(资料图片)

分享两个下载era5数据的脚本(根据实际仅需要改动时间、变量、气压层、时次即可使用)，由中国科学技术大学-大气科学先进计算实验室提供。在使用之前，需要先配置CDS API，点击https://cds.climate.copernicus.eu/api-how-to即可跳转至配置教程。

下载高空

#!/usr/bin/python3import cdsapiimport osimport pandas as pdimport numpy as npc = cdsapi.Client()var_list = ["geopotential", "relative_humidity", "temperature",            "u_component_of_wind", "v_component_of_wind"]start_date = "20070101"end_date = "20071231"start_year = int(start_date[:4])end_year = int(end_date[:4])year_list = np.arange(start_year, end_year+1)year_list = year_list.astype(str)for iyear in year_list:    path = "./" + iyear    if os.path.exists(path):        print("file exist")    else:        os.mkdir(path)    year_start_date = iyear+"0101"    year_end_date   = end_date if int(end_date) < int(iyear)*10000+1231 else iyear+"1231"    date_list = pd.date_range(year_start_date, year_end_date).strftime("%Y%m%d").tolist()    for idate in date_list:        date_name = path + "/" + idate + "-pl.grib"        if os.path.exists(date_name):            continue        c.retrieve(            "reanalysis-era5-pressure-levels",            {                "product_type": "reanalysis",                "format": "grib",                "pressure_level": [                    "1", "2", "3",                    "5", "7", "10",                    "20", "30", "50",                    "70", "100", "125",                    "150", "175", "200",                    "225", "250", "300",                    "350", "400", "450",                    "500", "550", "600",                    "650", "700", "750",                    "775", "800", "825",                    "850", "875", "900",                    "925", "950", "975",                    "1000",                ],                    "variable": var_list,                    "year": iyear,                    "month": idate[4:6],                    "day": idate[6:8],                    "time": [                        "00:00", "06:00", "12:00",                        "18:00",                    ],                },               date_name)print("ok")

下载地面

#!/usr/bin/python3import cdsapiimport osimport pandas as pdimport numpy as npc = cdsapi.Client()var_list = ["10m_u_component_of_wind", "10m_v_component_of_wind", "2m_dewpoint_temperature",            "2m_temperature", "land_sea_mask", "mean_sea_level_pressure",            "sea_ice_cover", "sea_surface_temperature", "skin_temperature",            "snow_depth", "soil_temperature_level_1", "soil_temperature_level_2",            "soil_temperature_level_3", "soil_temperature_level_4", "surface_pressure",            "volumetric_soil_water_layer_1", "volumetric_soil_water_layer_2", "volumetric_soil_water_layer_3",            "volumetric_soil_water_layer_4"]start_date = "20070101"end_date = "20071231"start_year = int(start_date[:4])end_year = int(end_date[:4])year_list = np.arange(start_year, end_year+1)year_list = year_list.astype(str)for iyear in year_list:    path = "./" + iyear    if os.path.exists(path):        print("file exist")    else:        os.mkdir(path)    year_start_date = iyear+"0101"    year_end_date   = end_date if int(end_date) < int(iyear)*10000+1231 else iyear+"1231"    date_list = pd.date_range(year_start_date, year_end_date).strftime("%Y%m%d").tolist()    for idate in date_list:        date_name = path + "/" + idate + "-sfc.grib"        if os.path.exists(date_name):            continue        c.retrieve(            "reanalysis-era5-single-levels",            {                "product_type": "reanalysis",                "format": "grib",                "variable": var_list,                "year": iyear,                "month": idate[4:6],                "day": idate[6:8],                "time": [                    "00:00", "06:00", "12:00",                    "18:00",                ],                },                date_name)print("ok")

并行下载

气象学家公众号提供了并行下载的脚本，有需要的可以了解一下

并行下载ERA5数据的python脚本