Add SFCSHP prepbufr scripts to SPOC

[nicholasesposito]

Add ADPSFC prepbufr scripts to the SPOC repository.

These are the high-level scripts.

• Build the backend
• Write scripts
• Compare to GSI diag and GSI IODA
• Compare to low level outputs, generated by global workflow using gdasprepatmiodaobs.

All of the comparisons are done here:
https://docs.google.com/presentation/d/1CQCtbzntg2hXruLKEqqEVuKsMizE8h2DjPmAcka29eg/edit?pli=1#slide=id.g2fc02b94591_0_31

Notes:

• Contains obstypes 180, 183, 280, 282, 284

• There are slightly fewer obs in the high level backend outputs compared to GSI IODA and the low level API output. This is because the scripts I wrote are inclusively bounded in the beginning but not the end (-3 hours from cycle time to +2.999999 hours from cycle time). GSI and the low level api are inclusive at -3 hours and + 3 hours, though this may be a bug in the GSI as it is not supposed to include up to +3 and should end at +2.99999. EDIT: This was indeed a bug. I made changes to my GSI so if the time was +3 hours, it was cycled out.

• height (ZOB), stationElevation (ELV), and pressure (POB) are placed in the MetaData.
  airTemperature, virtualTemperature, stationPressure, windNorthward, windEastward and specificHumidity are ObsValue and simulated. Because there is stationPressure and it uses the Height QM (ZQM mnemonic) is influenced by pressure obs at the surface, stationElevation is in here as well.

• sequenceNumber which is also Obs Subtype is added. Can be either 0 or 1. The Sequence Numbers values for data points that are in both GSI and backend are the same.

• Adding prepbufrDataLevelCategory because in readPrepbufr, if ( cat /=0 ) cycle loop_k_levs

• For the MetaData, there was some data over land that was QC'd out when PrepUseFlag or GsiUseFlag was turned on.

• For airTemperature, there are small differences without QC that decrease when QC is added. Less than 100 datapoints and less than 0.2 K difference.

• virtualTemperature is similar without QC but once QC is applied, the outputs in regards to datacounts, mean and stdev are the exact same between GSIs and bufr backend.

• For windNorthward and windEastward. the backend had the largest amount of data points. But differences in mean and standard deviation were < 0.2 m/s

• For specificHumidity, all plots were the exact same.