The Cloud Reflectance (CRef) is calculated statistically in every grid box from the values of the Reflectance PARASOL and MODIS co-located to the CALIPSO orbit and the Cloud Fraction observed by CALIPSO in the same grid box and for the same day. We use the co-located data PARASOL in one constant direction : TETAv=27°±2.5°,PHIs-PHIv=320°±2.5° (the same angle is used in the PARASOL simulator). Full description of the CRef product is found in Konsta et al., 2011. Practically, for any given day and any horizontal grid box (here 2°x2°), we built the probability density function (PDF) and the cumulative distribution function (CDF) of the collocated PARASOL (6km) and MODIS (1km and 250m) reflectances values. For each day and each 2°x2° horizontal grid box, the Cloud Fraction (CF) is given by CALIPSO-GOCCP and the CF percentage of the higher values of the CDF of the reflectance defines the full resolution cloud reflectances. These full resolution cloud reflectances are averaged on the grid (2°x2°) and this defines the Cloud Reflectance (CRef). The use of Cloud Reflectance (CRef) instead of the Reflectance (R) gives a description of the cloud properties at the highest possible spatial resolution (defined by the resolution of the Level 1 observation), it excludes the contamination by clear sky surrounding region with spatial extension larger than the pixel resolution.
Cloud reflectance files contain monthly means and daily means of instantaneous values of Cloud Reflectance PARASOL, MODIS-1km and MODIS-250m averaged on the 2°x2° grid.
- Konsta D., H. Chepfer, and J.-L. Dufresne, 2011: "A process oriented representation of tropical oceanic clouds for climate model evaluation, based on a statiscal analysis of daytime A-Train high spatial resolution observations", Climate Dynamics, submitted
Exemple of plot:
The following figures show the Cloud Reflectance PARASOL MODIS-1km and MODIS-250m for 2 years of data (January 2007- December 2008).
Ftp data access: CRef
please contact Dimitra Konsta if you have any question or comment about MULTI-SENSORS dataset.