Cloud Top Pressure Retrieval Using Polarized and Oxygen A-band Measurements from GF5 and PARASOL Satellites

Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the CTP.However,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent applications.This study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)model.LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP.Additionally,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COT<10 to improve the algorithm's stability.The synergistic method was then applied to the directional polarized camera(DPC)and Polarized and Directionality of the Earth’s Reflectance(POLDER)measurements for evaluation,and the resulting retrieval accuracy of the POLDER-based measurements(RMSEPOLDER=205.176 hPa,RMSEDPC=171.141 hPa,R^(2)POLDER=0.636,R^(2)DPC=0.663,respectively)were higher than that of the MODIS and POLDER Rayleigh pressure measurements.The synergistic algorithm also showed good performance with the application of DPC data.This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.

Calculating the Climatology and Anomalies of Surface Cloud Radiative Effect Using Cloud Property Histograms and Cloud Radiative Kernels

Cloud radiative kernels(CRK)built with radiative transfer models have been widely used to analyze the cloud radiative effect on top of atmosphere(TOA)fluxes,and it is expected that the CRKs would also be useful in the analyses of surface radiative fluxes,which determines the regional surface temperature change and variability.In this study,CRKs at the surface and TOA were built using the Rapid Radiative Transfer Model(RRTM).Longwave cloud radiative effect(CRE)at the surface is primarily driven by cloud base properties,while TOA CRE is primarily decided by cloud top properties.For this reason,the standard version of surface CRK is a function of latitude,longitude,month,cloud optical thickness(τ)and cloud base pressure(CBP),and the TOA CRK is a function of latitude,longitude,month,τand cloud top pressure(CTP).Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present,the surface CRKs on CBP-τhistograms were converted to CTP-τfields using the statistical relationship between CTP,CBP andτobtained from collocated CloudSat and MODIS observations.For both climate model outputs and satellites observations,the climatology of surface CRE and cloud-induced surface radiative anomalies calculated with the surface CRKs and cloud property histograms are well correlated with those calculated from surface radiative fluxes.The cloud-induced surface radiative anomalies reproduced by surface CRKs and MODIS cloud property histograms are not affected by spurious trends that appear in Clouds and the Earth's Radiant Energy System(CERES)surface irradiances products.