ECMWF (IFS and AIFS)#

This tutorial demonstrates how to download the ECMWF Open Data Integrated Forecast System (IFS) and Artificial Intelligence IFS (AIFS).

Recent and archived IFS and AIFS data is freely available from ECMWF in GRIB2 format (👀 Read more).

“The data that are becoming available are based on a range of high-resolution forecasts (HRES – 9 km horizontal resolution) and ensemble forecasts (ENS – 18 km horizontal resolution).

- Media Statement

29 February 2024: Update from 0.4 degree resolution to 0.25 degree resolution

- Media Statement

Data Availability#

Jan 18, 2023, IFS 0.4 degree resolution first available
Feb 1, 2024 IFS 0.25 degree resolution first available
Feb 1, 2024 AIFS 0.25 degree resolution first available
May ?, 2024 IFS 0.4 degree discontinued.

Model Types#

ECMWF provides data for two different models

model="ifs" ECMWF Integrated Forecast System
model="aifs" ECMWF Artificial Intelligence Integrated Forecast System

Data Source#

`prioriy=`	Data source	Archive Duration
`"ecmwf"`	ECMWF Open Data Server	last 4 days
`"azure"`	Microsoft Azure	2022-01-21 to present
`"aws"`	Amazon Web Services	2023-01-18 to present

Products#

Note: the aifs only has the oper product.

`product=`	Product Description	Available model runs
`"oper"`	operational high-resolution forecast, atmospheric fields	`00z`, `12z`,
`"wave"`	wave forecasts	`00z`, `12z`,
`"scda"`	short cut-off high-resolution forecast, atmospheric fields (also known a high-frequency products)”,	`06z`, `18z`
`"scwv"`	short cut-off high-resolution forecast, ocean wave fields (also known a high-frequency products)”,	`06z`, `18z`
`"enfo"`	ensemble forecast, atmospheric fields	`00z`, `06z`, `12z`, `18z`
`"waef"`	ensemble forecast, ocean wave fields,	`00z`, `06z`, `12z`, `18z`
`"mmsf"`	multi-model seasonal forecasts fields from the ECMWF model only.	?

Model initialized at 00z, 06z, 12z, 18z, but not all products are available every hour.

[1]:

from herbie import Herbie

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

from herbie import paint
from herbie.toolbox import EasyMap, pc

Integrated Forecast System (IFS)#

IFS data is only available at 0.4 degree prior to February 1, 2024. After that date, the IFS is available at 0.25 degree resolution.

[2]:

H = Herbie("2024-03-1", model="ifs", product="oper", fxx=12)

H.grib, H.idx

✅ Found ┊ model=ifs ┊ product=oper ┊ 2024-Mar-01 00:00 UTC F12 ┊ GRIB2 @ azure ┊ IDX @ azure

[2]:

('https://ai4edataeuwest.blob.core.windows.net/ecmwf/20240301/00z/ifs/0p25/oper/20240301000000-12h-oper-fc.grib2',
 'https://ai4edataeuwest.blob.core.windows.net/ecmwf/20240301/00z/ifs/0p25/oper/20240301000000-12h-oper-fc.index')

[3]:

# Show the inventory
H.inventory()

[3]:

