Daily data

In this example, we load a year of daily data and make some static plots with Matplotlib.

import matplotlib.pyplot as plt

import uscrn

Get data

With uscrn.get_data(), we get back a pandas.DataFrame.

%%time

df = uscrn.get_data(2019, "daily")

Show code cell output Hide code cell output

Discovering files...
155 files found
https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01/2019/CRND0103-2019-AK_Aleknagik_1_NNE.txt
...
https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01/2019/CRND0103-2019-WY_Sundance_8_NNW.txt
Reading files...

[Parallel(n_jobs=-2)]: Using backend LokyBackend with 11 concurrent workers.
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CPU times: total: 2.08 s
Wall time: 17 s

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df

	wban	lst_date	crx_vn	longitude	latitude	t_daily_max	t_daily_min	t_daily_mean	t_daily_avg	p_daily_calc	...	soil_moisture_5_daily	soil_moisture_10_daily	soil_moisture_20_daily	soil_moisture_50_daily	soil_moisture_100_daily	soil_temp_5_daily	soil_temp_10_daily	soil_temp_20_daily	soil_temp_50_daily	soil_temp_100_daily
0	23583	2019-10-09	2.514	-158.610001	59.279999	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	23583	2019-10-10	2.514	-158.610001	59.279999	NaN	NaN	NaN	NaN	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	23583	2019-10-11	2.514	-158.610001	59.279999	7.7	-2.4	2.7	2.5	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	23583	2019-10-12	2.514	-158.610001	59.279999	6.2	-3.5	1.3	0.8	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	23583	2019-10-13	2.514	-158.610001	59.279999	6.3	-2.8	1.7	1.2	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
56289	94088	2019-12-27	2.622	-104.440002	44.520000	-0.4	-10.1	-5.2	-4.9	0.0	...	NaN	NaN	0.313	0.348	0.457	-0.5	-0.1	0.3	1.1	2.2
56290	94088	2019-12-28	2.622	-104.440002	44.520000	-5.0	-11.0	-8.0	-8.5	0.0	...	NaN	NaN	NaN	0.347	0.455	-0.7	-0.3	0.2	1.1	2.2
56291	94088	2019-12-29	2.622	-104.440002	44.520000	-9.0	-11.2	-10.1	-10.1	2.8	...	NaN	NaN	NaN	0.346	0.456	-0.8	-0.4	0.1	1.0	2.1
56292	94088	2019-12-30	2.622	-104.440002	44.520000	-5.8	-13.0	-9.4	-9.3	0.0	...	NaN	NaN	NaN	0.346	0.456	-1.1	-0.5	0.1	1.0	2.1
56293	94088	2019-12-31	2.622	-104.440002	44.520000	1.6	-7.6	-3.0	-2.2	0.0	...	NaN	NaN	NaN	0.347	0.454	-1.2	-0.6	0.0	0.9	2.0

56294 rows × 28 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56294 entries, 0 to 56293
Data columns (total 28 columns):
 #   Column                   Non-Null Count  Dtype         
---  ------                   --------------  -----         
 0   wban                     56294 non-null  object        
 1   lst_date                 56294 non-null  datetime64[ns]
 2   crx_vn                   56024 non-null  object        
 3   longitude                56294 non-null  float32       
 4   latitude                 56294 non-null  float32       
 5   t_daily_max              55580 non-null  float32       
 6   t_daily_min              55579 non-null  float32       
 7   t_daily_mean             55577 non-null  float32       
 8   t_daily_avg              55552 non-null  float32       
 9   p_daily_calc             54890 non-null  float32       
 10  solarad_daily            50009 non-null  float32       
 11  sur_temp_daily_type      56294 non-null  object        
 12  sur_temp_daily_max       49745 non-null  float32       
 13  sur_temp_daily_min       49746 non-null  float32       
 14  sur_temp_daily_avg       49746 non-null  float32       
 15  rh_daily_max             49019 non-null  float32       
 16  rh_daily_min             49019 non-null  float32       
 17  rh_daily_avg             49019 non-null  float32       
 18  soil_moisture_5_daily    33154 non-null  float32       
 19  soil_moisture_10_daily   35001 non-null  float32       
 20  soil_moisture_20_daily   28039 non-null  float32       
 21  soil_moisture_50_daily   27850 non-null  float32       
 22  soil_moisture_100_daily  27044 non-null  float32       
 23  soil_temp_5_daily        39246 non-null  float32       
 24  soil_temp_10_daily       39583 non-null  float32       
 25  soil_temp_20_daily       31306 non-null  float32       
 26  soil_temp_50_daily       30825 non-null  float32       
 27  soil_temp_100_daily      30656 non-null  float32       
dtypes: datetime64[ns](1), float32(24), object(3)
memory usage: 6.9+ MB

