福衛七號：多個檔案分析
(使用Python)

• 下載福衛七號的某一天整天的大氣觀測資料，例如：2024年的第65天(2024/3/5) https://tacc.cwa.gov.tw/data-service/fs7rt_tdpc/level2/wetPf2/2024.065/，下載後的檔案儲存在"C:\fs7data\fs7_2024065"的資料夾中。

import os

import netCDF4 as nc

import csv

# 資料夾路徑

folde
r_path = r"C:\fs7data\fs7_2024065"

# CSV 檔案路徑

output_csv = r"C:\fs7data\results.csv"

# 開啟 CSV 檔案，設置寫入模式，並設置 CSV 寫入器

with open(output_csv, mode='w', newline='') as csvfile:

    fieldnames = ['File', 'Min_Temperature', 'Corresponding_Height', 'Corresponding_Latitude', 'Corresponding_Longitude']

    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

    # 寫入 CSV 標題行

    writer.writeheader()

    # 取得資料夾中所有檔案

    file_list = os.listdir(folder_path)

    # 迭代處理每個檔案

    for file_name in file_list:

        if file_name.endswith('_nc'):  # 確認檔案是 NetCDF 檔案

            file_path = os.path.join(folder_path, file_name)

            # 開啟 NetCDF 檔案

            dataset = nc.Dataset(file_path, 'r')

            try:

                # 讀取溫度(Temp)、高度(MSL_alt)、經度(lat)、緯度(lon)資料

                temp = dataset.variables['Temp'][:]

                msl_alt = dataset.variables['MSL_alt'][:]

                lat = dataset.variables['lat'][:]

                lon = dataset.variables['lon'][:]

                # 找出溫度最低處的索引

                min_temp_index = temp.argmin()

                # 取得溫度最低處的溫度、高度、經度、緯度資料

                min_temp = temp[min_temp_index]

                corresponding_msl_alt = msl_alt[min_temp_index]

                corresponding_lat = lat[min_temp_index]

                corresponding_lon = lon[min_temp_index]

                # 寫入 CSV 檔案

                writer.writerow({

                    'File': file_name,

                    'Min_Temperature': min_temp,

                    'Corresponding_Height': corresponding_msl_alt,

                    'Corresponding_Latitude': corresponding_lat,

                    'Corresponding_Longitude': corresponding_lon

                })

            except KeyError as e:

                #print(f"檔案 '{file_name}' 中找不到所需的變數: {e}")

            finally:

                # 關閉 NetCDF 檔案

                dataset.close()

import csv  # 導入csv模組

# 設定檔案路徑

file_path = r"C:\fs7data\results.csv"

# 讀取 CSV 檔案並顯示內容

with open(file_path, mode='r', newline='') as csvfile:

    reader = csv.reader(csvfile)

    for row in reader:

        print(', '.join(row))

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import pandas as pd

# 設定 CSV 檔案路徑

file_path = r"C:\fs7data\results.csv"

# 讀取 CSV 檔案為 DataFrame

df = pd.read_csv(file_path)

# 提取資料

heights = df['Corresponding_Height']

latitudes = df['Corresponding_Latitude']

longitudes = df['Corresponding_Longitude']

temperatures = df['Min_Temperature']

# 篩選符合範圍的數據

mask_longitude = (longitudes >= -180) & (longitudes <= 180)

mask_latitude = (latitudes >= -90) & (latitudes <= 90)

filtered_heights = heights[mask_longitude & mask_latitude]

filtered_latitudes = latitudes[mask_longitude & mask_latitude]

filtered_longitudes = longitudes[mask_longitude & mask_latitude]

filtered_temperatures = temperatures[mask_longitude & mask_latitude]

# 繪製3D圖形

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

# 設置圖形標題和座標軸標籤

ax.set_title('Min Temperature Distribution')

ax.set_xlabel('Corresponding Latitude')

ax.set_ylabel('Corresponding Longitude')

ax.set_zlabel('Corresponding Height')

# 繪製3D散點圖，顏色以溫度資料呈現

sc = ax.scatter(filtered_latitudes, filtered_longitudes, filtered_heights,

                c=filtered_temperatures, cmap='coolwarm', marker='o')

# 添加色條

cbar = fig.colorbar(sc)

cbar.set_label('Min Temperature')

plt.show()

import matplotlib.pyplot as plt

import pandas as pd

# 設定 CSV 檔案路徑

file_path = r"C:\fs7data\results.csv"

# 讀取 CSV 檔案為 DataFrame

df = pd.read_csv(file_path)

