[Staging #FZM-387181]: Code for CCL in NSHARP?

Hello again,

> Thank you for providing the MetPy code for CCL.  I'll have our software 
> engineers take a look at it.

Excellent!  Again, feel free to reach out to address@hidden with any questions 
about MetPy.


> For the GEMPAK software package that's no longer supported/maintained, does 
> this include NSHARP related information posted at 
> https://unidata.github.io/awips2/cave/nsharp/?

No, AWIPS2 is still supported / in development.  For what it's worth we only 
have so much control over what we can add to it, and I'm not familiar enough 
with it myself to know how difficult that would be... but my guess is 
considerably.

NSHARP's history started with the first AWIPS, and that functionality was added 
to GEMPAK with the same name, though I don't know how identical those two were. 
 SHARPpy is a separate attempt at radiosonde interrogation suite.  Here too I'm 
not exactly sure how much overlap there is between the two, but it kept the 
SHARP naming out of familiarity; this _can_ be thought of as a Python 
implementation of NSHARP, though it's not directly derived from either AWIPS1/2 
or GEMPAK (i.e. all new code).  I believe it was a group within SPC that 
developed it initially.  The bottom line here is that yes, AWIPS2 is supported 
(including NSHARP) though these are all separate entities.


> Can you help me better understand the relationship between NSHARP, SHARPpy, 
> and MetPy?  Googling has given me the impression that NSHARP leverages 
> SHARPpy some or all code/components.  This is the first I've heard of MetPy.  
> Do you think MetPy offers more benefits over NSHARP and SHARPpy as far as 
> calculations being faster and/or more representative of the actual 
> atmospheric column?

Sure.  I mentioned the differences between NSHARP and SHARPpy above, both of 
these are specific tools for interrogating radiosonde data.  MetPy, however, is 
a general meteorological package.  It contains tools to help you retrieve, 
calculate and visualize all sorts of data in many different ways.  A common 
application actually is to visualize sounding data, I'll post a link to that 
example below.  But MetPy can do more than just soundings, it can work with 
radar, satellite, gridded data and so on.  It's a pretty big multi-tool.


> I'm definitely interested in leveraging MetPy.

That's fantastic!  Here are some resources that should help you get started:

MetPy Documentation Homepage:
https://unidata.github.io/MetPy/latest/index.html

Example Gallery:
https://unidata.github.io/MetPy/latest/examples/index.html

A friendly getting started page:
https://unidata.github.io/MetPy/latest/userguide/startingguide.html

Here's an example of plotting a SKEW-T diagram:
https://unidata.github.io/MetPy/latest/examples/Advanced_Sounding.html#sphx-glr-examples-advanced-sounding-py

If you have more specific questions about MetPy, here is the support page 
listing the different avenues in which we provide support:
https://unidata.github.io/MetPy/latest/userguide/SUPPORT.html


I hope this helps, please let me know if you have any other questions.

Best,
-Mike


Ticket Details
===================
Ticket ID: FZM-387181
Department: Support GEMPAK
Priority: Normal
Status: Closed
===================
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