NOAA Global Monitoring Laboratory (GML) Halocarbons (and a few other gases) Data Loader

Simple methods for loading NOAA/GML halocarbon data from the NOAA/GML FTP site located at: https://gml.noaa.gov/aftp/data/hats/

The current list of gases available are: C2Cl4, C2H2, C2H6, C3H8, CCl4, CF4, CH2Cl2, CH3Br, CH3CCl3, CH3Cl, F11, F113, F114, F115, F12, F13, HCFC123, HCFC124, HCFC133a, HCFC141b, HCFC142b, HCFC22, HFC125, HFC134a, HFC143a, HFC152a, HFC227ea, HFC236fa, HFC32, HFC365mfc, HFO1234yf, HFO1234ze, N2O, NF3, OCS, PFC116, PFC218, SF6, SO2F2, h1211, h1301, h2402, i-butane, i-pentane, n-butane, n-hexane, n-pentane

The example below shows how to use the loader method. The following keywords are available: program, freq, gapfill, and addlocation

program

There are several measurement programs for halocarbon data. The loader method in the class HATS_Loader will return monthly mean flask data measured on the M3 mass spectrometer instrument. To select data from a different measurement program use the program keyword. Valid flask measurement programs include 'M3', 'otto', 'oldgc'. Use 'CATS' or 'RITS' for in situ measurement programs. The 'combined' or 'combo' program can be used for the following gases: N2O, SF6, F11, F12, F113, CCl4.

freq

The freq keyword is short for measurement frequency. All programs return monthly means or medians. 'freq' can be set to 'daily' or 'hourly' for the in situ measurement programs.

gapfill

When the gapfill keyword is set to True and the frequency of data (freq keyword) is 'monthly', a seasonally interpolation is done to fill any missing data. The dataframe returned will have five columns: mf, sd, model, gf, gfsd. Where the seasonal model is used to gapfill missing data. The gapfilled data is 'gf' in the dataframe.

addlocation

By default, latitude, longitude, and sample elevation are added to the dataframe. Set addlocation to False to exclude these fields.

The loader returns a Python Pandas multi-index dataframe where the index is a three letter site code and the measurement date. Columns returned are dry mole fraction in parts-per-trillion (ppt) (except for N2O which is in parts-per-billion) and one standard deviation of the mean of air measurements. Columns are denoted as 'mf' for mole fraction and 'sd' for standard deviation.

Igor Pro Halocarbons Loader

The HATS FTP Data.ipf file are Igor Pro functions to load data from the GML FTP site. They are similar to the Python functions but do not have gap fill methods.

Examples:

import pandas as pd

import halocarbons_loader
hats = halocarbons_loader.HATS_Loader()
df = hats.loader('F11')

Loading data for F11
File URL: ftp://ftp.cmdl.noaa.gov/hats/cfcs/cfc11/flasks/GCMS/CFC11b_GCMS_flask.txt
Please consult the header in the file listed above for PI and contact information.
>>> df
                         mf      sd      lat       lon   elev
site date                                                    
alt  2010-04-01  240.780000  0.3400  82.4508  -62.5072  185.0
     2010-05-01  241.050000  0.3575  82.4508  -62.5072  185.0
     2010-06-01  240.670000  0.2275  82.4508  -62.5072  185.0
     2010-07-01  239.495000  0.4150  82.4508  -62.5072  185.0
     2010-08-01  239.425000  0.2525  82.4508  -62.5072  185.0
...                     ...     ...      ...       ...    ...
thd  2020-06-01  224.972500  0.2575  41.0541 -124.1510  107.0
     2020-07-01  224.785000  0.1850  41.0541 -124.1510  107.0
     2020-08-01  224.355000  0.1225  41.0541 -124.1510  107.0
     2020-09-01  223.883333  0.1000  41.0541 -124.1510  107.0
     2020-10-01  224.330000  0.0750  41.0541 -124.1510  107.0

[1799 rows x 5 columns]

To load in situ data from the CATS program try:

import halocarbons_loader
hats = halocarbons_loader.HATS_Loader()

df = hats.loader('F11', program='CATS')
Loading data for F11
File URL: ftp://ftp.cmdl.noaa.gov/hats/cfcs/cfc11/insituGCs/CATS/monthly/brw_F11_MM.dat
File URL: ftp://ftp.cmdl.noaa.gov/hats/cfcs/cfc11/insituGCs/CATS/monthly/nwr_F11_MM.dat
File URL: ftp://ftp.cmdl.noaa.gov/hats/cfcs/cfc11/insituGCs/CATS/monthly/mlo_F11_MM.dat
File URL: ftp://ftp.cmdl.noaa.gov/hats/cfcs/cfc11/insituGCs/CATS/monthly/smo_F11_MM.dat
File URL: ftp://ftp.cmdl.noaa.gov/hats/cfcs/cfc11/insituGCs/CATS/monthly/spo_F11_MM.dat
File URL: ftp://ftp.cmdl.noaa.gov/hats/cfcs/cfc11/insituGCs/CATS/monthly/sum_F11_MM.dat
Please consult the header in the files listed above for PI and contact information.

Working with Pandas multi-index.

import pandas as pd
import matplotlib.pyplot as plt

df['mf']['brw'].plot()
df['mf']['spo'].plot()
plt.show()

# or use the cross section method (xs)
df.xs('brw').mf.plot()
plt.show()

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