I want to calculate daily solar_radiation_flux from 2019 to present by 'surface_solar_radiation_downwards‘ in era5-land dataset.  But the result is higher in winter and lower in summer.


import cdstoolbox as ct

@ct.application(title='Points data query')
@ct.output.livefigure()
#@ct.output.download()
def application():
srad1 = \
ct.catalogue.retrieve(
'reanalysis-era5-land',
{
#'product_type': 'reanalysis',
'variable': 'surface_solar_radiation_downwards',
'grid': ['0.1', '0.1'],

'year': ['2017'],
'month': [
'01', '02', '03', '04', '05', '06',
'07', '08', '09', '10', '11', '12'
],
'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','06:00','12:00','18:00'
'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',
],
'area':[53.560860,73.557701,13.159306,134.773926]

}
)
srad2 = \
ct.catalogue.retrieve(
'reanalysis-era5-single-levels',
{
'product_type': 'reanalysis',
'variable': 'surface_solar_radiation_downwards',
'grid': ['0.1', '0.1'],

'year': ['2017'],
'month': [
'01', '02', '03', '04', '05', '06',
'07', '08', '09', '10', '11', '12'
],
'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','06:00','12:00','18:00'
'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',
],
'area':[53.560860,73.557701,13.159306,134.773926]
}
)
srad3 = \
ct.catalogue.retrieve(
'sis-agrometeorological-indicators',
{
'variable': 'solar_radiation_flux',
'statistics': '24_hour_mean',
'year': '2017',
'month': [
'01', '02', '03', '04', '05', '06',
'07', '08', '09', '10', '11', '12'

],
'grid': ['0.1', '0.1'],
'area':[53.560860,73.557701,13.159306,134.773926]
}
)
lon=125.090837
lat=45.800623
srad1 = ct.geo.extract_point(srad1, lon=lon, lat=lat)
srad2 = ct.geo.extract_point(srad2, lon=lon, lat=lat)
srad3 = ct.geo.extract_point(srad3, lon=lon, lat=lat)
srad1 = ct.cube.resample(srad1, freq='day', dim='time',how='mean', closed='right')
srad2 = ct.cube.resample(srad2, freq='day', dim='time',how='mean', closed='right')
#srad3 = ct.cube.resample(srad3, freq='day', dim='time',how='mean', closed='right')
srad1 = srad1*24/3600*2
srad2 = srad2/12
srad3 = srad3/12
scatter_kwargs1 = {
'mode': 'lines',
'yaxis': 'y1',
'name': 'reanalysis-era5-land',
}
scatter_kwargs2 = {
'mode': 'lines',
'yaxis': 'y1',
'name': 'reanalysis-era5-single-levels',
}
scatter_kwargs3 = {
'mode': 'lines',
'yaxis': 'y1',
'name': 'sis-agrometeorological-indicators',
}
srads = ct.chart.line(srad1,scatter_kwargs=scatter_kwargs1)
srads = ct.chart.line(srad2,fig=srads,scatter_kwargs=scatter_kwargs2)
srads = ct.chart.line(srad3,fig=srads,scatter_kwargs=scatter_kwargs3)

return srads

4 Comments

  1. Vivien MAVEL

    Dear Xiangyi,

    Do you have a Toolbox workflow associated with the plot you attached?

    Which location on Earth did you do the plot for? If it is in the Northern hemisphere I do see higher values in the summer and lower in the winter.

    Regards.

    Vivien

    1. xiangyi wang

      Hi Vivien ,


      I have  added my script. Can you help me check the problem in my script. In fact, I don't know much about the conversion of solar radiation units

      import cdstoolbox as ct

      @ct.application(title='Points data query')
      @ct.output.livefigure()
      #@ct.output.download()
      def application():
      srad1 = \
      ct.catalogue.retrieve(
      'reanalysis-era5-land',
      {
      #'product_type': 'reanalysis',
      'variable': 'surface_solar_radiation_downwards',
      'grid': ['0.1', '0.1'],

      'year': ['2017'],
      'month': [
      '01', '02', '03', '04', '05', '06',
      '07', '08', '09', '10', '11', '12'
      ],
      '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','06:00','12:00','18:00'
      '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',
      ],
      'area':[53.560860,73.557701,13.159306,134.773926]

      }
      )
      srad2 = \
      ct.catalogue.retrieve(
      'reanalysis-era5-single-levels',
      {
      'product_type': 'reanalysis',
      'variable': 'surface_solar_radiation_downwards',
      'grid': ['0.1', '0.1'],

      'year': ['2017'],
      'month': [
      '01', '02', '03', '04', '05', '06',
      '07', '08', '09', '10', '11', '12'
      ],
      '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','06:00','12:00','18:00'
      '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',
      ],
      'area':[53.560860,73.557701,13.159306,134.773926]
      }
      )
      srad3 = \
      ct.catalogue.retrieve(
      'sis-agrometeorological-indicators',
      {
      'variable': 'solar_radiation_flux',
      'statistics': '24_hour_mean',
      'year': '2017',
      'month': [
      '01', '02', '03', '04', '05', '06',
      '07', '08', '09', '10', '11', '12'

      ],
      'grid': ['0.1', '0.1'],
      'area':[53.560860,73.557701,13.159306,134.773926]
      }
      )
      lon=125.090837
      lat=45.800623
      srad1 = ct.geo.extract_point(srad1, lon=lon, lat=lat)
      srad2 = ct.geo.extract_point(srad2, lon=lon, lat=lat)
      srad3 = ct.geo.extract_point(srad3, lon=lon, lat=lat)
      srad1 = ct.cube.resample(srad1, freq='day', dim='time',how='mean', closed='right')
      srad2 = ct.cube.resample(srad2, freq='day', dim='time',how='mean', closed='right')
      #srad3 = ct.cube.resample(srad3, freq='day', dim='time',how='mean', closed='right')
      srad1 = srad1*24/3600*2
      srad2 = srad2/12
      srad3 = srad3/12
      scatter_kwargs1 = {
      'mode': 'lines',
      'yaxis': 'y1',
      'name': 'reanalysis-era5-land',
      }
      scatter_kwargs2 = {
      'mode': 'lines',
      'yaxis': 'y1',
      'name': 'reanalysis-era5-single-levels',
      }
      scatter_kwargs3 = {
      'mode': 'lines',
      'yaxis': 'y1',
      'name': 'sis-agrometeorological-indicators',
      }
      srads = ct.chart.line(srad1,scatter_kwargs=scatter_kwargs1)
      srads = ct.chart.line(srad2,fig=srads,scatter_kwargs=scatter_kwargs2)
      srads = ct.chart.line(srad3,fig=srads,scatter_kwargs=scatter_kwargs3)

      return srads

  2. Vivien MAVEL

    Dear Xiangyi,

    The resampling operations you are doing look right to me but I do not quite understand the conversion you are performing:

    srad1 = srad1 * 24 / 3600 * 2
srad2 = srad2 / 12
srad3 = srad3 / 12

    From and to which unit are you intending to convert the values?

    1. xiangyi wang

      Hi Vivien,


      The unit of the model I'm using is MJ /m2 /day.    1W/m² ＝ 0．0864 MJ/(m² · d)？,I calculated from this formula is not correct

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