Synergistic multi-sensor and multi-frequency retrieval of cloud ice water path constrained by CloudSat collocations

Abstract

The cloud ice water path (IWP) is one of the major parameters that have a strong influence on earth׳s radiation budget. Onboard satellite sensors are recognized as valuable tools to measure the IWP in a global scale. Albeit, active sensors such as the Cloud Profiling Radar (CPR) onboard the CloudSat satellite has better capability to measure the ice water content profile, thus, its vertical integral, IWP, than any passive microwave (MW) or infrared (IR) sensors. In this study, we investigate the retrieval of IWP from MW and IR sensors, including AMSU-A, MHS, and HIRS instruments on-board the N19 satellite, such that the retrieval is consistent with the CloudSat IWP estimates. This is achieved through the collocations between the passive satellite measurements and CloudSat scenes. Potential benefit of synergistic multi-sensor multi-frequency retrieval is investigated. Two modeling approaches are explored for the IWP retrieval – generalized linear model (GLM) and neural network (NN). The investigation has been carried out over both ocean and land surface types. The MW/IR synergy is found to be retrieved more accurate IWP than the individual AMSU-A, MHS, or HIRS measurements. Both GLM and NN approaches have been able to exploit the synergistic retrievals.

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