📍 Nearest Points

WARNING: This is deprecated in favor of ds.herbie.pick_points

This demonstration to get the value at the nearest latitude/longitude point builds on info from these sources:

• Stack Overflow: https://stackoverflow.com/questions/58758480/xarray-select-nearest-lat-lon-with-multi-dimension-coordinates

• MetPy Details: https://unidata.github.io/MetPy/latest/tutorials/xarray_tutorial.html?highlight=assign_y_x

from herbie import Herbie
from metpy.units import units
import matplotlib.pyplot as plt
from toolbox import EasyMap, pc, ccrs
import numpy as np
import pandas as pd
import xarray as xr
from shapely.geometry import MultiPoint
from toolbox.gridded_data import pluck_points

H = Herbie("2021-9-23")
ds = H.xarray("TMP:2 m")

✅ Found ┊ model=hrrr ┊ product=sfc ┊ 2021-Sep-23 00:00 UTC F00 ┊ GRIB2 @ aws ┊ IDX @ aws
👨🏻‍🏭 Created directory: [/home/blaylock/data/hrrr/20210923]

ds

<xarray.Dataset>
Dimensions:              (y: 1059, x: 1799)
Coordinates:
    time                 datetime64[ns] 2021-09-23
    step                 timedelta64[ns] 00:00:00
    heightAboveGround    float64 2.0
    latitude             (y, x) float64 21.14 21.15 21.15 ... 47.86 47.85 47.84
    longitude            (y, x) float64 237.3 237.3 237.3 ... 299.0 299.0 299.1
    valid_time           datetime64[ns] 2021-09-23
Dimensions without coordinates: y, x
Data variables:
    t2m                  (y, x) float32 297.7 297.7 297.8 ... 288.6 288.6 288.6
    gribfile_projection  object None
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
    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....
    local_grib:              /home/blaylock/data/hrrr/20210923/subset_1beffd5...
    search:            TMP:2 m

xarray.Dataset

• Dimensions:
  • y: 1059
  • x: 1799
• Coordinates: (6)
  • time
    ()
    datetime64[ns]
    2021-09-23

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array('2021-09-23T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    00:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array(0, dtype='timedelta64[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]])

  • 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]])

  • valid_time
    ()
    datetime64[ns]
    2021-09-23

    standard_name :

    time

    long_name :

    time

    array('2021-09-23T00:00:00.000000000', dtype='datetime64[ns]')

• Data variables: (2)
  • t2m
    (y, x)
    float32
    297.7 297.7 297.8 ... 288.6 288.6

    GRIB_paramId :

    167

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    1905141

    GRIB_typeOfLevel :

    heightAboveGround

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    lambert

    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_shortName :

    2t

    GRIB_stepRange :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    air_temperature

    grid_mapping :

    gribfile_projection

    array([[297.7128, 297.7128, 297.7753, ..., 302.1503, 302.1503, 302.1503],
           [297.7128, 297.7753, 297.7753, ..., 302.1503, 302.1503, 302.1503],
           [297.7128, 297.7128, 297.7753, ..., 302.1503, 302.1503, 302.1503],
           ...,
           [287.2128, 287.2128, 287.2128, ..., 288.7128, 288.7128, 288.6503],
           [287.2128, 287.2128, 287.2128, ..., 288.6503, 288.6503, 288.6503],
           [287.1503, 287.2128, 287.2128, ..., 288.5878, 288.5878, 288.5878]],
          dtype=float32)

  • 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)

• Indexes: (0)
• Attributes: (12)

  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

  remote_grib :

  https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20210923/conus/hrrr.t00z.wrfsfcf00.grib2

  local_grib :

  /home/blaylock/data/hrrr/20210923/subset_1beffd54__hrrr.t00z.wrfsfcf00.grib2

  search :

  TMP:2 m

Herbie has a custom xarray accessor called nearest_point to select values nearest a specified latitude and longitude point. The points argument accepts several different inputs.

• a tuple of (lon, lat)

• a list of tuples [(lon, lat), (lon, lat), (lon, lat)]

• a Pandas DataFrame with columns longitude and latitude (the Index will be used to name the points).

• a Shapely geometry Point or MultiPoint object.

