Homework notebooks, examples, and data files


We chose to look at the WHOTS mooring data, with its ongoing multi-year time series of upper-ocean and atmospheric variables from Station ALOHA, 100 km north of Oahu.


We started with a notebook illustrating how to download and read some unusual current meter data from the Indian Ocean. A couple of emailed assignments were based on modifying this notebook.


The notebook below includes examples of histograms, PDFs, and CDFs; a demonstration of the Central Limit Theorem; and hints to help you get started with the first item of Homework 1.


Run the notebook yourself, experimenting with parameters such as the number of points and the plotting options. Watch for any questions I might have left in the notebook–they might be questions you need to answer in class.


For items 2 and 3 in Homework 1, the following notebook provides a starting point.

The data file is automatically downloaded by the notebook from the following location:


For the homework, Python users are encouraged to start with the notebook above; there is no need to use the matfile below unless you would like to compare it to the data as downloaded by the notebook.


In the file below, the data are sampled mostly at weekly intervals, but the sampling at the end is daily. Time is on the matlab datenum scale.

The file below contains a more recent estimate of SSH anomaly rather than SSH referenced to an estimate of the geoid, but for the purposes of this homework the reference is irrelevant. Time is in days since the beginning of the year 2000. Sampling is uniform at once per day. If you would like to learn how to easily access AVISO altimetry data like this from Python, first register with AVISO (http://www.aviso.altimetry.fr/en/data/data-access/registration-form.html) to get a username and password, and then contact me for instructions on updating and using the OpenDAP AVISO interface in pycurrents.


A current meter record from the Hawaii Island side of the Alenuihaha Channel has a nice island-trapped wave signal in addition to the expected tide. We look at this signal using spectral estimation, complex demodulation, and filtering.


Here is a file from Mark with annual mean sea level at Honolulu, in mm: