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 herbie.toolbox import EasyMap, pc

from herbie import paint

import matplotlib.pyplot as plt

[9]:

H = Herbie(
    "2024-01-01 06:00",
    model="rtma",
    product="anl",
)

✅ Found ┊ model=rtma ┊ product=anl ┊ 2024-Jan-01 06:00 UTC F00 ┊ GRIB2 @ aws ┊ IDX @ aws

[10]:

# Show available products
H.PRODUCTS

[10]:

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

[11]:

H.SOURCES

[11]:

{'aws': 'https://noaa-rtma-pds.s3.amazonaws.com/rtma2p5.20240101/rtma2p5.t06z.2dvaranl_ndfd.grb2_wexp',
 'nomads': 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/rtma/prod/rtma2p5.20240101/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.

[12]:

H.download(verbose=True)  # verbose will show you download progress

👨🏻‍🏭 Created directory: [/home/blaylock/data/rtma/20240101]
✅ Success! Downloaded RTMA from aws                 
        src: https://noaa-rtma-pds.s3.amazonaws.com/rtma2p5.20240101/rtma2p5.t06z.2dvaranl_ndfd.grb2_wexp
        dst: /home/blaylock/data/rtma/20240101/rtma2p5.t06z.2dvaranl_ndfd.grb2_wexp

[12]:

PosixPath('/home/blaylock/data/rtma/20240101/rtma2p5.t06z.2dvaranl_ndfd.grb2_wexp')

[13]:

H.inventory()

[13]:
grib_message start_byte end_byte range reference_time valid_time variable level forecast_time search_this
0 1 0 7490117.0 0-7490117 2024-01-01 06:00:00 2024-01-01 06:00:00 HGT surface anl :HGT:surface:anl
1 2 7490118 14980235.0 7490118-14980235 2024-01-01 06:00:00 2024-01-01 06:00:00 PRES surface anl :PRES:surface:anl
2 3 14980236 21065992.0 14980236-21065992 2024-01-01 06:00:00 2024-01-01 06:00:00 TMP 2 m above ground anl :TMP:2 m above ground:anl
3 4 21065993 27619869.0 21065993-27619869 2024-01-01 06:00:00 2024-01-01 06:00:00 DPT 2 m above ground anl :DPT:2 m above ground:anl
4 5 27619870 33237505.0 27619870-33237505 2024-01-01 06:00:00 2024-01-01 06:00:00 UGRD 10 m above ground anl :UGRD:10 m above ground:anl
5 6 33237506 38855141.0 33237506-38855141 2024-01-01 06:00:00 2024-01-01 06:00:00 VGRD 10 m above ground anl :VGRD:10 m above ground:anl
6 7 38855142 45877139.0 38855142-45877139 2024-01-01 06:00:00 2024-01-01 06:00:00 SPFH 2 m above ground anl :SPFH:2 m above ground:anl
7 8 45877140 51494775.0 45877140-51494775 2024-01-01 06:00:00 2024-01-01 06:00:00 WDIR 10 m above ground anl :WDIR:10 m above ground:anl
8 9 51494776 56644290.0 51494776-56644290 2024-01-01 06:00:00 2024-01-01 06:00:00 WIND 10 m above ground anl :WIND:10 m above ground:anl
9 10 56644291 62261926.0 56644291-62261926 2024-01-01 06:00:00 2024-01-01 06:00:00 GUST 10 m above ground anl :GUST:10 m above ground:anl
10 11 62261927 68815803.0 62261927-68815803 2024-01-01 06:00:00 2024-01-01 06:00:00 VIS surface anl :VIS:surface:anl
11 12 68815804 77242163.0 68815804-77242163 2024-01-01 06:00:00 2024-01-01 06:00:00 CEIL cloud ceiling anl :CEIL:cloud ceiling:anl
12 13 77242164 NaN 77242164- 2024-01-01 06:00:00 2024-01-01 06:00:00 TCDC entire atmosphere (considered as a single layer) anl :TCDC:entire atmosphere (considered as a singl... 
[14]:

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

[14]:

<xarray.Dataset> Size: 75MB
Dimensions:              (y: 1597, x: 2345)
Coordinates:
    time                 datetime64[ns] 8B 2024-01-01T06:00:00
    step                 timedelta64[ns] 8B 00:00:00
    heightAboveGround    float64 8B 2.0
    latitude             (y, x) float64 30MB 19.23 19.23 19.24 ... 54.38 54.37
    longitude            (y, x) float64 30MB 233.7 233.7 233.8 ... 300.9 301.0
    valid_time           datetime64[ns] 8B 2024-01-01T06:00:00
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    t2m                  (y, x) float32 15MB 294.8 294.8 294.8 ... 257.1 257.1
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:             /home/blaylock/data/rtma/20240101/rtma2p5.t06z.2...
    local_grib:              /home/blaylock/data/rtma/20240101/subset_33efba7...
    search:                  TMP:2 m
[15]:

