🏃🏼‍♂️ FastHerbie

Often, data from several GRIB2 files is needed (range of datetimes and/or forecast lead time). FastHerbie() use multithreading to help you efficiently create multiple Herbie objects. Then you can download many files using multiprocessing. When you open the data with xarray, it will concatenate the DataSets for a range of model runs and forecast lead times.

In this example, we will get the F00-F05 forecasts for each of the runs initialized between 00z-06z on January 1, 2022 (a total of 36 Herbie objects).

from herbie import FastHerbie
import pandas as pd

# Create a range of dates
DATES = pd.date_range(
    start="2022-03-01 00:00",
    periods=6,
    freq="1h",
)

# Create a range of forecast lead times
fxx = list(range(0, 6))

DATES, fxx

(DatetimeIndex(['2022-03-01 00:00:00', '2022-03-01 01:00:00',
                '2022-03-01 02:00:00', '2022-03-01 03:00:00',
                '2022-03-01 04:00:00', '2022-03-01 05:00:00'],
               dtype='datetime64[ns]', freq='h'),
 [0, 1, 2, 3, 4, 5])

# Make FastHerbie Object.
FH = FastHerbie(DATES, model="hrrr", fxx=fxx)
FH

<herbie.fast.FastHerbie at 0x7b0e50233b60>

At it’s core, FastHerbie uses multithreading to make a list of Herbie objects. The list of Herbie objects is stored in the objects property.

FH.objects, len(FH.objects)

([▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 00:00 UTC F00 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 00:00 UTC F01 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 00:00 UTC F02 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 00:00 UTC F03 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 00:00 UTC F04 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 00:00 UTC F05 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 01:00 UTC F00 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 01:00 UTC F01 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 01:00 UTC F02 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 01:00 UTC F03 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 01:00 UTC F04 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 01:00 UTC F05 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 02:00 UTC F00 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 02:00 UTC F01 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 02:00 UTC F02 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 02:00 UTC F03 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 02:00 UTC F04 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 02:00 UTC F05 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 03:00 UTC F00 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 03:00 UTC F01 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 03:00 UTC F02 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 03:00 UTC F03 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 03:00 UTC F04 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 03:00 UTC F05 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 04:00 UTC F00 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 04:00 UTC F01 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 04:00 UTC F02 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 04:00 UTC F03 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 04:00 UTC F04 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 04:00 UTC F05 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 05:00 UTC F00 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 05:00 UTC F01 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 05:00 UTC F02 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 05:00 UTC F03 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 05:00 UTC F04 ┊ source=aws,
  ▌▌Herbie HRRR model sfc product initialized 2022-Mar-01 05:00 UTC F05 ┊ source=aws],
 36)

You can download those Herbie objects as full files or subsets.

# Download full file
# (commented out because it would take a long time, but you get the idea)
# FH.download()

# Download subset
FH.download("TMP:2 m")

[PosixPath('/home/blaylock/data/hrrr/20220301/subset_51e4fd54__hrrr.t02z.wrfsfcf01.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_514bfd54__hrrr.t02z.wrfsfcf04.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_72effd54__hrrr.t01z.wrfsfcf00.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_b4effd54__hrrr.t03z.wrfsfcf00.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9be4fd54__hrrr.t00z.wrfsfcf01.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_b4e4fd54__hrrr.t03z.wrfsfcf01.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_51effd54__hrrr.t02z.wrfsfcf00.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9b21fd54__hrrr.t00z.wrfsfcf02.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9b4bfd54__hrrr.t00z.wrfsfcf04.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_7221fd54__hrrr.t01z.wrfsfcf02.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5121fd54__hrrr.t02z.wrfsfcf02.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_721afd54__hrrr.t01z.wrfsfcf05.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_72e4fd54__hrrr.t01z.wrfsfcf01.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_511afd54__hrrr.t02z.wrfsfcf05.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_b421fd54__hrrr.t03z.wrfsfcf02.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9b1afd54__hrrr.t00z.wrfsfcf05.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_724bfd54__hrrr.t01z.wrfsfcf04.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_7215fd54__hrrr.t01z.wrfsfcf03.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_b415fd54__hrrr.t03z.wrfsfcf03.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_b41afd54__hrrr.t03z.wrfsfcf05.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9c1afd54__hrrr.t04z.wrfsfcf05.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9c21fd54__hrrr.t04z.wrfsfcf02.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9c15fd54__hrrr.t04z.wrfsfcf03.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9c4bfd54__hrrr.t04z.wrfsfcf04.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9beffd54__hrrr.t00z.wrfsfcf00.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9ce4fd54__hrrr.t04z.wrfsfcf01.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5eeffd54__hrrr.t05z.wrfsfcf00.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5e15fd54__hrrr.t05z.wrfsfcf03.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9b15fd54__hrrr.t00z.wrfsfcf03.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5115fd54__hrrr.t02z.wrfsfcf03.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5ee4fd54__hrrr.t05z.wrfsfcf01.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_9ceffd54__hrrr.t04z.wrfsfcf00.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5e1afd54__hrrr.t05z.wrfsfcf05.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5e4bfd54__hrrr.t05z.wrfsfcf04.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_5e21fd54__hrrr.t05z.wrfsfcf02.grib2'),
 PosixPath('/home/blaylock/data/hrrr/20220301/subset_b44bfd54__hrrr.t03z.wrfsfcf04.grib2')]

