计算 netcdf 文件的变量相关性并在地图上绘制 Python

Question

给定两个 nc 文件 here:

data/CMIP6_UKESM1-0-LL_Lmon_piControl_r1i1p1f2_gpp_1960-3059.nc
data/CMIP6_UKESM1-0-LL_Amon_piControl_r1i1p1f2_tas_1960-3059.nc

读取第一个文件：

from netCDF4 import Dataset
import numpy as np

ds1 = Dataset('data/CMIP6_UKESM1-0-LL_Lmon_piControl_r1i1p1f2_gpp_1960-3059.nc')
print(ds1.variables.keys()) # get all variable names

输出：

odict_keys(['gpp', 'time', 'time_bnds', 'lat', 'lat_bnds', 'lon', 'lon_bnds', 'clim_season', 'season_year'])

读取第二个文件：

ds2 = Dataset('data/CMIP6_UKESM1-0-LL_Amon_piControl_r1i1p1f2_tas_1960-3059.nc')
print(ds2.variables.keys())

输出：

odict_keys(['tas', 'time', 'time_bnds', 'lat', 'lat_bnds', 'lon', 'lon_bnds', 'clim_season', 'height', 'season_year'])

检查gpp变量：

gpp = ds1.variables['gpp'] # gpp variable
print(gpp)

输出：

<class 'netCDF4._netCDF4.Variable'>
float32 gpp(time, lat, lon)
    _FillValue: 1e+20
    standard_name: gross_primary_productivity_of_biomass_expressed_as_carbon
    long_name: Carbon Mass Flux out of Atmosphere Due to Gross Primary Production on Land [kgC m-2 s-1]
    units: kg m-2 s-1
    cell_methods: area: mean where land time: mean
    coordinates: clim_season season_year
unlimited dimensions: 
current shape = (3300, 144, 192)
filling on

检查tas变量：

tas = ds2.variables['tas'] # tas variable
print(tas)

输出：

<class 'netCDF4._netCDF4.Variable'>
float32 tas(time, lat, lon)
    _FillValue: 1e+20
    standard_name: air_temperature
    long_name: Near-Surface Air Temperature
    units: K
    cell_methods: area: time: mean
    coordinates: clim_season height season_year
unlimited dimensions: 
current shape = (3300, 144, 192)
filling on

现在我想计算 gpp 和 tas 之间的相关性，然后在地图上绘制它们的相关值。

我该怎么做？谢谢。

Answer 1

你应该可以使用我的软件包 nctoolkit (https://nctoolkit.readthedocs.io/en/latest/) 轻松地做到这一点。

我的理解是您想绘制每个网格单元的时间相关系数。在那种情况下：

import nctoolkit as nc
files = ["data/CMIP6_UKESM1-0-LL_Lmon_piControl_r1i1p1f2_gpp_1960-3059.nc",
"data/CMIP6_UKESM1-0-LL_Amon_piControl_r1i1p1f2_tas_1960-3059.nc"]
ds = nc.open_data(files)
ds.merge()
ds.cor_time(var1 = "gpp", var2 =  "tas")
ds.plot()

如果你想要每个时间步长变量之间的空间相关系数，你可以使用以下行代替倒数第二行：

ds.cor_space(var1 = "gpp", var2 =  "tas")

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