NRT data

NRT data#

import time

import matplotlib.pyplot as plt
import pandas as pd

import uscrn

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Recent hourly data#

With uscrn.get_nrt_data(), we can load recent data (near-real-time) from USCRN by specifying the period we want and from which dataset. Here, we request the 6 most recent files.

now = pd.Timestamp.now("UTC")
print(now)

df = uscrn.get_nrt_data(
    (-6, None),
    "hourly",
    n_jobs=2,
)

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2026-07-10 13:45:58.213669+00:00

Discovering files...

  Looking for files in these years
  - 2026

Found 6 file(s) to load
https://www.ncei.noaa.gov/pub/data/uscrn/products/hourly02/updates/2026/CRN60H0203-202607100700.txt
...
https://www.ncei.noaa.gov/pub/data/uscrn/products/hourly02/updates/2026/CRN60H0203-202607101200.txt
Reading files...

[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.

[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:    1.5s

[Parallel(n_jobs=2)]: Done   4 out of   6 | elapsed:    1.8s remaining:    0.9s

[Parallel(n_jobs=2)]: Done   6 out of   6 | elapsed:    2.0s finished

df
wban utc_time lst_time crx_vn longitude latitude t_calc t_hr_avg t_max t_min ... soil_moisture_5 soil_moisture_10 soil_moisture_20 soil_moisture_50 soil_moisture_100 soil_temp_5 soil_temp_10 soil_temp_20 soil_temp_50 soil_temp_100
0 03047 2026-07-10 06:00:00 2026-07-10 00:00:00 2.622 -102.809998 31.620001 27.000000 27.400000 27.900000 27.000000 ... 0.041 0.033 0.036 0.037 0.028 32.0 34.000000 33.700001 34.400002 31.500000
1 03048 2026-07-10 06:00:00 2026-07-09 23:00:00 2.622 -106.889999 34.360001 27.600000 28.200001 28.600000 27.600000 ... 0.052 0.091 0.081 0.061 0.058 33.0 35.000000 34.500000 31.299999 29.299999
2 03054 2026-07-10 06:00:00 2026-07-10 00:00:00 2.622 -102.769997 33.959999 24.799999 25.299999 26.299999 24.799999 ... 0.102 0.112 0.123 0.114 0.139 31.1 32.599998 31.400000 28.400000 24.799999
3 03055 2026-07-10 06:00:00 2026-07-10 00:00:00 2.622 -101.589996 36.599998 22.000000 21.799999 22.200001 21.400000 ... 0.103 0.185 0.179 0.243 0.163 28.0 28.200001 27.100000 24.900000 24.299999
4 03060 2026-07-10 06:00:00 2026-07-09 23:00:00 2.622 -107.690002 38.540001 16.900000 17.600000 18.900000 16.900000 ... 0.042 0.116 0.175 0.221 0.184 25.0 25.799999 24.600000 20.500000 17.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
907 96404 2026-07-10 11:00:00 2026-07-10 02:00:00 2.514 -141.210007 62.740002 10.700000 10.700000 10.700000 10.700000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
908 96405 2026-07-10 11:00:00 2026-07-10 02:00:00 2.514 -145.350006 60.470001 4.700000 5.000000 6.000000 4.600000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
909 96407 2026-07-10 11:00:00 2026-07-10 02:00:00 2.515 -159.000000 66.559998 12.300000 12.600000 13.100000 12.300000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
910 96408 2026-07-10 11:00:00 2026-07-10 02:00:00 2.515 -150.869995 63.450001 10.500000 10.600000 10.700000 10.500000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
911 96409 2026-07-10 11:00:00 2026-07-10 02:00:00 2.514 -149.399994 68.650002 9.300000 9.500000 11.800000 8.300000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

912 rows × 36 columns

Sometimes, a few sites can have times an hour earlier than the others. See the notes in uscrn.get_nrt_data() for more details.

def func(x):
    nx = len(x)
    if nx == 0:
        return ""
    elif nx < 10:
        return sorted(x.unique())
    else:
        return f"..."

(
    df.utc_time
    .value_counts()
    .sort_index()
    .to_frame()
    .assign(
        wbans=df.groupby("utc_time")["wban"].apply(func),
    )
)
count wbans
utc_time
2026-07-10 06:00:00 152 ...
2026-07-10 07:00:00 152 ...
2026-07-10 08:00:00 152 ...
2026-07-10 09:00:00 152 ...
2026-07-10 10:00:00 152 ...
2026-07-10 11:00:00 152 ...

In these files, for example, site WBAN 13301 has data for multiple earlier days included.