	grib_message	start_byte	end_byte	range	reference_time	valid_time	step	param	levelist	levtype	number	domain	expver	class	type	stream	search_this
0	1	0	798588	0-798588	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	tp	NaN	sfc	NaN	g	0001	od	fc	oper	:tp:sfc:g:0001:od:fc:oper
1	2	798588	1327999	798588-1327999	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	r	500	pl	NaN	g	0001	od	fc	oper	:r:500:pl:g:0001:od:fc:oper
2	3	1327999	1817293	1327999-1817293	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	gh	850	pl	NaN	g	0001	od	fc	oper	:gh:850:pl:g:0001:od:fc:oper
3	4	1817293	2534675	1817293-2534675	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	u	925	pl	NaN	g	0001	od	fc	oper	:u:925:pl:g:0001:od:fc:oper
4	5	2534675	3268614	2534675-3268614	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	v	925	pl	NaN	g	0001	od	fc	oper	:v:925:pl:g:0001:od:fc:oper
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
78	79	58285210	59656192	58285210-59656192	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	d	250	pl	NaN	g	0001	od	fc	oper	:d:250:pl:g:0001:od:fc:oper
79	80	59656192	59759143	59656192-59759143	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	ro	NaN	sfc	NaN	g	0001	od	fc	oper	:ro:sfc:g:0001:od:fc:oper
80	81	59759143	61078909	59759143-61078909	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	vo	250	pl	NaN	g	0001	od	fc	oper	:vo:250:pl:g:0001:od:fc:oper
81	82	61078909	62399470	61078909-62399470	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	d	50	pl	NaN	g	0001	od	fc	oper	:d:50:pl:g:0001:od:fc:oper
82	83	62399470	63520541	62399470-63520541	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	vo	50	pl	NaN	g	0001	od	fc	oper	:vo:50:pl:g:0001:od:fc:oper

83 rows × 17 columns

[5]:

# Show just 10-m U and V wind
H.inventory(":10[u|v]:")

[5]:

	grib_message	start_byte	end_byte	range	reference_time	valid_time	step	param	levelist	levtype	number	domain	expver	class	type	stream	search_this
46	47	28201794	29068335	28201794-29068335	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	10u	NaN	sfc	NaN	g	0001	od	fc	oper	:10u:sfc:g:0001:od:fc:oper
47	48	29068335	29928681	29068335-29928681	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	10v	NaN	sfc	NaN	g	0001	od	fc	oper	:10v:sfc:g:0001:od:fc:oper

[6]:

# Get 2-m temperature as an xarray Dataset
ds = H.xarray(":2t:", verbose=True)
ds

👨🏻‍🏭 Created directory: [/home/blaylock/data/ifs/20240301]
📇 Download subset: ▌▌Herbie IFS model oper product initialized 2024-Mar-01 00:00 UTC F12 ┊ source=azure
 cURL from https://ai4edataeuwest.blob.core.windows.net/ecmwf/20240301/00z/ifs/0p25/oper/20240301000000-12h-oper-fc.grib2
Found 1 grib messages.
Download subset group 1
  41  :2t:sfc:g:0001:od:fc:oper
curl -s --range 24427114-25090915 "https://ai4edataeuwest.blob.core.windows.net/ecmwf/20240301/00z/ifs/0p25/oper/20240301000000-12h-oper-fc.grib2" > "/home/blaylock/data/ifs/20240301/subset_e0127a9f__20240301000000-12h-oper-fc.grib2"
💾 Saved the subset to /home/blaylock/data/ifs/20240301/subset_e0127a9f__20240301000000-12h-oper-fc.grib2

[7]:

ds.t2m.plot()

[7]:

<matplotlib.collections.QuadMesh at 0x7fb7ef104da0>

../../_images/gallery_ecmwf_models_ecmwf_7_1.png

0.4 degree IFS#

Again, the 0.4 degree IFS was available starting January 2023, but will be discontinued in May 2024 in favor of the 0.25 degree data. You can still access these files as long as they exist.

[8]:

# Accessing historical
H = Herbie("2023-07-04", model="ifs", product="oper", fxx=12)
H.grib

✅ Found ┊ model=ifs ┊ product=oper ┊ 2023-Jul-04 00:00 UTC F12 ┊ GRIB2 @ azure ┊ IDX @ azure

[8]:

'https://ai4edataeuwest.blob.core.windows.net/ecmwf/20230704/00z/0p4-beta/oper/20230704000000-12h-oper-fc.grib2'

Eccodes-style Index Files#

The ECMWF index files are different than the wgrib2-style index files, so pay close attention to how you should select the field you want.

