Data Analysis and Visualisation in Climate Science
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Data Analysis and Visualisation in Climate Science
A Programmer's Guide
About the Book
Master the tools well so you can focus on the science
The latest version is a substantial update of the book including 132 extra pages with many more code examples.
v1.0 includes the following updates:
- code base update to Python 3.11
- extensive new chapter on Xarray
- plotting code examples updated
- streamline plot examples added
- introduction to pathlib library
- introduction to Micromamba
- broken cross-references and URLs fixed
v0.3 includes the following updates:
- code updated to Python 3.8
- code example for trend line plot using linear regression
- section added on glob.glob()
- introduction to f-strings and associated code updates
- minor code updates
- hi-resolution Cartopy map features
- fixed skew-t plot monotonicity issue
- improved solutions for creating sequences of dates
- some more NumPy solutions
- fixed some cross-references
v0.2 includes the following updates:
- Hovmoeller plots updated and additional example added
- Unnecessary np.meshgrid() commands removed from code examples
- Improved code for contour plot time axis formatting
- Working with Dates/Times section added (datetime module)
- Wind vector plot section added including examples
- Skew-T plot section added including examples
- Section on multiple line plots updated
- Plotting variables added to Table 8.1.2.1
- ax.quiver() command added to Table 8.1.2.1
- ax.subplots() commands updated (nrows and ncols)
- Typos fixed
- Cross-references and table/code/figure numbering fixed
About the Author
Table of Contents
-
- Preface
- Acknowledgements
-
1 Introduction
- 1.1 Overview and Objective
- 1.2 Concept of Local and Remote Machines
- 1.3 Software
-
2 Climate Data
- 2.1 Climate Data Overview
- 2.2 Data Use Licences
- 2.3 Data Quality
- 2.4 Accessing Climate Data
-
2.5 Types of Climate Data
- 2.5.1 Analyses and Reanalyses Products
- 2.5.2 Climate and NWP Model Output
- 2.5.3 Point observations
-
2.6 Data File Formats
- 2.6.1 Plain Text and ASCII
- 2.6.2 Binary
- 2.6.3 GRIB
- 2.6.4 netCDF
- 2.6.5 PP
-
3 Unix
-
3.1 Introduction to Unix
- 3.1.1 Linux Distributions
- 3.1.2 Desktop versus Server
- 3.1.3 High Performance Computing on a Server
-
3.2 Accessing a Remote Server
- 3.2.1 Remote Server Login Details
- 3.2.2 Virtual Private Network (VPN)
- 3.2.3 X Window System (X11 forwarding)
- 3.2.4 Connecting to a Remote Server
-
3.3 First Steps on the Unix server
- 3.3.1 The Terminal Window
- 3.3.2 The Shell
- 3.3.3 Linux Directory Structure and Home Directory
- 3.3.4 Quota
- 3.3.5 File Transfer to and from the Server
- 3.3.6 Mapping the Linux Home Directory as a Remote Network Drive
-
3.4 Some More Unix Server Basics
- 3.4.1 Unix Command Syntax}
- 3.4.2 Manual Pages
- 3.4.3 Editing Text Files
- 3.4.4 Full versus Relative Paths
- 3.4.5 Special Characters
-
3.5 Working with Files and Directories
- 3.5.1 Creating Text Files and Directories
- 3.5.2 Listing Files and Directories
- 3.5.3 Moving Around in the Directory Tree
- 3.5.4 Copying, Moving, Renaming and Deleting Files and Directories
-
3.6 Advanced Unix Commands
- 3.6.1 Examining Text Files
- 3.6.2 File and Directory Properties
- 3.6.3 File Permissions
- 3.6.4 Changing File Permissions and Ownership
- 3.6.5 Changing the Unix Account Password
- 3.6.6 Redirecting Command Output
- 3.6.7 Finding Files
- 3.6.8 File Compression and Archives
- 3.6.9 Download Files from the Command Line
-
3.7 Long-running Jobs
- 3.7.1 GNU Screen (recommended)
-
3.1 Introduction to Unix
-
4 Multi-dimensional Gridded Datasets
- 4.1 The Earth’s Coordinate System and Realms
- 4.2 The Model Grid
- 4.3 Grid Indexing and Geographical Referencing of Data Points
- 4.4 The Time Dimension
-
4.5 Horizontal Resolutions and Grid Types
- 4.5.1 Spectral Resolution
- 4.5.2 Full and Reduced Gaussian Grid
- 4.5.3 Regular latitude-longitude Grid
-
4.6 Vertical Level Types
- 4.6.1 Pressure, Potential Temperature and Potential Vorticity Levels
- 4.6.2 Sigma (Model) Levels
- 4.6.3 Sigma-Hybrid Levels
-
5 The netCDF File Format
- 5.1 Introduction to the netCDF File Format
-
5.2 netCDF File Headers
- 5.2.1 Exploring netCDF File Headers with ncdump
- 5.2.2 Exploring netCDF File Headers with CDO
- 5.2.3 Exploring netCDF File Headers with ncview
- 5.3 Packed netCDF Files
- 5.4 netCDF File Format Conventions
-
6 Python - Concepts and Work Environment
- 6.1 Python Overview
-
6.2 Python Concepts
- 6.2.1 Python Modules and Packages
- 6.2.2 Package Dependencies
- 6.2.3 Package Managers, Repositories and Channels
- 6.2.4 Virtual Environments for Python
- 6.2.5 Conda, Mamba or Micromamba?
