard.google.com/d/1lOgVwnqQLvNRPAOEVEGnWXm8FSTuPYQWbteptKrslTM/viewer?f=10

Slack channel

ohw23_proj_sst

Ideation Presentation

https://docs.google.com/presentation/d/1eQKSdFHNGMDqGJMY4d-yGnNm4UrUj5kIS2mLQGPMZC8/edit#slide=id.g239da66eda5_25_5

Final presentation

https://docs.google.com/presentation/d/1uUAIsuj9bxOFMVeIG_h5Bs-ZGDrRodldlz2FHfj4TbE/edit#slide=id.p

Project google drive

https://drive.google.com/drive/folders/1M0o_R4aoDxU9XJOtLEHe90bma-Jn-jM9

Result

ERA5 Dataset

Model Architecture Val MSE Early Stopping Environment
Transformer 0.1902 Triggered CPU Friendly, <32G RAM
ConvLSTM 0.027 Triggered GPU Needed, <32G RAM
ConvLSTM + Transformer 0.0493 Not Triggered GPU Needed, <128G RAM

MUR Dataset

Model Architecture Val MSE Early Stopping
3D CNN 0.2041 Triggered
ConvLSTM 0.0480 Not Triggered