[9]:

H = Herbie("2024-03-1", model="ifs", product="oper", fxx=12)

# Show the search_help
print(H.search_help)

✅ Found ┊ model=ifs ┊ product=oper ┊ 2024-Mar-01 00:00 UTC F12 ┊ GRIB2 @ azure ┊ IDX @ azure

Use regular expression to search for lines in the index file.
Here are some examples you can use for the ecCodes-style `search`

Look at the ECMWF GRIB Parameter Database
https://apps.ecmwf.int/codes/grib/param-db


product=`oper` or `enfo`
======================== ==============================================
search=                  GRIB messages that will be downloaded
======================== ==============================================
":2t:"                   2-m temperature
":2d:"                   2-m dew point temperature
":10u:"                  10-m u wind vector
":10v:"                  10-m v wind vector
":10[uv]:                10-m u and 10-m v wind
":[tuvr]:"               Temp, u/v wind, RH (all levels)
":500:"                  All variables on the 500 hPa level
":gh:500"                Geopotential height only at 500 hPa
":gh:"                   Geopotential height (all pressure levels)
":t:"                    Temperature (all pressure levels)
":q:"                    Specific Humidity (all pressure levels)
":r:"                    Relative humidity (all pressure levels)
":v:"                    v wind vector (all pressure levels)
":u:"                    u wind vector (all pressure levels)
":w:"                    Vertical velocity (Pascals per second)
":lsm:"                  Land-sea mask
":ttr:"                  Top net long-wave (thermal) radiation
":ssrd:"                 Surface short-wave (solar) radiation downwards
":ssr:"                  Surface net short-wave (solar) radiation
":strd:"                 Surface long-wave (thermal) radiation downwards
":str:"                  Surface net long-wave (thermal) radiation
":swvl1:"                Volumetric soil water layer 1 (depth 0 meters)
":swvl2:"                Volumetric soil water layer 2 (depth 7 meters)
":swvl3:"                Volumetric soil water layer 3 (depth 28 meters)
":swvl4:"                Volumetric soil water layer 4 (depth 100 meters)
":skt:"                  Skin (surface) temperature
":d:"                    Divergence (all levels)
":st:"                   Soil temperature
":stl2:"                 Soil temperature level 2 (depth 7 meters)
":tp:"                   Total precipitation
":ro:"                   Run-off
":asn:"                  Snow albedo
":msl:"                  Mean sea level pressure
":sp:"                   Surface pressure
":cape:"                 CAPE
":tcwv:"                 Total column vertically integrated water vapor
":vo:"                   Relative vorticity

product=`wave` or `waef`
======================== ==============================================
search=                  GRIB messages that will be downloaded
======================== ==============================================
":swh:"                  Significant height of wind waves + swell
":mwp:"                  Mean wave period
":mwd:"                  Mean wave direction
":pp1d:"                 Peak wave period
":mp2:"                  Mean zero-crossing wave period

If you need help with regular expression, search the web or look at
this cheatsheet: https://www.petefreitag.com/cheatsheets/regex/.

Here is an example: https://regex101.com/r/niNjwp/1

When considering search queries, pay attention to the “search_this” column; that columns is used for the regex search.

[10]:

H.inventory()

[10]:

	grib_message	start_byte	end_byte	range	reference_time	valid_time	step	param	levelist	levtype	number	domain	expver	class	type	stream	search_this
0	1	0	798588	0-798588	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	tp	NaN	sfc	NaN	g	0001	od	fc	oper	:tp:sfc:g:0001:od:fc:oper
1	2	798588	1327999	798588-1327999	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	r	500	pl	NaN	g	0001	od	fc	oper	:r:500:pl:g:0001:od:fc:oper
2	3	1327999	1817293	1327999-1817293	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	gh	850	pl	NaN	g	0001	od	fc	oper	:gh:850:pl:g:0001:od:fc:oper
3	4	1817293	2534675	1817293-2534675	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	u	925	pl	NaN	g	0001	od	fc	oper	:u:925:pl:g:0001:od:fc:oper
4	5	2534675	3268614	2534675-3268614	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	v	925	pl	NaN	g	0001	od	fc	oper	:v:925:pl:g:0001:od:fc:oper
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
78	79	58285210	59656192	58285210-59656192	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	d	250	pl	NaN	g	0001	od	fc	oper	:d:250:pl:g:0001:od:fc:oper
79	80	59656192	59759143	59656192-59759143	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	ro	NaN	sfc	NaN	g	0001	od	fc	oper	:ro:sfc:g:0001:od:fc:oper
80	81	59759143	61078909	59759143-61078909	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	vo	250	pl	NaN	g	0001	od	fc	oper	:vo:250:pl:g:0001:od:fc:oper
81	82	61078909	62399470	61078909-62399470	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	d	50	pl	NaN	g	0001	od	fc	oper	:d:50:pl:g:0001:od:fc:oper
82	83	62399470	63520541	62399470-63520541	2024-03-01	2024-03-01 12:00:00	0 days 12:00:00	vo	50	pl	NaN	g	0001	od	fc	oper	:vo:50:pl:g:0001:od:fc:oper

