参考1: link.
参考2: link.

# coding:utf-8
# __author__ ='Y'
import netCDF4 as nc
from matplotlib import pyplot as plt
from matplotlib.patches import Polygon
import numpy as np
from mpl_toolkits.basemap import Basemap
from scipy import interpolate
import warnings
from PIL import Image



def Interpolation(lon, lat, sst, times=1):
    sst[sst>50] = 20
    # sst=np.squeeze(sst)
    func = interpolate.interp2d(lon,lat,sst,kind='linear')
    lon_new = np.linspace(min(lon),max(lon),720*times)
    lat_new = np.linspace(min(lat),max(lat),1440*times)
    sst_new = func(lon_new,lat_new)#xnew, ynew是一维的，输出znew是二维的
    return lon_new, lat_new, sst_new

def drawing(lon_new, lat_new, sst_new, savefilepath=r'D:\\sst分布图\\未处理\\sst.png'):
    # 定位到具体经纬度
    map = Basemap(llcrnrlon = 0.125, llcrnrlat = -89.875, urcrnrlon = 359.875, urcrnrlat = 89.875)
    lon, lat = np.meshgrid(lon_new, lat_new)
    # plt.figure(figsize=(9, 7.88)) # 设置画布大小
    plt.figure (figsize=(14.4,7.2 ))  # 设置画布大小
    ax = plt.gca()
    plt.style.use('classic')
    # # 绘制经纬线
    map.drawparallels(np.arange(-90., 90., 30.), labels=[1,0,0,0], fontsize=10)  # 纬线
    map.drawmeridians(np.arange(-180., 180., 30.), labels=[0,0,0,1], fontsize=10)  # 经线
    map.drawcoastlines ()

    #basemap有海岸线调用函数了，不用再加海岸线
    # map.readshapefile(r'M:\全球海岸线\GSHHS_h_L1', name='country', color='w')
    # for shp in map.country:
    #     poly = Polygon(xy=shp, facecolor='w') # 填充
    #     ax.add_patch(poly)

        # 添加Colorbar
    # cbar = plt.get_cmap('rainbow')
    colormesh = map.pcolormesh(lon, lat, sst_new)
    #横着
    # cbar = map.colorbar(colormesh, location='bottom', label="contour lines", pad="10%")
    #竖着
    cbar = map.colorbar (colormesh, location='right', pad="10%")
    momo = nc.Dataset (openfilepath)
    cbar.set_label (momo.variables['sst'].units)
    # cbar.ax.tick_params (labelsize=16)
    # cbar = plt.colorbar ()
    # cbar.set_ticks (np.linspace (-3, 45))

    # 去掉图片边框
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    # plt.subplots_adjust(left=0, bottom=0, right=1, top=1, hspace = 0, wspace = 0) # 让图片铺满画布
    # 添加标题、单位
    cbar.set_label("℃")
    plt.title('Sea Surface Temperature')

    plt.savefig(savefilepath)
    # plt.show()

def setalpha(openfilepath = r'D:\\sst分布图\\未处理\\sst.png', savefilepath = r'D:\\sst分布图\\透明化\\sst_alpha.png'):
    # 将图片中的白色改为透明色
    img = Image.open(openfilepath)  # 读取照片
    img = img.convert('RGBA')    # 转换格式，确保像素包含alpha通道
    width, height = img.size     # 长度和宽度
    for i in range(0,width):     # 遍历所有长度的点
        for j in range(0,height):       # 遍历所有宽度的点
            data = img.getpixel((i,j))  # 获取一个像素
            if (data.count(255) == 4):  # RGBA都是255，改成透明色
                img.putpixel((i,j),(255,255,255,0))
    img.save(savefilepath)  # 保存图片

if __name__ == '__main__':
    openfilepath=r'M:\SST_data\sst.day.mean.2012.nc'
        # 忽略警告
    warnings.filterwarnings ("ignore")
        # 显示全部数据
    np.set_printoptions (threshold=np.inf)
        # 读取nc文件
    f = nc.Dataset (openfilepath)
        # print(dataset.variables.keys()) # 打印变量的属性值
        # 读取数据
    lat = f.variables['lat'][:]
    lon = f.variables['lon'][:]
    time = f.variables['time'][:]
    sst = f.variables['sst'][:]
        # 关闭nc文件
    f.close ()
    sst=sst[3,:,:]
    lon_new=lon
    lat_new=lat
    sst_new=sst
    sst[sst == -9.96921e+36] = 0
    # lon_new, lat_new, sst_new = Interpolation(lon, lat, sst, times=1)
    drawing(lon_new, lat_new, sst_new, savefilepath=r'D:\\sst分布图\\未处理\\2020'+'.png')
    setalpha(openfilepath=r'D:\\sst分布图\\未处理\\2020'+'.png', savefilepath=r'D:\\sst分布图\\透明化\\2020'+'.png')
    print('已完成{}号sst的绘制'.format(3))

结果