2Ludwig-Maximilians-Universität München
3German Aerospace Center
4Federal Office of Meteorology and Climatology MeteoSwiss
wget
and curl
are the two standard tools that are available on most Linux and macOS computers. wget
contains a feature for downloading a list of files:
wget -x -nH -i 'https://opendata.physik.lmu.de/pt52a-nhm92/?list'
curl
is missing a feature like that, but the same functionality can be created by combining curl
and xargs
:
curl 'https://opendata.physik.lmu.de/pt52a-nhm92/?list' | xargs -I URL -n1 bash -c 'curl --create-dirs -o ${1:31} ${1}' -- URL
MODIS Black-Sky Albedo Climatology (2013-2022)
Authors: Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, Cedric Wehrum
Contact: giulia.roccetti@eso.org giuli.roccetti@gmail.com
Related publication: HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution, Roccetti et al., 2024, https://doi.org/10.5194/egusphere-2024-167
The dataset has 365 files, each corresponding to a day of the year (DOY), containing the seven MODIS climatology spectral bands.
Spectral bands of MODIS in the VIS/NIR providing information about surface albedo.
band | wavelength [nm] |
---|---|
1 | 645 |
2 | 858 |
3 | 469 |
4 | 555 |
5 | 1240 |
6 | 1640 |
7 | 2130 |
For each pixel we provide the values of the black-sky albedo for the seven MODIS spectral bands, as well as a flag indicating at which step of the climatological average the pixel has been filled.
Flag values and corresponding meaning:
0 - Missing pixel
1 - 41 - Filled with the mean value between (DOY - n) and (DOY + n), if both values are present
42 - Filled with the mean over 10 days around the DOYs before and after the current DOY with filled values until local solar zenith angle of 90°
43-46 - Filled with a spatial average over a square of side length m = [3,5,7,9] pixels
47 - Filled with the mean over all longitudes and a latitude band of 2° around the actual latitude
48 - Filled with the mean over all DOYs for that pixels
49 - Ocean
Description of the variables in the netCDF4 files:
variable name | dimension | resolution |
---|---|---|
Latitude (lat) | 21600 | 30 arcsec |
Longitude (lon) | 43200 | 30 arcsec |
Albedo_BSA_Band1 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_Origin_Band1 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_BSA_Band2 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_Origin_Band2 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_BSA_Band3 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_Origin_Band3 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_BSA_Band4 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_Origin_Band4 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_BSA_Band5 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_Origin_Band5 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_BSA_Band6 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_Origin_Band6 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_BSA_Band7 | lat, lon | 30 arcsec x 30 arcsec |
Albedo_Origin_Band7 | lat, lon | 30 arcsec x 30 arcsec |
Temporal evolution of Earth's albedo for MODIS band 1.
Example to read and plot the data with python and netCDF4:
import netCDF4 as nc
import matplotlib.pyplot as plt
data = nc.Dataset("DOY001.nc","r",format='NETCDF4')
albedo = data.variables["Albedo_BSA_Band2"]
fig, axs = plt.subplots(1,2,figsize=(20,8))
im = axs[0].imshow(albedo[7500:12500,10500:15000], vmin=0, vmax=0.4)
fig.colorbar(im, ax=axs[0])
im = axs[1].imshow(albedo[10650:11050,10550:10900], vmin=0, vmax=0.4)
fig.colorbar(im, ax=axs[1])
Acknowledgements
We acknowledge NASA LAADS DAAC for providing the MCD43D42-48 data products.