我试图在一组地理区域上绘制气候模拟数据和观测数据之间的差异。
要创建气候模拟的地图我正在使用此代码
import matplotlib.pyplot as plt
import iris
import iris.plot as iplt
import cartopy
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import iris.analysis.cartography
def main():
#bring in all the models we need and give them a name
CCCma = '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/AFR_44_tas/ERAINT/1979-2012/tas_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'
#Load exactly one cube from given file
CCCma = iris.load_cube(CCCma)
#we are only interested in the latitude and longitude relevant to Malawi
Malawi = iris.Constraint(grid_longitude=lambda v: 31 <= v <= 36.5, \
grid_latitude=lambda v: -18. <= v <= -8.)
CCCma = CCCma.extract(Malawi)
#time constraint to make all series the same
iris.FUTURE.cell_datetime_objects = True
t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
CCCma = CCCma.extract(t_constraint)
#Convert units to match, CORDEX data is in Kelvin but Observed data in Celsius, we would like to show all data in Celsius
CCCma.convert_units('Celsius')
#plot map with physical features
cmap = plt.cm.afmhot_r
ax = plt.axes(projection=cartopy.crs.PlateCarree())
ax.add_feature(cartopy.feature.COASTLINE)
ax.add_feature(cartopy.feature.BORDERS)
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
#set map boundary
ax.set_extent([31, 36.5, -8,-18])
#set axis tick marks
ax.set_xticks([32, 34, 36])
ax.set_yticks([-9, -11, -13, -15, -17])
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
data = CCCma
#take mean of data over all time
plot = iplt.contourf(data.collapsed('time', iris.analysis.MEAN), \
cmap=cmap, levels=[15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],\
extend='both')
#add colour bar index
plt.colorbar(plot)
#give map a title
plt.title('RCP4.5 Mean Temperature 1989-2008', fontsize=10)
plt.show()
if __name__ == '__main__':
main()
如何修改它以区分两个数据集？我试过这段代码：
import matplotlib.pyplot as plt
import iris
import iris.plot as iplt
import cartopy
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import iris.analysis.cartography
#this file is split into parts as follows:
#PART 1: load and format CORDEX models
#PART 2: load and format observed data
#PART 3: format data
#PART 4: plot data
def main():
#PART 1: CORDEX MODELS
#bring in all the models we need and give them a name
CCCma = '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/AFR_44_tas/ERAINT/1979-2012/tas_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'
#Load exactly one cube from given file
CCCma = iris.load_cube(CCCma)
#we are only interested in the latitude and longitude relevant to Malawi
Malawi = iris.Constraint(grid_longitude=lambda v: 31 <= v <= 36.5, \
grid_latitude=lambda v: -18. <= v <= -8.)
CCCma = CCCma.extract(Malawi)
#time constraint to make all series the same
iris.FUTURE.cell_datetime_objects = True
t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
CCCma = CCCma.extract(t_constraint)
#PART 2: OBSERVED DATA
#bring in all the files we need and give them a name
CRU= '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/Actual_Data/cru_ts4.00.1901.2015.tmp.dat.nc'
#Load exactly one cube from given file
CRU = iris.load_cube(CRU, 'near-surface temperature')
#we are only interested in the latitude and longitude relevant to Malawi
Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36., \
latitude=lambda v: -17. <= v <= -9.)
CRU = CRU.extract(Malawi)
#time constraint to make all series the same
iris.FUTURE.cell_datetime_objects = True
t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
CRU = CRU.extract(t_constraint)
#PART 3: FORMAT DATA
#Convert units to match
CCCma.convert_units('Celsius')
CRU.convert_units('Celsius')
#Take difference between two datasets
Bias_CCCma = CCCma-CRU
#PART 4: PLOT MAP
#plot map with physical features
cmap = plt.cm.afmhot_r
ax = plt.axes(projection=cartopy.crs.PlateCarree())
ax.add_feature(cartopy.feature.COASTLINE)
ax.add_feature(cartopy.feature.BORDERS)
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
#set map boundary
ax.set_extent([31, 36.5, -8,-18])
#set axis tick marks
ax.set_xticks([32, 34, 36])
ax.set_yticks([-9, -11, -13, -15, -17])
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
data = Bias_CCCma
#take mean of data over all time
plot = iplt.contourf(data.collapsed('time', iris.analysis.MEAN), \
cmap=cmap, levels=[15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],\
extend='both')
#add colour bar index
plt.colorbar(plot)
#give map a title
plt.title('RCP4.5 Mean Temperature 1989-2008', fontsize=10)
plt.show()
if __name__ == '__main__':
main()
然而，这给了我以下错误：
ValueError: This operation cannot be performed as there are differing coordinates (grid_latitude, grid_longitude, time) remaining which cannot be ignored.
