384 lines
15 KiB
Python
384 lines
15 KiB
Python
"""
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ERA5 Download- and Stitch-Tool
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by Marcel Frommelt
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"""
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"""
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INPUT SECTION
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"""
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folder = "ERA5" # name for output folder (either already existing or not, if not it will be created)
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ident1 = "McMurdo" # unique identifier for ERA5 ascent data (suggestion: launch location)
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ident2 = "Antarctica" # unique identifier for ERA float and radiation data (e. g. "southern polar region")
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"""
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Balloon launch location on ground (LATitude/LONgitude in degrees):
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"""
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start_lat = -77.8535 # McMurdo
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start_lon = 167.2022
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# ---------------------------------
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# start_lat = 67.887382 # Kiruna
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# start_lon = 21.081452
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# ---------------------------------
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# start_lat = 78.22 # Svalbard
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# start_lon = 15.65
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"""
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Geographical boundaries for atmospheric data (in degrees):
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"""
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# north_lim = 90
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# west_lim, east_lim = -180, 180 # suggestion for northern polar region
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# south_lim = 45
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north_lim = -45
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west_lim, east_lim = -180, 180 # suggestion for southern polar region
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south_lim = -90
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"""
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Start and end time for atmospheric data:
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"""
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startdate = '2019-12-15' # also acts as launch data of ERA5 data for ascent
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enddate = '2020-01-17'
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ascent_window = 10 # time length of ERA5 data for ascent phase in (full) days from start date
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"""
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Operations you want to be performed:
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"""
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ascent_only = False # True # get only ERA5 data for ascent (e. g. for new location or time within same region)
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download = False # True # download raw ERA5 files
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stitch = True # False # create stitched files
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delete = True # False # delete original files
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"""
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EXECUTE SECTION
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"""
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import os
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import cdsapi
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from datetime import datetime
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from datetime import date
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import numpy as np
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import xarray as xr
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from netCDF4 import Dataset
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def ERAsingle(yr, mon, dayrange, nlim, slim, elim, wlim, 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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'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(yr),
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'month': str(mon),
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'day': dayrange,
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'area': [
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nlim, wlim, slim, elim,
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],
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'format': 'netcdf',
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})
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single.download(name)
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def ERAlevelAscent(yr, mon, dayrange, start_lat, start_lon, name):
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nlim = start_lat + 10.0
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slim = start_lat - 10.0
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elim = start_lon + 10.0
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wlim = start_lon - 10.0
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ascent = 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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'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(yr),
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'month': str(mon),
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'day': dayrange,
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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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nlim, wlim, slim, elim,
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],
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'format': 'netcdf',
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})
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ascent.download(name)
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def ERAlevelFloat(yr, mon, dayrange, nlim, slim, elim, wlim, 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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'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(yr),
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'month': str(mon),
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'day': dayrange,
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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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nlim, wlim, slim, elim,
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],
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'format': 'netcdf',
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})
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floating.download(name)
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try:
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os.makedirs(folder)
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except FileExistsError:
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pass
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start = datetime.fromisoformat(startdate)
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end = datetime.fromisoformat(enddate)
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"""
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Download of ERA5-Data:
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"""
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if download == True:
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if start.month == 12:
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next_month = 1
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year2 = start.year + 1
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else:
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next_month = start.month + 1
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year2 = start.year
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if start.month == end.month:
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month = 'same'
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elif end.month - start.month == 1 or end.month - start.month == -11:
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month = 'next'
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else:
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month = 'more'
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if start.day + ascent_window > (date(year2, next_month, 1) - date(start.year, start.month, 1)).days:
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short_ascent = 0
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startdays = [str(start.day + i).zfill(2) for i in range(
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(date(year2, next_month, 1) - date(start.year, start.month, 1)).days - start.day + 1)]
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endascent = [str(i + 1).zfill(2) for i in range(ascent_window - len(startdays) + 1)]
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else:
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short_ascent = 1
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startdays = [str(start.day + i).zfill(2) for i in range(
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(date(year2, next_month, 1) - date(start.year, start.month, 1)).days - start.day + 1)]
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ascentdays = [str(start.day + i).zfill(2) for i in range(ascent_window)]
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if end.year == start.year:
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same_year = 1
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else:
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same_year = 0
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if month == 'more':
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days = [str(i + 1).zfill(2) for i in range(31)]
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if month != 'same':
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endfloat = [str(i + 1).zfill(2) for i in range(end.day)]
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if short_ascent == 1:
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ERAlevelAscent(start.year, start.month, ascentdays, start_lat, start_lon, os.path.join(folder, "ascent1.nc"))
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else:
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ERAlevelAscent(start.year, start.month, startdays, start_lat, start_lon, os.path.join(folder, "ascent1.nc"))
