[1]:

!test -f temp.bufr || wget https://sites.ecmwf.int/repository/pdbufr/test-data/temp.bufr --output-document=temp.bufr

How to extract radiosonde data with the generic reader?¶

[2]:

import pdbufr

The input BUFR data contains radiosonde observations. Each message represents a given station and contains vertical profiles on a set of pressure levels.

In this notebook we read this data with the generic reader, which is the default reader.

Example 1¶

Getting the WMO station ID and location for each message. Notice that since “WMO_station_id” is a computed key it appears as the last column in the resulting dataframe.

[3]:

df = pdbufr.read_bufr("temp.bufr", columns=("WMO_station_id", "latitude", "longitude"))
df.head()

[3]:

	latitude	longitude	WMO_station_id
0	58.47	-78.08	71907
1	53.75	-73.67	71823
2	-90.00	0.00	89009
3	18.43	-69.88	78486
4	21.98	-159.33	91165

Example 2¶

Extracting the same data but only for a given station.

[4]:

df = pdbufr.read_bufr(
    "temp.bufr",
    columns=("WMO_station_id", "latitude", "longitude"),
    filters={"WMO_station_id": 71907},
)
df

[4]:

	latitude	longitude	WMO_station_id
0	58.47	-78.08	71907

Example 3¶

Getting temperature profiles for two stations.

[5]:

df = pdbufr.read_bufr(
    "temp.bufr",
    columns=("WMO_station_id", "data_datetime", "pressure", "airTemperature"),
    filters={"WMO_station_id": [71823, 71907]},
)
df.head()

[5]:

	pressure	airTemperature	data_datetime	WMO_station_id
0	100300.0	258.3	2008-12-08 12:00:00	71907
1	100000.0	259.7	2008-12-08 12:00:00	71907
2	99800.0	261.1	2008-12-08 12:00:00	71907
3	99100.0	261.7	2008-12-08 12:00:00	71907
4	92500.0	258.1	2008-12-08 12:00:00	71907

[6]:

df.tail()

[6]:

	pressure	airTemperature	data_datetime	WMO_station_id
44	30000.0	218.9	2008-12-08 12:00:00	71823
45	29700.0	218.9	2008-12-08 12:00:00	71823
46	25000.0	221.1	2008-12-08 12:00:00	71823
47	23200.0	223.1	2008-12-08 12:00:00	71823
48	20500.0	221.5	2008-12-08 12:00:00	71823

Example 4¶

Extracting temperature profiles for a given station only on standard pressure levels when temperature >= -30 C.

[7]:

df = pdbufr.read_bufr(
    "temp.bufr",
    columns=("WMO_station_id", "data_datetime", "pressure", "airTemperature"),
    filters={"WMO_station_id": 71907, "verticalSoundingSignificance": 32, "airTemperature": slice(243.16, None)},
)
df

[7]:

	pressure	airTemperature	data_datetime	WMO_station_id
0	100000.0	259.7	2008-12-08 12:00:00	71907
1	92500.0	258.1	2008-12-08 12:00:00	71907
2	85000.0	253.1	2008-12-08 12:00:00	71907

Example 5¶

Extracting temperature, dewpoint and wind values on 500 hPa in the tropics:

[8]:

df = pdbufr.read_bufr(
    "temp.bufr",
    columns=(
        "latitude",
        "longitude",
        "pressure",
        "airTemperature",
        "dewpointTemperature",
        "windDirection",
        "windSpeed",
    ),
    filters={"latitude": slice(-20, 20), "pressure": 50000},
)
df

[8]:

	latitude	longitude	pressure	airTemperature	dewpointTemperature	windDirection	windSpeed
0	18.43	-69.88	50000.0	267.5	247.5	275	4.0
1	18.03	-63.12	50000.0	267.5	244.5	340	5.0
2	13.47	144.78	50000.0	270.3	249.3	110	12.0
3	7.08	171.38	50000.0	268.9	226.9	220	6.0
4	7.45	151.83	50000.0	269.9	249.9	50	18.0
...	...	...	...	...	...	...	...
69	-10.17	123.67	50000.0	269.5	267.0	70	11.0
70	-1.18	136.12	50000.0	264.1	262.6	80	8.0
71	-2.17	106.13	50000.0	268.9	263.9	135	6.0
72	2.83	-60.70	50000.0	267.5	258.5	90	8.0
73	-3.67	-69.67	50000.0	266.7	266.6	350	4.0

74 rows × 7 columns