Dataset and variable attributes are stored under .attrs.

list(df.attrs)

['which', 'title', 'created', 'source', 'attrs', 'notes']

some_keys = ["which", "title", "created", "source"]
for key in some_keys:
    print(f"{key:{max(len(key) for key in some_keys) + 1}}", df.attrs[key])

which    daily
title    U.S. Climate Reference Network (USCRN) | daily | 2019
created  2024-01-27 15:50:51.784080+00:00
source   https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01

Plot temperature

vn = "t_daily_avg"

df.attrs["attrs"][vn]

{'name': 't_daily_avg',
 'long_name': 'daily average air temperature',
 'units': 'degree_Celsius',
 'description': 'Average air temperature, in degrees C. See Note F.',
 'dtype': 'float32',
 'xarray_only': False,
 'categories': False}

df_ = df[["wban", "lst_date", "latitude", "longitude", vn]]

fig, ax = plt.subplots(figsize=(10, 5), tight_layout=True)

# Individual site time series
df_.groupby("wban").plot(x="lst_date", y="t_daily_avg", ax=ax, c="0.7", lw=0.5, alpha=0.35, legend=False)

# Mean and median
agg = df_.groupby(df_.lst_date)[[vn]].agg(("mean", "median"))
agg[vn]["median"].plot(x="lst_date", y="t_daily_avg", ax=ax, c="0.3", lw=1.8, ls="--", legend=False)
agg[vn]["mean"].plot(x="lst_date", y="t_daily_avg", ax=ax, c="0.3", lw=2, legend=False)

ax.set_title(df.attrs["title"], loc="left", size=10)

ax.set(
    xlabel="",
    ylabel=f"{df.attrs['attrs'][vn]['long_name']} [{df.attrs['attrs'][vn]['units']}]",
);

Soil with xarray

With uscrn.to_xarray(), we convert to xarray.Dataset.

ds = uscrn.to_xarray(df)

# Shorten notes so less space taken viewing the Dataset
lines = ds.attrs["notes"].splitlines()
for i, line in enumerate(lines):
    if line.lstrip().startswith("D."):
        break
else:
    i = 7
ds.attrs["notes"] = "\n".join(lines[:i] + [f"... ({len(lines) - i} more lines)"])

ds

<xarray.Dataset>
Dimensions:              (site: 155, time: 365, depth: 5)
Coordinates:
    wban                 (site) object '03047' '03048' ... '96408' '96409'
  * time                 (time) datetime64[ns] 2019-01-01 ... 2019-12-31
    longitude            (site) float32 -102.8 -106.9 -102.8 ... -150.9 -149.4
    latitude             (site) float32 31.62 34.36 33.96 ... 66.56 63.45 68.65
  * depth                (depth) float64 5.0 10.0 20.0 50.0 100.0
Dimensions without coordinates: site
Data variables: (12/16)
    crx_vn               (site, time) object '2.622' '2.622' ... '2.514' '2.514'
    t_daily_max          (site, time) float32 6.1 0.0 12.5 ... -26.4 -28.9 -29.4
    t_daily_min          (site, time) float32 -3.4 -3.2 -5.4 ... -32.8 -31.1
    t_daily_mean         (site, time) float32 1.3 -1.6 3.6 ... -30.4 -30.8 -30.3
    t_daily_avg          (site, time) float32 -1.5 -1.6 3.4 ... -30.7 -30.1
    p_daily_calc         (site, time) float32 0.0 0.0 0.0 0.0 ... 0.0 0.4 3.0
    ...                   ...
    sur_temp_daily_avg   (site, time) float32 -0.2 0.4 2.6 ... -32.3 -29.0 -28.8
    rh_daily_max         (site, time) float32 84.4 92.4 94.0 ... 75.1 81.4 79.0
    rh_daily_min         (site, time) float32 47.4 71.7 17.7 ... 51.6 68.7 68.6
    rh_daily_avg         (site, time) float32 75.6 81.8 54.7 ... 65.1 77.0 76.2
    soil_moisture_daily  (depth, site, time) float32 0.048 0.048 ... nan nan
    soil_temp_daily      (depth, site, time) float32 3.9 3.2 4.7 ... nan nan nan
Attributes:
    title:    U.S. Climate Reference Network (USCRN) | daily | 2019
    created:  2024-01-27 15:50:56.334536+00:00
    source:   https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01
    notes:    Notes from https://www.ncei.noaa.gov/pub/data/uscrn/products/da...

