hima8_pv/nc文件加载出太阳辐照数据.ipynb

In [1]:

import numpy as np
import netCDF4 as nc

In [10]:

from osgeo import gdal, osr, ogr

In [3]:

data = r"D:\Datasets\Himawari\pub\L2_PAR\20221107\18\H08_20221107_1800_RFL020_FLDK.02401_02401.nc"
nc_data = nc.Dataset(data)
nc_data

Out[3]:

<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF4 data model, file format HDF5):
    title: Himawari-08 AHI equal latitude-longitude map data
    id: H08_20221107_1800_RFL020_FLDK.02401_02401.nc
    date_created: 2022-11-07T18:25:18Z
    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 [4]:

list(nc_data.variables.keys())

Out[4]:

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

In [5]:

nc_data['PAR']

Out[5]:

<class 'netCDF4._netCDF4.Variable'>
int16 PAR(latitude, longitude)
    long_name: Photosynthetically active radiation
    units: umol/m^2/s
    scale_factor: 0.1
    add_offset: 0.0
    valid_min: 0
    valid_max: 25000
    missing_value: -32768
unlimited dimensions: 
current shape = (2401, 2401)
filling on, default _FillValue of -32767 used

In [6]:

nc_data['latitude']

Out[6]:

<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 [7]:

par = np.asarray(nc_data['PAR'][:])
par

Out[7]:

array([[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,
          0.     ],
       [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,
          0.     ],
       [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,
          0.     ],
       ...,
       [  0.     ,   0.     ,   0.     , ..., 181.6    , 139.7    ,
        144.40001],
       [  0.     ,   0.     ,   0.     , ..., 201.6    , 318.6    ,
        169.7    ],
       [  0.     ,   0.     ,   0.     , ..., 240.8    , 338.9    ,
        340.1    ]], dtype=float32)

In [14]:

import pandas as pd

In [42]:

lat = list(map(lambda x: round(x, 2), np.asarray(nc_data['latitude'][:])))
lon = list(map(lambda x: round(x, 2), np.asarray(nc_data['longitude'][:])))
print(len(lat), len(lon))
latMin, latMax, lonMin, lonMax = min(lat), max(lat), min(lon), max(lon)

2401 2401

In [9]:

# 分辨率
lat_Res = (latMax - latMin) / (lat.shape[0]-1)
lon_Res = (lonMax - lonMin) / (lon.shape[0]-1)

In [53]:

cols = [str(x) for x in lat]
rows = [str(x) for x in lon]

In [54]:

par_df = pd.DataFrame.from_records(par)
par_df.columns = cols
par_df.index = rows

In [58]:

par_df.tail()

Out[58]:

	60.0	59.95	59.9	59.85	59.8	59.75	59.7	59.65	59.6	59.55	...	-59.55	-59.6	-59.65	-59.7	-59.75	-59.8	-59.85	-59.9	-59.95	-60.0
199.8	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	160.699997	160.900009	155.400009	155.500000	142.100006	143.199997	143.699997	138.100006	138.300003	139.600006
199.85	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	153.800003	174.500000	147.300003	139.400009	139.600006	144.199997	144.199997	160.199997	142.199997	143.699997
199.9	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	164.199997	166.800003	151.000000	153.800003	153.900009	169.000000	169.300003	181.600006	139.699997	144.400009
199.95	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	152.900009	159.800003	184.300003	164.000000	164.199997	167.100006	167.600006	201.600006	318.600006	169.699997
200.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	149.199997	148.400009	148.600006	152.300003	152.800003	159.699997	240.699997	240.800003	338.899994	340.100006

5 rows × 2401 columns

In [68]:

par_df[par_df.index=='120.85']

Out[68]:

	60.0	59.95	59.9	59.85	59.8	59.75	59.7	59.65	59.6	59.55	...	-59.55	-59.6	-59.65	-59.7	-59.75	-59.8	-59.85	-59.9	-59.95	-60.0
120.85	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	12.5	27.4	53.100002	57.200001	11.1	11.900001	15.6	90.0	91.599998	94.900002

1 rows × 2401 columns

In [ ]: