Data Directories

The pipeline and none of the retrieval algorithms manipulate any data stored in the directories config.data.ground_pressure.path, config.data.atmospheric_profiles.path, and config.data.interferograms.path. The retrieval only writes to config.data.results.path.

Note

We can guarantee that this pipeline doesn’t modify any data from the input directories. We cannot guarantee this for all of the underlying retrieval algorithms, but we are very confident about that they also don’t manipulate any of the input data.

However, if you want to ensure the retrieval cannot manipulate any of your interferograms or ground pressure files, you can create a read-only mount of the directories. Only point the pipeline to those read-only directories.

See https://askubuntu.com/a/243390.

---

Interferograms

Point the config.data.interferograms.path variable to the directory your interferograms are stored in.

<config.data.interferograms.path>

ma

20220101

ma20210101.ifg.001

ma20210101.ifg.002

ma20210101.ifg.003

...

20220102

ma20210102.ifg.001

ma20210102.ifg.002

ma20210102.ifg.003

...

mb

20220101

...

...

In this example, ma, mb, and so on are the “sensor ids” used by us (see the next section about the EM27 metadata).

You must set the config.data.interferograms.ifg_file_regex value to a regex matching your files. In the example above, we can use ^$(SENSOR_ID)$(DATE).*ifg.\\d+$.

---

Ground Pressure Files

Point the config.data.ground_pressure.path variable to the directory where you store your local meteorological files, i.e. your ground pressure logs.

<config.data.ground_pressure.path>

ma

ground-pressure-ma-2021-01-01.csv

ground-pressure-ma-2021-01-02.csv

...

mb

ground-pressure-mb-2021-01-01.csv

...

...

You can fully specify the naming schema of these pressure files. For example, the example above matches the following naming schema: ^ground-pressure-$(SENSOR_ID)-$(YYYY)-$(MM)-$(DD).csv$. When looking for ground pressure files for a certain sensor id and date, the pipeline will replace the placeholders with the actual values. Available placeholders are $(SENSOR_ID), $(YYYY), $(YY), $(MM), and $(DD).

Note

Subdaily files: If multiple files match the regex, the pipeline will merge all of them into a single file which is then used for the retrieval.

Multi-day files: If each file contains the data of multiple days, the pipeline will filter the data for the specific day. Hence, you could also have monthly files using the schema ^ground-pressure-$(SENSOR_ID)-$(YYYY)-$(MM).csv$.

The second thing you need to specify is in which column to find the pressure and datetime data:

• Pressure column: Pass the name of the column with config.data.ground_pressure.pressure_column and specify the unit (either hPa or Pa) with config.data.ground_pressure.pressure_column_format.
• Datetime column: Pass exactly, one of the following
  • A datetime column containing both date and time information: datetime_column and datetime_column_format
  • One date column and one time column: date_column, date_column_format, time_column, and time_column_format
  • A column containing a Unix timestamp: unix_timestamp_column and unix_timestamp_column_format (format s, ms, us, or ns)

All Ground Pressure is assumed to be in UTC!

Examples

The following examples all use the pressure column config:

{
  "pressure_column": "pressure",
  "pressure_column_format": "hPa"
}

1

Datetime column config:

{
  "date_column": "utc-date",
  "date_column_format": "%Y-%m-%d",
  "time_column": "utc-time",
  "time_column_format": "%H:%M:%S"
}

Corresponding pressure files:

pressure,utc-date,utc-time
997.05,2022-06-02,00:00:49
997.06,2022-06-02,00:01:49
997.06,2022-06-02,00:02:49

2

Datetime column config:

{
  "datetime_column": "utc-datetime",
  "datetime_column_format": "%Y-%m-%dT%H:%M:%S"
}

Corresponding pressure files:

pressure,utc-datetime
997.05,2022-06-02T00:00:49
997.06,2022-06-02T00:01:49
997.06,2022-06-02T00:02:49

3

Datetime column config:

{
  "unix_timestamp_column": "utc-datetime",
  "unix_timestamp_column_format": "s"
}

Corresponding pressure files:

pressure,unix-timestamp
997.05,1654128049
997.06,1654128109
997.06,1654128169

---

Atmospheric Profiles

Point the config.data.atmospheric_profiles.path variable to the directory you want to store the atmospheric profiles.