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

p = ax.pcolormesh(
    ds.longitude,
    ds.latitude,
    ds.t2m - 273.15,
    transform=pc,
    **paint.NWSTemperature.kwargs2,
)
plt.colorbar(
    p,
    ax=ax,
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    **paint.NWSTemperature.cbar_kwargs2,
)

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")

[15]:

Text(1.0, 1.0, '2 metre temperature')
../../_images/gallery_noaa_models_rtma-urma_9_1.png

RTMA Precipitation#

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

[18]:

H = Herbie(
    "2024-06-01 06:00",
    model="rtma",
    product="pcp",
)
H.inventory()

✅ Found ┊ model=rtma ┊ product=pcp ┊ 2024-Jun-01 06:00 UTC F00 ┊ GRIB2 @ aws ┊ IDX @ aws

[18]:
grib_message start_byte range reference_time valid_time variable level forecast_time search_this
0 1 0 0- 2024-06-01 05:00:00 2024-06-01 05:00:00 APCP 0 m above mean sea level 0-1 hour acc fcst :APCP:0 m above mean sea level:0-1 hour acc fcst 
[21]:

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

/home/blaylock/GITHUB/Herbie/herbie/core.py:1097: UserWarning: Will not remove GRIB file because it previously existed.
  warnings.warn("Will not remove GRIB file because it previously existed.")

[21]:

<xarray.Dataset> Size: 59MB
Dimensions:              (y: 1377, x: 2145)
Coordinates:
    time                 datetime64[ns] 8B 2024-06-01T05:00:00
    step                 timedelta64[ns] 8B 01:00:00
    heightAboveSea       float64 8B 0.0
    latitude             (y, x) float64 24MB ...
    longitude            (y, x) float64 24MB ...
    valid_time           datetime64[ns] 8B ...
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    tp                   (y, x) float32 12MB nan nan nan nan ... nan nan nan nan
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:             /home/blaylock/data/rtma/20240601/rtma2p5.202406...
    local_grib:              /home/blaylock/data/rtma/20240601/rtma2p5.202406...
    search:                  None
[22]:

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

p = ax.pcolormesh(
    ds.longitude,
    ds.latitude,
    ds.tp,
    transform=pc,
    **paint.NWSPrecipitation.kwargs2,
)
plt.colorbar(
    p,
    ax=ax,
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    **paint.NWSPrecipitation.cbar_kwargs2,
)

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")

[22]:

Text(1.0, 1.0, 'Total Precipitation')
../../_images/gallery_noaa_models_rtma-urma_13_1.png

RTMA Alaska#

[2]:

H = Herbie(
    "2024-01-01 06:00",
    model="rtma_ak",
    product="anl",
)

✅ Found ┊ model=rtma_ak ┊ product=anl ┊ 2024-Jan-01 06:00 UTC F00 ┊ GRIB2 @ aws ┊ IDX @ aws

[3]:

H.inventory()

[3]:
grib_message start_byte end_byte range reference_time valid_time variable level forecast_time search_this
0 1 0 3644461.0 0-3644461 2024-01-01 06:00:00 2024-01-01 06:00:00 HGT surface anl :HGT:surface:anl
1 2 3644462 7288923.0 3644462-7288923 2024-01-01 06:00:00 2024-01-01 06:00:00 PRES surface anl :PRES:surface:anl
2 3 7288924 10250081.0 7288924-10250081 2024-01-01 06:00:00 2024-01-01 06:00:00 TMP 2 m above ground anl :TMP:2 m above ground:anl
3 4 10250082 13211239.0 10250082-13211239 2024-01-01 06:00:00 2024-01-01 06:00:00 DPT 2 m above ground anl :DPT:2 m above ground:anl
4 5 13211240 16172397.0 13211240-16172397 2024-01-01 06:00:00 2024-01-01 06:00:00 UGRD 10 m above ground anl :UGRD:10 m above ground:anl
5 6 16172398 19133555.0 16172398-19133555 2024-01-01 06:00:00 2024-01-01 06:00:00 VGRD 10 m above ground anl :VGRD:10 m above ground:anl
6 7 19133556 22094713.0 19133556-22094713 2024-01-01 06:00:00 2024-01-01 06:00:00 SPFH 2 m above ground anl :SPFH:2 m above ground:anl
7 8 22094714 24828103.0 22094714-24828103 2024-01-01 06:00:00 2024-01-01 06:00:00 WDIR 10 m above ground anl :WDIR:10 m above ground:anl
8 9 24828104 27561493.0 24828104-27561493 2024-01-01 06:00:00 2024-01-01 06:00:00 WIND 10 m above ground anl :WIND:10 m above ground:anl
9 10 27561494 30294883.0 27561494-30294883 2024-01-01 06:00:00 2024-01-01 06:00:00 GUST 10 m above ground anl :GUST:10 m above ground:anl
10 11 30294884 33483809.0 30294884-33483809 2024-01-01 06:00:00 2024-01-01 06:00:00 VIS surface anl :VIS:surface:anl
11 12 33483810 37583808.0 33483810-37583808 2024-01-01 06:00:00 2024-01-01 06:00:00 CEIL cloud ceiling anl :CEIL:cloud ceiling:anl
12 13 37583809 NaN 37583809- 2024-01-01 06:00:00 2024-01-01 06:00:00 TCDC entire atmosphere (considered as a single layer) anl :TCDC:entire atmosphere (considered as a singl... 
[4]:

