我正在尝试计算相对涡度，即dV / dX - dU / dY，我目前正在使用numpy上的梯度函数。这是我的代码如下。我想知道是否有更好的方法来做这个，而不是在我想做dU / dY时尝试重塑数组。有一个更好的分化方法，给定一个数字只有U和Y的两个矩阵，我想区分你的Y.
import numpy as np
import netCDF4
import matplotlib.pyplot as plt
from numpy import *
import decimal
from netCDF4 import Dataset
ncfile= Dataset('test.nc','r')
#--------------------Reading in Variables---------------------------------#
lon = ncfile.variables['lon'][:]
lat = ncfile.variables['lat'][:]
UWind850 = ncfile.variables['U'][:,22,:,:] (time, level,lat,lon)
VWind850 = ncfile.variables['V'][:,22,:,:] (time, level,lat,lon)
time = ncfile.variables['time'][:]
MSLP = ncfile.variables['PSL'][:]
# Variable[time,Longitude,Latitude]
#These values are equivalent to I,J and L in the netCDF file
t = 30 #time
x = 300 #longitude
y = 240 #latitude
#-----------------------Calculating Vorticity-----------------------------#
dX = np.gradient(lon) #shape 300
dY = np.gradient(lat) #shape 240
#VWind850.shape (30,240,300)
#UWind850.shape (30,240,300)
dV = (np.gradient(VWind850))
#dV.shape(3,30,240,300) --The extra "3" dimension is caused by the gradient because
#Its creating a Matrix for gradients by (time,latitude,longitude)
Vgradient = dV[2]/dX
UWindTemp = np.reshape(UWind850,(30,300,240)) # I am reshaping so I can divide by dY
dU = (np.gradient(UWindTemp))
Ugradient = dU[2]/dY
Ugradient = np.reshape(Ugradient,(30,240,300)) # Taking it back to normal
VORT= Vgradient - Ugradient
# VORT.shape(time, latitude, longitude)
#-------------------------------------------------------------------------#