- 6.2.6 Micromamba
- 6.2.7 Conda
-
6.3 Python Code Development Solutions
- 6.3.1 Python Code Editors
- 6.3.2 Python IDEs
- 6.3.3 Browser-based Python Code Editing
- 6.3.4 The IPython Command Line
-
7 Python - Programming Basics
-
7.1 Basic Python Programming Building Blocks
- 7.1.1 Declaring Variables
- 7.1.2 Variable Types and Conversion Between them
- 7.1.3 Functions
- 7.1.4 Methods and Attributes
- 7.1.5 Controlling the Code Flow
-
7.2 Applying Python in Climate Data Analysis
- 7.2.1 Error Messages when Running Code
- 7.2.2 Looping Through Input Files
- 7.2.3 Reading Data Files Into NumPy Variables
- 7.2.4 Executing Unix System Commands from Within Python
-
7.3 A Brief Introduction to Numpy
- 7.3.1 Creating Numpy Arrays
- 7.3.2 Indexing NumPy Arrays
- 7.3.3 Saving and Loading NumPy Variables
- 7.3.4 Some NumPy Solutions
-
7.4 Working with Dates and Times
- 7.4.1 Creating Date Objects
-
7.4.2 Converting Datetime Objects to Strings (
strftime) - 7.4.3 Creating a NumPy Array with Datetime Objects
-
7.5 Tips and Solutions
-
7.5.1 Managing Paths and Filenames Using
pathlib - 7.5.2 String Formatting of Numbers
- 7.5.3 Zero-padding Integer Values in Filenames
- 7.5.4 Calculate Height from Geopotential with MetPy
-
7.5.1 Managing Paths and Filenames Using
-
7.1 Basic Python Programming Building Blocks
-
8 Python - Data Analysis with Xarray
- 8.1 What is Xarray?
-
8.2 Xarray Basics
- 8.2.1 Xarray Terminology
- 8.2.2 Import Xarray into Python Script
-
8.3 Reading in netCDF Files Using Xarray
- 8.3.1 Reading in a Single netCDF File
- 8.3.2 Reading in Multiple netCDF Files
- 8.3.3 Reading in Very Large (memory-intensive) netCDF Files
- 8.3.4 Additional Considerations for Reading in netCDF Files
- 8.4 DataSets vs DataArrays
-
8.5 Exploring File Content
- 8.5.1 Print DataSet
- 8.5.2 Print DataArray
- 8.5.3 Accessing DataArray Elements
-
8.6 Dates and Times in Xarray
- 8.6.1 Retrieving Date/Time Information Using Datetime Accessors
- 8.6.2 Passing Date/Time Information to DataArray Methods
-
8.7 Selections
- 8.7.1 Selecting Variables
- 8.7.2 sel() vs isel()
- 8.7.3 Selecting Spatial Subsets (Geographical Regions)
- 8.7.4 Selecting Vertical Levels
- 8.7.5 Selecting Timesteps
-
8.8 Resampling - Downsampling the Time Dimension
- 8.8.1 Resampling to Daily Statistics
- 8.8.2 Resampling to Monthly Statistics
- 8.8.3 Resampling to Yearly Statistics
-
8.9 Aggregation - Calculating Statistics Over Dimensions
- 8.9.1 Aggregation Over the Time Domain
- 8.9.2 Aggregation Over the Spatial Domain
- 8.9.3 Aggregation Over the Vertical Domain
- 8.9.4 Aggregation Over the Zonal Domain
- 8.9.5 Aggregation Over the Meridional Domain
- 8.9.6 Aggregation for Hovmöller Plots
-
8.10 The Split-Apply-Combine Concept
- 8.10.1 Group by Hour
- 8.10.2 Group by Month
- 8.10.3 Group by Season
-
8.11 Interpolation
- 8.11.1 Simple Interpolation Example
- 8.11.2 Spatial Interpolation
- 8.11.3 Interpolation Between two Geographical Points
-
8.12 Xarray Computations
- 8.12.1 Direct Application of Arithmetic Operation
- 8.12.2 NumPy Universial Functions - ufunc()
- 8.12.3 Xarray Universal Functions - apply_ufunc()
- 8.13 Xarray MetPy Integration
-
8.14 More Xarray Methods and Attributes to Explore
