Observations from spaceborne microwave (MW) and infrared (IR) passive sensors are the backbone of current satellite meteorology, essential for data assimilation into modern numerical weather prediction and climate benchmarking. In this context, over the last decades, the study and the analysis of cloud microphysics have received increasing attention to better understand cloud feedbacks on climate. MW and IR observations from space offer complementary features concerning cloud microphysics, and various tools have been developed to retrieve cloud parameters such as the effective radius of water and ice clouds. However, MW-IR synergy for cloud investigation is currently under-explored. In this framework, innovative processing methods, such as those based on the use of Artificial Intelligence (AI), which can run on large databases and can handle hundreds of input variables from different sensors, such as those operating in hyperspectral and multispectral channels of the infrared and the microwave bands, such as the New Generation Atmospheric Sounding Interferometer (IASI-NG) and the Microwave Sounder (MWS) of the EPS second generation (EPSSG) platforms whose forthcoming launch is scheduled from 2024 onwards. A regression framework has been implemented based on the combined use of Random Forest (RF) regression and the principal components analysis (PCA) of IASI-NG and MWS observations to input the RF regressors. The supervised learning of liquid and ice water clouds' effective radii was carried out based on this framework. In conclusion, the regression analysis shows good agreement between reference and retrieved effective radius, with 80% correlation and root-mean-square error (RMSE) of 0.68 μm for liquid and 11.6 μm for ice cloud effective radius.
Cloud microphysics in terms of their liquid/ice water content and particle size are the principal factors addressed to study and understand the behavior behind the climate change phenomenon. Based on remotely sensed measurements, in the last decades, some evidence exists that an increase in temperature leads to an increase in cloud liquid water content (CLWC). The temperature dependence of ice water content (CIWC) is also evident from measurements of midlatitude cirrus clouds. Hence, innovative methods, such as those based on the use of Artificial Intelligence (AI) allowing a more relevant investigation of how clouds influence the hydrological cycle and radiative components of the Earth's climate system, are required. This work investigates the capability of a statistical regression scheme of CLWC and CIWC, implemented through the use of a multilayer feed-forward neural network (NN). The whole methodology is applied to a set of simulated IASI-NG L1C and MWS acquisitions, covering the global scale. The NN regression analysis shows good agreement with the test data. The retrieved cloud liquid water and ice profiles have an accuracy of 20 to 60% depending on the given layer. Finally, the layer with the maximum concentration is accurately identified.
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