You can also read the data with xarray. You’ll see here that there are new dimensions for step and time.

ds = FH.xarray("TMP:2 m")
ds

/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/core.py:1257: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(

<xarray.Dataset> Size: 305MB
Dimensions:              (step: 6, time: 6, y: 1059, x: 1799)
Coordinates:
  * step                 (step) timedelta64[ns] 48B 00:00:00 ... 05:00:00
  * time                 (time) datetime64[ns] 48B 2022-03-01 ... 2022-03-01T...
    heightAboveGround    float64 8B 2.0
    latitude             (y, x) float64 15MB 21.14 21.15 21.15 ... 47.85 47.84
    longitude            (y, x) float64 15MB 237.3 237.3 237.3 ... 299.0 299.1
    valid_time           (step, time) datetime64[ns] 288B 2022-03-01 ... 2022...
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    t2m                  (step, time, y, x) float32 274MB 292.7 292.7 ... 262.5
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   hrrr
    product:                 sfc
    description:             High-Resolution Rapid Refresh - CONUS
    search:                  TMP:2 m

xarray.Dataset

• Dimensions:
  • step: 6
  • time: 6
  • y: 1059
  • x: 1799
• Coordinates: (7)
  • step
    (step)
    timedelta64[ns]
    00:00:00 01:00:00 ... 05:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([             0,  3600000000000,  7200000000000, 10800000000000,
           14400000000000, 18000000000000], dtype='timedelta64[ns]')

  • time
    (time)
    datetime64[ns]
    2022-03-01 ... 2022-03-01T05:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2022-03-01T00:00:00.000000000', '2022-03-01T01:00:00.000000000',
           '2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
           '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000'],
          dtype='datetime64[ns]')

  • heightAboveGround
    ()
    float64
    2.0

    long_name :

    height above the surface

    units :

    m

    positive :

    up

    standard_name :

    height

    array(2.)

  • latitude
    (y, x)
    float64
    21.14 21.15 21.15 ... 47.85 47.84

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([[21.138123  , 21.14511004, 21.1520901 , ..., 21.1545089 ,
            21.14753125, 21.14054663],
           [21.16299459, 21.1699845 , 21.17696744, ..., 21.17938723,
            21.1724067 , 21.16541921],
           [21.18786863, 21.19486142, 21.20184723, ..., 21.20426802,
            21.19728462, 21.19029425],
           ...,
           [47.78955926, 47.799849  , 47.81012868, ..., 47.81369093,
            47.80341474, 47.79312849],
           [47.81409316, 47.82438621, 47.8346692 , ..., 47.83823259,
            47.8279531 , 47.81766354],
           [47.8386235 , 47.84891986, 47.85920615, ..., 47.86277069,
            47.85248789, 47.84219502]], shape=(1059, 1799))

  • longitude
    (y, x)
    float64
    237.3 237.3 237.3 ... 299.0 299.1

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([[237.280472  , 237.30713868, 237.3338097 , ..., 287.6569408 ,
            287.68361332, 287.71028151],
           [237.27297501, 237.29964881, 237.32632695, ..., 287.66442108,
            287.69110073, 287.71777603],
           [237.26547368, 237.2921546 , 237.31883986, ..., 287.6719057 ,
            287.69859247, 287.7252749 ],
           ...,
           [225.9351904 , 225.97171577, 226.00825329, ..., 298.97907406,
            299.0156158 , 299.05214538],
           [225.91986142, 225.95639874, 225.99294822, ..., 298.99437498,
            299.03092868, 299.06747022],
           [225.90452027, 225.94106954, 225.97763099, ..., 299.00968806,
            299.04625373, 299.08280723]], shape=(1059, 1799))

  • valid_time
    (step, time)
    datetime64[ns]
    2022-03-01 ... 2022-03-01T10:00:00

    standard_name :

    time

    long_name :

    time

    array([['2022-03-01T00:00:00.000000000', '2022-03-01T01:00:00.000000000',
            '2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
            '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000'],
           ['2022-03-01T01:00:00.000000000', '2022-03-01T02:00:00.000000000',
            '2022-03-01T03:00:00.000000000', '2022-03-01T04:00:00.000000000',
            '2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000'],
           ['2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
            '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000',
            '2022-03-01T06:00:00.000000000', '2022-03-01T07:00:00.000000000'],
           ['2022-03-01T03:00:00.000000000', '2022-03-01T04:00:00.000000000',
            '2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000',
            '2022-03-01T07:00:00.000000000', '2022-03-01T08:00:00.000000000'],
           ['2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000',
            '2022-03-01T06:00:00.000000000', '2022-03-01T07:00:00.000000000',
            '2022-03-01T08:00:00.000000000', '2022-03-01T09:00:00.000000000'],
           ['2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000',
            '2022-03-01T07:00:00.000000000', '2022-03-01T08:00:00.000000000',
            '2022-03-01T09:00:00.000000000', '2022-03-01T10:00:00.000000000']],
          dtype='datetime64[ns]')