- 8.14.1 Some Useful Xarray Methods
- 8.14.2 Some Useful Xarray Attributes
- 8.15 Xarray Plotting
-
8.16 Applying Xarray in Climate Computations
- 8.16.1 The 1997 Indian Ocean Dipole Event
- 8.16.2 Rainfall Variability in the Sahel
- 8.16.3 Winds at In Salah (Algeria)
-
9 Python - Creating Plots
-
9.1 Matplotlib
- 9.1.1 Setting up a Plotting Page (Figure and Axes)
- 9.1.2 Main Plotting Commands
- 9.1.3 Colour Names and Colour Maps
-
9.2 Line Plots
- 9.2.1 Line Plot with Labels
- 9.2.2 Line Plot with Arrows
- 9.2.3 Multiple Lines Plot with Markers and Legend
- 9.2.4 Multiple Lines Plot with two Scales
- 9.2.5 Multiple Lines Plot with Standard Deviation
-
9.3 Scatter Plots
- 9.3.1 Scatter Plot with a Legend
- 9.3.2 Scatter Plot with Divergent Colour Bar
- 9.3.3 Scatter Plot on a Map with Colour Bar and Legend
- 9.3.4 Adding Trend Line Based on Linear Regression
-
9.4 Map Plots
- 9.4.1 Cartopy Map Projections
- 9.4.2 Cartopy Data Transformations
- 9.4.3 High-Resolution Map Features
- 9.4.4 Simple Map of SST Anomalies
- 9.4.5 Map with Stipples for Statistical Significance
-
9.5 Bar Graphs
- 9.5.1 Anomalies Bar Graph
-
9.6 Hovmöller Plots
- 9.6.1 Hovmöller Plot with Time as a Function of Latitude
- 9.6.2 Hovmöller Plot with Time as a Function of Pressure
-
9.7 Vertical Cross-Section Plots
- 9.7.1 Meridional Cross-Section
- 9.7.2 Vertical Cross-Section Between two Points
-
9.8 Skew-T Plots
- 9.8.1 Brief Introduction to Skew-T Plots
- 9.8.2 Simple Skew-T Plot
- 9.8.3 Multiple Skew-T Plots
-
9.9 Vector and Streamline Plots
- 9.9.1 Black Wind Vectors on Filled Colour Contours
- 9.9.2 Coloured Wind Vectors
- 9.9.3 Streamline Plot
-
9.10 Looping Through Multiple Panels
-
9.10.1 Multiple Line Plots (
axes.flatmethod) -
9.10.2 Multiple Line Plots (
pop()function) -
9.10.3 Multiple Map Plots (
axes.flatmethod)
-
9.10.1 Multiple Line Plots (
-
9.1 Matplotlib
-
10 Data Analysis with CDO
- 10.1 What is CDO?
- 10.2 Useful CDO Resources
-
10.3 Basic Syntax of CDO Commands
- 10.3.1 CDO Options
- 10.3.2 CDO Operator Categories
- 10.3.3 Using Multiple CDO Operators
- 10.3.4 CDO Operator Parameters
- 10.3.5 CDO Command Input and Output Files
- 10.4 Merging Files
-
10.5 Selections
- 10.5.1 Selecting Variables
- 10.5.2 Selecting Spatial Subsets (Geographical Regions)
- 10.5.3 Selecting Vertical Levels
- 10.5.4 Selecting Time Subsets
-
10.6 Basic Statistics
- 10.6.1 Statistics over the Time Domain
- 10.6.2 Statistics over the Spatial Domain
- 10.6.3 Statistics over the Vertical Domain
- 10.6.4 Statistics over the Zonal Domain
- 10.6.5 Statistics over the Meridional Domain
- 10.6.6 Statistics over Ensembles
-
10.7 Interpolations
- 10.7.1 Interpolation to a new horizontal grid (remapping)
- 10.7.2 Interpolation in the Vertical Domain
- 10.7.3 Interpolation in the Time Domain
-
10.8 Basic Arithmetic
- 10.8.1 Artithmetic Between Two Files
- 10.8.2 Arithmetic Using a Constant Value
-
10.9 Applying CDO in Climate Computations
- 10.9.1 Indian Ocean Dipole Example
- 10.9.2 Sahel Rainfall Variability Example
- 10.9.3 Creating a Land-Sea Mask File
- 10.10 Using CDO with Python
-
Appendix
- List of Acronyms
- Notes
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