Overview of analysis and visualization tools for netCDF data
Overview
Teaching: 0 min
Exercises: 0 minQuestions
Tools
Objectives
panoply, python, ncl, nco, cdo, etc.
CDO (Climate Data Operators)
NCO (netCDF Operators)
NCL (Ncar Command Language)
Ncview (a netCDF visual browser)
- See a screenshot of ncview in action.
- Quick introduction: Everything you need to know to get working in 5 minutes.
Panoply (a netCDF, HDF and GRIB Data Viewer)
Python (programming language)
This is what will mostly be used during this course.
Learn to program with Python
- Programming in Python
- Python for Atmosphere and Ocean Scientists
- Working with Spatio-temporal data in Python
- Reproducible research with Interactive Jupyter Dashboards
- Making a lat-lon reference plot
- Understanding the transform and projection keywords
- Basic plotting of CMIP5 data
- Introduction to python with xarray
- Rename pandas dataframe columns
- Append Pandas dataframes
- select rows and columns in Pandas DataFrames
- Replacing values in Pandas
- Dropping columns in a Pandas dataframe
- Xarray: Calculating Seasonal Averages from Timeseries of Monthly Means
- Xarray Plotting
- Xarray indexing and selecting
- Intermediate Python III: Xarray for Multidimensional Data
Make your python codes more readable and more efficient
- Improve your python programming using python classes and Object Oriented programming
- Using xarray and dask with netCDF data
- Memory usage: Real and Lazy data with iris
- Memory Profiler
Customize your plots
- Customizing colorbars
- Adjust subplots
- Cartopy map gridlines and tick labels
- tick labels example
- Making subplots
- Overlap with contour hatching
Distribute your code
Key Points
Some of the tools available to manipulate netCDF files