xarray.Dataset

• Dimensions:
  • site: 155
  • time: 365
  • depth: 5
• Coordinates: (5)
  • wban
    (site)
    object
    '03047' '03048' ... '96408' '96409'

    long_name :

    WBAN number

    description :

    The station WBAN number.

    array(['03047', '03048', '03054', '03055', '03060', '03061', '03062', '03063',
           '03067', '03072', '03074', '03728', '03733', '03739', '03758', '03759',
           '03761', '04125', '04126', '04127', '04128', '04131', '04136', '04137',
           '04138', '04139', '04140', '04141', '04222', '04223', '04236', '04237',
           '04990', '04994', '12987', '13301', '21514', '21515', '22016', '23583',
           '23801', '23802', '23803', '23904', '23906', '23907', '23908', '23909',
           '25379', '25380', '25381', '25382', '25522', '25630', '26494', '26562',
           '26563', '26564', '26565', '26655', '26656', '27516', '53131', '53132',
           '53136', '53138', '53139', '53149', '53150', '53151', '53152', '53154',
           '53155', '53182', '53877', '53878', '53926', '53927', '53960', '53961',
           '53968', '53974', '54794', '54795', '54796', '54797', '54808', '54810',
           '54811', '54854', '54856', '54902', '54903', '54932', '54933', '54937',
           '56401', '63826', '63828', '63829', '63831', '63838', '63849', '63850',
           '63855', '63856', '63857', '63858', '63862', '63867', '63868', '63869',
           '63891', '63892', '63893', '63894', '63895', '63896', '63897', '63898',
           '63899', '64756', '64757', '64758', '73801', '73802', '73803', '92821',
           '92826', '92827', '93243', '93245', '94059', '94060', '94074', '94075',
           '94077', '94078', '94079', '94080', '94081', '94082', '94084', '94085',
           '94088', '94644', '94645', '94995', '94996', '96404', '96405', '96406',
           '96407', '96408', '96409'], dtype=object)

  • time
    (time)
    datetime64[ns]
    2019-01-01 ... 2019-12-31

    description :

    The Local Standard Time (LST) date of the observation.

    array(['2019-01-01T00:00:00.000000000', '2019-01-02T00:00:00.000000000',
           '2019-01-03T00:00:00.000000000', ..., '2019-12-29T00:00:00.000000000',
           '2019-12-30T00:00:00.000000000', '2019-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • longitude
    (site)
    float32
    -102.8 -106.9 ... -150.9 -149.4

