Thivin Abeywickrama Excellent question! Forecasting sea surface temperature (SST) spatiotemporal variability is highly relevant for climate studies. Here is a general suggested approach with Python:

1) Load netCDF SST data into Pandas dataframe with DatetimeIndex and coordinate columns

2) Split into training and test sets. Training set for fitting models.

3) Extract time series for each lat-lon location. Apply decomposition to remove seasonality.

4) Iterate through locations, fitting ARIMA or LSTM models on each pre-processed timeseries.

5) Make predictions on test set at each location. Evaluate errors. Optimize models.

6) Forecast into future dates using best models. Inverse transform forecasts to undo decomposition.

7) Plot maps of predicted temperatures using matplotlib/cartopy by location coordinates.

8) Generate final netCDF forecast output for downstream use.

The key is fitting models tailored for each location's timeseries patterns. We can exchange code specifics if helpful. This workflow should provide a robust statistical forecast capturing both temporal and spatial relationships. Let me know if any part needs further explanation!

Qamar Ul Islam Do you have python code for this? Could you suggest any relevant materials for this? And please explain how to forecast considering each lat-lon location.

Thanks

This is not a programming question. It's a time-series question. Imagine measuring temperature in your back yard every hour for a day. You could use that to make predictions, but they might not be very useful, because weather changes from day to day. So, you need more than a day. Maybe you measure for 3 days. That would be better. But maybe those were 3 warm days, which are followed by 3 cool days. Etc.

Depending on your background, you might start by reading books on time-series analysis. Then move on to books about ocean physics. And then climate physics. You will soon see that statistical prediction is a weak approach, and that dynamical models are required. That takes you from the domain of reading and plotting with python to the domain of building PhD-level scientific and computing skills. The latter go way beyond plotting with python; you'll need to deploy supercomputers to run models that were took many person-decades to develop and take person-years to learn to run. Oh, and the end result will be a model prediction that will not agree with other model predictions to within the error bars we want for climate prediction.