155 lines
6.0 KiB
Python
155 lines
6.0 KiB
Python
import cdsapi
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from input.user_input import *
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# start_lat = 78.22 Svalbard
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# start_lon = 15.65
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# start_lat = 67.887382 Kiruna
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# start_lon = 21.081452
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startdays = ['23', '24', '25', '26', '27', '28', '29', '30', '31']
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days = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31']
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endascent = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22']
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endfloat = ['1']
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def ERAsingle(year, month, days, north_lim, south_lim, east_lim, west_lim, name):
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single = cdsapi.Client().retrieve(
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'reanalysis-era5-single-levels',
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{
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'product_type': 'reanalysis',
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'format': 'netcdf',
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'variable': [
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'cloud_base_height', 'high_cloud_cover', 'low_cloud_cover', 'medium_cloud_cover',
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'skin_temperature', 'surface_net_solar_radiation', 'surface_net_thermal_radiation',
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'surface_pressure', 'surface_solar_radiation_downwards', 'surface_thermal_radiation_downward_clear_sky',
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'surface_thermal_radiation_downwards', 'toa_incident_solar_radiation',
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'top_net_solar_radiation', 'top_net_thermal_radiation', 'total_cloud_cover',
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'total_sky_direct_solar_radiation_at_surface',
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],
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'time': [
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'00:00', '01:00', '02:00',
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'03:00', '04:00', '05:00',
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'06:00', '07:00', '08:00',
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'09:00', '10:00', '11:00',
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'12:00', '13:00', '14:00',
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'15:00', '16:00', '17:00',
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'18:00', '19:00', '20:00',
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'21:00', '22:00', '23:00',
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],
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'year': str(year),
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'month': str(month),
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'day': days,
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'area': [
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north_lim, west_lim, south_lim, # north_lim, west_lim, south_lim, # North, West, South 72, -111, 67,
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east_lim, # east_lim, # East 22,
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],
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})
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single.download(name)
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def ERAlevelAscend(year, month, days, start_lat, start_lon, name):
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north_lim = start_lat + 10.0
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south_lim = start_lat - 10.0
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east_lim = start_lon - 10.0
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west_lim = start_lon + 10.0
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ascend = cdsapi.Client().retrieve(
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'reanalysis-era5-pressure-levels',
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{
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'product_type': 'reanalysis',
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'format': 'netcdf',
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'variable': [
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'geopotential', 'temperature', 'u_component_of_wind',
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'v_component_of_wind', 'vertical_velocity',
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],
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'pressure_level': [
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'1', '2', '3',
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'5', '7', '10',
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'20', '30', '50',
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'70', '100', '125',
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'150', '175', '200',
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'225', '250', '300',
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'350', '400', '450',
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'500', '550', '600',
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'650', '700', '750',
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'775', '800', '825',
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'850', '875', '900',
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'925', '950', '975',
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'1000',
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],
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'year': str(year),
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'month': str(month),
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'day': days,
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'time': [
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'00:00', '01:00', '02:00',
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'03:00', '04:00', '05:00',
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'06:00', '07:00', '08:00',
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'09:00', '10:00', '11:00',
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'12:00', '13:00', '14:00',
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'15:00', '16:00', '17:00',
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'18:00', '19:00', '20:00',
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'21:00', '22:00', '23:00',
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],
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'area': [
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north_lim, west_lim, south_lim,
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east_lim,
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],
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})
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ascend.download(name)
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def ERAlevelFloat(year, month, days, north_lim, south_lim, east_lim, west_lim, name):
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floating = cdsapi.Client().retrieve(
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'reanalysis-era5-pressure-levels',
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{
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'product_type': 'reanalysis',
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'format': 'netcdf',
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'variable': [
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'geopotential', 'temperature', 'u_component_of_wind',
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'v_component_of_wind', 'vertical_velocity',
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],
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'pressure_level': [
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'1', '2', '3',
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'5', '7', '10',
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'20',
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],
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'year': str(year),
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'month': str(month),
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'day': days,
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'time': [
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'00:00', '01:00', '02:00',
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'03:00', '04:00', '05:00',
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'06:00', '07:00', '08:00',
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'09:00', '10:00', '11:00',
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'12:00', '13:00', '14:00',
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'15:00', '16:00', '17:00',
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'18:00', '19:00', '20:00',
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'21:00', '22:00', '23:00',
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],
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'area': [
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north_lim, west_lim, south_lim,
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east_lim,
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],
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})
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floating.download(name)
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##ERAsingle(2018, 5, startdays, 90, 45, 180, -180, "single_2018_1.nc")
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##ERAsingle(2018, 6, days, 90, 45, 180, -180, "single_2018_2.nc")
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##ERAsingle(2018, 7, days, 90, 45, 180, -180, "single_2018_3.nc")
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##ERAsingle(2018, 8, endfloat, 90, 45, 180, -180, "single_2018_4.nc")
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##ERAlevelAscend(2018, 5, startdays, start_lat, start_lon, "ascend_2018_kiruna_1.nc") # start_lat = 67.887382 Kiruna
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ERAlevelAscend(2018, 6, endascent, start_lat, start_lon, "ascend_2018_kiruna_2.nc") # start_lon = 21.081452
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ERAlevelFloat(2018, 5, startdays, 90, 45, -180, 180, "float_2018_1.nc")
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ERAlevelFloat(2018, 6, days, 90, 45, -180, 180, "float_2018_2.nc")
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ERAlevelFloat(2018, 7, days, 90, 45, -180, 180, "float_2018_3.nc")
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ERAlevelFloat(2018, 8, endfloat, 90, 45, -180, 180, "float_2018_4.nc")
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#ERAlevelAscend(2016, 7, ['12', '13', '14'], start_lat, start_lon, "ascend_2016_kiruna_new.nc")
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#ERAsingle(2016, 7, ['12', '13', '14', '15', '16', '17', '18', '19'], 90, 45, 180, -180, "single_2016_new.nc")
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#ERAlevelFloat(2016, 7, ['12', '13', '14', '15', '16', '17', '18', '19'], 90, 45, -180, 180, "float_2016_new.nc")
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