Comparison between the first Odin-SMR, Aura MLS and CloudSat retrievals of cloud ice mass in the upper tropical troposphere
1Department of Radio and Space Science, Chalmers University of Technology, Gothenburg, Sweden
2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
3Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
Abstract. Emerging microwave satellite techniques are expected to provide improved global measurements of cloud ice mass. CloudSat, Aura MLS and Odin-SMR fall into this category and first cloud ice retrievals from these instruments are compared. The comparison is made for partial ice water columns above 12 km, following the SMR retrieval product. None of the instruments shows significant false cloud detections and a consistent view of the geographical distribution of cloud ice is obtained, but differences on the absolute levels exist. CloudSat gives the lowest values, with an overall mean of 2.12 g/m2. A comparable mean for MLS is 4.30 g/m2. This relatively high mean can be an indication of overestimation of the vertical altitude of cloud ice by the MLS retrievals. The vertical response of SMR has also some uncertainty, but this does not affect the comparison between MLS and CloudSat. SMR observations are sensitive to cloud inhomogeneities inside the footprint and some compensation is required. Results in good agreement with CloudSat, both in regard of the mean and probability density functions, are obtained for a weak compensation, while a simple characterisation of the effect indicates the need for stronger compensation. The SMR mean was found to be 1.89/2.62/4.10 g/m2 for no/selected/strongest compensation, respectively. Assumptions about the particle size distribution are a consideration for all three instruments, and constitute the dominating retrieval uncertainty for CloudSat. The comparison indicates a retrieval accuracy of about 40% (3.1±1.2 g/m2). This number is already very small compared to uncertainties of cloud ice parametrisation in atmospheric models, but can be decreased further through a better understanding of main retrieval error sources.