# 提取資料

heights = df['Corresponding_Height']

latitudes = df['Corresponding_Latitude']

longitudes = df['Corresponding_Longitude']

temperatures = df['Min_Temperature']

# 篩選符合範圍的數據

mask_longitude = (longitudes >= -180) & (longitudes <= 180)

mask_latitude = (latitudes >= -90) & (latitudes <= 90)

filtered_latitudes = latitudes[mask_longitude & mask_latitude]

filtered_longitudes = longitudes[mask_longitude & mask_latitude]

filtered_heights = heights[mask_longitude & mask_latitude]

filtered_temperatures = temperatures[mask_longitude & mask_latitude]

# 繪製平面圖

plt.figure(figsize=(10, 8))

scatter = plt.scatter(filtered_longitudes, filtered_latitudes, c=filtered_heights, cmap='viridis', marker='o')

# 設置圖形標題和軸標籤

plt.title('Latitude vs Longitude with Height Colormap')

plt.xlabel('Corresponding Longitude')

plt.ylabel('Corresponding Latitude')

# 添加色條

cbar = plt.colorbar(scatter)

cbar.set_label('Corresponding Height')

plt.grid(True)

plt.show()

import matplotlib.pyplot as plt

import numpy as np

from scipy.interpolate import griddata

import pandas as pd

# 設定 CSV 檔案路徑

file_path = r"C:\fs7data\results.csv"

# 讀取 CSV 檔案為 DataFrame

df = pd.read_csv(file_path)

# 提取資料

heights = df['Corresponding_Height']

latitudes = df['Corresponding_Latitude']

longitudes = df['Corresponding_Longitude']

temperatures = df['Min_Temperature']

# 篩選符合範圍的數據

mask_longitude = (longitudes >= -180) & (longitudes <= 180)

mask_latitude = (latitudes >= -90) & (latitudes <= 90)

filtered_latitudes = latitudes[mask_longitude & mask_latitude]

filtered_longitudes = longitudes[mask_longitude & mask_latitude]

filtered_heights = heights[mask_longitude & mask_latitude]

# 設置格點網格

x = np.linspace(min(filtered_longitudes), max(filtered_longitudes), 100)

y = np.linspace(min(filtered_latitudes), max(filtered_latitudes), 100)

X, Y = np.meshgrid(x, y)

# 內插溫度資料

Z = griddata((filtered_longitudes, filtered_latitudes), filtered_heights, (X, Y), method='linear')

# 繪製填滿的等高線圖

plt.figure(figsize=(10, 8))

plt.tripcolor(X.flatten(), Y.flatten(), Z.flatten(), cmap='viridis', shading='gouraud')

# 設置圖形標題和軸標籤

plt.title('Interpolated Filled Contour Plot')

plt.xlabel('Corresponding Longitude')

plt.ylabel('Corresponding Latitude')

# 添加色條

plt.colorbar(label='Corresponding Height')

plt.grid(True)

plt.show()

下載簡易的海岸線向量檔，並儲存在"C:\fs7data\ne_110m_coastline"資料夾中
https://www.naturalearthdata.com/downloads/110m-physical-vectors/

安裝geopandas 和 cartopy函式庫

pip install geopandas cartopy

import pandas as pd

import matplotlib.pyplot as plt

import cartopy.crs as ccrs

from cartopy.feature import NaturalEarthFeature

# Read data from CSV

file_path = r'C:\fs7data\results.csv'

df = pd.read_csv(file_path)

# Extract data

lats = df['Corresponding_Latitude']

lons = df['Corresponding_Longitude']

temps = df['Min_Temperature']

# Create a figure and axis with Cartopy projection

fig, ax = plt.subplots(figsize=(12, 8), subplot_kw={'projection': ccrs.PlateCarree()})

# Add coastline feature

coastline = NaturalEarthFeature(category='physical', name='coastline', scale='110m', edgecolor='black', facecolor='none')

ax.add_feature(coastline, linewidth=0.5)

# Scatter plot with temperature as color

sc = ax.scatter(lons, lats, c=temps, cmap='coolwarm', s=50, marker='o', alpha=0.7, edgecolors='k', linewidth=0.5)

# Colorbar

cbar = plt.colorbar(sc)

cbar.set_label('Min Temperature (°C)')

# Labels and title

ax.set_xlabel('Longitude')

ax.set_ylabel('Latitude')

ax.set_title('Min Temperature Distribution over Corresponding Coordinates')

# Show plot

plt.show()