python绘制WRF模拟的台风路径gif动图
以1713号台风“天鸽”为例，绘制WRF模拟的台风路径和上海台风所的最佳路径做对比。为了更好的对比台风的移动路径和移动快慢，制作动图。
以下为基于Python的实现，重点说明如何绘制台风符号和制作动图。
一. 绘制台风符号
台风符号代码如下：
def get_tc_marker():
u = np.array([,
,
[-1.243,5.433],
[-2.353,2.975],
[-2.578,0.092],
[-2.075,-1.795],
[-0.336,-2.870],
])
u[:,0] -= 0.098
codes = + *(len(u)-2) +
u = np.append(u, -u[::-1], axis=0)
codes += codes
return mpath.Path(3*u, codes, closed=False)
实际效果：
二. 制作动图
python制作动图使用imageio库，安装可使用如下命令：
conda install imageio -y
具体实现的函数：
def convert_img2gif(img_path,img_suffix,gif_name):
files = os.listdir(img_path)
files.sort()
image_list=[ os.path.join(img_path,img) for img in files]
frames = []
for image_name in image_list:
if image_name.endswith(img_suffix):
print(image_name)
frames.append(imageio.imread(image_name))
imageio.mimsave(gif_name, frames, 'GIF', duration = 0.5)
return None上述函数中，img_path是存放图片的路径，需要将此路径下的图片合并成动图。img_suffix是图片后缀，比如".png"或者".jpeg"。gif_name是生成的gif动图名称。可以修改duration的大小调节每帧的持续时间。
三 .最终效果
以下为WRF模拟的1713号台风“天鸽”和观测结果的对比：
从模拟结果看，除了前6小时WRF模拟结果偏快，其他时刻模拟的移动路径都慢于观测结果。(提取WRF模拟的台风路径可参考：Python提取WRF模拟的台风路径)
以下为全部代码：
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.io.shapereader import Reader
import cartopy.feature as cfeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import pandas as pd
import imageio
import os
def convert_img2gif(img_path,img_suffix,gif_name):
files = os.listdir(img_path)
files.sort()
image_list=[ os.path.join(img_path,img) for img in files]
frames = []
for image_name in image_list:
if image_name.endswith(img_suffix):
print(image_name)
frames.append(imageio.imread(image_name))
imageio.mimsave(gif_name, frames, 'GIF', duration = 0.5)
return None
def get_tc_marker():
u = np.array([,
,
[-1.243,5.433],
[-2.353,2.975],
[-2.578,0.092],
[-2.075,-1.795],
[-0.336,-2.870],
])
u[:,0] -= 0.098
codes = + *(len(u)-2) +
u = np.append(u, -u[::-1], axis=0)
codes += codes
return mpath.Path(3*u, codes, closed=False)
if __name__ == "__main__":
lonMax = 130.0
lonMin = 100.0
latMax = 30.0
latMin = 10.0
#read CMA-STI best track
fObs = 'cma_bst_hato.txt'
col_names =['date','grade','lat', 'lon', 'pres', 'vmax']
widths =
df = pd.read_fwf(fObs,usecols=,widths=widths,names=col_names)
date0 = df['lat'].values[:]
latObs= df['lat'].values[:]
lonObs= df['lon'].values[:]
latObs= np.array(latObs)/10
lonObs= np.array(lonObs)/10
#read WRF tc track
fWrf = 'wrf_tc_track.txt'
col_names =['date','lon', 'lat']
widths =
df = pd.read_fwf(fWrf,usecols=,widths=widths,names=col_names)
date = df['date'].values[:]
latWrf= df['lat'].values[:]
lonWrf= df['lon'].values[:]
latWrf= np.array(latWrf)
lonWrf= np.array(lonWrf)
# plot tc track
tc_marker = get_tc_marker()
for i in range(len(lonObs)):
fig = plt.figure(figsize=(12,10))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(,crs=ccrs.PlateCarree())
xticks = np.linspace(lonMin, lonMax, 6, endpoint=True)
yticks = np.linspace(latMin, latMax, 6, endpoint=True)
ax.set_xticks(xticks, crs=ccrs.PlateCarree())
ax.set_yticks(yticks, crs=ccrs.PlateCarree())
lon_formatter = LongitudeFormatter(zero_direction_label=False)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.gridlines()
ax.coastlines()
if i != 0 :
for it in range(1, i+1):
plt.plot((lonObs,lonObs), (latObs,latObs),color='blue',linewidth=1.5,transform=ccrs.PlateCarree())
plt.plot((lonWrf,lonWrf), (latWrf,latWrf),color='red' ,linewidth=1.5,transform=ccrs.PlateCarree())
plt.scatter(lonObs,latObs, s=10, marker=".", edgecolors="blue", facecolors='none', linewidth=1.5)
plt.scatter(lonWrf,latWrf, s=10, marker=".", edgecolors="red" , facecolors='none', linewidth=1.5)
plt.scatter(lonObs,latObs, s=10, marker=".", edgecolors="black", facecolors='none', linewidth=1.5)
plt.scatter(lonObs<i>,</i>latObs<span style="font-style: italic;"><span style="font-style: normal;">, s=350, marker=tc_marker, edgecolors="blue", facecolors='none', linewidth=2, label="OBS")
plt.scatter(lonWrf</span><span style="font-style: normal;">,latWrf</span><span style="font-style: normal;">, s=350, marker=tc_marker, edgecolors="red" , facecolors='none', linewidth=2, label="WRF")
plt.legend()
title = str(date</span><span style="font-style: normal;">) + " UTC"
plt.title(title)
fig_name = "fig_tc/"+str(date</span><span style="font-style: normal;">)+".png"
plt.savefig(fig_name)
#plt.show()
# convert png to gif
img_path = "fig_tc/"
gif_name = "TC1713_Hato.gif"
img_suffix = ".png"
convert_img2gif(img_path,img_suffix,gif_name)</span></span>
参考：
https://stackoverflow.com/questions/44726675/custom-markers-using-python-matplotlib
https://blog.csdn.net/weixin_51549690/article/details/109096105
文章来源于微信公众号：气海同途 顶一个！
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