  • gribfile_projection
    ()
    object
    None

    crs_wkt :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["unknown",ELLIPSOID["unknown",6371229,0,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Lambert Conic Conformal (2SP)",ID["EPSG",9802]],PARAMETER["Latitude of false origin",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8821]],PARAMETER["Longitude of false origin",262.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8822]],PARAMETER["Latitude of 1st standard parallel",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8823]],PARAMETER["Latitude of 2nd standard parallel",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8824]],PARAMETER["Easting at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8826]],PARAMETER["Northing at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8827]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6371229.0

    semi_minor_axis :

    6371229.0

    inverse_flattening :

    0.0

    reference_ellipsoid_name :

    unknown

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    unknown

    horizontal_datum_name :

    unknown

    projected_crs_name :

    unknown

    grid_mapping_name :

    lambert_conformal_conic

    standard_parallel :

    (38.5, 38.5)

    latitude_of_projection_origin :

    38.5

    longitude_of_central_meridian :

    262.5

    false_easting :

    0.0

    false_northing :

    0.0

    long_name :

    HRRR model grid projection

    array(None, dtype=object)

• Data variables: (1)
  • t2m
    (step, time, y, x)
    float32
    292.7 292.7 292.7 ... 262.5 262.5

    GRIB_paramId :

    167

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    1905141

    GRIB_typeOfLevel :

    heightAboveGround

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    lambert

    GRIB_uvRelativeToGrid :

    1

    GRIB_DxInMetres :

    3000.0

    GRIB_DyInMetres :

    3000.0

    GRIB_LaDInDegrees :

    38.5

    GRIB_Latin1InDegrees :

    38.5

    GRIB_Latin2InDegrees :

    38.5

    GRIB_LoVInDegrees :

    262.5

    GRIB_NV :

    0

    GRIB_Nx :

    1799

    GRIB_Ny :

    1059

    GRIB_cfName :

    air_temperature

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Lambert Conformal can be secant or tangent, conical or bipolar

    GRIB_iScansNegatively :

    0

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    1

    GRIB_latitudeOfFirstGridPointInDegrees :

    21.138123

    GRIB_latitudeOfSouthernPoleInDegrees :

    0.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    237.280472

    GRIB_longitudeOfSouthernPoleInDegrees :

    0.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_parameterName :

    Temperature

    GRIB_parameterUnits :

    K

    GRIB_shapeOfTheEarth :

    6

    GRIB_shortName :

    2t

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    air_temperature

    grid_mapping :

    gribfile_projection

    array([[[[292.7411 , 292.6786 , 292.6786 , ..., 299.1161 , 299.1161 ,
              299.1161 ],
             [292.6786 , 292.6786 , 292.6786 , ..., 299.1161 , 299.1161 ,
              299.1161 ],
             [292.6786 , 292.6786 , 292.6786 , ..., 299.1161 , 299.1161 ,
              299.1161 ],
             ...,
             [280.6786 , 280.6786 , 280.6786 , ..., 263.7411 , 263.7411 ,
              263.8036 ],
             [280.6786 , 280.6786 , 280.6786 , ..., 263.7411 , 263.8036 ,
              263.8036 ],
             [280.6786 , 280.6786 , 280.6161 , ..., 263.8036 , 263.8036 ,
              263.8661 ]],
    
            [[292.78296, 292.78296, 292.78296, ..., 299.09546, 299.03296,
              299.03296],
             [292.78296, 292.78296, 292.72046, ..., 299.03296, 299.03296,
              299.03296],
             [292.72046, 292.72046, 292.72046, ..., 299.03296, 299.03296,
              299.03296],
    ...
             [280.6357 , 280.6982 , 280.6982 , ..., 261.9482 , 262.0107 ,
              262.5732 ],
             [280.6357 , 280.6982 , 280.6982 , ..., 262.0107 , 262.1357 ,
              262.5107 ],
             [280.6357 , 280.6357 , 280.6357 , ..., 262.3857 , 262.4482 ,
              262.5732 ]],
    