83 rows × 17 columns

[18]:

H.inventory().param.unique()

[18]:

array(['tp', 'msl', '2t', '10u', 'gh', 't', 'u', 'v', 'st', 'r', 'skt',
       'sp', 'lsm', '10v', 'tcwv', 'q', 'd', 'vo', 'ro'], dtype=object)

Ok, now that we have some understanding of the index file, we can read the 2-m temperature data.

[11]:

ds = H.xarray(":2t:")
ds

[12]:

ax = EasyMap("50m", crs=ds.herbie.crs, figsize=[10, 10]).STATES().BORDERS().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=0.05, **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")

[12]:

Text(1.0, 1.0, '2 metre temperature')

../../_images/gallery_ecmwf_models_ecmwf_17_1.png

Now the same, but for wind. Use the Herbie accessor to compute wind speed and direction.

[14]:

H = Herbie("2024-03-01", model="ifs", product="oper")

# Get u and v wind component
ds = H.xarray(":10[u|v]:").herbie.with_wind()
ds

✅ Found ┊ model=ifs ┊ product=oper ┊ 2024-Mar-01 00:00 UTC F00 ┊ GRIB2 @ azure ┊ IDX @ azure

[16]:

ax = EasyMap("50m", crs=ds.herbie.crs, figsize=[10, 10]).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=0.05, **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.standard_name, loc="right")

[16]:

Text(1.0, 1.0, 'wind_speed')

../../_images/gallery_ecmwf_models_ecmwf_20_1.png

Now lets get the humidity and geopotential height at 500 hPa

[17]:

ds = H.xarray(":(?:q|gh):500")
ds

[19]:

# NOTE: transforming data to a Robinson projection can take some time...
ax = EasyMap("50m", crs=ccrs.Robinson(), figsize=[10, 10]).STATES().BORDERS().ax


# Color shade by specific humidity
p = ax.pcolormesh(ds.longitude, ds.latitude, ds.q, transform=pc, cmap="Greens")

plt.colorbar(
    p,
    ax=ax,
    orientation="horizontal",
    pad=0.05,
    label=f"{ds.q.GRIB_name} ({ds.q.units})",
)

# Contours for geopotential height
ax.contour(
    ds.longitude,
    ds.latitude,
    ds.gh,
    transform=pc,
    colors="k",
    linewidths=0.5,
    levels=range(0, 10_000, 60 * 2),
)


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(
    f"{ds.isobaricInhPa.item()} {ds.isobaricInhPa.units}\n{ds.q.GRIB_name}/{ds.gh.GRIB_name}",
    loc="right",
)

[19]:

Text(1.0, 1.0, '500.0 hPa\nSpecific humidity/Geopotential height')

../../_images/gallery_ecmwf_models_ecmwf_23_1.png

The Kernel crashed while executing code in 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.

