Assignment:
In this project, you will get a chance to use real scatterometter data to study tropical cyclones!
First, let’s take a look at Hurricane Bertha, which is a particularly interesting case to investigate. In the Summer of 2008, it was the first hurricane to form in the Atlantic and it broke the record for longevity, lasting 17 days during the month of July. More info at Bertha at NASA-EO.
1. Download the script qscathurr.m (for MATLAB users) or readscat.py (for Python users) and the Bertha hurricane dataset qscat_20080711v4. Save them both in the same directory. For those who prefer to work with Excel, you can also download the MATLAB script Transfer_to_excel.m (which, if run after the qscathurr.m file, will convert the output to Excel sheets where you can do further analysis).
2. Unzip the dataset file. Windows users: use 7-Zip. Linux users: in a terminal window, type gunzip followed by the full file name.
3. Run the Matlab script. The script reads in data from the scatterometer and produces plots that will help you understand the wind distribution during a hurricane event.
4. Investigate the plots:
- What does the streamline plot show?
- Where is the maximum wind speed? How does that value compare to typical ‘everyday’ winds?
- How does wind speed vary with radius? How about the azimuthal wind speed?
- Briefly comment on the contour plots of radial and azimuthal winds.
5. Add a calculation of the Rossby number to the script, and then plot it as a function of radius. See detailed instructions within the script.
6. You can now run the script with a different dataset of your choice:
- Choose a hurricane case from the Scatterometer Archive of Historical Storms between 1999 and 2009 (recent data is not as good due to instrumentation problems).
- Inspect the surface winds at different times along the path of the tropical cyclone. Try to find a pass that shows a strong hurricane.
- Note down the year, month, day and pass of the event.
- Download scatterometer data from here and unzip into your working folder. Avoid the 3 day average files. More information on the dataset can be found here.
- Adjust the center and the pass number in your Matlab script.
- Run the script and repeat the analysis above.