    array([-102.81, -106.89, -102.77, -101.59, -107.69, -108.5 , -106.52,
           -103.82, -100.96, -100.25, -106.74,  -79.36,  -79.47,  -75.93,
            -79.09,  -78.47,  -75.79, -119.65, -113.56, -116.75, -119.69,
           -110.71, -117.53, -113.01, -112.54, -119.64, -110.29, -124.32,
           -122.61, -121.45, -123.33, -123.81,  -96.62,  -95.87,  -98.06,
            -93.15, -155.58, -155.08, -103.21, -158.61,  -86.  ,  -87.74,
            -89.43,  -91.78,  -96.82,  -98.08,  -94.58,  -91.72, -135.33,
           -135.69, -131.59, -139.68, -155.92, -160.47, -147.51, -154.32,
           -150.45, -155.75, -148.46, -162.92, -164.08, -156.61, -111.17,
           -110.51, -116.02, -114.21, -117.14, -111.29, -119.82, -117.19,
           -119.88, -114.19, -112.34, -101.61,  -82.61,  -82.56,  -97.09,
            -97.11,  -91.87,  -92.12,  -95.72,  -96.61,  -70.93,  -70.95,
            -71.54,  -71.54,  -88.37,  -86.92,  -88.85,  -84.72,  -81.91,
            -93.29,  -90.17,  -92.99,  -99.13,  -99.48, -145.5 ,  -81.33,
            -84.47,  -84.45,  -89.07,  -84.75,  -86.23,  -83.39,  -85.13,
            -81.46,  -85.96,  -87.24,  -85.62,  -86.8 ,  -87.35,  -87.88,
            -86.61,  -88.14,  -87.62,  -87.64,  -87.71,  -86.  ,  -86.98,
            -86.57,  -87.05,  -73.74,  -79.78,  -76.25,  -87.59,  -86.31,
            -86.06,  -80.69,  -81.32,  -81.37, -120.88, -123.07, -105.21,
           -105.1 , -104.76, -105.54, -103.74, -108.67, -101.44, -103.38,
           -103.3 , -108.97, -102.17, -100.35, -104.44,  -68.7 ,  -67.88,
            -96.57,  -96.85, -141.21, -145.35, -154.13, -159.  , -150.87,
           -149.4 ], dtype=float32)

  • latitude
    (site)
    float32
    31.62 34.36 33.96 ... 63.45 68.65

    array([31.62, 34.36, 33.96, 36.6 , 38.54, 37.26, 35.86, 37.86, 38.87,
           32.04, 32.61, 33.15, 39.01, 37.29, 35.97, 38.  , 39.86, 44.56,
           43.46, 43.2 , 43.47, 43.66, 47.42, 45.16, 41.62, 41.85, 46.88,
           43.27, 40.65, 48.54, 44.42, 47.51, 43.73, 48.31, 26.53, 39.87,
           19.54, 19.65, 29.35, 59.28, 31.79, 31.88, 34.82, 35.82, 28.3 ,
           30.62, 37.43, 37.63, 57.06, 58.43, 55.05, 59.51, 58.21, 55.35,
           64.97, 60.2 , 60.72, 68.49, 70.16, 68.03, 61.35, 71.32, 32.24,
           31.59, 36.62, 39.01, 36.6 , 38.3 , 37.76, 33.44, 34.41, 32.84,
           35.76, 36.57, 35.49, 35.42, 36.12, 36.13, 30.09, 32.88, 31.78,
           39.1 , 43.17, 43.11, 41.49, 41.48, 40.05, 46.33, 41.84, 44.91,
           40.76, 41.56, 44.06, 46.11, 45.71, 46.77, 63.03, 33.36, 31.31,
           31.19, 32.34, 38.09, 37.25, 33.78, 36.01, 30.81, 34.29, 32.46,
           34.57, 34.2 , 34.66, 30.55, 32.85, 32.84, 32.72, 34.77, 34.45,
           34.69, 32.33, 38.89, 31.14, 41.79, 44.23, 42.44, 33.21, 31.92,
           33.57, 28.62, 25.9 , 27.15, 37.24, 38.32, 48.49, 48.31, 40.81,
           40.04, 42.42, 42.68, 42.07, 46.89, 45.52, 40.24, 48.97, 44.02,
           44.52, 44.93, 46.96, 40.85, 40.7 , 62.74, 60.47, 64.5 , 66.56,
           63.45, 68.65], dtype=float32)

  • depth
    (depth)
    float64
    5.0 10.0 20.0 50.0 100.0

    long_name :

    depth below surface

    units :

    cm

    array([  5.,  10.,  20.,  50., 100.])

