Hi David,

When you are working on paleoclimatology, there is a requirement for huge datasets, hence you will need robust calculation and efficiency. Moreover, it will also depend upon which datasets one is using. In general, MATLAB and R can work, though R is open source. Here is the link for seeing Datasets and Language used for analysis:

https://www.ncdc.noaa.gov/paleo-search/reports/all?dataTypeId=59&search=true

Cheers

Hi David,

When you are working on paleoclimatology, there is a requirement for huge datasets, hence you will need robust calculation and efficiency. Moreover, it will also depend upon which datasets one is using. In general, MATLAB and R can work, though R is open source. Here is the link for seeing Datasets and Language used for analysis:

https://www.ncdc.noaa.gov/paleo-search/reports/all?dataTypeId=59&search=true

Cheers

Hi David,

When I did my PhD in paleocenography I mainly used Fortran and Shell Script. Fortran is quite old fashioned, but most ocean models are written in Fortran. I used Shell Script for the postprocess and plot generation. At present, I also use Matlab, which is quite handy. I hope I helped.

Cheers,

Alba

Hi David,

I found Python useful. I agree with getting your head around Matlab. I did a course on that as I struggled teach myself without any programming background.

Good luck!

Lisa

Hi Nikul, Alba and Lisa,

Thank you very much for yours answers

David,

One other consideration for 3D models is using MITgcm. It's a model that I believe is FORTRAN based. I used it for ocean models in conjunction with MATLAB and was able to do some decent visualization in Vapor. http://mitgcm.org/public/r2_manual/latest/online_documents/manual.pdf

Matlab, Python, Fortran

Thanks you Larry and Robert

David, please consult the following link:

https://geosci.uchicago.edu/~rtp1/itr/Python.html

http://www.2blowhards.com/archives/2009/07/climate_models.html

http://ceur-ws.org/Vol-1536/paper11.pdf

https://www.osti.gov/pages/servlets/purl/1339572

http://www.muthar-alomar.com/wp-content/uploads/2013/01/Spatial-Interpolation_for-Climate.pdf