Week 8: Final Preparations

Hi everyone and welcome back to my blog!

Last week was spring break, so I took a refreshing break from this project. This week, I resumed working on the project, and I have decided to use ERA5 (ECMWF Reanalysis v5) reanalysis for data obtainment.

ERA5 has a series of useful variables that I will be using, including zonal and meridional wind (u and v wind vectors), precipitable moisture (this one’s important, since it’s the main facet of an atmospheric river), temperature, relative humidity (also important), MSLP (mean sea level pressure), and cloud cover. I discussed these variables in week 7 with the reanalysis data specialist at NCEP (Dr. Tallapragada’s colleague, Dr. Bentley), and she gave me some helpful advice on how to visualize and process this data, including the MetPy tools in Python.

I will be doing five case studies using the ERA5 reanalysis data for the following five tropical cyclone(s) which fed into landfalling atmospheric rivers in Europe.

• Alberto 2000
• Allison 2001
• Wilma + Alpha 2005
• Cristobal 2014
• Arthur 2014

I chose these ones for the sake of simplicity, and since there are literally two weeks left. In the future, I will work on this project (during the summer if possible) and that phase of the project will give me more time to take a look at more complex events like:

• 2008: Ike + Josephine + Hanna feed a complex moisture channel covering the entire Atlantic that impacts Europe
• Jerry 2013 contributed to a huge conveyor belt of moisture transport involving two massive extratropical cyclones
• 2004: Jeanne + Karl + Lisa feed another complex moisture channel consisting of multiple atmospheric rivers. This one looked so confusing I genuinely didn’t know what was even going on 😂

However, I still have my list of all the atmospheric river events attributed to tropical cyclones (including the ones above) in my notebook, which I will produce some more large-scale data charts. That would include: cyclone stage of life, when during the timeline of the hurricane season it took place (think early or late season), duration of events (minimum 24 hours), strength of the cyclone (I might scratch this one because I’m not exactly sure on its relevance), location of the storm (latitude and longitude), and frequency of events relative to hurricane season overall activity.

For that last one, I’m pretty sure that there’s going to be a lot of variation with no visible trend, but that’s just a hypothesis.

Next week will be a doozy, since I’ll be going head-first into the case study and reanalysis, with merely a week and a half remaining. See you then!