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把多年逐小时气温数据处理成
多年逐日最高最低平均气温数据
数据链接
import xarray as xrimport numpy as npimport os# 设置路径data_path = "J:/2m_Temperature/"output_path = "D:/lizhi/land_atmosphere_heatwaves/2m_tem/"# 创建输出目录（如果不存在）os.makedirs(output_path, exist_ok=True)# 分年处理数据years = np.arange(1940, 2024)for year in years:print(f"Processing year: {year}")# 获取当前年份所有文件的路径year_files = [os.path.join(data_path, f"era5.2mt.{year}{month:02d}{day:02d}.nc")for month in range(1, 13) for day in range(1, 32)if os.path.exists(os.path.join(data_path, f"era5.2mt.{year}{month:02d}{day:02d}.nc"))]# 初始化空的数据数组daily_max_t2m = Nonedaily_min_t2m = Nonedaily_mean_t2m = Nonefor file in year_files:try:# 逐个文件处理ds = xr.open_dataset(file)t2m = ds['t2m']# 获取并应用缩放因子和偏移量scale_factor = t2m.attrs.get('scale_factor', 1.0)add_offset = t2m.attrs.get('add_offset', 0.0)t2m = t2m * scale_factor + add_offset # 应用缩放因子和偏移量# 将温度从K转换为Ct2m = t2m - 273.15# 计算每日最高、最低和平均温度max_t2m = t2m.max(dim='time')min_t2m = t2m.min(dim='time')mean_t2m = t2m.mean(dim='time')# 累加每日数据if daily_max_t2m is None:daily_max_t2m = max_t2mdaily_min_t2m = min_t2mdaily_mean_t2m = mean_t2melse:daily_max_t2m = xr.concat([daily_max_t2m, max_t2m], dim='time')daily_min_t2m = xr.concat([daily_min_t2m, min_t2m], dim='time')daily_mean_t2m = xr.concat([daily_mean_t2m, mean_t2m], dim='time')# 关闭当前文件ds.close()except Exception as e:print(f"Error processing file {file}: {e}")# 将结果保存为NetCDF文件if daily_max_t2m is not None:output_ds = xr.Dataset({'daily_max_t2m': daily_max_t2m,'daily_min_t2m': daily_min_t2m,'daily_mean_t2m': daily_mean_t2m})output_file = os.path.join(output_path, f'daily_temperatures_{year}.nc')output_ds.to_netcdf(output_file)print(f"Year {year} results saved to {output_file}")print("所有年份的数据处理完毕！")
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