• Data variables: (16)
  • crx_vn
    (site, time)
    object
    '2.622' '2.622' ... '2.514' '2.514'

    long_name :

    station datalogger version number

    description :

    The version number of the station datalogger program that was in effect at the time of the observation. Note: This field should be treated as text (i.e. string).

    array([['2.622', '2.622', '2.622', ..., '2.622', '2.622', '2.622'],
           ['2.622', '2.622', '2.622', ..., '2.622', '2.622', '2.622'],
           ['2.622', '2.622', '2.622', ..., '2.622', '2.622', '2.622'],
           ...,
           ['2.515', '2.515', '2.515', ..., '2.515', '2.515', '2.515'],
           ['2.515', '2.515', '2.515', ..., '2.515', '2.515', '2.515'],
           ['2.514', '2.514', '2.514', ..., '2.514', '2.514', '2.514']],
          dtype=object)

  • t_daily_max
    (site, time)
    float32
    6.1 0.0 12.5 ... -26.4 -28.9 -29.4

    long_name :

    daily maximum air temperature

    units :

    degree_Celsius

    description :

    Maximum air temperature, in degrees C. See Note F.

    array([[  6.1,   0. ,  12.5, ...,  10.1,  11.1,  13.8],
           [  1.2,  -2.4,  -2.5, ...,   2.5,   3.6,   6. ],
           [ -5. ,   0.5,  11.4, ...,   5.1,   5.9,  10.4],
           ...,
           [ -2. , -11.5, -19.1, ..., -23.4, -18.7, -21.8],
           [  1.8,  -2.9, -11.1, ..., -20.3,  -7.4, -15.8],
           [ -4.9,  -6.8,  -7.1, ..., -26.4, -28.9, -29.4]], dtype=float32)

  • t_daily_min
    (site, time)
    float32
    -3.4 -3.2 -5.4 ... -32.8 -31.1

    long_name :

    daily minimum air temperature

    units :

    degree_Celsius

    description :

    Minimum air temperature, in degrees C. See Note F.

    array([[ -3.4,  -3.2,  -5.4, ...,   0.4,  -6.6,  -2.2],
           [ -9. , -12.3, -16.7, ...,  -5.7,  -7.3,  -6.1],
           [ -8.5,  -8.8,  -8.1, ...,  -3.7,  -8.5,  -8.1],
           ...,
           [-11.6, -20.4, -33.4, ..., -37.9, -27.8, -32.6],
           [ -9.5, -15.3, -21. , ..., -30.6, -25.3, -21.8],
           [-14.2, -14.2, -24.6, ..., -34.4, -32.8, -31.1]], dtype=float32)

  • t_daily_mean
    (site, time)
    float32
    1.3 -1.6 3.6 ... -30.4 -30.8 -30.3

    long_name :

    (t_daily_max + t_daily_min) / 2

    units :

    degree_Celsius

    description :

    Mean air temperature, in degrees C, calculated using the typical historical approach: (T_DAILY_MAX + T_DAILY_MIN) / 2. See Note F.

    array([[  1.3,  -1.6,   3.6, ...,   5.2,   2.3,   5.8],
           [ -3.9,  -7.3,  -9.6, ...,  -1.6,  -1.9,  -0.1],
           [ -6.8,  -4.1,   1.7, ...,   0.7,  -1.3,   1.1],
           ...,
           [ -6.8, -15.9, -26.3, ..., -30.7, -23.3, -27.2],
           [ -3.8,  -9.1, -16.1, ..., -25.5, -16.4, -18.8],
           [ -9.6, -10.5, -15.9, ..., -30.4, -30.8, -30.3]], dtype=float32)

  • t_daily_avg
    (site, time)
    float32
    -1.5 -1.6 3.4 ... -30.2 -30.7 -30.1

    long_name :

    daily average air temperature

    units :

    degree_Celsius

    description :

    Average air temperature, in degrees C. See Note F.

    array([[ -1.5,  -1.6,   3.4, ...,   5. ,   2. ,   5.5],
           [ -3.3,  -6.6,  -9. , ...,  -1.5,  -2.5,  -1.3],
           [ -6.6,  -4.8,   0.3, ...,   0.1,  -1.9,   0.1],
           ...,
           [ -5.7, -16.4, -28.1, ..., -29.7, -20.7, -28.6],
           [ -2.3,  -8.5, -16.6, ..., -27. , -16.2, -20.2],
           [ -9.2, -11.1, -17.5, ..., -30.2, -30.7, -30.1]], dtype=float32)