<config.data.atmospheric_profiles.path>

GGG2014

20210101_48N011E.map

20210101_48N011E.mod

20210101_48N012E.map

20210101_48N012E.mod

20210102_48N011E.map

...

GGG2020

2021010100_48N011E.map

2021010100_48N011E.mod

2021010100_48N011E.vmr

2021010100_48N012E.map

2021010100_48N012E.mod

2021010100_48N012E.vmr

2021010103_48N011E.map

...

Note

Since this pipeline includes a fully automated downloader for this data, you can point it to an empty directory and let the pipeline populate it.

---

Results

The pipeline populates the results directory in the following way:

<config.data.results.path>

proffast-2.3

GGG2020

ma

failed

successful

20210101

input_files

invers20ma_210101_a.inp

pcxs20ma_210101.inp

preprocess5ma_210101.inp

logfiles

container.log

inv_output.log

pcxs_output.log

preprocess_output.log

pylot_38218.log

comb_invparms_ma_SN061_210101-210101.csv

opus_file_stats.csv

about.json

pylot_config.yml

pylot_log_format.yml

... (more files depending on retrieval algorithm)

mb

failed

successful

GGG2014

...

The about.json file in each successful retrieval directory contains all information required to reproduce the respective retrieval results. The structure of the directories in failed/ and successful/ is the same - the outputs are moved to successful/ if the retrieval has produced a final CSV file and to failed otherwise.

---

Bundle Exports

With config.bundles, you can specify a list of bundles to produce from the raw retrieval results. The script will generate one bundle per sensor, retrieval algorithm, and atmospheric profile. For example, when using the following bundle config:

{
  "dst_dir": "/some/path/where/the/bundle_should/be/written/to",
  "output_formats": ["csv", "parquet"],
  "from_datetime": "2024-05-10T00:00:00+0000",
  "to_datetime": "2024-07-09T23:59:59+0000",
  "retrieval_algorithms": ["proffast-2.2", "proffast-2.4"],
  "atmospheric_profile_models": ["GGG2020"],
  "sensor_ids": ["ma", "mb"]
}

… the following bundles will be generated in the output directory /some/path/where/the/bundle_should/be/written/to:

/some/path/where/the/bundle_should_be/written/to

em27-retrieval-bundle-ma-proffast-2.2-GGG2020-20240510-20240709.csv

em27-retrieval-bundle-ma-proffast-2.2-GGG2020-20240510-20240709.parquet

em27-retrieval-bundle-ma-proffast-2.4-GGG2020-20240510-20240709.csv

em27-retrieval-bundle-ma-proffast-2.4-GGG2020-20240510-20240709.parquet

em27-retrieval-bundle-mb-proffast-2.2-GGG2020-20240510-20240709.csv

em27-retrieval-bundle-mb-proffast-2.2-GGG2020-20240510-20240709.parquet

em27-retrieval-bundle-mb-proffast-2.4-GGG2020-20240510-20240709.csv

em27-retrieval-bundle-mb-proffast-2.4-GGG2020-20240510-20240709.parquet

The output files will include all data from the <config.data.results.path> directory that matches the time period. The CSV and Parquet files contain the same data - just in two different tabular formats. They keep all columns from the raw retrieval algorithm but add four more columns utc, retrieval_time, location_id and campaign_ids:

• utc: parsed from the UTC/HHMMSS_ID columns to have a consistent timestamp format
• retrieval_time: the timestamp when the retrieval was finished
• location_id: the location ID of the sensor at that time
• campaign_ids: the campaign IDs that match this datapoint separated by a + sign

