Application of Active Spaceborne Remote Sensing for Understanding Biases Between Passive Cloud Water Path Retrievals

Accurate observations of cloud water path (Wc) are of importance as a metric against which to evaluate global weather and climate models. Our results demonstrate that major discrepancies persist in the modeled Wc for the most recent results of the fifth Coupled Model Intercomparison Project (CMIP5). The discordance in model representation of cloud water can partly be explained by inconsistencies in the various global observational datasets based on passive solar reflectance and emitted microwave observations.

We explore bias between the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the MODerate resolution Imaging Spectroradiometer (MODIS) cloud liquid water path (Wc) products with the aid of coincident active observations from the CloudSat radar and CALIPSO lidar. In terms of detection, the active observations provide precise separation of cloudy from clear sky and precipitating from non-precipitating clouds. In addition they offer a unique quantification of precipitation water path (Wp) in warm clouds. Central to this study they also provide an independent quantification of Wc that is based on an accurate surface reference technique (SRT), which serves as an independent arbiter between the two passive sensing approaches. The results herein establish the potential for CloudSat and CALIPSO to provide an independent assessment of bias between the conventional passive remote sensing methods from reflected solar and emitted microwave radiation.

MODIS has an inherent sampling bias due to daytime sampling and the inability to sample beneath ice cloud. This sampling bias is estimated to be -28% highlighting the importance of satellite simulators when comparing Wc from models to MODIS or other solar reflectance observations. After applying a common data filter to the observations to account for sampling biases, AMSR-E is biased high relative to MODIS in the global mean by 26.4 gm^-2. The contribution of 4 potential sources for this bias are investigated by exploiting the active observations: (1) bias in MODIS related to solar zenith angle dependence accounts for -2.3 gm^-2, (2) bias in MODIS due to undersampling of cloud edges and this clouds accounts for 4.2 gm^-2, (3) A wind speed and water vapor dependent bias in the AMSR-E retrieval accounts for 17.0 gm^-2, (4) precipitation occurrence is associated with a -1.0 gm^-2 bias.

The methodology outlined here is now being employed to develop a bias corrected Wc climatology based on a 25+ year record from passive microwave observation of Wc.