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RTMA and URMA#

This tutorial shows you how to download the Real-Time Mesoscale Analysis (RTMA) and Un-Restricted Mesoscale Analysis (URMA) products with the Herbie Python package.

  • For RTMA, use model='rtma'

  • For RTMA Alaska, use model='rtma_ak'

  • For URMA, use model='urma'

  • For URMA Alaska, use model='urma_ak'

[1]:

from herbie import Herbie
from toolbox import EasyMap, pc
from paint.standard2 import cm_tmp, cm_pcp, cm_wind

import matplotlib.pyplot as plt
import cartopy.crs as ccrs

[2]:

H = Herbie(
    "2023-01-01 06:00",
    model="rtma",
    product="anl",
)

✅ Found ┊ model=rtma ┊ product=anl ┊ 2023-Jan-01 06:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ None

[3]:

# Show available products
H.PRODUCTS

[3]:

{'anl': 'Analysis',
 'err': 'Analysis Forecast Error',
 'ges': 'Forecasts',
 'pcp': 'Precipitation Field'}

[4]:

H.SOURCES

[4]:

{'aws': 'https://noaa-rtma-pds.s3.amazonaws.com/rtma2p5.20230101/rtma2p5.t06z.2dvaranl_ndfd.grb2_wexp',
 'nomads': 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/rtma/prod/rtma2p5.20230101/rtma2p5.t06z.2dvaranl_ndfd.grb2_wexp'}

Since there is no index file, we need to download the full file before we can look at the contents with wgrib2.

[5]:

H.download()

[5]:

PosixPath('/p/cwfs/blaylock/data/rtma/20230101/rtma2p5.t06z.2dvaranl_ndfd.grb2_wexp')

[6]:

H.inventory()

[6]:
grib_message start_byte end_byte range reference_time valid_time variable level forecast_time search_this
0 1 0 7490118 0-7490118 2023-01-01 06:00:00 2023-01-01 06:00:00 HGT surface anl :HGT:surface:anl
1 2 7490118 14980236 7490118-14980236 2023-01-01 06:00:00 2023-01-01 06:00:00 PRES surface anl :PRES:surface:anl
2 3 14980236 21065993 14980236-21065993 2023-01-01 06:00:00 2023-01-01 06:00:00 TMP 2 m above ground anl :TMP:2 m above ground:anl
3 4 21065993 27151750 21065993-27151750 2023-01-01 06:00:00 2023-01-01 06:00:00 DPT 2 m above ground anl :DPT:2 m above ground:anl
4 5 27151750 32769386 27151750-32769386 2023-01-01 06:00:00 2023-01-01 06:00:00 UGRD 10 m above ground anl :UGRD:10 m above ground:anl
5 6 32769386 38387022 32769386-38387022 2023-01-01 06:00:00 2023-01-01 06:00:00 VGRD 10 m above ground anl :VGRD:10 m above ground:anl
6 7 38387022 45409020 38387022-45409020 2023-01-01 06:00:00 2023-01-01 06:00:00 SPFH 2 m above ground anl :SPFH:2 m above ground:anl
7 8 45409020 51026656 45409020-51026656 2023-01-01 06:00:00 2023-01-01 06:00:00 WDIR 10 m above ground anl :WDIR:10 m above ground:anl
8 9 51026656 56644292 51026656-56644292 2023-01-01 06:00:00 2023-01-01 06:00:00 WIND 10 m above ground anl :WIND:10 m above ground:anl
9 10 56644292 62261928 56644292-62261928 2023-01-01 06:00:00 2023-01-01 06:00:00 GUST 10 m above ground anl :GUST:10 m above ground:anl
10 11 62261928 68815805 62261928-68815805 2023-01-01 06:00:00 2023-01-01 06:00:00 VIS surface anl :VIS:surface:anl
11 12 68815805 77242165 68815805-77242165 2023-01-01 06:00:00 2023-01-01 06:00:00 CEIL cloud ceiling anl :CEIL:cloud ceiling:anl
12 13 77242165 77242165- 2023-01-01 06:00:00 2023-01-01 06:00:00 TCDC entire atmosphere (considered as a single layer) anl :TCDC:entire atmosphere (considered as a singl... 
[7]:

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

[7]:

