Use climatological aerosol optical thickness

BaptisteVandecrux commented 1 year ago

Article:

https://www.tandfonline.com/doi/full/10.1080/16000889.2019.1623639

MACv2 properties are at ftp://ftp-projects.mpimet.mpg.de/aerocom/climatology/MACv2_2018/.

The data are placed in several subdirectories and a README file describes data content of file-names

/550nm (mid-visible) aerosol properties at 550nm wavelength
/CCN lower cloud-base condensation nuclei and critical radii at diff. supersaturation
/detail ancillary data for radiative transfer simulations
/documents some documentation and figure
/forcing MACv2 associated radiative effects
/programs fortran programs for the MAC v2 climatology
/program_force fortran program for MAC associated radiative effects
/retrieval MACv2 fields for under-determined solar reflection based AOD retrievals
/spectral 2005 optical data at 3 different spectral resolutions: 20, 30 (RRTM), 31 bands
/time same as in/spectral … but data for different years (from 1850 to 2100)

AdrienWehrle commented 1 year ago

Code to download 3hrs CAMS AOD 550:


#!/usr/bin/env python
import calendar
from ecmwfapi import ECMWFDataServer
# You need to obtain your own API key and email (username) to run this script
server = ECMWFDataServer(url="https://api.ecmwf.int/v1",key="Your API key",email="Your email")

def retrieve_cams_reanalysis():
    yearStart = 2004                                   
    yearEnd = 2004
    monthStart = 1
    monthEnd = 12
    for year in list(range(yearStart, yearEnd + 1)):
        for month in list(range(monthStart, monthEnd + 1)):
            startDate = '%04d%02d%02d' % (year, month, 1)
            numberOfDays = calendar.monthrange(year, month)[1]
            lastDate = '%04d%02d%02d' % (year, month, numberOfDays)
            target = "cams_aod_3h_%04d%02d.nc" % (year, month)
            requestDates = (startDate + "/TO/" + lastDate)
            cams_reanalysis_request(requestDates, target)

def cams_reanalysis_request(requestDates, target):
    server.retrieve({
        "class": "mc",                                  # do not change
        "dataset": "cams_reanalysis",                   # do not change
        "expver": "eac4",                               # do not change
        "stream": "oper",                               # do not change
        "type": "an",                                   # analysis (versus forecast, fc)
        "date": requestDates,                           # dates, set automatically from above
        "levtype": "sfc",                                # pressure level data (versus surface, sfc, and model level, ml)
        "param": "207.210/213.210/214.210/215.210/216.210",                       # here: Dust Aerosol mixing ration and Relative humidity (r); see http://apps.ecmwf.int/codes/grib/param-db
        "time": "00/03/06/09/12/15/18/21",
        "grid": "0.625/0.5",
        "area": "90/-180/-90/180",
        "format": "netcdf",
        "target": target,
        })
if __name__ == '__main__':
    retrieve_cams_reanalysis()

Supplementary in https://www.sciencedirect.com/science/article/abs/pii/S1352231019308556

AdrienWehrle commented 1 year ago

Sorry, didn't see you are referring to a different product!

GEUS-SICE / pySICE

Use climatological aerosol optical thickness #31