[ncl-talk] Computing cape

[Bill Ladwig]

Hi Andrew,

This may not be helpful, but you might be able to use wrf_cape_3d instead
to help mask your undesired data. The cape_2d and cape_3d routines are the
same, with the main difference being that the cape_2d computes an averages
parcel in the lowest 500 m and computes cape for that parcel, whereas
cape_3d is going to treat every grid box as a parcel and compute the cape
for each grid box. While I'm not sure how scientifically valid this is, you
could mask off the cape_3d values that are below your pressure level of
interest, then average the values above it (up to 500 m) and see if that
works better. It should produce something similar to what cape_2d would
produce if the values were masked. However, is creating an average parcel
and lifting it the same as lifting several parcels and then averaging them?
I'm not sure off the top of my head, but for the lowest 500m, it's probably
not that bad.

The cape_3d routine also works on single vertical columns, so you could use
cape_2d for the entire grid first. Then over the region that you want to
correct, use the technique above and replace the cape_2d values with those
averages. This might help save the computational cost associated with
cape_3d, and will only impact the area that you're trying to correct.

Or, yeah, maybe just using the GFS cape is better.

Also, if I completely misunderstood what you're trying to do, please let me
know and I'll try again.

Hope this helps,

Bill




On Fri, Mar 2, 2018 at 1:37 PM, Andrew Kren - NOAA Affiliate <
andrew.kren at noaa.gov> wrote:

> Dear ncl-talk,
>
> I am computing CAPE using the ncl function wrf_cape_2d.
>
> I am computing this for the ERA-5 reanalysis dataset and my output GFS
> forecast files. When I compute the CAPE for both datasets, I get some
> comparable values between the two datasets, but also some differences. The
> most striking example is over the mountains, such as the Himalayas, where
> ERA5 has no cape, but GFS exceeds 6000 J/kg.
>
> I did some examining to see what this was and plotted the vertical plot of
> temperature for ERA5 and GFS over that mountain area. Turns out that GFS
> has a higher lapse rate below 600 mb compared to ERA5.
>
> I guess my question is, would there be any way to rectify this? I could
> mask values below the surface, but then the function would not account for
> missing values, correct? Or should I use the GFS derived CAPE (which
> doesn't have any cape over the Tibetan plateau).
>
> Any ideas or suggestions are appreciated.
>
> Thanks,
>
> --
> Andrew Kren
> Assistant Scientist
> CIMAS & NOAA/AOML
> 325 Broadway, Boulder, CO 80305
> (303) 497-5418
>
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