  • p_daily_calc
    (site, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.4 3.0

    long_name :

    daily total precipitation

    units :

    mm

    description :

    Total amount of precipitation, in mm. See Note F.

    array([[0. , 0. , 0. , ..., 0. , 0. , 0. ],
           [2.6, 0.2, 0. , ..., 0. , 0. , 0. ],
           [0. , 0. , 0. , ..., 0.2, 0. , 0. ],
           ...,
           [0. , 0.3, 0. , ..., 0. , 2.4, 0. ],
           [0. , 3.7, 0. , ..., 0. , 0.3, 1.4],
           [nan, nan, nan, ..., 0. , 0.4, 3. ]], dtype=float32)

  • solarad_daily
    (site, time)
    float32
    2.64 2.42 12.41 ... 0.0 0.0 0.0

    long_name :

    daily total solar radiation

    units :

    MJ m-2

    description :

    Total solar energy, in MJ m-2, calculated from the hourly average global solar radiation rates and converted to energy by integrating over time.

    array([[ 2.64,  2.42, 12.41, ..., 12.36, 13.21, 12.9 ],
           [ 5.68,  9.23, 12.65, ..., 11.49, 11.56, 10.58],
           [ 2.84,  6.3 , 12.04, ..., 10.52, 12.22, 12.08],
           ...,
           [ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],
           [ 0.08,  0.07,  0.  , ...,  0.  ,  0.  ,  0.  ],
           [ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ]], dtype=float32)

  • sur_temp_daily_type
    (site, time)
    object
    'C' 'C' 'C' 'C' ... 'C' 'C' 'C' 'C'

    long_name :

    type of infrared surface temperature measurement

    description :

    Type of infrared surface temperature measurement. 'R' denotes raw measurements, 'C' denotes corrected measurements, and 'U' indicates unknown/missing. See Note G.

    array([['C', 'C', 'C', ..., 'C', 'C', 'C'],
           ['C', 'C', 'C', ..., 'C', 'C', 'C'],
           ['C', 'C', 'C', ..., 'C', 'C', 'C'],
           ...,
           ['C', 'C', 'C', ..., 'C', 'C', 'C'],
           ['C', 'C', 'C', ..., 'C', 'C', 'C'],
           ['C', 'C', 'C', ..., 'C', 'C', 'C']], dtype=object)

  • sur_temp_daily_max
    (site, time)
    float32
    2.4 3.8 16.2 ... -26.5 -26.3 -27.6

    long_name :

    daily maximum infrared surface temperature

    units :

    degree_Celsius

    description :

    Maximum infrared surface temperature, in degrees C.

    array([[  2.4,   3.8,  16.2, ...,  18.6,  20.4,  19.2],
           [  1.5,  -2.8,  -3.6, ...,   3.2,   6.7,   9.2],
           [ -4. ,   5.1,  13. , ...,   9.8,  11.9,  13.2],
           ...,
           [ -3.2, -11.3, -19.8, ..., -24.5, -18.8, -22.1],
           [ -3.8,  -5.1, -15.8, ..., -21.9, -12.7, -17.9],
           [ -8.5,  -9.9,  -8.8, ..., -26.5, -26.3, -27.6]], dtype=float32)

  • sur_temp_daily_min
    (site, time)
    float32
    -3.3 -1.6 -5.7 ... -32.9 -29.8

    long_name :

    daily minimum infrared surface temperature

    units :

    degree_Celsius

    description :

    Minimum infrared surface temperature, in degrees C.

    array([[ -3.3,  -1.6,  -5.7, ...,  -2.8,  -7.2,  -4.1],
           [-12.6, -17.3, -19.5, ...,  -8.5,  -9.7,  -8.2],
           [ -8.3,  -9. ,  -8.4, ...,  -3.7,  -7.8,  -7.4],
           ...,
           [-14.2, -23.2, -38.1, ..., -40.1, -32.4, -36.7],
           [-16.9, -16.2, -33.9, ..., -33.3, -24.4, -23.7],
           [-18.1, -16.7, -27.5, ..., -36.7, -32.9, -29.8]], dtype=float32)