            [[292.7087 , 292.7087 , 292.6462 , ..., 298.8962 , 298.8962 ,
              298.8962 ],
             [292.7087 , 292.6462 , 292.6462 , ..., 298.8962 , 298.8962 ,
              298.8962 ],
             [292.6462 , 292.5837 , 292.5837 , ..., 298.8962 , 298.8962 ,
              298.8337 ],
             ...,
             [280.6462 , 280.7087 , 280.7087 , ..., 261.9587 , 262.0212 ,
              262.5212 ],
             [280.6462 , 280.7087 , 280.7087 , ..., 262.0212 , 262.0837 ,
              262.5212 ],
             [280.6462 , 280.6462 , 280.6462 , ..., 262.3962 , 262.4587 ,
              262.5212 ]]]], shape=(6, 6, 1059, 1799), dtype=float32)

• Attributes: (10)

  GRIB_edition :

  2

  GRIB_centre :

  kwbc

  GRIB_centreDescription :

  US National Weather Service - NCEP

  GRIB_subCentre :

  0

  Conventions :

  CF-1.7

  institution :

  US National Weather Service - NCEP

  model :

  hrrr

  product :

  sfc

  description :

  High-Resolution Rapid Refresh - CONUS

  search :

  TMP:2 m

# plot matrix at a single point
a = ds.isel(y=500, x=500)
a["step"] = a["step"].dt.total_seconds() / 3600  # have to convert to hours to plot
a.t2m.plot() # Note: Diagonals are the same valid time

<matplotlib.collections.QuadMesh at 0x7b0e4cf57e00>

If multiple hypercubes are returned by cfgrib when reading with xarray, then you still get a list of Datasets

FH.inventory("TMP:(?:[8,5]00 mb|2 m)")

	grib_message	start_byte	end_byte	range	reference_time	valid_time	variable	level	forecast_time	search_this	FILE
0	15	7631631	8193915.0	7631631-8193915	2022-03-01 00:00:00	2022-03-01 00:00:00	TMP	500 mb	anl	:TMP:500 mb:anl	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
1	71	36318346	37549890.0	36318346-37549890	2022-03-01 00:00:00	2022-03-01 00:00:00	TMP	2 m above ground	anl	:TMP:2 m above ground:anl	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
2	15	7635185	8203254.0	7635185-8203254	2022-03-01 00:00:00	2022-03-01 01:00:00	TMP	500 mb	1 hour fcst	:TMP:500 mb:1 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
3	71	43581050	44810228.0	43581050-44810228	2022-03-01 00:00:00	2022-03-01 01:00:00	TMP	2 m above ground	1 hour fcst	:TMP:2 m above ground:1 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
4	15	7962155	8519233.0	7962155-8519233	2022-03-01 00:00:00	2022-03-01 02:00:00	TMP	500 mb	2 hour fcst	:TMP:500 mb:2 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
...	...	...	...	...	...	...	...	...	...	...	...
67	71	44489436	45801367.0	44489436-45801367	2022-03-01 05:00:00	2022-03-01 08:00:00	TMP	2 m above ground	3 hour fcst	:TMP:2 m above ground:3 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
68	15	8192881	8752530.0	8192881-8752530	2022-03-01 05:00:00	2022-03-01 09:00:00	TMP	500 mb	4 hour fcst	:TMP:500 mb:4 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
69	71	44688940	46005220.0	44688940-46005220	2022-03-01 05:00:00	2022-03-01 09:00:00	TMP	2 m above ground	4 hour fcst	:TMP:2 m above ground:4 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
70	15	8285779	8844677.0	8285779-8844677	2022-03-01 05:00:00	2022-03-01 10:00:00	TMP	500 mb	5 hour fcst	:TMP:500 mb:5 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...
71	71	43827545	45148222.0	43827545-45148222	2022-03-01 05:00:00	2022-03-01 10:00:00	TMP	2 m above ground	5 hour fcst	:TMP:2 m above ground:5 hour fcst	https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrr...

72 rows × 11 columns

ds = FH.xarray("TMP:(?:[8,5]00 mb|2 m)", remove_grib=False)
len(ds)

/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(
/home/blaylock/GITHUB/Herbie/src/herbie/fast.py:347: FutureWarning: In a future version of xarray the default value for coords will change from coords='different' to coords='minimal'. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set coords explicitly.
  ds = xr.combine_nested(

2

ds[0]

<xarray.Dataset> Size: 305MB
Dimensions:              (step: 6, time: 6, y: 1059, x: 1799)
Coordinates:
  * step                 (step) timedelta64[ns] 48B 00:00:00 ... 05:00:00
  * time                 (time) datetime64[ns] 48B 2022-03-01 ... 2022-03-01T...
    heightAboveGround    float64 8B 2.0
    latitude             (y, x) float64 15MB 21.14 21.15 21.15 ... 47.85 47.84
    longitude            (y, x) float64 15MB 237.3 237.3 237.3 ... 299.0 299.1
    valid_time           (step, time) datetime64[ns] 288B 2022-03-01 ... 2022...
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    t2m                  (step, time, y, x) float32 274MB 292.7 292.7 ... 262.5
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   hrrr
    product:                 sfc
    description:             High-Resolution Rapid Refresh - CONUS
    search:                  TMP:(?:[8,5]00 mb|2 m)

xarray.Dataset

• Dimensions:
  • step: 6
  • time: 6
  • y: 1059
  • x: 1799
• Coordinates: (7)
  • step
    (step)
    timedelta64[ns]
    00:00:00 01:00:00 ... 05:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([             0,  3600000000000,  7200000000000, 10800000000000,
           14400000000000, 18000000000000], dtype='timedelta64[ns]')