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ERAlevelAscent(year2, next_month, endascent, start_lat, start_lon, os.path.join(folder, "ascent2.nc"))
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if ascent_only == True:
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pass
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else:
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if month == 'same':
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ERAsingle(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single1.nc"))
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ERAlevelFloat(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float1.nc"))
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elif month == 'next':
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ERAsingle(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single1.nc"))
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ERAsingle(year2, end.month, endfloat, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single2.nc"))
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ERAlevelFloat(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float1.nc"))
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ERAlevelFloat(year2, end.month, endfloat, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float2.nc"))
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else:
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ERAsingle(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single1.nc"))
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ERAlevelFloat(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float1.nc"))
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if same_year == 1:
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for m in range(end.month - start.month - 1):
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ERAsingle(start.year, next_month + m, days, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single" + str(m + 2) + ".nc"))
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ERAlevelFloat(start.year, next_month + m, days, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float" + str(m + 2) + ".nc"))
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ERAsingle(start.year, end.month, endfloat, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single" + str(end.month - start.month + 1) + ".nc"))
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ERAlevelFloat(start.year, end.month, endfloat, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float" + str(end.month - start.month + 1) + ".nc"))
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else:
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count1 = 0
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count2 = 0
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for m in range(12 - start.month):
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ERAsingle(start.year, start.month + 1 + m, days, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single" + str(m + 2) + ".nc"))
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ERAlevelFloat(start.year, start.month + 1 + m, days, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float" + str(m + 2) + ".nc"))
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count1 = m + 2
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for m in range(end.month - 1):
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ERAsingle(end.year, 1 + m, days, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single" + str(count1 + m + 2) + ".nc"))
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ERAlevelFloat(end.year, 1 + m, days, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float" + str(count1 + m + 2) + ".nc"))
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count2 = count1 + m + 2
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ERAsingle(end.year, end.month, endfloat, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "single" + str(count2 + 1) + ".nc"))
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ERAlevelFloat(end.year, end.month, endfloat, north_lim, south_lim, east_lim, west_lim,
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os.path.join(folder, "float" + str(count2 + 1) + ".nc"))
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else:
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pass
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floatfiles = []
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singlefiles = []
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ascentfiles = []
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for (root, dirs, files) in os.walk(folder):
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for name in files:
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if name.startswith("float"):
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floatfiles.append(os.path.join(folder, str(name)))
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elif name.startswith("single"):
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singlefiles.append(os.path.join(folder, str(name)))
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elif name.startswith("ascent"):
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ascentfiles.append(os.path.join(folder, str(name)))
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else:
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pass
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"""
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Stitching of multiple *.nc-files to one:
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"""
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if stitch == True:
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startfile = Dataset(floatfiles[0], "r", format="NETCDF4")
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endfile = Dataset(floatfiles[-1], "r", format="NETCDF4")
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tstart = int(startfile.variables['time'][0])
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tend = int(endfile.variables['time'][-1])
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startfile.close()
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endfile.close()
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print("Stitching files, please wait...")
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if ascent_only == True:
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pass
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else:
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with xr.open_mfdataset(floatfiles, chunks={'time': 100}, combine="nested", engine='netcdf4', concat_dim="time", parallel=True) as df1:
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df1 = df1.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df1.to_netcdf(os.path.join(folder, "FLOAT_" + str(ident2) + "_" + str(start.year) + str(start.month) + "_to_" + str(end.year) + str(end.month) + ".nc"),
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mode='w', format="NETCDF4", engine="netcdf4",
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encoding={"z": {"dtype": "float32"}, "t": {"dtype": "float32"}, "u": {"dtype": "float32"}, "v": {"dtype": "float32"}, "w": {"dtype": "float32"}})
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with xr.open_mfdataset(singlefiles, chunks={'time': 500}, combine="nested", engine='netcdf4', concat_dim="time", parallel=True) as df2:
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df2 = df2.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df2.to_netcdf(os.path.join(folder, "RAD_" + str(ident2) + "_" + str(start.year) + str(start.month) + "_to_" + str(end.year) + str(end.month) + ".nc"),
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mode='w', format="NETCDF4",
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engine="netcdf4",
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encoding={"cbh": {"dtype": "float32"}, "hcc": {"dtype": "float32"},
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"lcc": {"dtype": "float32"},
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"mcc": {"dtype": "float32"}, "skt": {"dtype": "float32"},
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"ssr": {"dtype": "float32"},
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"str": {"dtype": "float32"}, "sp": {"dtype": "float32"},
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"ssrd": {"dtype": "float32"},
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"strdc": {"dtype": "float32"}, "strd": {"dtype": "float32"},
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"tisr": {"dtype": "float32"},
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"tsr": {"dtype": "float32"}, "ttr": {"dtype": "float32"},
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"tcc": {"dtype": "float32"},
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"fdir": {"dtype": "float32"}})
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startfile = Dataset(ascentfiles[0], "r", format="NETCDF4")
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endfile = Dataset(ascentfiles[-1], "r", format="NETCDF4")
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tstart = int(startfile.variables['time'][0])
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tend = int(endfile.variables['time'][-1])
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startfile.close()
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endfile.close()
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with xr.open_mfdataset(ascentfiles, chunks={'time': 800}, combine="nested", engine='netcdf4', concat_dim="time", parallel=True) as df3:
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df3 = df3.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df3.to_netcdf(os.path.join(folder, "ASCENT_" + str(ident1) + "_" + str(start.year) + str(start.month) + ".nc"),
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mode='w', format="NETCDF4", engine="netcdf4",
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encoding={"z": {"dtype": "float32"}, "t": {"dtype": "float32"}, "u": {"dtype": "float32"},
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"v": {"dtype": "float32"}, "w": {"dtype": "float32"}})
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print("Stitching finished!")
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else:
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pass
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"""
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Deleting original files:
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"""
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if delete == True:
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print("Deleting obsolete files.")
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for (root, dirs, files) in os.walk("ERA5"):
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for name in files:
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if os.path.join(folder, name) in floatfiles + singlefiles + ascentfiles:
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os.remove(os.path.join(folder, name))
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else:
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pass |