hima8_pv/ftp_hima8.ipynb

In [27]:

import netCDF4 as nc

In [32]:

data = nc.Dataset('./data/2022-10-09/PAR_Minutes_Download/H08_20221009_0500_RFL020_FLDK.02401_02401.nc')

In [33]:

data

Out[33]:

<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF4 data model, file format HDF5):
    title: Himawari-08 AHI equal latitude-longitude map data
    id: H08_20221009_0500_RFL020_FLDK.02401_02401.nc
    date_created: 2022-10-09T05:26:29Z
    pixel_number: 2401
    line_number: 2401
    upper_left_latitude: 60.0
    upper_left_longitude: 80.0
    grid_interval: 0.05
    band_number: 6
    algorithm_version: 0201
    Ancillary meteorological data: JMA forcast
    Ancillary ozone data: JMA objective analysis
    BRDF correction: on (Morel and Maritorena 2001)
    dimensions(sizes): latitude(2401), longitude(2401), band(6), time(1), geometry(17)
    variables(dimensions): float32 latitude(latitude), float32 longitude(longitude), int32 band_id(band), float64 start_time(time), float64 end_time(time), float64 geometry_parameters(geometry), int16 TAOT_02(latitude, longitude), int16 TAAE(latitude, longitude), int16 PAR(latitude, longitude), int16 SWR(latitude, longitude), int16 UVA(latitude, longitude), int16 UVB(latitude, longitude), uint8 QA_flag(latitude, longitude)
    groups: 

In [34]:

data.variables.keys()

Out[34]:

dict_keys(['latitude', 'longitude', 'band_id', 'start_time', 'end_time', 'geometry_parameters', 'TAOT_02', 'TAAE', 'PAR', 'SWR', 'UVA', 'UVB', 'QA_flag'])

In [35]:

data.variables['SWR']

Out[35]:

<class 'netCDF4._netCDF4.Variable'>
int16 SWR(latitude, longitude)
    long_name: Shortwave radiation
    units: W/m^2
    scale_factor: 0.05
    add_offset: 0.0
    valid_min: 0
    valid_max: 26000
    missing_value: -32768
unlimited dimensions: 
current shape = (2401, 2401)
filling on, default _FillValue of -32767 used

In [38]:

data.variables['latitude']

Out[38]:

<class 'netCDF4._netCDF4.Variable'>
float32 latitude(latitude)
    long_name: latitude
    units: degrees_north
unlimited dimensions: 
current shape = (2401,)
filling on, default _FillValue of 9.969209968386869e+36 used

In [2]:

import cv2

In [3]:

img = cv2.imread('./data/2022-10-09/PAR_Minutes_Analysis/20221009_0500.tif', 2)
img

Out[3]:

array([[559.4    , 559.45   , 559.5    , ..., 326.30002, 332.75   ,
        331.55002],
       [559.55   , 559.65   , 559.9    , ..., 331.7    , 331.7    ,
        331.55002],
       [556.8    , 559.15   , 559.4    , ..., 337.95   , 337.75   ,
        331.95   ],
       ...,
       [816.45   , 816.35004, 816.45   , ..., 541.65   , 543.35004,
        527.65   ],
       [817.45   , 815.25   , 814.5    , ..., 543.4    , 413.5    ,
        398.75   ],
       [817.5    , 813.35004, 817.3    , ..., 544.05   , 239.1    ,
        339.75   ]], dtype=float32)

In [4]:

import matplotlib.pyplot as plt

In [9]:

plt.imshow(img, "Greys_r")

Out[9]:

<matplotlib.image.AxesImage at 0x18f79cd60c8>

In [ ]: