NASA官網(wǎng)下載的高分辨率底圖(https://www.naturalearthdata.com/downloads/10m-raster-data/10m-cross-blend-hypso/)
非常漂亮，效果如下：

image.png

那么畫圖的時候，想用這個圖來作為底圖。需要用到imshow函數(shù)。

首先加載庫

import matplotlib.pyplot as plt
import matplotlib.colors as colors
from cartopy.io.shapereader import Reader
from shapely.geometry import Polygon,Point
from shapely.geometry.multipolygon import MultiPolygon
from shapely.ops import linemerge,polygonize_full
from shapely.ops import cascaded_union
from cartopy.mpl.patch import geos_to_path
import matplotlib.patches as mpatches
import pandas as pd
import numpy as np
import os,datetime
import sys
import cmaps
import xarray as xr
import netCDF4 as nc
from scipy.io import loadmat
import cartopy.crs as ccrs
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import sys, os
import importlib
import matplotlib
from pylab import mpl
font = {'family' : 'Arial',
    'weight' : 'medium',
    'size'   : 20,
    'style'  : 'normal'}
font0 = {'family' : 'Arial',
    'weight' : 'medium',
    'size'   : 20,
    'style'  : 'italic'}
mpl.rcParams['mathtext.fontset'] = 'custom'
mpl.rcParams['mathtext.it'] = 'Arial:italic'
mpl.rcParams['mathtext.rm'] = 'Arial'
matplotlib.rc('font', **font)
matplotlib.rc('text', usetex=False)

以全球歷史臺風路徑為例。首先讀取數(shù)據(jù)

dt=xr.open_dataset(r'C:\Users\wangnan\Desktop\Allstorms.ibtracs_wmo.v03r08.nc')
lat=dt.lat_wmo.values
lon=dt.lon_wmo.values
speed=dt.wind_wmo.values
ns,nt=np.shape(speed)

設置填色

print('{highlight:#^50s}'.format(highlight='load RGB '))
#rgb=([27,41,224],[47,81,224],[81,120,234],[130,173,239],[231,186,100],[255,124,0],[255,69,0])
rgb=([61,135,255],[117,211,255],[210,245,255],[255,217,217],[255,115,115],[255,64,64],[255,13,13],)
rgb=np.array(rgb)/255.0
icmap=colors.ListedColormap(rgb,name='my_color')
cmap_color=icmap  #.reversed()

畫圖

xstep = 60    #x axis step
ystep = 60    #y axis step
fig=plt.figure(num="figure1",figsize=(22,8))   ###?????fig
clevs=np.array([0,17,33,43,50,59,70,80])  #填色范圍，依據(jù)中心速度劃分臺風等級
proj=ccrs.PlateCarree()
fig,ax1 = plt.subplots(1, 1,dpi=300, subplot_kw={'projection': proj})
# ax1.stock_img()#自帶的低分辨率的底圖
# NASA官網(wǎng)下載的高分辨率底圖(https://www.naturalearthdata.com/downloads/10m-raster-data/10m-cross-blend-hypso/)
ax1.imshow(plt.imread(r'C:\Users\wangnan\Desktop\HYP_LR_SR_W_DR\HYP_LR_SR_W_DR.tif')
, origin='upper', transform=ccrs.PlateCarree(), extent=[-180, 180, -90, 90])
ax1.coastlines()
ax1.set_xticks(np.arange(-180, 181, 60), crs = proj)
ax1.set_yticks(np.arange(-90, 91,30), crs = proj)
ax1.xaxis.set_major_formatter(LongitudeFormatter())
ax1.yaxis.set_major_formatter(LatitudeFormatter())
#ax1.set_xlim(60,120)   #這里控制底圖范圍
#ax1.set_ylim(0,60)
ax1.minorticks_on()
#————————————————

for i in range(nt):
    normc = colors.BoundaryNorm(clevs, ncolors=cmap_color.N, clip=True)
    c3=plt.scatter(lon[:,i],lat[:,i],s=1,c=speed[:,i],norm=normc,cmap=cmap_color,alpha=0.8)
plt.tick_params(labelsize=12)
position = fig.add_axes([0.235, 0.11, 0.58, 0.030])
clevs=    np.array     ([8.5, 25,  38,  46.5, 54.5, 64.5, 75])
cbar = plt.colorbar( c3,cax=position,ticks=clevs,orientation='horizontal',)  
cbar.ax.set_xticklabels(['TD\n <17', 'TS\n 17-33', 'C1\n 33-43',\
                         'C2\n 43-50',  'C3\n 50-59',  'C4\n 59-70',  'C5\n >70'],size=12)
cbar.set_label('wind speed (m/s)',size=12,weight='normal')
plt.savefig('./typhoon_track.png', dpi=300, bbox_inches='tight')
plt.show()

image.png