  • time
    (time)
    datetime64[ns]
    2022-03-01 ... 2022-03-01T05:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2022-03-01T00:00:00.000000000', '2022-03-01T01:00:00.000000000',
           '2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
           '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000'],
          dtype='datetime64[ns]')

  • heightAboveGround
    ()
    float64
    2.0

    long_name :

    height above the surface

    units :

    m

    positive :

    up

    standard_name :

    height

    array(2.)

  • latitude
    (y, x)
    float64
    21.14 21.15 21.15 ... 47.85 47.84

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([[21.138123  , 21.14511004, 21.1520901 , ..., 21.1545089 ,
            21.14753125, 21.14054663],
           [21.16299459, 21.1699845 , 21.17696744, ..., 21.17938723,
            21.1724067 , 21.16541921],
           [21.18786863, 21.19486142, 21.20184723, ..., 21.20426802,
            21.19728462, 21.19029425],
           ...,
           [47.78955926, 47.799849  , 47.81012868, ..., 47.81369093,
            47.80341474, 47.79312849],
           [47.81409316, 47.82438621, 47.8346692 , ..., 47.83823259,
            47.8279531 , 47.81766354],
           [47.8386235 , 47.84891986, 47.85920615, ..., 47.86277069,
            47.85248789, 47.84219502]], shape=(1059, 1799))

  • longitude
    (y, x)
    float64
    237.3 237.3 237.3 ... 299.0 299.1

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([[237.280472  , 237.30713868, 237.3338097 , ..., 287.6569408 ,
            287.68361332, 287.71028151],
           [237.27297501, 237.29964881, 237.32632695, ..., 287.66442108,
            287.69110073, 287.71777603],
           [237.26547368, 237.2921546 , 237.31883986, ..., 287.6719057 ,
            287.69859247, 287.7252749 ],
           ...,
           [225.9351904 , 225.97171577, 226.00825329, ..., 298.97907406,
            299.0156158 , 299.05214538],
           [225.91986142, 225.95639874, 225.99294822, ..., 298.99437498,
            299.03092868, 299.06747022],
           [225.90452027, 225.94106954, 225.97763099, ..., 299.00968806,
            299.04625373, 299.08280723]], shape=(1059, 1799))

  • valid_time
    (step, time)
    datetime64[ns]
    2022-03-01 ... 2022-03-01T10:00:00

    standard_name :

    time

    long_name :

    time

    array([['2022-03-01T00:00:00.000000000', '2022-03-01T01:00:00.000000000',
            '2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
            '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000'],
           ['2022-03-01T01:00:00.000000000', '2022-03-01T02:00:00.000000000',
            '2022-03-01T03:00:00.000000000', '2022-03-01T04:00:00.000000000',
            '2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000'],
           ['2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
            '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000',
            '2022-03-01T06:00:00.000000000', '2022-03-01T07:00:00.000000000'],
           ['2022-03-01T03:00:00.000000000', '2022-03-01T04:00:00.000000000',
            '2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000',
            '2022-03-01T07:00:00.000000000', '2022-03-01T08:00:00.000000000'],
           ['2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000',
            '2022-03-01T06:00:00.000000000', '2022-03-01T07:00:00.000000000',
            '2022-03-01T08:00:00.000000000', '2022-03-01T09:00:00.000000000'],
           ['2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000',
            '2022-03-01T07:00:00.000000000', '2022-03-01T08:00:00.000000000',
            '2022-03-01T09:00:00.000000000', '2022-03-01T10:00:00.000000000']],
          dtype='datetime64[ns]')

  • gribfile_projection
    ()
    object
    None

    crs_wkt :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["unknown",ELLIPSOID["unknown",6371229,0,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Lambert Conic Conformal (2SP)",ID["EPSG",9802]],PARAMETER["Latitude of false origin",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8821]],PARAMETER["Longitude of false origin",262.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8822]],PARAMETER["Latitude of 1st standard parallel",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8823]],PARAMETER["Latitude of 2nd standard parallel",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8824]],PARAMETER["Easting at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8826]],PARAMETER["Northing at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8827]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6371229.0

    semi_minor_axis :

    6371229.0

    inverse_flattening :

    0.0

    reference_ellipsoid_name :

    unknown

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    unknown

    horizontal_datum_name :

    unknown

    projected_crs_name :

    unknown

    grid_mapping_name :

    lambert_conformal_conic

    standard_parallel :

    (38.5, 38.5)

    latitude_of_projection_origin :

    38.5

    longitude_of_central_meridian :

