NRT data

NRT data#

import time

import matplotlib.pyplot as plt
import pandas as pd

import uscrn

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,
)

Hide code cell output

 
2026-08-03 16:27:35.761839+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/CRN60H0203-202608031000.txt
...
https://www.ncei.noaa.gov/pub/data/uscrn/products/hourly02/updates/2026/CRN60H0203-202608031500.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 2026-08-03 09:00:00 2026-08-03 03:00:00 2.622 -102.809998 31.620001 23.000000 23.900000 26.200001 23.000000 ... 0.021 0.019 0.022 0.032 0.030 29.6 32.799999 33.500000 33.400002 31.900000
1 03048 2026-08-03 09:00:00 2026-08-03 02:00:00 2.622 -106.889999 34.360001 26.000000 26.299999 27.000000 25.900000 ... 0.060 0.090 0.073 0.063 0.173 30.5 33.000000 33.700001 32.500000 31.200001
2 03054 2026-08-03 09:00:00 2026-08-03 03:00:00 2.622 -102.769997 33.959999 20.299999 21.100000 21.500000 20.299999 ... 0.214 0.208 0.241 0.151 0.237 24.4 27.100000 28.400000 28.500000 26.500000
3 03055 2026-08-03 09:00:00 2026-08-03 03:00:00 2.622 -101.589996 36.599998 23.700001 23.799999 24.200001 23.600000 ... 0.059 0.132 0.167 0.250 0.164 29.4 29.799999 29.100000 27.700001 27.000000
4 03060 2026-08-03 09:00:00 2026-08-03 02:00:00 2.622 -107.690002 38.540001 20.700001 20.799999 21.600000 20.299999 ... 0.146 0.194 0.179 0.241 0.181 23.5 24.799999 25.299999 22.799999 18.900000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
901 96407 2026-08-03 14:00:00 2026-08-03 05:00:00 2.515 -159.000000 66.559998 14.400000 14.600000 14.800000 14.400000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
902 96408 2026-08-03 14:00:00 2026-08-03 05:00:00 2.515 -150.869995 63.450001 14.200000 14.600000 15.300000 14.000000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
903 96409 2026-08-03 12:00:00 2026-08-03 03:00:00 2.514 -149.399994 68.650002 10.300000 10.800000 11.700000 10.300000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
904 96409 2026-08-03 13:00:00 2026-08-03 04:00:00 2.514 -149.399994 68.650002 9.700000 9.900000 10.400000 9.700000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
905 96409 2026-08-03 14:00:00 2026-08-03 05:00:00 2.514 -149.399994 68.650002 9.800000 9.600000 9.800000 9.500000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

906 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
2026-08-03 08:00:00 2 [94059, 94060]
2026-08-03 09:00:00 150 ...
2026-08-03 10:00:00 151 ...
2026-08-03 11:00:00 150 ...
2026-08-03 12:00:00 151 ...
2026-08-03 13:00:00 151 ...
2026-08-03 14:00:00 151 ...

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/a109852dba312f7c4f590d39c6c8c7b58da817fb77a81d31d8d7b43c3e7b9ab6.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/c230bf61adf48f36f3d4320ca7d19410c0ac1cf0df5a2b77c478f8cfc1139580.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"))

Hide code cell output

 
/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")

Hide code cell output

 
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/CRND0103-202608022359.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 2026-08-02 2.622 -102.809998 31.620001 37.000000 17.600000 27.299999 28.299999 0.0 ... 0.022 0.019 0.023 0.033 0.031 36.599998 34.099998 33.599998 32.900002 32.000000
1 03048 2026-08-02 2.622 -106.889999 34.360001 36.799999 20.799999 28.799999 29.200001 0.0 ... 0.069 0.094 0.075 0.063 0.217 35.700001 34.900002 33.500000 32.200001 31.200001
2 03054 2026-08-02 2.622 -102.769997 33.959999 33.400002 17.799999 25.600000 25.500000 0.0 ... 0.229 0.222 0.249 0.139 0.240 27.700001 28.100000 28.000000 28.500000 26.700001
3 03055 2026-08-02 2.622 -101.589996 36.599998 38.700001 16.000000 27.299999 28.500000 0.0 ... 0.062 0.133 0.165 0.249 0.164 31.100000 30.100000 28.600000 27.600000 26.799999
4 03060 2026-08-02 2.622 -107.690002 38.540001 31.000000 16.200001 23.600000 23.400000 0.0 ... 0.153 0.199 0.178 0.239 0.181 27.100000 26.200001 25.000000 22.400000 18.900000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
146 96404 2026-08-02 2.514 -141.210007 62.740002 27.700001 5.900000 16.799999 17.600000 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
147 96405 2026-08-02 2.514 -145.350006 60.470001 14.100000 9.900000 12.000000 12.300000 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
148 96407 2026-08-02 2.515 -159.000000 66.559998 18.600000 9.300000 14.000000 13.500000 7.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
149 96408 2026-08-02 2.515 -150.869995 63.450001 22.000000 11.100000 16.600000 16.799999 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
150 96409 2026-08-02 2.514 -149.399994 68.650002 13.100000 7.600000 10.300000 10.100000 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

151 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/91039cb86a17ebb1b29b8ae9a648fc6217ef21d32cf7ed035c038ebf7ba319cc.png