都是可以直接用ERA5_processer.py进行处理 # 下载高空数据示例# 所需的变量和高度层进入ERA5_downloader.py手动修改pythonERA5_downloader.py\--start_date2023-04-01\--end_date2023-04-01\--datasetpressure-level\--save_dirdata/ERA5_high/\--area40,89,31,103\--log_file./test.log# 下载地面数据...
plt2.set_ylabel('Pressure Level (hPa)') plt2.invert_yaxis() # 压力随高度增加而减小,因此反转y轴 plt2.legend() plt2.grid(True) wind_direction1.plot(y='level', marker='o', linestyle='-.', label=f'wind_direction 31.8667N, 117.2833E', ax=plt3) wind_direction2.plot(y='level', ma...
常规的思维,如前所述,就是比较等压面的气压Plevel和该位置正下方地面的地面气压Psp(Surface Pressure),如果等压面气压小于地面气压(即Plevel<Psfc),则该等压面位于地面之上,该气压层的气象要素(比如温度,风速)有实际意义,若该等压面位于地面之下,则该气压层的气象要素没有实际意义。 但是,实际工作中,一般用的是海平...
def download_era5_data(year, month, day, download_dir): dataset ="derived-era5-pressure-levels-daily-statistics" request = { "product_type":"reanalysis", "variable": ["geopotential"], "year": year, "month": [month], "day": [day], "pressure_level": [ "300","500","700", "850...
(month)# convert np.int64 to regular int type# dataInfoDict["pressure_level"]=level # convert np.int64 to regular int typedataInfoDict["variable"]=varname# convert np.int64 to regular int typedataInfoDict["day"]=dayListdataInfoDict["area"]=areadataInfoDict["grid"]=griddataInfoDict["...
'surface_pressure','mean_sea_level_pressure','10m_u_component_of_wind','10m_v_component_of_wind','2m_temperature','sea_surface_temperature','skin_temperature','2m_dewpoint_temperature','snow_depth','sea_ice_cover','land_sea_mask','soil_type','soil_temperature_level_1','soil_temperatur...
Image Name 最近排队下载实在慢的很 变量需要 高空数据需要以下变量: 'geopotential', 'relative_humidity', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind' 地面数据需要以下变量: 'surface_pressure', 'mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v...
air_pressure_at_mean_sea_level Data Zarr 2 metre dewpoint temperature dew_point_temperature_at_2_metres Data Zarr Total precipitation precipitation_amount_1hour_Accumulation Data Zarr Maximum temperature at 2 metres since previous post-processing ...
ax.plot(wind_speed[0].values,nearest_point['level'].values,'b',linewidth=2)# 使用蓝色线条绘制风速曲线 # 设置轴标签 plt.xlabel('Wind Speed (m/s)')plt.ylabel('Pressure Level (hPa)')plt.title(f'Wind Speed Profile at ({target_lat:.2f}, {target_lon:.2f})')# 反转 y 轴(从大气顶...
Pressure level data (download.py) import cdsapi c = cdsapi.Client() c.retrieve( 'reanalysis-era5-pressure-levels', { 'product_type': 'reanalysis', 'format': 'grib', 'variable': [ 'geopotential', 'relative_humidity', 'temperature','specific_humidity', 'u_component_of_wind', 'v_compo...