    262.5

    false_easting :

    0.0

    false_northing :

    0.0

    long_name :

    HRRR model grid projection

    array(None, dtype=object)

• Data variables: (1)
  • t2m
    (step, time, y, x)
    float32
    292.7 292.7 292.7 ... 262.5 262.5

    GRIB_paramId :

    167

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    1905141

    GRIB_typeOfLevel :

    heightAboveGround

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    lambert

    GRIB_uvRelativeToGrid :

    1

    GRIB_DxInMetres :

    3000.0

    GRIB_DyInMetres :

    3000.0

    GRIB_LaDInDegrees :

    38.5

    GRIB_Latin1InDegrees :

    38.5

    GRIB_Latin2InDegrees :

    38.5

    GRIB_LoVInDegrees :

    262.5

    GRIB_NV :

    0

    GRIB_Nx :

    1799

    GRIB_Ny :

    1059

    GRIB_cfName :

    air_temperature

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Lambert Conformal can be secant or tangent, conical or bipolar

    GRIB_iScansNegatively :

    0

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    1

    GRIB_latitudeOfFirstGridPointInDegrees :

    21.138123

    GRIB_latitudeOfSouthernPoleInDegrees :

    0.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    237.280472

    GRIB_longitudeOfSouthernPoleInDegrees :

    0.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_parameterName :

    Temperature

    GRIB_parameterUnits :

    K

    GRIB_shapeOfTheEarth :

    6

    GRIB_shortName :

    2t

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    air_temperature

    grid_mapping :

    gribfile_projection

    array([[[[292.7411 , 292.6786 , 292.6786 , ..., 299.1161 , 299.1161 ,
              299.1161 ],
             [292.6786 , 292.6786 , 292.6786 , ..., 299.1161 , 299.1161 ,
              299.1161 ],
             [292.6786 , 292.6786 , 292.6786 , ..., 299.1161 , 299.1161 ,
              299.1161 ],
             ...,
             [280.6786 , 280.6786 , 280.6786 , ..., 263.7411 , 263.7411 ,
              263.8036 ],
             [280.6786 , 280.6786 , 280.6786 , ..., 263.7411 , 263.8036 ,
              263.8036 ],
             [280.6786 , 280.6786 , 280.6161 , ..., 263.8036 , 263.8036 ,
              263.8661 ]],
    
            [[292.78296, 292.78296, 292.78296, ..., 299.09546, 299.03296,
              299.03296],
             [292.78296, 292.78296, 292.72046, ..., 299.03296, 299.03296,
              299.03296],
             [292.72046, 292.72046, 292.72046, ..., 299.03296, 299.03296,
              299.03296],
    ...
             [280.6357 , 280.6982 , 280.6982 , ..., 261.9482 , 262.0107 ,
              262.5732 ],
             [280.6357 , 280.6982 , 280.6982 , ..., 262.0107 , 262.1357 ,
              262.5107 ],
             [280.6357 , 280.6357 , 280.6357 , ..., 262.3857 , 262.4482 ,
              262.5732 ]],
    
            [[292.7087 , 292.7087 , 292.6462 , ..., 298.8962 , 298.8962 ,
              298.8962 ],
             [292.7087 , 292.6462 , 292.6462 , ..., 298.8962 , 298.8962 ,
              298.8962 ],
             [292.6462 , 292.5837 , 292.5837 , ..., 298.8962 , 298.8962 ,
              298.8337 ],
             ...,
             [280.6462 , 280.7087 , 280.7087 , ..., 261.9587 , 262.0212 ,
              262.5212 ],
             [280.6462 , 280.7087 , 280.7087 , ..., 262.0212 , 262.0837 ,
              262.5212 ],
             [280.6462 , 280.6462 , 280.6462 , ..., 262.3962 , 262.4587 ,
              262.5212 ]]]], shape=(6, 6, 1059, 1799), dtype=float32)

• Attributes: (10)

  GRIB_edition :

  2

  GRIB_centre :

  kwbc

  GRIB_centreDescription :

  US National Weather Service - NCEP

  GRIB_subCentre :

  0

  Conventions :

  CF-1.7

  institution :

  US National Weather Service - NCEP

  model :

  hrrr

  product :

  sfc

  description :

  High-Resolution Rapid Refresh - CONUS

  search :

  TMP:(?:[8,5]00 mb|2 m)

ds[1]