<xarray.Dataset>
Dimensions:              (y: 1597, x: 2345)
Coordinates:
    time                 datetime64[ns] 2023-01-01T06:00:00
    step                 timedelta64[ns] 00:00:00
    heightAboveGround    float64 2.0
    latitude             (y, x) float64 19.23 19.23 19.24 ... 54.38 54.38 54.37
    longitude            (y, x) float64 233.7 233.7 233.8 ... 300.9 300.9 301.0
    valid_time           datetime64[ns] 2023-01-01T06:00:00
Dimensions without coordinates: y, x
Data variables:
    t2m                  (y, x) float32 294.6 294.7 294.6 ... 264.0 264.0 264.0
    gribfile_projection  object None
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          4
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   rtma
    product:                 anl
    description:             CONUS Real-Time Mesoscale Analysis (RTMA)
    remote_grib:             /p/cwfs/blaylock/data/rtma/20230101/rtma2p5.t06z...
    local_grib:              /p/cwfs/blaylock/data/rtma/20230101/subset_c8efb...
    search:            TMP:2 m
[8]:

ax = EasyMap("50m", crs=ds.herbie.crs, figsize=[10, 8]).STATES().ax

p = ax.pcolormesh(
    ds.longitude,
    ds.latitude,
    ds.t2m,
    transform=pc,
    **cm_tmp(units="K").cmap_kwargs,
)
plt.colorbar(
    p,
    ax=ax,
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    **cm_tmp(units="K").cbar_kwargs,
)

ax.set_title(
    f"{ds.model.upper()}: {H.product_description}\nValid: {ds.valid_time.dt.strftime('%H:%M UTC %d %b %Y').item()}",
    loc="left",
)
ax.set_title(ds.t2m.GRIB_name, loc="right")

[8]:

Text(1.0, 1.0, '2 metre temperature')
../../../_images/user_guide_tutorial_model_notebooks_rtma_9_1.png

RTMA Precipitation#

The precipitation data is found in a different file, but Herbie will locate it when you produce='pcp'.

[9]:

H = Herbie(
    "2023-01-01 06:00",
    model="rtma",
    product="pcp",
)

✅ Found ┊ model=rtma ┊ product=pcp ┊ 2023-Jan-01 06:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ None

[10]:

H.download()

[10]:

PosixPath('/p/cwfs/blaylock/data/rtma/20230101/rtma2p5.2023010106.pcp.184.grb2')

[11]:

ds = H.xarray()
ds["tp"] = ds.tp.where(ds.tp > 0)
ds

/p/home/blaylock/BB_python/Herbie/herbie/core.py:1048: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")

[11]:

<xarray.Dataset>
Dimensions:              (y: 1377, x: 2145)
Coordinates:
    time                 datetime64[ns] 2023-01-01T05:00:00
    step                 timedelta64[ns] 01:00:00
    heightAboveSea       float64 0.0
    latitude             (y, x) float64 ...
    longitude            (y, x) float64 ...
    valid_time           datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    tp                   (y, x) float32 nan nan nan nan nan ... nan nan nan nan
    gribfile_projection  object None
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          4
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   rtma
    product:                 pcp
    description:             CONUS Real-Time Mesoscale Analysis (RTMA)
    remote_grib:             /p/cwfs/blaylock/data/rtma/20230101/rtma2p5.2023...
    local_grib:              /p/cwfs/blaylock/data/rtma/20230101/rtma2p5.2023...
    search:            None
[12]:

ax = EasyMap("50m", crs=ds.herbie.crs, figsize=[10, 8], dark=True).STATES().OCEAN().ax

p = ax.pcolormesh(
    ds.longitude,
    ds.latitude,
    ds.tp,
    transform=pc,
    **cm_pcp(units="mm").cmap_kwargs,
)
plt.colorbar(
    p,
    ax=ax,
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    **cm_pcp().cbar_kwargs,
)

ax.set_title(
    f"{ds.model.upper()}: {H.product_description}\nValid: {ds.valid_time.dt.strftime('%H:%M UTC %d %b %Y').item()}",
    loc="left",
)
ax.set_title(ds.tp.GRIB_name, loc="right")

[12]:

Text(1.0, 1.0, 'Total Precipitation')
../../../_images/user_guide_tutorial_model_notebooks_rtma_14_1.png

RTMA Alaska#

[13]:

H = Herbie(
    "2023-01-01 06:00",
    model="rtma_ak",
    product="anl",
)

✅ Found ┊ model=rtma_ak ┊ product=anl ┊ 2023-Jan-01 06:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ None

[14]:

H.download()

[14]:

PosixPath('/p/cwfs/blaylock/data/rtma_ak/20230101/akrtma.t06z.2dvaranl_ndfd_3p0.grb2')

[15]:

H.inventory()

