Application of active spaceborne remote sensing for understanding biases...

The core information for this publication's citation.: 
Lebsock, M., and H. Su (2014), Application of active spaceborne remote sensing for understanding biases between passive cloud water path retrievals, J. Geophys. Res., 119, 8962-8979, doi:10.1002/2014JD021568.
Abstract: 

Bias between the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) version 2 and the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 5.1 cloud liquid water path (Wc) products are explored with the aid of coincident active observations from the CloudSat radar and the CALIPSO lidar. In terms of detection, the active observations provide precise separation of cloudy from clear sky and precipitating from nonprecipitating clouds. In addition, they offer a unique quantification of precipitation water path (Wp) in warm clouds. They also provide an independent quantification of Wc that is based on an accurate surface reference technique, which is an independent arbiter between the two passive 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. 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 RMS difference in the regional patterns is 32.4 gm-2, which highlights a large geographical dependence in the bias which is related to the tropical transitions from stratocumulus to cumulus cloud regimes. The contributions of four 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 accounts for 4.2 gm-2, (3) a wind speed and water vapor-dependent “clear-sky biase” in the AMSR-E retrieval accounts for 6.3 gm-2, and (4) evidence suggests that much of the remaining 18 gm-2 bias is related to the assumed partitioning of the observed emission signal between cloud and precipitation water in the AMSR-E retrieval. This is most evident through the correlations between the regional mean patterns of Wp and the Wc bias within the latitudes of 30°N and 30°S, suggesting that the assumption of a regionally invariant cloud/precipitation partitioning in the AMSR-E algorithm is the likely causal factor.

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CloudSat