python解析风云4B,生成红外云图、可见光云图、水汽云图

文章目录

- 概要
- 整体流程
- 直接干货拿着就用!
- 小结

概要

提示：python实现数据读取跟png图片生成

整体流程

提示：了解hdf文件格式,使用pyhton库解析文件生成想要的卫星云图
风云4B测试数据文件请到官网下载,如果想知道hdf文件格式可下载HDFView 直接查看,但是当你打开这个文章你就不用管,直接代码就能用,

红外云图为通道Channel14 所以需要颜色,可根据实际需求自己设置cmap颜色,目前不做演示,可见光云图为通道Channel02,水汽云图为通道通道Channel11,真彩色云图需要根据通道Channel01,通道Channel02,通道Channel03进行通道融合处理,本文章不做演示,跳转这里
python解析风云4B生成真彩云图
https://blog.csdn.net/qq_38197010/article/details/147305867
大致思路:三个通道对于RGB三个颜色管道，然后合并成一个三通道图像

直接干货拿着就用!

from netCDF4 import Dataset
import matplotlib.pyplot as plt
import math
from numpy import deg2rad, rad2deg, arctan, arcsin, tan, sqrt, cos, sin
import numpy as np
from mpl_toolkits.basemap import Basemap
import h5py
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
import matplotlib.colors as mcolorsdata_file = '\\Z_SATE_C_BAWX_20250306061714_P_FY4B-_AGRI--_N_DISK_1050E_L1-_FDI-_MULT_NOM_20250306060000_20250306061459_4000M_V0001.HDF'
# 读取省级边界的 GeoJSON 或 Shapefile
china_province_shapefile = 'china.json'  # 替换为实际的 Shapefile 路径
reader = shpreader.Reader(china_province_shapefile)
ea = 6378.137  # 地球的半长轴[km]
eb = 6356.7523  # 地球的短半轴[km]
h = 42164  # 地心到卫星质心的距离[km]
λD = deg2rad(104.7)  # 卫星星下点所在经度
# 列偏移
COFF = {"0500M": 10991.5,"1000M": 5495.5,"2000M": 2747.5,"4000M": 1373.5}
# 列比例因子
CFAC = {"0500M": 81865099,"1000M": 40932549,"2000M": 20466274,"4000M": 10233137}
LOFF = COFF  # 行偏移
LFAC = CFAC  # 行比例因子def latlon2linecolumn(lat, lon, resolution):"""经纬度转行列(lat, lon) → (line, column)resolution：文件名中的分辨率{'0500M', '1000M', '2000M', '4000M'}line, column"""# Step1.检查地理经纬度# Step2.将地理经纬度的角度表示转化为弧度表示lat = deg2rad(lat)lon = deg2rad(lon)# Step3.将地理经纬度转化成地心经纬度eb2_ea2 = eb ** 2 / ea ** 2λe = lonφe = arctan(eb2_ea2 * tan(lat))# Step4.求Recosφe = cos(φe)re = eb / sqrt(1 - (1 - eb2_ea2) * cosφe ** 2)# Step5.求r1,r2,r3λe_λD = λe - λDr1 = h - re * cosφe * cos(λe_λD)r2 = -re * cosφe * sin(λe_λD)r3 = re * sin(φe)# Step6.求rn,x,yrn = sqrt(r1 ** 2 + r2 ** 2 + r3 ** 2)x = rad2deg(arctan(-r2 / r1))y = rad2deg(arcsin(-r3 / rn))# Step7.求c,lcolumn = COFF[resolution] + x * 2 ** -16 * CFAC[resolution]line = LOFF[resolution] + y * 2 ** -16 * LFAC[resolution]return np.rint(line).astype(np.uint16), np.rint(column).astype(np.uint16)# 中国范围
x_min = 10
x_max = 60
y_min = 70
y_max = 140column = math.ceil((x_max - x_min) / 0.04)
row = math.ceil((y_max - y_min) / 0.04)
print(row, column)
ynew = np.linspace(y_min, y_max, row)  # 获取网格y
xnew = np.linspace(x_min, x_max, column)  # 获取网格x
xnew, ynew = np.meshgrid(xnew, ynew)  # 生成xy二维数组
data_grid = np.zeros((row, column))  # 声明一个二维数组keyword = "NOMChannel"cmap = mcolors.LinearSegmentedColormap.from_list("smooth_gradient", colors, N=256)
# cmap = mcolors.LinearSegmentedColormap.from_list("custom_IR", colors, N=256)
# cmap = plt.cm.viridis# 1. 读取观测数据文件
with h5py.File(data_file, 'r') as f_data:type = f_data['Data'].keys()nc_obj=f_data['Data'];index = {}r_data = {}for k in type:if str(k)!= 'NOMChannel14':continueif str(k).find(keyword) == 0:value = nc_obj[k][:]for i in range(row):for j in range(column):lat = xnew[i][j]lon = ynew[i][j]fy_line = 0fy_column = 0if index.get((lat, lon)) == None:# 查找行列并记录下来下次循环使用fy_line, fy_column = latlon2linecolumn(lat, lon, "4000M")index[(lat, lon)] = fy_line, fy_columnelse:fy_line, fy_column = index.get((lat, lon))data_grid[i][j] = value[fy_line, fy_column]r_data[k] = data_gridimg = plt.figure()ax = img.add_subplot(111)m = Basemap(llcrnrlon=y_min, llcrnrlat=x_min, urcrnrlon=y_max, urcrnrlat=x_max)# 4. 创建地图画布plt.figure(figsize=(12, 8))ax = plt.axes(projection=ccrs.PlateCarree())  # 确保使用平面投影# 关键修正：移除转置操作if 'Channel14' in k:img = ax.pcolormesh(ynew, xnew, data_grid,  # 直接使用bt_datacmap='viridis', # 更改为彩色映射vmin=data_grid.min(),  # 动态范围vmax=data_grid.max(),transform=ccrs.PlateCarree())else:img = ax.pcolormesh(ynew, xnew, data_grid,  # 直接使用bt_datacmap='gray',  # 更改为彩色映射# vmin=data_grid.min(),  # 动态范围# vmax=data_grid.max(),transform=ccrs.PlateCarree())# 4. 使用Cartopy内置方法添加边界ax.add_geometries(reader.geometries(),  # 自动处理所有几何类型crs=ccrs.PlateCarree(),  # 数据坐标系（WGS84）edgecolor='white',facecolor='none',  # 不填充颜色linewidth=0.6)# 添加地理要素ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linestyle=':',linewidth=0.5,color='white')ax.add_feature(cfeature.BORDERS.with_scale('50m'),  linewidth=0.5,color='white')# 保存图像plt.savefig(f'./images/FY4B_China_IR_Color {k}.png', dpi=150, bbox_inches='tight')print("通道" + k + "绘制完成")plt.close()