Mean Sea Level Pressure & Wind Speed at 850 hPa¶

Hint

Execute the notebook on the training platform >>

Note: this notebook is lightly modified from an existing ECMWF notebook. See the original ECMWF notebook example.

Retrieve Data¶

This product takes in input 3 parameters :

In this example, we will use:

cdsapi Client to download the data
ecmwf.data library to read and process the data
magpye to plot the result

Load required libraries

import cdsapi
import ecmwf.data as ecdata
from magpye import GeoMap

[OPTIONAL] Specify URL and Atmosphere Data Store key

This step is optional as we have already downloaded the data for you.

URL='https://ads.atmosphere.copernicus.eu/api/v2'
KEY='######################################'

CAMS global atmospheric composition forecasts¶

[OPTIONAL] Retrieve CAMS global atmospheric composition forecasts from the Atmosphere Data Store¶

This step is optional as we have already downloaded the data for you. The following two code blocks have been commented out as a result.

Link: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts?tab=overview

Variables: Mean sea level pressure, 10m u-component of wind, 10m v-component of wind
Type: Forecasts
Resolution: 0.4 deg x 0.4 deg

'''
c = cdsapi.Client(url=URL, key=KEY)

c.retrieve(
    'cams-global-atmospheric-composition-forecasts',
    {
        'date': '2021-08-05/2021-08-05',
        'type': 'forecast',
        'format': 'grib',
        'time': '00:00',
        'variable': [
            'u_component_of_wind', 'v_component_of_wind',
        ],
        'leadtime_hour': '12',
        'pressure_level': '850',
    },
    './20210805_cams_forecast_wind850.grib')
'''

'''
c.retrieve(
    'cams-global-atmospheric-composition-forecasts',
    {
        'date': '2021-08-05/2021-08-05',
        'type': 'forecast',
        'format': 'grib',
        'time': '00:00',
        'variable': 'mean_sea_level_pressure',
        'leadtime_hour': '12',
    },
    './20210805_cams_forecast_mslp.grib')
'''

pl_filename = '../data/cams/2021/20210805_cams_forecast_wind850.grib'
sfc_filename = '../data/cams/2021/20210805_cams_forecast_mslp.grib'

Reading and processing the data¶

Now we can use ecmwf.data to read the files.

wind850 = ecdata.read(pl_filename)
msl = ecdata.read(sfc_filename)

The describe() function will give us the overview of the dataset.

wind850.describe()

parameter	typeOfLevel	level	date	time	step	paramId	class	stream	type	experimentVersionNumber
u	isobaricInhPa	850	20210805	0	12	131	mc	oper	fc	0001
v	isobaricInhPa	850	20210805	0	12	132	mc	oper	fc	0001

And an overview of one parameter, where we can see more information, such as units or type of level.

msl.describe('msl')

shortName	msl
name	Mean sea level pressure
paramId	151
units	Pa
typeOfLevel	surface
level	0
date	20210805
time	0
step	12
class	mc
stream	oper
type	fc
experimentVersionNumber	0001

We can use ls() function to list all the fields in the file we downloaded.

wind850.ls()

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	gridType
Message
0	ecmf	u	isobaricInhPa	850	20210805	0	12	fc	regular_ll
1	ecmf	v	isobaricInhPa	850	20210805	0	12	fc	regular_ll

In order to calculate the speed, we need to filter out u and v component and use the speed() function for the calculation.

u = wind850.select(shortName ='u')
v = wind850.select(shortName ='v')
speed = ecdata.speed(u,v)
speed.describe()

parameter	typeOfLevel	level	date	time	step	paramId	class	stream	type	experimentVersionNumber
ws	isobaricInhPa	850	20210805	0	12	10	mc	oper	fc	0001

Mean sea level pressure data has unites Pa, but we want to plot it in hPa, therefore we need to convert it.

msl /= 100

Plotting the data¶

And finally, we can plot the data on the map.

fig = GeoMap(area_name='europe')

fig.coastlines(land_colour="cream",resolution="medium")

fig.contour_shaded(speed, style="green_speed")
fig.contour_lines(msl, style="black_i5")

fig.coastlines(resolution="medium")
fig.gridlines()

fig.title(["Mean sea level pressure and wind speed at 850 hPa", 
           "START TIME: <grib_info key='base-date' format='%a %d %B %Y %H' where='shortName=msl'/> ",
           "VALID TIME: <grib_info key='valid-date' format='%a %d %B %Y %H' where='shortName=msl'/>, STEP: <grib_info key='step' where='shortName=msl' />"])
fig.legend()
fig.footer("© European Centre for Medium-Range Weather Forecasts (ECMWF) Source: www.ecmwf.int Licence: CC-BY-4.0 and ECMWF Terms of Use (https://apps.ecmwf.int/datasets/licences/general/)", logo='ecmwf')
fig.show()

../_images/figure6_CAMS_mslp_wind850_31_0.png

References¶

Copernicus Atmosphere Monitoring Service Information 2021
The code in this notebook was adapted from the following sources:
- origin: https://github.com/ecmwf/notebook-examples/blob/master/opencharts/medium-mslp-wind850.ipynb
- copyright: 2020, ECMWF
- license: Apache-2.0
- retrieved: 2022-06-28 by Sabrina Szeto

Return to the case study

Assessing pre-fire risk with next-generation satellites: Mediterranean Fires Case Study
Mean sea level pressure and wind speed at 850 hPa

FANGS - Fire Applications with Next-Generation Satellites

Mean Sea Level Pressure & Wind Speed at 850 hPa

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