<xarray.Dataset> Size: 305MB
Dimensions:              (step: 6, time: 6, y: 1059, x: 1799)
Coordinates:
  * step                 (step) timedelta64[ns] 48B 00:00:00 ... 05:00:00
  * time                 (time) datetime64[ns] 48B 2022-03-01 ... 2022-03-01T...
    isobaricInhPa        float64 8B 500.0
    latitude             (y, x) float64 15MB 21.14 21.15 21.15 ... 47.85 47.84
    longitude            (y, x) float64 15MB 237.3 237.3 237.3 ... 299.0 299.1
    valid_time           (step, time) datetime64[ns] 288B 2022-03-01 ... 2022...
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    t                    (step, time, y, x) float32 274MB 262.7 262.7 ... 238.5
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   hrrr
    product:                 sfc
    description:             High-Resolution Rapid Refresh - CONUS
    search:                  TMP:(?:[8,5]00 mb|2 m)

xarray.Dataset

• Dimensions:
  • step: 6
  • time: 6
  • y: 1059
  • x: 1799
• Coordinates: (7)
  • step
    (step)
    timedelta64[ns]
    00:00:00 01:00:00 ... 05:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([             0,  3600000000000,  7200000000000, 10800000000000,
           14400000000000, 18000000000000], dtype='timedelta64[ns]')

  • time
    (time)
    datetime64[ns]
    2022-03-01 ... 2022-03-01T05:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2022-03-01T00:00:00.000000000', '2022-03-01T01:00:00.000000000',
           '2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
           '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000'],
          dtype='datetime64[ns]')

  • isobaricInhPa
    ()
    float64
    500.0

    long_name :

    pressure

    units :

    hPa

    positive :

    down

    stored_direction :

    decreasing

    standard_name :

    air_pressure

    array(500.)

  • latitude
    (y, x)
    float64
    21.14 21.15 21.15 ... 47.85 47.84

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([[21.138123  , 21.14511004, 21.1520901 , ..., 21.1545089 ,
            21.14753125, 21.14054663],
           [21.16299459, 21.1699845 , 21.17696744, ..., 21.17938723,
            21.1724067 , 21.16541921],
           [21.18786863, 21.19486142, 21.20184723, ..., 21.20426802,
            21.19728462, 21.19029425],
           ...,
           [47.78955926, 47.799849  , 47.81012868, ..., 47.81369093,
            47.80341474, 47.79312849],
           [47.81409316, 47.82438621, 47.8346692 , ..., 47.83823259,
            47.8279531 , 47.81766354],
           [47.8386235 , 47.84891986, 47.85920615, ..., 47.86277069,
            47.85248789, 47.84219502]], shape=(1059, 1799))

  • longitude
    (y, x)
    float64
    237.3 237.3 237.3 ... 299.0 299.1

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([[237.280472  , 237.30713868, 237.3338097 , ..., 287.6569408 ,
            287.68361332, 287.71028151],
           [237.27297501, 237.29964881, 237.32632695, ..., 287.66442108,
            287.69110073, 287.71777603],
           [237.26547368, 237.2921546 , 237.31883986, ..., 287.6719057 ,
            287.69859247, 287.7252749 ],
           ...,
           [225.9351904 , 225.97171577, 226.00825329, ..., 298.97907406,
            299.0156158 , 299.05214538],
           [225.91986142, 225.95639874, 225.99294822, ..., 298.99437498,
            299.03092868, 299.06747022],
           [225.90452027, 225.94106954, 225.97763099, ..., 299.00968806,
            299.04625373, 299.08280723]], shape=(1059, 1799))

  • valid_time
    (step, time)
    datetime64[ns]
    2022-03-01 ... 2022-03-01T10:00:00

    standard_name :

    time

    long_name :

    time

    array([['2022-03-01T00:00:00.000000000', '2022-03-01T01:00:00.000000000',
            '2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
            '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000'],
           ['2022-03-01T01:00:00.000000000', '2022-03-01T02:00:00.000000000',
            '2022-03-01T03:00:00.000000000', '2022-03-01T04:00:00.000000000',
            '2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000'],
           ['2022-03-01T02:00:00.000000000', '2022-03-01T03:00:00.000000000',
            '2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000',
            '2022-03-01T06:00:00.000000000', '2022-03-01T07:00:00.000000000'],
           ['2022-03-01T03:00:00.000000000', '2022-03-01T04:00:00.000000000',
            '2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000',
            '2022-03-01T07:00:00.000000000', '2022-03-01T08:00:00.000000000'],
           ['2022-03-01T04:00:00.000000000', '2022-03-01T05:00:00.000000000',
            '2022-03-01T06:00:00.000000000', '2022-03-01T07:00:00.000000000',
            '2022-03-01T08:00:00.000000000', '2022-03-01T09:00:00.000000000'],
           ['2022-03-01T05:00:00.000000000', '2022-03-01T06:00:00.000000000',
            '2022-03-01T07:00:00.000000000', '2022-03-01T08:00:00.000000000',
            '2022-03-01T09:00:00.000000000', '2022-03-01T10:00:00.000000000']],
          dtype='datetime64[ns]')

  • gribfile_projection
    ()
    object
    None

    crs_wkt :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["unknown",ELLIPSOID["unknown",6371229,0,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Lambert Conic Conformal (2SP)",ID["EPSG",9802]],PARAMETER["Latitude of false origin",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8821]],PARAMETER["Longitude of false origin",262.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8822]],PARAMETER["Latitude of 1st standard parallel",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8823]],PARAMETER["Latitude of 2nd standard parallel",38.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8824]],PARAMETER["Easting at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8826]],PARAMETER["Northing at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8827]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6371229.0

    semi_minor_axis :