# Get values nearest two points

dsi = ds.herbie.nearest_points(
    points=[(-100, 40), (-115, 34)],
    names=["point_a", "point_b"],
)
dsi

/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "t2m".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'

<xarray.Dataset>
Dimensions:              (point: 2)
Coordinates:
    time                 datetime64[ns] 2021-09-23
    step                 timedelta64[ns] 00:00:00
    heightAboveGround    float64 2.0
    latitude             (point) float64 40.01 33.99
    longitude            (point) float64 260.0 245.0
    valid_time           datetime64[ns] 2021-09-23
    metpy_crs            object Projection: lambert_conformal_conic
    y                    (point) float64 1.707e+05 -3.483e+05
    x                    (point) float64 -2.135e+05 -1.609e+06
  * point                (point) <U7 'point_a' 'point_b'
    point_latitude       (point) int64 40 34
    point_longitude      (point) int64 -100 -115
Data variables:
    t2m                  (point) float32 298.5 310.2
    gribfile_projection  (point) object None None
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
    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....
    local_grib:              /home/blaylock/data/hrrr/20210923/subset_1beffd5...
    search:            TMP:2 m

xarray.Dataset

• Dimensions:
  • point: 2
• Coordinates: (12)
  • time
    ()
    datetime64[ns]
    2021-09-23

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array('2021-09-23T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    00:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array(0, dtype='timedelta64[ns]')

  • heightAboveGround
    ()
    float64
    2.0

    long_name :

    height above the surface

    units :

    m

    positive :

    up

    standard_name :

    height

    _metpy_axis :

    vertical

    array(2.)

  • latitude
    (point)
    float64
    40.01 33.99

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    _metpy_axis :

    latitude

    array([40.00871026, 33.99302795])

  • longitude
    (point)
    float64
    260.0 245.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    _metpy_axis :

    longitude

    array([259.9936499 , 245.00082479])

  • valid_time
    ()
    datetime64[ns]
    2021-09-23

    standard_name :

    time

    long_name :

    time

    array('2021-09-23T00:00:00.000000000', dtype='datetime64[ns]')

  • metpy_crs
    ()
    object
    Projection: lambert_conformal_conic

    array(<metpy.plots.mapping.CFProjection object at 0x7f68f688a790>,
          dtype=object)

  • y
    (point)
    float64
    1.707e+05 -3.483e+05

    units :

    meter

    standard_name :

    projection_y_coordinate

    array([ 170693.84744334, -348306.15255666])

  • x
    (point)
    float64
    -2.135e+05 -1.609e+06

    units :

    meter

    standard_name :

    projection_x_coordinate

    array([ -213520.14252193, -1608520.14252193])

  • point
    (point)
    <U7
    'point_a' 'point_b'

    array(['point_a', 'point_b'], dtype='<U7')

  • point_latitude
    (point)
    int64
    40 34

    array([40, 34])

  • point_longitude
    (point)
    int64
    -100 -115

    array([-100, -115])

• Data variables: (2)
  • t2m
    (point)
    float32
    298.5 310.2

    GRIB_paramId :

    167

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    1905141

    GRIB_typeOfLevel :

    heightAboveGround

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    lambert

    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_shortName :

    2t

    GRIB_stepRange :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    air_temperature

    grid_mapping :

    gribfile_projection

    array([298.5253, 310.2128], dtype=float32)

  • gribfile_projection
    (point)
    object
    None 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, None], dtype=object)

• Indexes: (1)
  • point
    PandasIndex

    PandasIndex(Index(['point_a', 'point_b'], dtype='object', name='point'))

• Attributes: (12)

  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

  remote_grib :

  https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20210923/conus/hrrr.t00z.wrfsfcf00.grib2

  local_grib :

  /home/blaylock/data/hrrr/20210923/subset_1beffd54__hrrr.t00z.wrfsfcf00.grib2

  search :

  TMP:2 m

ds.herbie.plot()

plt.scatter(dsi.longitude, dsi.latitude, transform=pc)

for i in dsi.t2m:
    plt.text(
        i.longitude.item(),
        i.latitude.item(),
        f"{i.point.item()}\n2m Temp={i.item():.1f}",
        transform=pc,
    )

cfgrib variable: t2m
GRIB_cfName air_temperature
GRIB_cfVarName t2m
GRIB_name 2 metre temperature
GRIB_units K
GRIB_typeOfLevel heightAboveGround

/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "t2m".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'

# Alternatively, we can specify our points of interest using a DataFrame

df = pd.DataFrame(
    {
        "longitude": range(-120, -95, 2),
        "latitude": range(25, 50, 2),
    }
)

dsi2 = ds.herbie.nearest_points(points=df)
dsi2

/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "t2m".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'