Proffast 1.0 bundle example:

utc,HHMMSS_ID,ground_pressure,lat,lon,alt,sza,azi,XH2O,XAIR,XCO2,XCH4,XCH4_S5P,XCO,retrieval_time,location_id,campaign_ids
2022-06-02T05:13:49.000000+0000,51349.0,998.2,48.148,16.438,180.0,70.1,-101.45,3316.9,1.00387,418.077,1.8772,0.0,0.0,2024-09-11T22:48:42.000000+0000,ZEN,both+only-mc
2022-06-02T05:14:04.000000+0000,51404.0,998.2,48.148,16.438,180.0,70.06,-101.41,3317.72,1.00343,417.989,1.87669,0.0,0.0,2024-09-11T22:48:42.000000+0000,ZEN,both+only-mc
2022-06-02T05:14:19.000000+0000,51419.0,998.2,48.148,16.438,180.0,70.02,-101.37,3317.16,1.00421,417.361,1.87585,0.0,0.0,2024-09-11T22:48:42.000000+0000,ZEN,both+only-mc
...

Proffast 2.4 bundle example:

utc,spectrum,ground_pressure,lat,lon,alt,sza,azi,XH2O,XAIR,XCO2,XCH4,XCO2_STR,XCO,XCH4_S5P,H2O,O2,CO2,CH4,CO,CH4_S5P,retrieval_time,location_id,campaign_ids
2022-06-02T05:13:55.000000+0000,220602_051349SN.BIN,998.2,48.148,16.438,180.0,70.1,-101.45,3435.8,0.998586,420.051,1.88495,0.0,0.0,0.0,7.24389e26,4.46289e28,8.89103e25,4.01976e23,0.0,0.0,2024-09-11T22:50:05.000000+0000,ZEN,both+only-mc
2022-06-02T05:14:09.000000+0000,220602_051404SN.BIN,998.19,48.148,16.438,180.0,70.06,-101.41,3436.61,0.998166,419.96,1.88445,0.0,0.0,0.0,7.24253e26,4.46095e28,8.88534e25,4.01701e23,0.0,0.0,2024-09-11T22:50:05.000000+0000,ZEN,both+only-mc
2022-06-02T05:14:24.000000+0000,220602_051419SN.BIN,998.19,48.148,16.438,180.0,70.02,-101.37,3435.96,0.998954,419.327,1.88353,0.0,0.0,0.0,7.24683e26,4.46442e28,8.87892e25,4.01823e23,0.0,0.0,2024-09-11T22:50:05.000000+0000,ZEN,both+only-mc
...

Filtering by Campaign ID can be done with one line of code:

import polars as pl

df = pl.read_parquet()

df = df.filter(pl.col("campaign_ids").str.split("+").list.contains("muccnet"))

For example our MUCCnet campaign config looks like this:

{
  "campaign_id": "muccnet",
  "from_datetime": "2019-09-13T00:00:00+0000",
  "to_datetime": "2100-01-01T23:59:59+0000",
  "sensor_ids": ["ma", "mb", "mc", "md", "me"],
  "location_ids": ["TUM_I", "FEL", "GRAE", "OBE", "TAU", "DLR_2", "DLR_3"]
}

… the dataframe filtered by the campaign ID muccnet will only contain dat that has been generated between 2019-09-13 and 2100-01-01 by the sensors ma, mb, mc, md, and me at the locations TUM_I, FEL, GRAE, OBE, TAU, DLR_2, and DLR_3.

---

Logs

The logs are stored in data/logs by default. Set ERP_LOGS_DIR to use another existing base directory; the pipeline creates its retrieval and retrieval/archive directories automatically:

data/logs

retrieval

20240106-23-54_main.log

Log file a fill retrieval run.

20240106-22-55_generous-easley.log

Log file of an individual container.

20240106-23-10_eloquent-oppenheimer.log

Log file of an individual container.

archive

container

old container logs

main

old main logs

The files are either from containers (startingdate-startingtime_containername.log) or from the main process (startingdate-startingtime_main.log), which orchestrates the containers.