Analysis and Modelling of TTL ice crystals based on in-situ light scattering patterns


Even though there are numerous studies on cirrus clouds and its influence on climate, lack of detailed information on its microphysical properties like ice crystal geometry, still exists. Challenges like instrumental limitations and scarcity of observational data could be the reasons behind it. But this knowledge gap has only heightened the error in climate model predictions. Therefore, this study is focused on the Tropical Tropopause Layer (TTL), where cirrus clouds can be seen, and the temperature bias is higher. Since the shape and surface geometry of ice crystals greatly influence the temperature, a detailed understanding of these ice crystals is necessary. So, this paper will look in-depth on finding the morphology of different types of ice crystals in the TTL. The primary objective of this research is to analyse the scattering patterns of ice crystals in the TTL cirrus and find their characteristics like shape and size distributions. As cirrus is a high cloud, it plays a crucial role in the Earth-atmosphere radiation balance and by knowing the scattering properties of ice crystals, their impact on the radiative balance can be estimated. This research further helps to broaden the understanding of the general scattering properties of TTL ice crystals, to support climate modelling and contribute towards more accurate climate prediction. An investigation into the light scattering data is presented. The data consist of 2D scattering patterns of ice crystals of size 1-100μm captured by the Aerosol Ice Interface Transition Spectrometer (AIITS) between the scattering angles 6° and 25° at the wavelength of 532nm. The images were taken during the NERC and NASA Co-ordinated Airborne Studies in the Tropics and Airborne Tropical Tropopause Experiment (known as the CAST-ATTREX campaign) on 5th March 2015 at an altitude between 15-16km over the Eastern Pacific. The features in the scattering patterns are analysed to identify the crystal habit, as they vary with the geometry of the crystal. After the analysis phase, the model crystals of specific types and sizes are generated using an appropriate computer program. The scattering data of the model crystals are then simulated using a Beam Tracing Model (BTM) based on physical optics, as geometric optics doesn’t produce the required information and exact methods (like T-matrix or Discrete Dipole Approximation) are either unsuitable for large size parameters or time-consuming. The simulated scattering pattern of a model crystal is then compared against that of the AIITS to find the characteristics like shape, surface texture and size of the ice crystals. By successive testing and further analysis, the crystal sizes are estimated. Since the manual analysis of scattering patterns is time-consuming, a pilot study on Deep Learning Network has been undertaken to classify the scattering patterns. Previous studies have shown that there are high concentrations of small ice crystals in TTL cirrus. However, these crystals, especially <30μm, are often misclassified due to the limited resolution of the imaging instruments, or even considered as shattered ice. Through this research it was possible to explore both the crystal habit and its surface texture with greater accuracy as the scattering patterns captured by the AIITS are analysed instead of crystal images. It was found that most of the crystals are quasi-spheroidal in shape and that there is indeed an abundance of smaller crystals <30μm. It was also found that over a quarter of the crystal population has rough surfaces

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