import cdstoolbox as ct
# Efficiently process 1 year of ERA5 data
# The most efficient way to process a large amount of hourly ERA5 data
# is to retrieve 1 month at a time, process these data, and append the
# results to an output file.
# In the example below, the daily mean 2m temperature is calculated for 2019.
@ct.application(title='Download data')
@ct.output.download()
def download_application():
count=1
for yr in ['2019']:
for mn in [ '01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12'
]:
data = ct.catalogue.retrieve(
'reanalysis-era5-single-levels',
{
'product_type': 'reanalysis',
'variable': '2m_temperature',
'year': yr,
'month': mn,
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30', '31'
],
'time': [
'00:00', '01:00', '02:00',
'03:00', '04:00', '05:00',
'06:00', '07:00', '08:00',
'09:00', '10:00', '11:00',
'12:00', '13:00', '14:00',
'15:00', '16:00', '17:00',
'18:00', '19:00', '20:00',
'21:00', '22:00', '23:00',
],
}
)
day_mean=ct.climate.daily_mean(data,keep_attrs=True)
if count == 1:
day_mean_all=day_mean
else:
day_mean_all=ct.cube.concat([day_mean_all, day_mean], dim='time')
count = count + 1
return day_mean_all
