Place these four scripts in a subdirectory called templates
of the directory in which you have downloaded get.sh.
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
area is North/West/South/East
server = ECMWFDataServer()
time: 00:00:00/06:00:00/12:00:00/18:00:00
129.128 - z - geopotential
172.128 - lsm - land-sea mask
time: 00:00:00/06:00:00/12:00:00/18:00:00
130.128 - t - temperature
131.128 - u - u component of wind
132.128 - v - v component of wind
133.128 - q - specific humidity
server.retrieve({
"class": "ei",
"dataset": "interim",
"date": "START_DATE/to/END_DATE",
"expver": "1",
"grid": "0.75/0.75",
"levelist": "1",
"levtype": "ml",
"param": "129.128/172.128",
"step": "0",
"stream": "oper",
"time": "00:00:00/06:00:00/12:00:00/18:00:00",
"type": "an",
"target": "CHANGEMEML0",
"area" : "80/-50/20/50",
})
server.retrieve({
"class": "ei",
"dataset": "interim",
"date": "START_DATE/to/END_DATE",
"expver": "1",
"grid": "0.75/0.75",
"levelist": "1/to/60",
"levtype": "ml",
"param": "130.128/131.128/132.128/133.128",
"step": "0",
"stream": "oper",
"time": "00:00:00/06:00:00/12:00:00/18:00:00",
"type": "an",
"target": "CHANGEMEML1",
"area" : "80/-50/20/50",
})
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
area is North/West/South/East
server = ECMWFDataServer()
time: 00:00:00/06:00:00/12:00:00/18:00:00
129.128 - z - geopotential
130.128 - t - temperature
133.128 - q - specific humidity
server.retrieve({
"class": "ei",
"dataset": "interim",
"date": "START_DATE/to/END_DATE",
"expver": "1",
"grid": "0.75/0.75",
"levelist": "100",
"levtype": "pl",
"param": "129.128/130.128/133.128",
"step": "0",
"stream": "oper",
"time": "00:00:00/06:00:00/12:00:00/18:00:00",
"type": "an",
"target": "CHANGEMEPL",
"area" : "80/-50/20/50",
})
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
area is North/West/South/East
server = ECMWFDataServer()
time: 00:00:00/06:00:00/12:00:00/18:00:00
129.128 - z - geopotential
131.128 - u - u component of wind
132.128 - v - v component of wind
133.128 - q - specific humidity
server.retrieve({
"class": "ei",
"dataset": "interim",
"date": "START_DATE/to/END_DATE",
"expver": "1",
"grid": "0.75/0.75",
"levelist": "2000",
"levtype": "pv",
"param": "129.128/131.128/132.128/133.128",
"step": "0",
"stream": "oper",
"time": "00:00:00/06:00:00/12:00:00/18:00:00",
"type": "an",
"target": "CHANGEMEPV",
"area" : "80/-50/20/50",
})
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
area is North/West/South/East
server = ECMWFDataServer()
Codes - variables - name:
time: 00:00:00/06:00:00/12:00:00/18:00:00
31.128 - ci - sea ice cover
33.128 - rsn - snow density
34.128 - sst - sea surface temperature
39.128 - swvl1 - volumetric soil water layer 1
40.128 - swvl2 - volumetric soil water layer 2
41.128 - swvl3 - volumetric soil water layer 3
42.128 - swvl4 - volumetric soil water layer 4
134.128 - sp - surface pressure
139.128 - stl1 - soil temperature level 1
141.128 - sd - snow depth
151.128 - msl - mean sea level pressure
165.128 - 10u - 10 metre U wind component
166.128 - 10v - 10 metre V wind component
167.128 - 2t - 2 metre temperature
168.128 - 2d - 2 metre dewpoint temperature
170.128 - stl2 - soil temperature level 2
183.128 - stl3 - soil temperature level 3
235.128 - skt - skin temperature
236.128 - stl4 - soil temperature level 4
time: 00:00:00/06:00:00/12:00:00/18:00:00
230.140 - mwd - mean wave direction
232.140 - mwp - mean wave period
time: 00:00:00/06:00:00/12:00:00/18:00:00
31.128 - ci - sea ice cover
33.128 - rsn - snow density
34.128 - sst - sea surface temperature
39.128 - swvl1 - volumetric soil water layer 1
40.128 - swvl2 - volumetric soil water layer 2
41.128 - swvl3 - volumetric soil water layer 3
42.128 - swvl4 - volumetric soil water layer 4
134.128 - sp - surface pressure
139.128 - stl1 - soil temperature level 1
141.128 - sd - snow depth
151.128 - msl - mean sea level pressure
165.128 - 10u - 10 metre U wind component
166.128 - 10v - 10 metre V wind component
167.128 - 2t - 2 metre temperature
168.128 - 2d - 2 metre dewpoint temperature
170.128 - stl2 - soil temperature level 2
183.128 - stl3 - soil temperature level 3
235.128 - skt - skin temperature
236.128 - stl4 - soil temperature level 4
server.retrieve({
"class": "ei",
"dataset": "interim",
"date": "START_DATE/to/END_DATE",
"expver": "1",
"grid": "0.75/0.75",
"levtype": "sfc",
"param": "31.128/33.128/34.128/39.128/40.128/41.128/42.128/134.128/139.128/141.128/151.128/165.128/166.128/167.128/168.128/170.128/183.128/235.128/236.128",
"step": "0",
"stream": "oper",
"time": "00:00:00/06:00:00/12:00:00/18:00:00",
"type": "an",
"target": "CHANGEMESFC0",
"area" : "80/-50/20/50",
})
server = ECMWFDataServer()
server.retrieve({
"class": "ei",
"dataset": "interim",
"date": "START_DATE/to/END_DATE",
"expver": "1",
"grid": "0.75/0.75",
"levtype": "sfc",
"param": "230.140/232.140",
"step": "0",
"stream": "wave",
"time": "00:00:00/06:00:00/12:00:00/18:00:00",
"type": "an",
"target": "CHANGEMESFC2",
"area" : "80/-50/20/50",
})
Don't use forecast (type=fc) data, only analysis (type=an).
"date": "START_DATE/to/END_DATE",
"param": "31.128/33.128/34.128/39.128/40.128/41.128/42.128/134.128/139.128/141.128/151.128/165.128/166.128/167.128/168.128/170.128/183.128/235.128/236.128",
"time": "00:00:00/12:00:00",
"target": "CHANGEMESFC1",