86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
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'''
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批量下载每小时平均ECMWF风场数据
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'''
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# -*- coding: utf-8 -*-
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import cdsapi
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# from ecmwfapi import ECMWFDataServer
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import calendar
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#在运行前先安装cdsapi包
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# 创建 ECMWF 数据服务器客户端
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# server = ECMWFDataServer()
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c = cdsapi.Client()
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# dic = {
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# 'product_type': 'reanalysis', #选择数据集
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# 'format': 'netcdf', #选择数据格式
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# 'variable': [
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# 'Mean_sea_level_pressure', 'Sea_surface_temperature','2m_temperature',
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# '100m_u_component_of_wind', '100m_v_component_of_wind', '10m_u_component_of_neutral_wind',
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# '10m_u_component_of_wind', '10m_v_component_of_neutral_wind', '10m_v_component_of_wind',
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# '10m_wind_gust_since_previous_post_processing', 'instantaneous_10m_wind_gust',
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# ],
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# 'year': '',
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# 'month': '',
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# 'day': [],
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# 'time': [
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# '00:00', '01:00', '02:00', '03:00', '04:00', '05:00',
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# '06:00', '07:00', '08:00', '09:00', '10:00', '11:00',
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# '12:00', '13:00', '14:00', '15:00', '16:00', '17:00',
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# '18:00', '19:00', '20:00', '21:00', '22:00', '23:00'
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# ]
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# }
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dic = {
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'product_type': 'reanalysis', #选择数据集
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'format': 'netcdf', #选择数据格式
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'download_format': 'unarchived',
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'variable': [
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'mean_sea_level_pressure', 'sea_surface_temperature','2m_temperature',
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'100m_u_component_of_wind', '100m_v_component_of_wind', '10m_u_component_of_neutral_wind',
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'10m_u_component_of_wind', '10m_v_component_of_neutral_wind', '10m_v_component_of_wind',
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# '10m_wind_gust_since_previous_post_processing', 'instantaneous_10m_wind_gust',
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],
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'year': [],
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'month': [],
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'day': [],
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'time': [
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'00:00', '01:00', '02:00', '03:00', '04:00', '05:00',
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'06:00', '07:00', '08:00', '09:00', '10:00', '11:00',
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'12:00', '13:00', '14:00', '15:00', '16:00', '17:00',
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'18:00', '19:00', '20:00', '21:00', '22:00', '23:00'
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]
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}
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# 批量下载1979年到2021年所有月份数据
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for i in range(2022, 2023): # 2025年数据
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for j in range(2, 3): # 2月数据
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day_num = calendar.monthrange(i, j)[1] # 根据年月，获取当月日数
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for day_indx in range(day_num):
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dic['year'] = [str(i)]
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dic['month'] = [str(j).zfill(2)]
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dic['day'] = [str(day_indx+1).zfill(2)]
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filename = 'F:\\ECMWF\\once_time\\total_precipitation_' + str(i) + str(j).zfill(2)+str(day_indx+1).zfill(2) + '.nc' # 文件存储路径
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print(filename)
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# try:
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# c.retrieve('reanalysis-era5-single-levels', dic, filename).download()
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# except Exception as e:
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# print(f"An error occurred while downloading data for {i}-{j}: {e}")
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c.retrieve('reanalysis-era5-single-levels', dic, filename) # 下载数据
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# 仅下载2022年6月份数据（某一年的某一个月数据）
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# i = 2022
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# j = 6
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# day_num = calendar.monthrange(i, j)[1] # 根据年月，获取当月日数
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# dic['year'] = str(i)
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# dic['month'] = str(j).zfill(2)
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# dic['day'] = [str(d).zfill(2) for d in range(1, day_num + 1)]
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# filename = 'G:\\data_ecmwf\\once_time\\total_precipitation' + str(i) + str(j).zfill(2) + '.nc' # 文件存储路径
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# c.retrieve('reanalysis-era5-single-levels', dic, filename) # 下载数据
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