<xarray.Dataset>
Dimensions:              (point: 13)
Coordinates:
    time                 datetime64[ns] 2021-09-23
    step                 timedelta64[ns] 00:00:00
    heightAboveGround    float64 2.0
    latitude             (point) float64 25.01 27.01 29.0 ... 44.99 46.99 49.0
    longitude            (point) float64 240.0 242.0 244.0 ... 260.0 262.0 264.0
    valid_time           datetime64[ns] 2021-09-23
    metpy_crs            object Projection: lambert_conformal_conic
    y                    (point) float64 -1.23e+06 -1.056e+06 ... 1.176e+06
    x                    (point) float64 -2.305e+06 -2.053e+06 ... 1.105e+05
  * point                (point) int64 0 1 2 3 4 5 6 7 8 9 10 11 12
    point_latitude       (point) int64 25 27 29 31 33 35 37 39 41 43 45 47 49
    point_longitude      (point) int64 -120 -118 -116 -114 ... -102 -100 -98 -96
Data variables:
    t2m                  (point) float32 22.38 22.5 20.69 ... 20.56 19.63 19.69
    gribfile_projection  (point) object None None None None ... None None None
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
    remote_grib:             https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr....
    local_grib:              /home/blaylock/data/hrrr/20210923/subset_1beffd5...
    search:            TMP:2 m

xarray.Dataset

• Dimensions:
  • point: 13
• Coordinates: (12)
  • time
    ()
    datetime64[ns]
    2021-09-23

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array('2021-09-23T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    00:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array(0, dtype='timedelta64[ns]')

  • heightAboveGround
    ()
    float64
    2.0

    long_name :

    height above the surface

    units :

    m

    positive :

    up

    standard_name :

    height

    _metpy_axis :

    vertical

    array(2.)

  • latitude
    (point)
    float64
    25.01 27.01 29.0 ... 46.99 49.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    _metpy_axis :

    latitude

    array([25.01149156, 27.0123049 , 28.99553121, 31.00913604, 33.01159741,
           34.99772694, 36.99455544, 39.00261339, 40.99343655, 42.99842805,
           44.9875233 , 46.9892808 , 49.00312348])

  • longitude
    (point)
    float64
    240.0 242.0 244.0 ... 262.0 264.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    _metpy_axis :

    longitude

    array([240.00381947, 242.00807551, 244.01635007, 245.98584048,
           247.98948018, 249.99685233, 252.00364844, 254.01148864,
           255.989288  , 258.00570024, 259.99217111, 261.98494105,
           263.98778749])

  • valid_time
    ()
    datetime64[ns]
    2021-09-23

    standard_name :

    time

    long_name :

    time

    array('2021-09-23T00:00:00.000000000', dtype='datetime64[ns]')

  • metpy_crs
    ()
    object
    Projection: lambert_conformal_conic

    array(<metpy.plots.mapping.CFProjection object at 0x7f68f6486510>,
          dtype=object)

  • y
    (point)
    float64
    -1.23e+06 -1.056e+06 ... 1.176e+06

    units :

    meter

    standard_name :

    projection_y_coordinate

    array([-1230306.15255666, -1056306.15255666,  -879306.15255666,
            -693306.15255666,  -504306.15255666,  -312306.15255666,
            -114306.15255666,    89693.84744334,   296693.84744334,
             509693.84744334,   725693.84744334,   947693.84744334,
            1175693.84744334])

  • x
    (point)
    float64
    -2.305e+06 -2.053e+06 ... 1.105e+05

    units :

    meter

    standard_name :

    projection_x_coordinate

    array([-2304520.14252193, -2052520.14252193, -1809520.14252193,
           -1578520.14252193, -1353520.14252193, -1137520.14252193,
            -930520.14252193,  -732520.14252193,  -546520.14252193,
            -366520.14252193,  -198520.14252193,   -39520.14252193,
             110479.85747807])

  • point
    (point)
    int64
    0 1 2 3 4 5 6 7 8 9 10 11 12

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

  • point_latitude
    (point)
    int64
    25 27 29 31 33 ... 41 43 45 47 49

    array([25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49])

  • point_longitude
    (point)
    int64
    -120 -118 -116 ... -100 -98 -96

    array([-120, -118, -116, -114, -112, -110, -108, -106, -104, -102, -100,
            -98,  -96])

• Data variables: (2)
  • t2m
    (point)
    float32
    22.38 22.5 20.69 ... 19.63 19.69

    GRIB_paramId :

    167

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    1905141

    GRIB_typeOfLevel :

    heightAboveGround

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    lambert

    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_shortName :