我很确定这不会那么简单，但我不确定如何解决它。有任何想法吗？ TIA！
答案 0 :(得分：0)
我的猜测是CCCma和CRU在不同的网格上，所以当你试图减去它们时会出现错误。您可能需要先将它们插入到同一网格中（否则，iris如何知道您希望结果位于哪个网格上？）。
答案 1 :(得分：0)
Iris对于匹配二进制操作的立方体坐标是非常严格的，并且有一个open issue讨论是否以及如何使它更灵活地为版本2准备。同时，如果你的立方体是相同的形状并且你不介意加载数据，你可以这样做
Bias_CCCma = CCCma - CRU.data
如果你的立方体是不同的形状（即模型在不同的网格上，正如Jeremy建议的那样），或者你不想加载数据，有几点需要注意：
regrid其中一个立方体与另一个相匹配。grid_latitude和grid_longitude与latitude和longitude相同，则可以rename在其中一个多维数据集上的网格坐标。您还需要确保其他坐标元数据匹配（例如var_name通常是一个问题）。time坐标几乎可以肯定是由于您在previous question中发现的单位不匹配。我认为如果您重新排序代码以首先进行平均时间然后采取差异（二进制操作不必太关心标量坐标），我认为这个问题应该消失。答案 2 :(得分：0)
谢谢大家的回答。最后，我需要首先对数据进行重新编译，正如@RuthC建议的那样。
所以代码改为看起来像这样：
import matplotlib.pyplot as plt
import matplotlib.cm as mpl_cm
import numpy as np
from cf_units import Unit
import iris
import iris.plot as iplt
import cartopy
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import iris.analysis.cartography
import iris.coord_categorisation as iriscc
#this file is split into parts as follows:
#PART 1: load and format CORDEX models
#PART 2: load and format observed data
#PART 3: format data
#PART 4: plot data
def main():
iris.FUTURE.netcdf_promote=True
#PART 1: CORDEX MODELS
#bring in all the models we need and give them a name
CCCma = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_tasmax/ERAINT/1979-2012/tasmax_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'
#Load exactly one cube from given file
CCCma = iris.load_cube(CCCma)
#remove flat latitude and longitude and only use grid latitude and grid longitude to make consistent with the observed data, also make sure all of the longitudes are monotonic
lats = iris.coords.DimCoord(CORDEX.coord('latitude').points[:,0], \
standard_name='latitude', units='degrees')
lons = CORDEX.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, \
standard_name='longitude', units='degrees')
CORDEX.remove_coord('latitude')
CORDEX.remove_coord('longitude')
CORDEX.remove_coord('grid_latitude')
CORDEX.remove_coord('grid_longitude')
CORDEX.add_dim_coord(lats, 1)
CORDEX.add_dim_coord(lons, 2)
#PART 2: OBSERVED DATA
#bring in all the files we need and give them a name
CRU= '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/Actual_Data/cru_ts4.00.1901.2015.tmp.dat.nc'
#Load exactly one cube from given file
CRU = iris.load_cube(CRU, 'near-surface temperature')
#PART 3: FORMAT DATA
#Regrid observed data onto rotated pole grid
CRU = CRU.regrid(CORDEX, iris.analysis.Linear())
#we are only interested in the latitude and longitude relevant to Malawi
Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36.5, \
latitude=lambda v: -17. <= v <= -9.)
CORDEX = CORDEX.extract(Malawi)
CRU = CRU.extract(Malawi)
#time constraint to make all series the same
iris.FUTURE.cell_datetime_objects = True
t_constraint = iris.Constraint(time=lambda cell: 1990<= cell.point.year <= 2008)
CORDEX = CORDEX.extract(t_constraint)
CRU = CRU.extract(t_constraint)
#Convert units to match
CORDEX.convert_units('Celsius')
CRU.unit = Unit('Celsius') # This fixes CRU which is in 'Degrees Celsius' to read 'Celsius'
#add years to data
iriscc.add_year(CORDEX, 'time')
iriscc.add_year(CRU, 'time')
#We are interested in plotting the data for the average of the time period.
CORDEX = CORDEX.collapsed('time', iris.analysis.MEAN)
CRU = CRU.collapsed(['time'], iris.analysis.MEAN)
#Take difference between two datasets
Bias = CRU-CORDEX
#PART 4: PLOT MAP
#load color palette
colourA = mpl_cm.get_cmap('brewer_YlOrRd_09')
#plot map with physical features
ax = plt.axes(projection=cartopy.crs.PlateCarree())
ax.add_feature(cartopy.feature.COASTLINE)
ax.add_feature(cartopy.feature.BORDERS)
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
#set map boundary
ax.set_extent([32.5, 36., -9, -17])
#set axis tick marks
ax.set_xticks([33, 34, 35])
ax.set_yticks([-10, -12, -14, -16])
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
#plot data and set colour range
plot = iplt.contourf(CORDEX, cmap=colourA, levels=np.arange(13,32,1), extend='both')
#add colour bar index and a label
plt.colorbar(plot, label='Celsius')
#give map a title
plt.title('Tasmax 1990-2008 - CanRCM4_ERAINT ', fontsize=10)
#save the image of the graph and include full legend
plt.savefig('ERAINT_CCCma_Tasmax_MAP_Annual', bbox_inches='tight')
plt.show()
if __name__ == '__main__':
main()