Update examples for surface and aircraft and fix any bugs that come up

[rschwant]

I'm re-running all the examples for surface and aircraft datasets. I updated the yaml file to state they have been tested with MELODIES-MONET, finding all the data and putting them in a consistent space, and fix any bugs that come up in the process.

THIS IS A DRAFT I STILL HAVE A COUPLE MORE EXAMPLES TO UPDATE.

These updates include:
-Cleaned up all the examples for the surface and aircraft results.
-For the aircraft examples, moved FIREX-AQ examples to AEROMMA datasets as this will be easier to describe in the paper to use a more recent campaign. All examples are now stored on Meiyu or Hera in one location, so I can easily run these later on.
-Cleaned up a couple duplicative files or files that are outdated and are replaced by more complex examples as we do not need to maintain both options
-Added a note for all YAML files I tested and updated that they work with MELODIES MONET version 1.1
-Fixed violin plot y_labels to default to the obs name if no label provided
-Added a note to the curtain plot YAML file example that the plot only plots all model data and obs data regardless of how you set rem_obs_nan currently, but we will add more options in the future.
-The secondary y-axis for altitude was sometimes not working properly based on you set the ylimits and intervals. So switched to plotting with pandas since the ax1 uses pandas and this was causing an inconsistency sometimes with then using matplotlib. And made the ylimits and intervals optional so users can also just let matplotlib determine the scaling or specify desired behavior if they don’t like the default. I also added that the secondary y-axis would average like the ax1 data for consistency too. This now works for all cases that I tested. Updated all examples for this including the ones on the docs.
-Copied new curtain plot from AEROMMA to replace the old FIREX-AQ plot in the docs for consistency with these examples.
-Talked to the aerosol observationalists with the AEROMMA data and they agreed we can just use the standard temperature and pressure of 273.15 K and 1013.25 mb/hPa for all of their data. So delete the AMS and BC data versions of convert_std_to_amb for consistency and updated the example to just use the generic function convert_std_to_amb instead. And update the standard temperature to 273.15 K in this function. This is much easier to explain to the community and the observationalists approve of this approach.
-Delete redundant save and read yaml file and jupyter notebooks which is already in the doc examples.
-Updated the SUNVEx ground example to pair with O3, NO, and NO2 because current example was paring PM2.5 with Org aerosol so that everything in the example now is scientifically correct.
-Update fire mobile case to be SUNVEx for one of the mobile drives for consistency and hard to find all these older datasets.