    6371229.0

    inverse_flattening :

    0.0

    reference_ellipsoid_name :

    unknown

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    unknown

    horizontal_datum_name :

    unknown

    projected_crs_name :

    unknown

    grid_mapping_name :

    lambert_conformal_conic

    standard_parallel :

    (38.5, 38.5)

    latitude_of_projection_origin :

    38.5

    longitude_of_central_meridian :

    262.5

    false_easting :

    0.0

    false_northing :

    0.0

    long_name :

    HRRR model grid projection

    array(None, dtype=object)

• Data variables: (1)
  • t
    (step, time, y, x)
    float32
    262.7 262.7 262.7 ... 238.5 238.5

    GRIB_paramId :

    130

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    1905141

    GRIB_typeOfLevel :

    isobaricInhPa

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    lambert

    GRIB_uvRelativeToGrid :

    1

    GRIB_DxInMetres :

    3000.0

    GRIB_DyInMetres :

    3000.0

    GRIB_LaDInDegrees :

    38.5

    GRIB_Latin1InDegrees :

    38.5

    GRIB_Latin2InDegrees :

    38.5

    GRIB_LoVInDegrees :

    262.5

    GRIB_NV :

    0

    GRIB_Nx :

    1799

    GRIB_Ny :

    1059

    GRIB_cfName :

    air_temperature

    GRIB_cfVarName :

    t

    GRIB_gridDefinitionDescription :

    Lambert Conformal can be secant or tangent, conical or bipolar

    GRIB_iScansNegatively :

    0

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    1

    GRIB_latitudeOfFirstGridPointInDegrees :

    21.138123

    GRIB_latitudeOfSouthernPoleInDegrees :

    0.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    237.280472

    GRIB_longitudeOfSouthernPoleInDegrees :

    0.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Temperature

    GRIB_parameterName :

    Temperature

    GRIB_parameterUnits :

    K

    GRIB_shapeOfTheEarth :

    6

    GRIB_shortName :

    t

    GRIB_units :

    K

    long_name :

    Temperature

    units :

    K

    standard_name :

    air_temperature

    grid_mapping :

    gribfile_projection

    array([[[[262.68005, 262.68005, 262.68005, ..., 268.05505, 268.05505,
              268.05505],
             [262.68005, 262.68005, 262.68005, ..., 268.05505, 268.05505,
              268.05505],
             [262.68005, 262.68005, 262.68005, ..., 268.05505, 268.05505,
              268.05505],
             ...,
             [243.86755, 243.86755, 243.93005, ..., 235.49255, 235.49255,
              235.49255],
             [243.80505, 243.86755, 243.86755, ..., 235.49255, 235.49255,
              235.49255],
             [243.80505, 243.80505, 243.86755, ..., 235.49255, 235.49255,
              235.43005]],
    
            [[262.89017, 262.89017, 262.89017, ..., 268.07767, 268.07767,
              268.07767],
             [262.89017, 262.89017, 262.89017, ..., 268.07767, 268.07767,
              268.07767],
             [262.89017, 262.89017, 262.89017, ..., 268.07767, 268.07767,
              268.07767],
    ...
             [243.8261 , 243.8261 , 243.8261 , ..., 238.0136 , 238.0136 ,
              238.0136 ],
             [243.8261 , 243.8261 , 243.8261 , ..., 237.9511 , 237.9511 ,
              237.9511 ],
             [243.8261 , 243.8261 , 243.8261 , ..., 237.8886 , 237.8886 ,
              237.8886 ]],
    
            [[261.40863, 261.47113, 261.47113, ..., 268.65863, 268.65863,
              268.65863],
             [261.40863, 261.40863, 261.40863, ..., 268.65863, 268.65863,
              268.65863],
             [261.34613, 261.34613, 261.40863, ..., 268.65863, 268.65863,
              268.72113],
             ...,
             [243.90863, 243.90863, 243.90863, ..., 238.53363, 238.53363,
              238.53363],
             [243.90863, 243.90863, 243.90863, ..., 238.47113, 238.47113,
              238.53363],
             [243.90863, 243.90863, 243.90863, ..., 238.47113, 238.47113,
              238.47113]]]], shape=(6, 6, 1059, 1799), dtype=float32)

• Attributes: (10)

  GRIB_edition :

  2

  GRIB_centre :

  kwbc

  GRIB_centreDescription :

  US National Weather Service - NCEP

  GRIB_subCentre :

  0

  Conventions :

  CF-1.7

  institution :

  US National Weather Service - NCEP

  model :

  hrrr

  product :

  sfc

  description :

  High-Resolution Rapid Refresh - CONUS

  search :

  TMP:(?:[8,5]00 mb|2 m)