Ensemble Wave Products#

[16]:

H = Herbie("2022-01-26 00:00", model="ifs", product="waef")
H.inventory()

✅ Found ┊ model=ifs ┊ product=waef ┊ 2022-Jan-26 00:00 UTC F00 ┊ GRIB2 @ azure ┊ IDX @ azure

[16]:

	grib_message	start_byte	end_byte	range	reference_time	valid_time	step	param	levelist	levtype	number	domain	expver	class	type	stream	search_this
0	1	0	501693	0-501693	2022-01-26	2022-01-26	0 days	swh	NaN	sfc	38	g	0001	od	pf	waef	:swh:sfc:38:g:0001:od:pf:waef
1	2	501693	1003064	501693-1003064	2022-01-26	2022-01-26	0 days	swh	NaN	sfc	46	g	0001	od	pf	waef	:swh:sfc:46:g:0001:od:pf:waef
2	3	1003064	1504757	1003064-1504757	2022-01-26	2022-01-26	0 days	swh	NaN	sfc	43	g	0001	od	pf	waef	:swh:sfc:43:g:0001:od:pf:waef
3	4	1504757	2006096	1504757-2006096	2022-01-26	2022-01-26	0 days	swh	NaN	sfc	40	g	0001	od	pf	waef	:swh:sfc:40:g:0001:od:pf:waef
4	5	2006096	2507435	2006096-2507435	2022-01-26	2022-01-26	0 days	swh	NaN	sfc	NaN	g	0001	od	cf	waef	:swh:sfc:g:0001:od:cf:waef
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
250	251	125340988	125842349	125340988-125842349	2022-01-26	2022-01-26	0 days	pp1d	NaN	sfc	7	g	0001	od	pf	waef	:pp1d:sfc:7:g:0001:od:pf:waef
251	252	125842349	126343438	125842349-126343438	2022-01-26	2022-01-26	0 days	pp1d	NaN	sfc	29	g	0001	od	pf	waef	:pp1d:sfc:29:g:0001:od:pf:waef
252	253	126343438	126844527	126343438-126844527	2022-01-26	2022-01-26	0 days	pp1d	NaN	sfc	34	g	0001	od	pf	waef	:pp1d:sfc:34:g:0001:od:pf:waef
253	254	126844527	127345908	126844527-127345908	2022-01-26	2022-01-26	0 days	pp1d	NaN	sfc	NaN	g	0001	od	cf	waef	:pp1d:sfc:g:0001:od:cf:waef
254	255	127345908	127847247	127345908-127847247	2022-01-26	2022-01-26	0 days	mp2	NaN	sfc	NaN	g	0001	od	cf	waef	:mp2:sfc:g:0001:od:cf:waef

255 rows × 17 columns

[18]:

H = Herbie("2022-01-26", model="ifs", product="enfo")
H.inventory()

✅ Found ┊ model=ifs ┊ product=enfo ┊ 2022-Jan-26 00:00 UTC F00 ┊ GRIB2 @ azure ┊ IDX @ azure

[18]:

	grib_message	start_byte	end_byte	range	reference_time	valid_time	step	param	levelist	levtype	number	domain	expver	class	type	stream	search_this
0	1	0	609069	0-609069	2022-01-26	2022-01-26	0 days	2t	NaN	sfc	29	g	0001	od	pf	enfo	:2t:sfc:29:g:0001:od:pf:enfo
1	2	609069	1218138	609069-1218138	2022-01-26	2022-01-26	0 days	10u	NaN	sfc	27	g	0001	od	pf	enfo	:10u:sfc:27:g:0001:od:pf:enfo
2	3	1218138	1827207	1218138-1827207	2022-01-26	2022-01-26	0 days	10v	NaN	sfc	14	g	0001	od	pf	enfo	:10v:sfc:14:g:0001:od:pf:enfo
3	4	1827207	2436276	1827207-2436276	2022-01-26	2022-01-26	0 days	2t	NaN	sfc	2	g	0001	od	pf	enfo	:2t:sfc:2:g:0001:od:pf:enfo
4	5	2436276	3045345	2436276-3045345	2022-01-26	2022-01-26	0 days	10u	NaN	sfc	43	g	0001	od	pf	enfo	:10u:sfc:43:g:0001:od:pf:enfo
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4177	4178	2574120261	2574932280	2574120261-2574932280	2022-01-26	2022-01-26	0 days	d	200	pl	20	g	0001	od	pf	enfo	:d:200:pl:20:g:0001:od:pf:enfo
4178	4179	2574932280	2575744299	2574932280-2575744299	2022-01-26	2022-01-26	0 days	d	200	pl	41	g	0001	od	pf	enfo	:d:200:pl:41:g:0001:od:pf:enfo
4179	4180	2575744299	2576556318	2575744299-2576556318	2022-01-26	2022-01-26	0 days	d	200	pl	4	g	0001	od	pf	enfo	:d:200:pl:4:g:0001:od:pf:enfo
4180	4181	2576556318	2577368337	2576556318-2577368337	2022-01-26	2022-01-26	0 days	d	200	pl	50	g	0001	od	pf	enfo	:d:200:pl:50:g:0001:od:pf:enfo
4181	4182	2577368337	2578180356	2577368337-2578180356	2022-01-26	2022-01-26	0 days	d	200	pl	23	g	0001	od	pf	enfo	:d:200:pl:23:g:0001:od:pf:enfo