Plot temperature change time series#

fig, ax = plt.subplots(figsize=(7, 4))

df_ = df.copy()

tmax = df_.utc_time.max()
df_ = df_[df_.utc_time.between(tmax - pd.Timedelta("7h"), tmax)]

df_["utc_time_mid"] = df_["utc_time"] + pd.Timedelta("30min")
df_["t_hr_avg_k"] = df_["t_hr_avg"].add(273.15)
df_["dt_hr_avg"] = df_["t_hr_avg_k"].sub(df_.groupby("wban")["t_hr_avg_k"].transform("mean"))

df_[["utc_time", "dt_hr_avg"]].groupby("utc_time").mean().plot(
    color="0.3",
    linewidth=3,
    zorder=10,
    legend=False,
    ax=ax,
)

df_.groupby("wban").plot(
    x="utc_time",
    y="dt_hr_avg",
    color="0.5",
    linewidth=1,
    alpha=0.4,
    legend=False,
    xlabel="Time (UTC)",
    ylabel="NRT temperature anomaly  $\Delta T$  (°C)",
    ax=ax,
)

ax.set_title(df.attrs["title"], loc="left", size=8);
../_images/b520e5c1cc1a3cfe8c96b9b1d18eb4c7a0194c37b0422e1a4a1773924d2cd991.png

Plot current temperature#

fig, ax = plt.subplots(figsize=(7, 4.5))

ds = uscrn.to_xarray(df)

ds.isel(time=-1).plot.scatter(x="longitude", y="latitude", hue="t_hr_avg", ax=ax);
../_images/efc4ba745d42a44812c393fadfaecfc05434ccb017afab8ad1610452f37e8bc8.png

Specific period of hourly data#

Date selection works by file, not by the data inside the file. In general, the data are an hour behind the file date/time. See the notes in uscrn.get_nrt_data() for more details.

def get_nrt_hourly_period(period):
    a, b = period
    ap1 = pd.to_datetime(a) + pd.Timedelta(hours=1)
    bp1 = pd.to_datetime(b) + pd.Timedelta(hours=1)

    df = uscrn.get_nrt_data((ap1, bp1))

    time.sleep(0.5)  # for prints
    in_period = df.utc_time.between(a, b)
    print(
        f"Got {in_period.sum()}/{len(df)} ({in_period.sum() / len(df):.1%}) records "
        f"in desired period {a} to {b}"
    )
    outside = df.loc[~in_period, "utc_time"].value_counts()
    print(
        "Outside counts:",
        ", ".join(f"{time:%Y-%m-%d %H} ({count})" for time, count in outside.items())
    )

    dupe = df.duplicated(["wban", "utc_time"], keep=False)
    print(f"Got {dupe.sum()} ({dupe.sum() / len(df):.1%}) duplicates")

    return (
        df[in_period]
        .drop_duplicates(["wban", "utc_time"], keep="last")
        .reset_index(drop=True)
    )


df = get_nrt_hourly_period(("2024-02-09 16", "2024-02-09 20"))

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/home/docs/checkouts/readthedocs.org/user_builds/uscrn/envs/latest/lib/python3.11/site-packages/uscrn/data.py:778: UserWarning: Timestamp 2024-02-09 17:00:00 has no timezone, assuming UTC.
  warnings.warn(f"Timestamp {ts} has no timezone, assuming UTC.")
/home/docs/checkouts/readthedocs.org/user_builds/uscrn/envs/latest/lib/python3.11/site-packages/uscrn/data.py:778: UserWarning: Timestamp 2024-02-09 21:00:00 has no timezone, assuming UTC.
  warnings.warn(f"Timestamp {ts} has no timezone, assuming UTC.")

Discovering files...

  Looking for files in these years
  - 2024

Found 5 file(s) to load
https://www.ncei.noaa.gov/pub/data/uscrn/products/hourly02/updates/2024/CRN60H0203-202402091700.txt
...
https://www.ncei.noaa.gov/pub/data/uscrn/products/hourly02/updates/2024/CRN60H0203-202402092100.txt
Reading files...

[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.

[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:    0.2s

[Parallel(n_jobs=2)]: Done   3 out of   5 | elapsed:    0.5s remaining:    0.3s
[Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:    0.7s finished

Got 769/771 (99.7%) records in desired period 2024-02-09 16 to 2024-02-09 20
Outside counts: 2024-02-09 15 (2)
Got 6 (0.8%) duplicates