[15]:
grib_message start_byte end_byte range reference_time valid_time variable level forecast_time search_this
0 1 0 3644462 0-3644462 2023-01-01 06:00:00 2023-01-01 06:00:00 HGT surface anl :HGT:surface:anl
1 2 3644462 7288924 3644462-7288924 2023-01-01 06:00:00 2023-01-01 06:00:00 PRES surface anl :PRES:surface:anl
2 3 7288924 10250082 7288924-10250082 2023-01-01 06:00:00 2023-01-01 06:00:00 TMP 2 m above ground anl :TMP:2 m above ground:anl
3 4 10250082 13211240 10250082-13211240 2023-01-01 06:00:00 2023-01-01 06:00:00 DPT 2 m above ground anl :DPT:2 m above ground:anl
4 5 13211240 16172398 13211240-16172398 2023-01-01 06:00:00 2023-01-01 06:00:00 UGRD 10 m above ground anl :UGRD:10 m above ground:anl
5 6 16172398 18905788 16172398-18905788 2023-01-01 06:00:00 2023-01-01 06:00:00 VGRD 10 m above ground anl :VGRD:10 m above ground:anl
6 7 18905788 21866946 18905788-21866946 2023-01-01 06:00:00 2023-01-01 06:00:00 SPFH 2 m above ground anl :SPFH:2 m above ground:anl
7 8 21866946 24600336 21866946-24600336 2023-01-01 06:00:00 2023-01-01 06:00:00 WDIR 10 m above ground anl :WDIR:10 m above ground:anl
8 9 24600336 27333726 24600336-27333726 2023-01-01 06:00:00 2023-01-01 06:00:00 WIND 10 m above ground anl :WIND:10 m above ground:anl
9 10 27333726 30067116 27333726-30067116 2023-01-01 06:00:00 2023-01-01 06:00:00 GUST 10 m above ground anl :GUST:10 m above ground:anl
10 11 30067116 33256042 30067116-33256042 2023-01-01 06:00:00 2023-01-01 06:00:00 VIS surface anl :VIS:surface:anl
11 12 33256042 37356041 33256042-37356041 2023-01-01 06:00:00 2023-01-01 06:00:00 CEIL cloud ceiling anl :CEIL:cloud ceiling:anl
12 13 37356041 37356041- 2023-01-01 06:00:00 2023-01-01 06:00:00 TCDC entire atmosphere (considered as a single layer) anl :TCDC:entire atmosphere (considered as a singl... 
[16]:

ds = H.xarray("WIND:10 m")
ds

[16]:

<xarray.Dataset>
Dimensions:              (y: 1105, x: 1649)
Coordinates:
    time                 datetime64[ns] 2023-01-01T06:00:00
    step                 timedelta64[ns] 00:00:00
    heightAboveGround    float64 10.0
    latitude             (y, x) float64 40.53 40.54 40.55 ... 64.02 64.0 63.98
    longitude            (y, x) float64 181.4 181.5 181.5 ... 266.2 266.3 266.3
    valid_time           datetime64[ns] 2023-01-01T06:00:00
Dimensions without coordinates: y, x
Data variables:
    si10                 (y, x) float32 14.57 14.62 14.68 ... 1.67 1.67 1.68
    gribfile_projection  object None
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          4
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   rtma_ak
    product:                 anl
    description:             Alaska Real-Time Mesoscale Analysis (RTMA)
    remote_grib:             /p/cwfs/blaylock/data/rtma_ak/20230101/akrtma.t0...
    local_grib:              /p/cwfs/blaylock/data/rtma_ak/20230101/subset_c8...
    search:            WIND:10 m
[17]:

ax = EasyMap("50m", crs=ds.herbie.crs, figsize=[10, 8]).STATES().BORDERS().ax

p = ax.pcolormesh(
    ds.longitude,
    ds.latitude,
    ds.si10,
    transform=pc,
    **cm_wind().cmap_kwargs,
)
plt.colorbar(
    p,
    ax=ax,
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    **cm_wind().cbar_kwargs,
)

ax.set_title(
    f"{ds.model.upper()}: {H.product_description}\nValid: {ds.valid_time.dt.strftime('%H:%M UTC %d %b %Y').item()}",
    loc="left",
)
ax.set_title(ds.si10.GRIB_name, loc="right")

[17]:

Text(1.0, 1.0, '10 metre wind speed')
../../../_images/user_guide_tutorial_model_notebooks_rtma_20_1.png

RTMA vs URMA#

For fun, let’s compare the differences between the RTMA and the URMA analyses and see where the differences are for a particular time.

[18]:

from herbie import Herbie
import matplotlib.pyplot as plt
from toolbox import EasyMap, pc

[19]:

# Just open the entire GRIB2 files (opened in multiple DataSets)
rtma = Herbie("2023-05-22 00:00", model="rtma").xarray()
urma = Herbie("2023-05-22 00:00", model="urma").xarray()

✅ Found ┊ model=rtma ┊ product=anl ┊ 2023-May-22 00:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ aws

/p/home/blaylock/BB_python/Herbie/herbie/core.py:1048: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")

Note: Returning a list of [5] xarray.Datasets because cfgrib opened with multiple hypercubes.
✅ Found ┊ model=urma ┊ product=anl ┊ 2023-May-22 00:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ None

/p/home/blaylock/BB_python/Herbie/herbie/core.py:1048: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")

Note: Returning a list of [6] xarray.Datasets because cfgrib opened with multiple hypercubes.