4182 rows × 17 columns

Here is another examle, just for fun

[21]:

H = Herbie("2022-01-26", model="ifs", product="oper", fxx=12)
H

✅ Found ┊ model=ifs ┊ product=oper ┊ 2022-Jan-26 00:00 UTC F12 ┊ GRIB2 @ azure ┊ IDX @ azure

[21]:

▌▌Herbie IFS model oper product initialized 2022-Jan-26 00:00 UTC F12 ┊ source=azure

[22]:

# Download just the 10-m u and v winds
ds = H.download(search=":10(u|v):")
ds

/home/blaylock/GITHUB/Herbie/herbie/core.py:808: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
  logic = df.search_this.str.contains(search)

[22]:

PosixPath('/home/blaylock/data/ifs/20220126/subset_bf121a77__20220126000000-12h-oper-fc.grib2')

[23]:

# Retrieve the 500 hPa temperature as an xarray.Dataset
ds = H.xarray(search=":t:500:")
ds

[24]:

ds.t.plot()

[24]:

<matplotlib.collections.QuadMesh at 0x7f756deb8500>

../../_images/gallery_ecmwf_models_ecmwf_40_1.png

Artificial Intelligence IFS#

Download and plot ECMWF AIFS model data.

⚠ AIFS GRIB files before March 13, 2024, seemed to be encoded incorrectly as they are read as a vector instead of a grid. I’m not sure how this data can be recovered or reshaped.

[29]:

H = Herbie("2024-06-11", model="aifs", product="oper", fxx=12)

H.grib, H.idx

✅ Found ┊ model=aifs ┊ product=oper ┊ 2024-Jun-11 00:00 UTC F12 ┊ GRIB2 @ azure ┊ IDX @ azure

[29]:

('https://ai4edataeuwest.blob.core.windows.net/ecmwf/20240611/00z/aifs/0p25/oper/20240611000000-12h-oper-fc.grib2',
 'https://ai4edataeuwest.blob.core.windows.net/ecmwf/20240611/00z/aifs/0p25/oper/20240611000000-12h-oper-fc.index')

[30]:

H.inventory(":t:850")

[30]:

	grib_message	start_byte	end_byte	range	reference_time	valid_time	step	param	levelist	levtype	number	domain	expver	class	type	stream	search_this
32	33	25515811	26027206	25515811-26027206	2024-06-11	2024-06-11 12:00:00	0 days 12:00:00	t	850	pl	NaN	g	0001	ai	fc	oper	:t:850:pl:g:0001:ai:fc:oper

[42]:

ds = H.xarray(":t:850")
(ds.t - 273.15).plot(
    x="longitude",
    y="latitude",
    ax=EasyMap().ax,
    transform=pc,
    **paint.NWSTemperature.kwargs2,
    cbar_kwargs=paint.NWSTemperature.cbar_kwargs2
    | dict(orientation="horizontal", pad=0.01),
)
ds

../../_images/gallery_ecmwf_models_ecmwf_44_1.png

Attribution

Source www.ecmwf.int
Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/
Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.