earth: global ocean forecast updated every three hours!
Access to quality data is essential to understand marine processes. Over the last 20 years, ocean data portals have emerged and are routinely used by Scientific organizations, Research agencies and the Industry to better understand the complexity of the Ocean and its interactions with Climate and Life. These portals facilitate seamless access to marine data/services and promote the exchange and dissemination of ocean-related information.
Ocean scientists routinely perform data crunching to understand a particular system and need to access and query extensive lists of dataset.
Understanding how these data are stored, their origin and how to quickly retrieve particular information from them are what this module is all about!
Preamble
The information that is stored, processed, and exchanged, is at the heart of modern marine science.
Wave height measurements taken every day by a buoy offshore Sydney are data. A graph showing the evolution of the significant wave height over time, at a given place, is information. The fact that the number of extreme storms hitting Australian's coast increases as a result of climate change is knowledge. These three notions are very closely linked.
Roughly speaking, here is how you should use them:
- A piece of data provides a basic description, typically numerical for our purposes, of a given reality.
- Drawing on the collected data, information is obtained by organising and structuring data so as to derive meaning.
- By understanding the meaning of information, we obtain knowledge.
Lecture resources
One of the great challenges for Ocean Data users is to understand where and how to find technologies that make it possible to evaluate, validate, verify, and rank information to help them in their jobs. This involves understanding how the ocean data providers are organised, the main standards, vocabularies and formats which are used by the community as well as the best approach for accessing and querying these information routinely.
| HTML version (for Chrome or Safari) | PDF version |
For the labs
We will use Jupyter, a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. To access the module materials we will download via Kitematic a Docker container called usyd-oceancoasts. Please follow the documentation provided here on how to install the materials on your local computer or directly from the school computer labs.
A series of examples based on ipython notebooks is proposed to give you an introduction to marine data querying. There are several advantages of using python as a general data analysis language and the notebook environment is a versatile tool that is designed to be interactive, user-friendly, open-source and sharable. While there are many libraries available to perform data analysis in Python, here's a few to get you started:
- NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
- SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
- Pandas, also built on top of NumPy, offers data structures and operations for manipulating numerical tables and time series.
- Matplotlib is a 2D plotting library that can generate such data visualizations as histograms, power spectra, bar charts, and scatterplots with just a few lines of code.
- Built on NumPy, SciPy, and Matplotlib, Scikit-learn is a machine learning library that implements classification, regression, and clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests, and gradient boosting.
For a longer list of Python libraries useful for data science applications follow this link.
When the oceandata container has been installed via Kitematic and a volume has been attached to the container, you will be ready to start opening the ipython notebooks. The following notebooks are available:
- Using Basemap library to map Global Ocean Salinity from NASA via THREDDS data server.
- Analysing off-shore sydney wave buoy data from Australian Integrated Marine Observing System (IMOS) and historical NOAA WW3 model predictions for different locations.
- Extract Ocean Radar dataset for Turquoise Bay from IMOS and plot them on a map.
- Access via THREDDS protocol NetCDF forecast prediction dataset for Chesapeake bay (US) from FVCOM model, visualise it and extract relevant information.
| Labs materials | Docker Container usyd-oceancoasts |
IMOS ocean data
Australian marine and climate science data
Using the IMOS User Code Library with Python