[20]:

rtma[3]

[20]:

<xarray.Dataset>
Dimensions:              (y: 1597, x: 2345)
Coordinates:
    time                 datetime64[ns] 2023-05-22
    step                 timedelta64[ns] 00:00:00
    heightAboveGround    float64 2.0
    latitude             (y, x) float64 19.23 19.23 19.24 ... 54.38 54.38 54.37
    longitude            (y, x) float64 233.7 233.7 233.8 ... 300.9 300.9 301.0
    valid_time           datetime64[ns] 2023-05-22
Dimensions without coordinates: y, x
Data variables:
    t2m                  (y, x) float32 ...
    d2m                  (y, x) float32 ...
    sh2                  (y, x) float32 ...
    gribfile_projection  object None
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          4
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   rtma
    product:                 anl
    description:             CONUS Real-Time Mesoscale Analysis (RTMA)
    remote_grib:             /p/cwfs/blaylock/data/rtma/20230522/rtma2p5.t00z...
    local_grib:              /p/cwfs/blaylock/data/rtma/20230522/rtma2p5.t00z...
    search:            None
[21]:

urma[3]

[21]:

<xarray.Dataset>
Dimensions:              (y: 1597, x: 2345)
Coordinates:
    time                 datetime64[ns] 2023-05-22
    step                 timedelta64[ns] 00:00:00
    heightAboveGround    float64 2.0
    latitude             (y, x) float64 19.23 19.23 19.24 ... 54.38 54.38 54.37
    longitude            (y, x) float64 233.7 233.7 233.8 ... 300.9 300.9 301.0
    valid_time           datetime64[ns] 2023-05-22
Dimensions without coordinates: y, x
Data variables:
    t2m                  (y, x) float32 ...
    d2m                  (y, x) float32 ...
    sh2                  (y, x) float32 ...
    gribfile_projection  object None
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          4
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    model:                   urma
    product:                 anl
    description:             CONUS Un-Restricted Mesoscale Analysis (URMA)
    remote_grib:             /p/cwfs/blaylock/data/urma/20230522/urma2p5.t00z...
    local_grib:              /p/cwfs/blaylock/data/urma/20230522/urma2p5.t00z...
    search:            None

Just to confirm, both the RTMA and URMA have the same grid size and map projection.

[22]:

print("RTMA:", rtma[3].herbie.crs)
print("URMA:", urma[3].herbie.crs)

RTMA: +proj=lcc +ellps=sphere +a=6371200.0 +b=6371200.0 +lon_0=265.0 +lat_0=25.0 +x_0=0.0 +y_0=0.0 +lat_1=25.0 +lat_2=25.0 +no_defs +type=crs
URMA: +proj=lcc +ellps=sphere +a=6371200.0 +b=6371200.0 +lon_0=265.0 +lat_0=25.0 +x_0=0.0 +y_0=0.0 +lat_1=25.0 +lat_2=25.0 +no_defs +type=crs

[23]:

crs = rtma[3].herbie.crs

ax = EasyMap("50m", crs=crs).STATES().ax

art = ax.pcolormesh(
    rtma[3].longitude,
    rtma[3].latitude,
    (rtma[3].t2m - urma[3].t2m),
    transform=pc,
    cmap="PRGn",
    vmax=3,
    vmin=-3,
)

ax.set_title("RTMA minus URMA")

art = plt.colorbar(
    art,
    ax=ax,
    label="2 m Temperature Difference (Celsius)",
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    extend="both",
)
../../../_images/user_guide_tutorial_model_notebooks_rtma_28_0.png 
[24]:

crs = rtma[3].herbie.crs

ax = EasyMap("50m", crs=crs).STATES().ax

art = ax.pcolormesh(
    rtma[3].longitude,
    rtma[3].latitude,
    (rtma[3].d2m - urma[3].d2m),
    transform=pc,
    cmap="PRGn",
    vmax=3,
    vmin=-3,
)

ax.set_title("RTMA minus URMA")

art = plt.colorbar(
    art,
    ax=ax,
    label="2 m Dew Point Temperature Difference (Celsius)",
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    extend="both",
)
../../../_images/user_guide_tutorial_model_notebooks_rtma_29_0.png 
[25]:

crs = rtma[3].herbie.crs

ax = EasyMap("50m", crs=crs).STATES().ax

art = ax.pcolormesh(
    rtma[2].longitude,
    rtma[2].latitude,
    (rtma[2].si10 - urma[2].si10),
    transform=pc,
    cmap="PRGn",
    vmax=3,
    vmin=-3,
)

ax.set_title("RTMA minus URMA")

art = plt.colorbar(
    art,
    ax=ax,
    label="10 m Wind Speed Difference (m/s)",
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    extend="both",
)
../../../_images/user_guide_tutorial_model_notebooks_rtma_30_0.png 
[ ]:
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