df
wban utc_time lst_time crx_vn longitude latitude t_calc t_hr_avg t_max t_min ... soil_moisture_5 soil_moisture_10 soil_moisture_20 soil_moisture_50 soil_moisture_100 soil_temp_5 soil_temp_10 soil_temp_20 soil_temp_50 soil_temp_100
0 03047 2024-02-09 16:00:00 2024-02-09 10:00:00 2.622 -102.809998 31.620001 11.300000 10.0 11.300000 8.300000 ... 0.048 0.038 0.056 0.045 0.035 9.3 9.1 9.5 12.1 13.3
1 03048 2024-02-09 16:00:00 2024-02-09 09:00:00 2.622 -106.889999 34.360001 3.000000 2.3 3.100000 0.900000 ... 0.101 0.140 0.109 0.056 0.057 3.1 4.4 6.2 8.2 9.2
2 03054 2024-02-09 16:00:00 2024-02-09 10:00:00 2.622 -102.769997 33.959999 6.700000 6.5 6.700000 5.900000 ... 0.132 0.160 0.164 0.105 0.125 5.0 5.8 6.9 9.0 10.3
3 03055 2024-02-09 16:00:00 2024-02-09 10:00:00 2.622 -101.589996 36.599998 8.700000 6.7 8.700000 3.900000 ... 0.250 0.306 0.324 0.412 0.217 4.5 5.0 5.6 6.4 6.6
4 03060 2024-02-09 16:00:00 2024-02-09 09:00:00 2.622 -107.690002 38.540001 -7.400000 -8.4 -7.400000 -10.400000 ... NaN NaN NaN 0.173 0.152 -0.3 -0.3 -0.1 0.7 2.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
761 94996 2024-02-09 20:00:00 2024-02-09 14:00:00 2.622 -96.849998 40.700001 8.200000 7.8 8.400000 7.000000 ... 0.290 0.330 0.287 0.260 0.322 5.1 4.4 4.8 5.2 5.5
762 96404 2024-02-09 20:00:00 2024-02-09 11:00:00 2.514 -141.210007 62.740002 -21.700001 -22.9 -21.700001 -24.200001 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
763 96405 2024-02-09 20:00:00 2024-02-09 11:00:00 2.514 -145.350006 60.470001 3.000000 2.9 3.200000 2.600000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
764 96407 2024-02-09 20:00:00 2024-02-09 11:00:00 2.515 -159.000000 66.559998 -9.700000 -9.3 -8.700000 -11.100000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
765 96409 2024-02-09 20:00:00 2024-02-09 11:00:00 2.514 -149.399994 68.650002 -10.800000 -9.6 -8.000000 -11.100000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

766 rows × 36 columns

Recent daily data#

Here, we load the most recent daily data file.

df = uscrn.get_nrt_data(-1, "daily")

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Discovering files...

  Looking for files in these years
  - 2026

Found 1 file(s) to load
https://www.ncei.noaa.gov/pub/data/uscrn/products/daily01/updates/2026/CRND0103-202607092359.txt
Reading files...

[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.2s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s finished

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 03047 2026-07-09 2.622 -102.809998 31.620001 36.799999 23.500000 30.200001 29.900000 0.0 ... 0.051 0.043 0.038 0.037 0.028 35.000000 33.700001 33.299999 32.900002 31.299999
1 03048 2026-07-09 2.622 -106.889999 34.360001 37.299999 20.799999 29.000000 29.500000 0.0 ... 0.055 0.094 0.082 0.061 0.206 36.200001 34.599998 33.000000 30.900000 29.400000
2 03054 2026-07-09 2.622 -102.769997 33.959999 36.400002 18.700001 27.600000 28.700001 0.0 ... 0.106 0.115 0.122 0.113 0.138 32.000000 31.200001 29.900000 28.200001 24.799999
3 03055 2026-07-09 2.622 -101.589996 36.599998 40.099998 18.600000 29.299999 29.200001 15.1 ... 0.106 0.184 0.179 0.244 0.163 28.600000 28.000000 26.500000 24.799999 24.200001
4 03060 2026-07-09 2.622 -107.690002 38.540001 28.900000 14.700000 21.799999 21.200001 0.0 ... 0.045 0.116 0.173 0.221 0.184 27.100000 25.000000 23.200001 20.299999 17.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
147 96404 2026-07-09 2.514 -141.210007 62.740002 13.000000 7.100000 10.000000 10.200000 7.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
148 96405 2026-07-09 2.514 -145.350006 60.470001 19.299999 5.400000 12.400000 12.400000 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
149 96407 2026-07-09 2.515 -159.000000 66.559998 21.000000 14.200000 17.600000 17.600000 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
150 96408 2026-07-09 2.515 -150.869995 63.450001 11.200000 8.600000 9.900000 9.800000 3.7 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
151 96409 2026-07-09 2.514 -149.399994 68.650002 20.700001 7.400000 14.000000 15.700000 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

152 rows × 28 columns

fig, ax = plt.subplots(figsize=(7, 4.5))

ds = uscrn.to_xarray(df).squeeze()

ds.plot.scatter(x="longitude", y="latitude", hue="t_daily_max", ax=ax);
../_images/fd1dd4a8d7d2c1914ba0d9e9067c5eaf168ff928f7a06c448267e3e1de3d5899.png