Skip to content

HW02.md

Latest commit

 

History

History
executable file
·
32 lines (22 loc) · 2.17 KB

HW02.md

File metadata and controls

executable file
·
32 lines (22 loc) · 2.17 KB

ATMS 523 Weather and Climate Data Analytics

Project 2: dask & xarray for computing weather and climate diagnostics

Task: Create code using python xarray to organize and reduce climate data.

Task: Create code using python xarray to organize and reduce climate data. The goal of this analysis will be to detect global atmospheric circulation patterns (or teleconnections) associated with extreme daily precipitation in a certain part of the globe. You will

(1) Use the precipitation output of ERA-5 to compute a time series of daily precipitation at a given point closest to a city of your choosing. Choose a box of 5 x 5 deg lat-lon values over the grid box closest to the city you are examining. Save this data. Use a period of at least 10 years.

(2) Determine the 95% values of daily precipitation for the data created in (1). Plot a cumulative distribution function of all values daily precipitation values and illustrate the 95% value of daily precipitation in millimeters.

(3) Create a map of the composite mean precipitation on the 95% days identified in (2) over the continental USA, and a map of the anomaly of precipitation from the 1981-2010 mean. This field should be plotted on a Cartopy map centered at your city with a 40 x 40 degree lat-lon range.

Functionality Requirements:

  • The code must use dask and xarray for the data reduction, and matplotlib to plot the required visualizations.
  • You can develop the code using a Jupyter Notebook, or any other way; the repository should be submitted to GitHub, and include a descriptive README.md.

Formatting and Documentation Requirements:

  • The appropriate references for the data, including DOI numbers, for the datasets used.
  • The code must contain docstrings and comments where appropriate.
  • The code must be formatted in PEP8.

GitHub usage requirements:

  • The code must be submitted to GitHub Classroom.

Code submittal:

  • Submit a jupyter notebook with your results to Google Classroom by the end of the day Monday, February 12.

Video:

  • Submit a summary video of your research process and results to Canvas at the completion of your assignment. This should be submitted by the end of the day Wednesday, February 14.