Step 7: xarray cookbook

[kjschmidt913]

Take areal mean of grid to explore the data and see how changes impact data using the xarray cookbook. https://foundations.projectpythia.org/core/xarray/computation-masking.html

• by default cant use xarray with RADAR coords, which is why we convert to Cartesian coords

• Then, compare the areal mean to the ERA5 data

• RADAR data is fine resolution; want to aggregate across the areal mean

  • data.sel(lat = range(30, 40), lon = range(-95, -90)).mean([“latitude”, “longitude”])

• subsetting the grid at ARM site in Houston

  • ds.sel(lat=29.67, lon=-95.06, method=“nearest”)
  • this will show you what you lose for aggregating across coarse grid vs at single point for high resolution
  • Radar measures reflectivity - have some information about number and size of rain drops
    • Can use this to estimate the rainfall rate + accumulated precipitation, which is what is provided in reanalysis data

[kjschmidt913]

When you've completed this task, mark the checkbox by your name.

• [x] Braeden Cullen
• [x] Aadit Ambadkar
• [x] Sanjiv Parthasarathy
• [x] Isaac Darling