netcdf (.nc) 文件只有 -999 值?
netcdf (.nc) file only has -999 values?
我正在尝试分析来自 https://imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html 的降雨数据,特别是 'Gridded Rainfall (0.25 x 0.25) NetCDF' 文件。
我使用 netCDF4
加载数据,但我得到了这个奇怪的结果:
from netCDF4 import Dataset
import numpy as np
path = '2013.nc'
f = Dataset(path ,'r')
rain = np.array(f.variables['RAINFALL'][:,:,:])
print(rain[0][0])
输出:
[-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999.]
注意:这个特定文件没有问题,因为我每年的数据都得到相同的结果。你可以自己试试看。
我在python中加载数据是否有误?我下载的数据有问题吗?
latitude
和 longitude
变量不会给我这个问题
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
# file import
path = Path('../../Downloads/')
file = 'Clim_Pred_LRF_New_RF25_IMD0p252013.nc'
f = Dataset(path / file, 'r')
# select variable "Rainfall"
rain = np.array(f.variables['RAINFALL'][:,:,:])
# assign NaN value
rain[rain==-999.] = np.nan
# plot dataset for selected date (2013-01-18)
plt.pcolormesh(rain[17])
plt.colorbar()
数据看起来不错。
但是看看雨的形状:
print(rain.shape)
(365, 129, 135)
# --> (days, latitude, longitude).
对于 rain[0][0]
,您选择第一个时间步长 (Jan-01) 和第一个纬度 (6.5),这将 return 一个具有 NaN 值的一维数组,仅因为所有像素都是在印度以外。选择带有 0 的第二个和第三个维度将始终 return 只包含 -999 的数组。你可以试试 rain[17][100]
!
print(rain[17][100]
array([ nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, 1.2962954 , 0.99620295, 2.705826 ,
9.772656 , 11.187045 , 0. , 3.0871496 , 5.298688 ,
18.108587 , 34.280876 , 35.034836 , 41.41929 , 38.952507 ,
63.49905 , 92.398125 , 92.92495 , 80.57031 , 63.660515 ,
45.413662 , nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan],
dtype=float32)
我正在尝试分析来自 https://imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html 的降雨数据,特别是 'Gridded Rainfall (0.25 x 0.25) NetCDF' 文件。
我使用 netCDF4
加载数据,但我得到了这个奇怪的结果:
from netCDF4 import Dataset
import numpy as np
path = '2013.nc'
f = Dataset(path ,'r')
rain = np.array(f.variables['RAINFALL'][:,:,:])
print(rain[0][0])
输出:
[-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999.]
注意:这个特定文件没有问题,因为我每年的数据都得到相同的结果。你可以自己试试看。
我在python中加载数据是否有误?我下载的数据有问题吗?
latitude
和 longitude
变量不会给我这个问题
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
# file import
path = Path('../../Downloads/')
file = 'Clim_Pred_LRF_New_RF25_IMD0p252013.nc'
f = Dataset(path / file, 'r')
# select variable "Rainfall"
rain = np.array(f.variables['RAINFALL'][:,:,:])
# assign NaN value
rain[rain==-999.] = np.nan
# plot dataset for selected date (2013-01-18)
plt.pcolormesh(rain[17])
plt.colorbar()
数据看起来不错。
但是看看雨的形状:
print(rain.shape)
(365, 129, 135)
# --> (days, latitude, longitude).
对于 rain[0][0]
,您选择第一个时间步长 (Jan-01) 和第一个纬度 (6.5),这将 return 一个具有 NaN 值的一维数组,仅因为所有像素都是在印度以外。选择带有 0 的第二个和第三个维度将始终 return 只包含 -999 的数组。你可以试试 rain[17][100]
!
print(rain[17][100]
array([ nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, 1.2962954 , 0.99620295, 2.705826 ,
9.772656 , 11.187045 , 0. , 3.0871496 , 5.298688 ,
18.108587 , 34.280876 , 35.034836 , 41.41929 , 38.952507 ,
63.49905 , 92.398125 , 92.92495 , 80.57031 , 63.660515 ,
45.413662 , nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan],
dtype=float32)