  • sur_temp_daily_avg
    (site, time)
    float32
    -0.2 0.4 2.6 ... -32.3 -29.0 -28.8

    long_name :

    daily average infrared surface temperature

    units :

    degree_Celsius

    description :

    Average infrared surface temperature, in degrees C.

    array([[ -0.2,   0.4,   2.6, ...,   4.9,   2.8,   4.9],
           [ -4. ,  -8.2, -11.7, ...,  -4. ,  -4.3,  -2.7],
           [ -6.2,  -4. ,   0.7, ...,   1.1,  -0.4,   0.3],
           ...,
           [ -7.8, -16.7, -32.3, ..., -31.4, -21.9, -31.7],
           [ -8.6, -10.4, -24. , ..., -26.6, -17.2, -20.3],
           [-13.6, -14.2, -17.9, ..., -32.3, -29. , -28.8]], dtype=float32)

  • rh_daily_max
    (site, time)
    float32
    84.4 92.4 94.0 ... 75.1 81.4 79.0

    long_name :

    daily maximum relative humidity

    units :

    %

    description :

    Maximum relative humidity, in %. See Notes H and I.

    array([[84.4, 92.4, 94. , ..., 69.8, 78.6, 69.7],
           [80.2, 80.1, 76.7, ..., 55.6, 61.4, 69.3],
           [81.7, 85.9, 86.2, ..., 84.9, 85.4, 85.8],
           ...,
           [91.4, 83.9, 82.3, ..., 71.2, 78.1, 71.1],
           [75.8, 94.6, 87.4, ..., 82.8, 90.2, 87. ],
           [79.4, 92.5, 96.5, ..., 75.1, 81.4, 79. ]], dtype=float32)

  • rh_daily_min
    (site, time)
    float32
    47.4 71.7 17.7 ... 51.6 68.7 68.6

    long_name :

    daily minimum relative humidity

    units :

    %

    description :

    Minimum relative humidity, in %. See Notes H and I.

    array([[47.4, 71.7, 17.7, ..., 30.3, 19.2, 27.8],
           [47.5, 59. , 43. , ..., 23.3, 24.3, 24.9],
           [68.9, 56.7, 15.7, ..., 39.3, 29.1, 25.9],
           ...,
           [71.4, 75.2, 71.9, ..., 61.3, 54.7, 59.9],
           [39.8, 59.4, 23.1, ..., 73.2, 73.5, 80.1],
           [57.3, 68.5, 73. , ..., 51.6, 68.7, 68.6]], dtype=float32)

  • rh_daily_avg
    (site, time)
    float32
    75.6 81.8 54.7 ... 65.1 77.0 76.2

    long_name :

    daily average relative humidity

    units :

    %

    description :

    Average relative humidity, in %. See Notes H and I.

    array([[75.6, 81.8, 54.7, ..., 50.9, 46. , 46.2],
           [65.9, 68.6, 60.4, ..., 40.3, 43. , 46.7],
           [75.9, 73.2, 51.4, ..., 62.1, 62.5, 61.8],
           ...,
           [83.1, 79. , 75.8, ..., 65.4, 69.3, 65.7],
           [56.6, 79.6, 58.3, ..., 76.9, 84.7, 82.2],
           [69.5, 76.7, 85.6, ..., 65.1, 77. , 76.2]], dtype=float32)

  • soil_moisture_daily
    (depth, site, time)
    float32
    0.048 0.048 0.049 ... nan nan nan

    long_name :

    daily average soil moisture

    units :

    m3 m-3

    description :

    Average soil moisture, in fractional volumetric water content (m3 m-3). See Notes I and J.

    array([[[0.048, 0.048, 0.049, ..., 0.041, 0.038, 0.037],
            [  nan,   nan,   nan, ..., 0.136,   nan,   nan],
            [0.198,   nan,   nan, ..., 0.163, 0.177, 0.172],
            ...,
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan]],
    
           [[0.056, 0.055, 0.055, ..., 0.054, 0.052, 0.052],
            [0.104, 0.107, 0.112, ..., 0.136, 0.135, 0.135],
            [0.209, 0.208, 0.208, ..., 0.163, 0.162, 0.161],
            ...,
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan]],
    
