Choose two meteorologic stations from NOAA's Global Summary of the Month.

  1. at least 60 years of data for each station.
  2. choose stations with different characteristics, regarding mean annual precipitation, seasonality, extreme events, etc.


Analyze the data and make graphs showing the differences and similarities between the two locations you chose. Discuss about:

  1. mean annual precipitation and inter-annual variability.
  2. intra-annual variability (seasonality).
  3. extreme rainfall events and return times.

You will have two weeks to deliver your assignment. You should not hand in a dry document with only figures and code, I'm expecting text before and after each code/graph cell, explaining what you did, why you did it, and how it fits the story you are telling.

Your Jupyter Notebook should be fully functional: if we press Kernel > Restart & Run All, all the code must work without any errors.


All the assignment must be in one single Jupyter Notebook. Use markdown cells to discuss the analysis and results, and in code cells show all the code you used to produce the figures and data analysis. Leave only the code necessary for your analysis, delete unnecessary lines your wrote while analyzing your data. Don't forget to comment your code, just like we did during exercise sessions.

You can write in English or in Hebrew, but the text in the figures must be in English. If you choose to write the discussion in Hebrew, be aware that Jupyter Notebooks don't have native right-to-left language support:

ניתן לכתוב בעברית, למרות שזה לא נראה כ״כ טוב...

You can use some HTML code to achieve best results in Hebrew. Type the following

<p dir="rtl" style="text-align: right;">
עכשיו הרבה יותר טוב!

to get

עכשיו הרבה יותר טוב!

If you have many paragraphs in hebrew, do the following:

פסקה מספר 1.

פסקה מספר 2.

אם יש לכם כמה פסקאות, כל אחת מהן תהיה בתוך "dir" משלה

In my opinion it is too complicated to write in Hebrew in Jupyter Notebooks, just write in English, your grade will not be affected by typos nor less-than-perfect English proficiency.