    2t

    GRIB_stepRange :

    0

    GRIB_units :

    C

    long_name :

    2 metre temperature

    units :

    C

    standard_name :

    air_temperature

    grid_mapping :

    gribfile_projection

    array([22.375305, 22.500305, 20.687805, 30.562805, 38.250305, 28.812805,
           24.000305, 19.125305, 23.812805, 23.812805, 20.562805, 19.625305,
           19.687805], dtype=float32)

  • gribfile_projection
    (point)
    object
    None None None ... None None 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, None, None, None, None, None, None, None, None, None, None,
           None, None], dtype=object)

• Indexes: (1)
  • point
    PandasIndex

    PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='point'))

• Attributes: (12)

  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

  remote_grib :

  https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20210923/conus/hrrr.t00z.wrfsfcf00.grib2

  local_grib :

  /home/blaylock/data/hrrr/20210923/subset_1beffd54__hrrr.t00z.wrfsfcf00.grib2

  search :

  TMP:2 m

ds.herbie.plot()

plt.scatter(dsi2.longitude, dsi2.latitude, transform=pc)

for i in dsi2.t2m:
    plt.text(
        i.longitude.item(),
        i.latitude.item(),
        f"{i.point.item()}\n2m Temp={i.item():.1f}",
        transform=pc,
    )

cfgrib variable: t2m
GRIB_cfName air_temperature
GRIB_cfVarName t2m
GRIB_name 2 metre temperature
GRIB_units C
GRIB_typeOfLevel heightAboveGround

/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "t2m".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'
/home/blaylock/miniconda3/envs/herbie-dev/lib/python3.11/site-packages/metpy/xarray.py:382: UserWarning: More than one time coordinate present for variable "gribfile_projection".
  warnings.warn('More than one ' + axis + ' coordinate present for variable'

We can make timeseries from a point by plucking point from FastHerbie

from herbie import FastHerbie

DATES = pd.date_range("2022-01-01", periods=2, freq="6H")
fxx = range(18)
FH = FastHerbie(DATES=DATES, fxx=fxx, max_threads=1)
ds = FH.xarray("TMP:2 m")
ds

Canceled future for execute_request message before replies were done

The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.

dsi = ds.herbie.nearest_points(points=(-100, 40))
dsi

<xarray.Dataset>
Dimensions:              (time: 2, step: 18, point: 1)
Coordinates:
  * time                 (time) datetime64[ns] 2022-01-01 2022-01-01T06:00:00
  * step                 (step) timedelta64[ns] 00:00:00 01:00:00 ... 17:00:00
    heightAboveGround    float64 2.0
    latitude             float64 40.01
    longitude            float64 260.0
    valid_time           (step, time) datetime64[ns] 2022-01-01 ... 2022-01-0...
    metpy_crs            object Projection: lambert_conformal_conic
    y                    float64 1.707e+05
    x                    float64 -2.135e+05
Dimensions without coordinates: point
Data variables:
    t2m                  (point, step, time) float32 267.2 260.9 ... 256.0 258.3
    gribfile_projection  (point) object None
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:
  • time: 2
  • step: 18
  • point: 1
• Coordinates: (9)
  • time
    (time)
    datetime64[ns]
    2022-01-01 2022-01-01T06:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    _metpy_axis :

    time

    array(['2022-01-01T00:00:00.000000000', '2022-01-01T06:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    (step)
    timedelta64[ns]
    00:00:00 01:00:00 ... 17:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([             0,  3600000000000,  7200000000000, 10800000000000,
           14400000000000, 18000000000000, 21600000000000, 25200000000000,
           28800000000000, 32400000000000, 36000000000000, 39600000000000,
           43200000000000, 46800000000000, 50400000000000, 54000000000000,
           57600000000000, 61200000000000], dtype='timedelta64[ns]')

  • heightAboveGround
    ()
    float64
    2.0

    long_name :

    height above the surface

    units :

    m

    positive :

    up

    standard_name :

    height

    _metpy_axis :

    vertical

    array(2.)

