Dong's Projects
MSc Thesis Sea_level_variablity
Instance of ECCOv4-r2 used for adjoint sensitivity studies
Create animations, plots, and calculate summary statistics for MITgcm adjoint output
The Climate Data Toolbox for MATLAB
Supplement for 'A hybrid dynamical approach for seasonal prediction of sea-level anomalies: a pilot study for Charleston, South Carolina'
Example notebooks to convolve ecco sensitivities with other fields
Ocean state estimation framework
model Ekman spiral using a tank model
gcmfaces is a Matlab / Octave toolbox that handles gridded earth variables in generic fashion. Read more at:
A list of cool features of Git and GitHub.
A workshop on writing good scientific code.
My scripts for working on HPC (ARCHER2 specifically)
Course materials for Introduction to Introduction to Physical Oceanography (EESC4925)
MAIO project with Michael
:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.
M.I.T General Circulation Model master code and documentation repository
MITgcm setup for removing divergence from a 2D velocity field
Python scripts designed for my Weddell Sea and Amundsen Sea configurations of MITgcm.
adjoint_test
Tools to interact with MITgcm (setup, run, output, plot, etc)
NCTOOLBOX A Matlab toolbox for working with common data model datasets
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
oceanliner: observing system simulation experiments (OSSEs) to subsample high-resolution model output as if by gliders, ships, or other in situ platforms
Config files for my GitHub profile.
The PyEarthScience repository created by DKRZ (German Climate Computing Center) provides Python scripts and Jupyter notebooks in particular for scientific data processing and visualization used in climate science. It contains scripts for visualization, I/O, and analysis using PyNGL, PyNIO, xarray, cfgrib, xesmf, cartopy, and others.
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.