The multi-sensors A-train observations allow to make the correlation between the different cloud variables at the instantaneous time scale, and at high resolution (CALIPSO-GOCCP and MODIS, PARASOL). This allows to see how different key cloud properties (cloud fraction, cloud vertical distribution, cloud reflectance, a surrogate of the cloud optical depth) vary one as a function of the other and built pictures of cloud processes well suited for the evaluation of clouds in climate models.
The following figures describe the instantaneous relationship between the cloud cover, the cloud vertical distribution and the cloud reflectance.
2D histograms of instantaneous cloud reflectance (CR MODIS-250m) and cloud fraction (CF CALIPSO-GOCCP) at 2°x2° grid a) for all tropical oceanic clouds and b) for low tropical oceanic clouds (CF-low>90% CF-tot).
Statistical relationship between cloud reflectance (MODIS 250m) and the normalised cloud fraction profile (NCF3D=CF3D/CF CALIPSO-GOCCP) when CF-tot>0.1, over the tropical oceans.
Cloud Reflectance as a function of clouds top pressure (Ptop, where Ptop is defined as the highest level of low clouds where NCF3D>0.1) for four classes of CF (CF<0.2, 0.2<CF<0.5, 0.5<CF<0.8, and CF>0.8) for low tropical oceanic clouds.
The same pictures can be built from the models outputs and the simulator tools:
CF is given by the CALIPSO simulator and CRef = (R - (1 - RF) x CSR) / CF, where R is the reflectance given by the PARASOL simulator, and CSR is the Clear Sky Reflectance equal to 0.03 in PARASOL simulator for clear sky conditions.
- Konsta, D., H. Chepfer, and J.-L. Dufresne, 2011: A process oriented representation of tropical oceanic clouds for climate model evaluation, based on a statistical analysis of daytime A-train high spatial resolution observations, Climate Dynamics, submitted.
please contact Dimitra Konsta if you have any question or comment about MULTI-SENSORS Analysis dataset.