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

[4]:

<xarray.Dataset> Size: 36MB
Dimensions:              (y: 1105, x: 1649)
Coordinates:
    time                 datetime64[ns] 8B 2024-01-01T06:00:00
    step                 timedelta64[ns] 8B 00:00:00
    heightAboveGround    float64 8B 10.0
    latitude             (y, x) float64 15MB 40.53 40.54 40.55 ... 64.0 63.98
    longitude            (y, x) float64 15MB 181.4 181.5 181.5 ... 266.3 266.3
    valid_time           datetime64[ns] 8B 2024-01-01T06:00:00
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    si10                 (y, x) float32 7MB 3.4 3.49 3.49 ... 2.93 2.92 2.92
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:             https://noaa-rtma-pds.s3.amazonaws.com/akrtma.20...
    local_grib:              /home/blaylock/data/rtma_ak/20240101/subset_33ef...
    search:                  WIND:10 m
[5]:

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

p = ax.pcolormesh(
    ds.longitude,
    ds.latitude,
    ds.si10,
    transform=pc,
    **paint.NWSWindSpeed.kwargs2,
)
plt.colorbar(
    p,
    ax=ax,
    orientation="horizontal",
    pad=0.01,
    shrink=0.8,
    **paint.NWSWindSpeed.cbar_kwargs2,
)

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")

[5]:

Text(1.0, 1.0, '10 metre wind speed')
../../_images/gallery_noaa_models_rtma-urma_18_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.

[2]:

# Just open the entire GRIB2 files (opened in multiple DataSets)
rtma = Herbie("2024-06-11 00:00", model="rtma").xarray(verbose=True)
urma = Herbie("2024-06-11 00:00", model="urma").xarray(verbose=True)

✅ Found ┊ model=rtma ┊ product=anl ┊ 2024-Jun-11 00:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ aws

/home/blaylock/GITHUB/Herbie/herbie/core.py:1097: 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 ┊ 2024-Jun-11 00:00 UTC F00 ┊ GRIB2 @ local ┊ IDX @ aws

/home/blaylock/GITHUB/Herbie/herbie/core.py:1097: 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.

[7]:

rtma[3]

[7]:

<xarray.Dataset> Size: 105MB
Dimensions:              (y: 1597, x: 2345)
Coordinates:
    time                 datetime64[ns] 8B 2024-06-11
    step                 timedelta64[ns] 8B 00:00:00
    heightAboveGround    float64 8B 2.0
    latitude             (y, x) float64 30MB 19.23 19.23 19.24 ... 54.38 54.37
    longitude            (y, x) float64 30MB 233.7 233.7 233.8 ... 300.9 301.0
    valid_time           datetime64[ns] 8B 2024-06-11
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    t2m                  (y, x) float32 15MB ...
    d2m                  (y, x) float32 15MB ...
    sh2                  (y, x) float32 15MB ...
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:             /home/blaylock/data/rtma/20240611/rtma2p5.t00z.2...
    local_grib:              /home/blaylock/data/rtma/20240611/rtma2p5.t00z.2...
    search:                  None
[8]:

urma[3]

[8]:

<xarray.Dataset> Size: 105MB
Dimensions:              (y: 1597, x: 2345)
Coordinates:
    time                 datetime64[ns] 8B 2024-06-11
    step                 timedelta64[ns] 8B 00:00:00
    heightAboveGround    float64 8B 2.0
    latitude             (y, x) float64 30MB 19.23 19.23 19.24 ... 54.38 54.37
    longitude            (y, x) float64 30MB 233.7 233.7 233.8 ... 300.9 301.0
    valid_time           datetime64[ns] 8B 2024-06-11
    gribfile_projection  object 8B None
Dimensions without coordinates: y, x
Data variables:
    t2m                  (y, x) float32 15MB ...
    d2m                  (y, x) float32 15MB ...
    sh2                  (y, x) float32 15MB ...
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:             /home/blaylock/data/urma/20240611/urma2p5.t00z.2...
    local_grib:              /home/blaylock/data/urma/20240611/urma2p5.t00z.2...
    search:                  None

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

[9]:

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

[3]:

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="herbie.white",  # this is a Custom herbie colormap :)
    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/gallery_noaa_models_rtma-urma_25_0.png 
[4]:

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="herbie.white",
    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/gallery_noaa_models_rtma-urma_26_0.png 
[5]:

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="herbie.white",
    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",
)

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