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臺風是重大災害性天氣，臺風引起的直接災害通常由三方面造成，狂風、暴雨、風暴潮，除此以外臺風的這些災害極易誘發城市內澇、房屋倒塌、山洪、泥石流等次生災害，正因如此，臺風在科研和業務作業中是研究的重點，希望這次臺風路徑可視化可以給予大家一點點幫助，
臺風路徑的獲取
中國氣象局(CMA)
中國氣象局(CMA)的臺風最佳路徑資料集(BST)，BST是之后對歷史臺風路徑進行校正后發布的，其經緯度、強度、氣壓具有更高的可靠性，但是時間解析度為6小時，部分3小時，這一點不如觀測資料，下載地址：
http://tcdata.typhoon.org.cn/溫州臺風網
溫州臺風網的資料是實時發布資料的記錄，時間解析度最高達1小時，對于臺風軌跡具有更加精細化的表述，下載地址：
http://www.wztf121.com/
示例
匯入模塊并讀取資料，使用BST的2018年臺風路徑資料作為示例，已經將原始的txt檔案轉換為xls檔案，
import os, glob
import pandas as pd
import numpy as np
import shapely.geometry as sgeom
import matplotlib.pyplot as plt
from matplotlib.image import imread
from matplotlib.animation import FuncAnimation
import matplotlib.lines as mlines
import cartopy.crs as ccrs
import cartopy.feature as cfeat
from cartopy.mpl.ticker import LongitudeFormatter,LatitudeFormatter
import cartopy.io.shapereader as shpreader
import cartopy.io.img_tiles as cimgt
from PIL import Image
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('./2018typhoon.csv')定義等級色標
def get_color(level):
global color
if level == '熱帶低壓' or level == '熱帶擾動':
color='#FFFF00'
elif level == '熱帶風暴':
color='#6495ED'
elif level == '強熱帶風暴':
color='#3CB371'
elif level == '臺風':
color='#FFA500'
elif level == '強臺風':
color='#FF00FF'
elif level == '超強臺風':
color='#DC143C'
return color定義底圖函式
def create_map(title, extent):
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
url = 'http://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'
layer = 'BlueMarble_ShadedRelief'
ax.add_wmts(url, layer)
ax.set_extent(extent,crs=ccrs.PlateCarree())
gl = ax.gridlines(draw_labels=False, linewidth=1, color='k', alpha=0.5, linestyle='--')
gl.xlabels_top = gl.ylabels_right = False
ax.set_xticks(np.arange(extent[0], extent[1]+5, 5))
ax.set_yticks(np.arange(extent[2], extent[3]+5, 5))
ax.xaxis.set_major_formatter(LongitudeFormatter())
ax.xaxis.set_minor_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_formatter(LatitudeFormatter())
ax.yaxis.set_minor_locator(plt.MultipleLocator(1))
ax.tick_params(axis='both', labelsize=10, direction='out')
a = mlines.Line2D([],[],color='#FFFF00',marker='o',markersize=7, label='TD',ls='')
b = mlines.Line2D([],[],color='#6495ED', marker='o',markersize=7, label='TS',ls='')
c = mlines.Line2D([],[],color='#3CB371', marker='o',markersize=7, label='STS',ls='')
d = mlines.Line2D([],[],color='#FFA500', marker='o',markersize=7, label='TY',ls='')
e = mlines.Line2D([],[],color='#FF00FF', marker='o',markersize=7, label='STY',ls='')
f = mlines.Line2D([],[],color='#DC143C', marker='o',markersize=7, label='SSTY',ls='')
ax.legend(handles=[a,b,c,d,e,f], numpoints=1, handletextpad=0, loc='upper left', shadow=True)
plt.title(f'{title} Typhoon Track', fontsize=15)
return ax定義繪制單個臺風路徑方法，并繪制2018年第18號臺風溫比亞，
def draw_single(df):
ax = create_map(df['名字'].iloc[0], [110, 135, 20, 45])
for i in range(len(df)):
ax.scatter(list(df['經度'])[i], list(df['緯度'])[i], marker='o', s=20, color=get_color(list(df['強度'])[i]))
for i in range(len(df)-1):
pointA = list(df['經度'])[i],list(df['緯度'])[i]
pointB = list(df['經度'])[i+1],list(df['緯度'])[i+1]
ax.add_geometries([sgeom.LineString([pointA, pointB])], color=get_color(list(df['強度'])[i+1]),crs=ccrs.PlateCarree())
plt.savefig('./typhoon_one.png')
draw_single(df[df['編號']==1818])
定義繪制多個臺風路徑方法，并繪制2018年全年的全部臺風路徑，
def draw_multi(df):
L = list(set(df['編號']))
L.sort(key=list(df['編號']).index)
ax = create_map('2018', [100, 180, 0, 45])
for number in L:
df1 = df[df['編號']==number]
for i in range(len(df1)-1):
pointA = list(df1['經度'])[i],list(df1['緯度'])[i]
pointB = list(df1['經度'])[i+1],list(df1['緯度'])[i+1]
ax.add_geometries([sgeom.LineString([pointA, pointB])], color=get_color(list(df1['強度'])[i+1]),crs=ccrs.PlateCarree())
plt.savefig('./typhoon_multi.png')
draw_multi(df)
定義繪制單個臺風gif路徑演變方法，并繪制2018年第18號臺風的gif路徑圖，
def draw_single_gif(df):
for state in range(len(df.index))[:]:
ax = create_map(f'{df["名字"].iloc[0]} {df["時間"].iloc[state]}', [110, 135, 20, 45])
for i in range(len(df[:state])):
ax.scatter(df['經度'].iloc[i], df['緯度'].iloc[i], marker='o', s=20, color=get_color(df['強度'].iloc[i]))
for i in range(len(df[:state])-1):
pointA = df['經度'].iloc[i],df['緯度'].iloc[i]
pointB = df['經度'].iloc[i+1],df['緯度'].iloc[i+1]
ax.add_geometries([sgeom.LineString([pointA, pointB])], color=get_color(df['強度'].iloc[i+1]),crs=ccrs.PlateCarree())
print(f'正在繪制第{state}張軌跡圖')
plt.savefig(f'./{df["名字"].iloc[0]}{str(state).zfill(3)}.png', bbox_inches='tight')
# 將圖片拼接成影片
imgFiles = list(glob.glob(f'./{df["名字"].iloc[0]}*.png'))
images = [Image.open(fn) for fn in imgFiles]
im = images[0]
filename = f'./track_{df["名字"].iloc[0]}.gif'
im.save(fp=filename, format='gif', save_all=True, append_images=images[1:], duration=500)
draw_single_gif(df[df['編號']==1818])轉載請註明出處，本文鏈接：https://www.uj5u.com/houduan/186013.html
標籤：Python
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