  • latitude
    ()
    float64
    40.01

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    _metpy_axis :

    latitude

    array(40.00871026)

  • longitude
    ()
    float64
    260.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    _metpy_axis :

    longitude

    array(259.9936499)

  • valid_time
    (step, time)
    datetime64[ns]
    2022-01-01 ... 2022-01-01T23:00:00

    standard_name :

    time

    long_name :

    time

    array([['2022-01-01T00:00:00.000000000', '2022-01-01T06:00:00.000000000'],
           ['2022-01-01T01:00:00.000000000', '2022-01-01T07:00:00.000000000'],
           ['2022-01-01T02:00:00.000000000', '2022-01-01T08:00:00.000000000'],
           ['2022-01-01T03:00:00.000000000', '2022-01-01T09:00:00.000000000'],
           ['2022-01-01T04:00:00.000000000', '2022-01-01T10:00:00.000000000'],
           ['2022-01-01T05:00:00.000000000', '2022-01-01T11:00:00.000000000'],
           ['2022-01-01T06:00:00.000000000', '2022-01-01T12:00:00.000000000'],
           ['2022-01-01T07:00:00.000000000', '2022-01-01T13:00:00.000000000'],
           ['2022-01-01T08:00:00.000000000', '2022-01-01T14:00:00.000000000'],
           ['2022-01-01T09:00:00.000000000', '2022-01-01T15:00:00.000000000'],
           ['2022-01-01T10:00:00.000000000', '2022-01-01T16:00:00.000000000'],
           ['2022-01-01T11:00:00.000000000', '2022-01-01T17:00:00.000000000'],
           ['2022-01-01T12:00:00.000000000', '2022-01-01T18:00:00.000000000'],
           ['2022-01-01T13:00:00.000000000', '2022-01-01T19:00:00.000000000'],
           ['2022-01-01T14:00:00.000000000', '2022-01-01T20:00:00.000000000'],
           ['2022-01-01T15:00:00.000000000', '2022-01-01T21:00:00.000000000'],
           ['2022-01-01T16:00:00.000000000', '2022-01-01T22:00:00.000000000'],
           ['2022-01-01T17:00:00.000000000', '2022-01-01T23:00:00.000000000']],
          dtype='datetime64[ns]')

  • metpy_crs
    ()
    object
    Projection: lambert_conformal_conic

    array(<metpy.plots.mapping.CFProjection object at 0x000002554C95BAF0>,
          dtype=object)

  • y
    ()
    float64
    1.707e+05

    units :

    meter

    standard_name :

    projection_y_coordinate

    array(170693.84744334)

  • x
    ()
    float64
    -2.135e+05

    units :

    meter

    standard_name :

    projection_x_coordinate

    array(-213520.14252193)

• Data variables: (2)
  • t2m
    (point, step, time)
    float32
    267.2 260.9 265.1 ... 256.0 258.3

    GRIB_paramId :

    167

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    1905141

    GRIB_typeOfLevel :

    heightAboveGround

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    lambert

    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_shortName :

    2t

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    air_temperature

    grid_mapping :

    gribfile_projection

    array([[[267.207  , 260.86615],
            [265.1052 , 259.7516 ],
            [263.73575, 259.26343],
            [262.45215, 258.86182],
            [261.79544, 258.3954 ],
            [261.3382 , 257.6913 ],
            [260.85333, 257.09995],
            [259.9947 , 256.48898],
            [259.37546, 255.8175 ],
            [258.83777, 255.61026],
            [258.52417, 255.66951],
            [257.9538 , 256.18906],
            [256.8481 , 256.7597 ],
            [256.25223, 257.30005],
            [255.52495, 257.69382],
            [255.22339, 258.25775],
            [255.47006, 258.44235],
            [256.01837, 258.30005]]], dtype=float32)

  • gribfile_projection
    (point)
    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)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-01 00:00:00', '2022-01-01 06:00:00'], dtype='datetime64[ns]', name='time', freq=None))

  • step
    PandasIndex

    PandasIndex(TimedeltaIndex(['0 days 00:00:00', '0 days 01:00:00', '0 days 02:00:00',
                    '0 days 03:00:00', '0 days 04:00:00', '0 days 05:00:00',
                    '0 days 06:00:00', '0 days 07:00:00', '0 days 08:00:00',
                    '0 days 09:00:00', '0 days 10:00:00', '0 days 11:00:00',
                    '0 days 12:00:00', '0 days 13:00:00', '0 days 14:00:00',
                    '0 days 15:00:00', '0 days 16:00:00', '0 days 17:00:00'],
                   dtype='timedelta64[ns]', name='step', freq=None))

• 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

dsi.isel(time=0, point=0).t2m.plot(x="valid_time", marker=".", label="t+0h")
dsi.isel(time=1, point=0).t2m.plot(x="valid_time", marker=".", label="t+6h")
plt.legend()

<matplotlib.legend.Legend at 0x25500b9b370>