           [[0.052, 0.052, 0.052, ..., 0.058, 0.056, 0.055],
            [0.095, 0.096, 0.097, ..., 0.138, 0.136, 0.135],
            [0.32 , 0.317, 0.317, ..., 0.191, 0.19 , 0.19 ],
            ...,
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan]],
    
           [[0.052, 0.053, 0.052, ..., 0.069, 0.068, 0.068],
            [0.033, 0.034, 0.034, ..., 0.061, 0.061, 0.061],
            [  nan,   nan,   nan, ..., 0.125, 0.124, 0.124],
            ...,
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan]],
    
           [[0.046, 0.046, 0.045, ..., 0.042, 0.043, 0.042],
            [0.058, 0.059, 0.06 , ..., 0.06 , 0.059, 0.059],
            [0.108, 0.108, 0.109, ..., 0.215, 0.215, 0.215],
            ...,
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan],
            [  nan,   nan,   nan, ...,   nan,   nan,   nan]]], dtype=float32)

  • soil_temp_daily
    (depth, site, time)
    float32
    3.9 3.2 4.7 5.7 ... nan nan nan nan

    long_name :

    daily average soil temperature

    units :

    degree_Celsius

    description :

    Average soil temperature, in degrees C. See Notes I and J.

    array([[[ 3.9,  3.2,  4.7, ...,  9.4,  7.3,  7.5],
            [ 0. ,  0.1,  0.1, ...,  1.3,  0.4,  0.9],
            [ 1. ,  0.9,  1. , ...,  4.3,  2.3,  2.3],
            ...,
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
    
           [[ 5.9,  4.9,  5. , ..., 10.7,  8.6,  8.3],
            [ 0.7,  0.7,  0.7, ...,  2.3,  1.4,  1.6],
            [ 1.6,  1.2,  1.3, ...,  5.3,  3.3,  2.9],
            ...,
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
    
           [[ 6.9,  5.6,  5.7, ..., 11.6,  9.8,  9.2],
            [ 1.7,  1.7,  1.6, ...,  3.4,  2.4,  2.3],
            [ 3.3,  2.5,  2.5, ...,  6.6,  5. ,  4.3],
            ...,
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
    
           [[ 8.7,  7.7,  7.2, ..., 12.6, 11.6, 10.8],
            [ 3.9,  3.7,  3.6, ...,  4.9,  4.2,  3.8],
            [ nan,  nan,  nan, ...,  8.6,  7.7,  6.9],
            ...,
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
    
           [[10.1,  9.6,  9.1, ..., 13.2, 12.8, 12.1],
            [ 5.7,  5.4,  5.2, ...,  6. ,  5.7,  5.3],
            [ 9.1,  8.9,  8.7, ..., 10.4, 10.4, 10.2],
            ...,
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan],
            [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
                   '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',
                   '2019-01-09', '2019-01-10',
                   ...
                   '2019-12-22', '2019-12-23', '2019-12-24', '2019-12-25',
                   '2019-12-26', '2019-12-27', '2019-12-28', '2019-12-29',
                   '2019-12-30', '2019-12-31'],
                  dtype='datetime64[ns]', name='time', length=365, freq=None))

  • depth
    PandasIndex

    PandasIndex(Index([5.0, 10.0, 20.0, 50.0, 100.0], dtype='float64', name='depth'))

• Attributes: (4)

  title :

  U.S. Climate Reference Network (USCRN) | daily | 2019

  created :

  2024-01-27 15:50:56.334536+00:00

  source :

  https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01

  notes :

  Notes from https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01/readme.txt: A. All fields are separated from adjacent fields by at least one space. B. Leading zeros are omitted. C. Missing data are indicated by the lowest possible integer for a given column format, such as -9999.0 for 7-character fields with one decimal place or -99.000 for 7-character fields with three decimal places. ... (41 more lines)

ds.soil_temp_daily.mean(dim="site", keep_attrs=True).plot.contourf(levels=20, size=4.5, aspect=2.5)

ax = plt.gca()
ax.invert_yaxis()

ax.set_title(f"{ds.title}\nAll-site average", loc="left", size=10)

ax.set(xlabel="");