Calculating ML CCL

[Cjseng]

Hello! I'm working on a project and am looking to find a way to calculate the ML CCL utilizing MetPy. I see there's a way to calculate the mixed_layer, and of course the CCL calculation, but do we have a way to combine the two?

Edit: For a bit of context, the ML (Mixed Layer) is being considered as using the mean values between the surface and 100mb above the surface for calculations.

[dopplershift]

ccl can be passed an optional mixed_layer_depth argument to have it calculate the starting point as the mixed layer over that depth. So you'd probably want to use:

import metpy.calc as mpcalc
from metpy.units import units

# Read data
p, t, td = ...

ccl = mpcalc.ccl(p, t, td, mixed_layer_depth=100 * units.hPa)

[Cjseng]

Doing this seemed to match the data I had from yesterday perfectly, but it's off by nearly 200mb today. Weird! I appreciate the insight.

[dopplershift]

If you can share the data (or site & date/time for a standard radiosonde) we're always happy to double check.

[Cjseng]

Index Calculations.zip

Here's a zip folder with some example data and my Python code. After doing some more reading on my end, it appears the data that I'm given is primarily using the Mixed Layer or the Most Unstable level for most of its calculations, so it makes sense that many of my indices don't match when I'm utilizing MetPy. Indices that are noticably different are typically the SWEAT, TT (which is odd. Mathematically MetPy is correct, so I'm not sure what my source is doing on their end), and MU CAPE & CIN.

The ML CCL for both days provided seem to be fine, within about 10-15mb which I assume can be attributed to differences in how the parcel path is calculated. I'm not sure that would explain the 200mb difference from the other day but unfortunately I no longer have that data.

[dopplershift]

Your conclusions sound reasonable. Just FYI, MetPy does have most_unstable_parcel if you'd like to use that to construct an appropriate profile to use in calculations.

[Cjseng]

I'll have to toy around with it! Thank you again. This package has been extraordinarily helpful for me.