Assessment of ice cloud modeling capabilities for the irregularly shaped Voronoi models in climate simulations with CAM5

Climate models and satellite remote sensing applications require accurate descriptions of ice cloud optical and radiative properties through parameterization of their scattering properties. While abundant irregularly shaped ice particle habits present a challenge for modelling ice clouds. An irregularly shaped ice particle habit (Voronoi model) has been developed and recently suggested to be effective in inferring the microphysical and radiative properties of ice clouds from Himawari-8 and GCOM-C satellite measurements. As a continuation of previous work by Letu et al. (2016), in this study, we develop a broadband ice cloud scheme based on the Voronoi model through parameterization for use in the Community Atmosphere Model, Version 5 (CAM5). With single scattering properties of Voronoi model, ice cloud bulk scattering properties are integrate over particle size distributions of 11 field campaigns and are parameterized over particle effective diameter. The new ice cloud scheme is compared with four ice cloud schemes (the Yi, Mitchell, Baum-yang and Fu scheme), and is evaluated through the General circulation model version of the Rapid Radiative Transfer Model (RRTMG), and simulations of the top of atmosphere (TOA) shortwave and longwave cloud forcing (SWCF and LWCF) in CAM5. The Clouds and the Earth's Radiant Energy System (CERES) satellite data was selected as validation data. Results indicated that the Voronoi scheme can minimize differences between the satellite-based measurements and CAM5 simulations of global TOA SWCF compared to other four schemes, but performance is not significant for TOA LWCF. For tropical ice clouds, Voronoi scheme has advantages of ice cloud modelling capabilities for shortwave (SW) and longwave (LW) spectrum over other four schemes. In general, it is found that the Voronoi model has advantages over conventional ice cloud schemes and is sufficient for ice cloud modelling in climate simulations with CAM5.

The Impact of Scattering Clouds when Studying Exoplanet Emission Spectra with JWST

<p>Observational studies of exoplanets show that many of them contain some form of cloud coverage. The current modelling techniques used in emission to account for the clouds tend to require prior knowledge of the cloud condensing species as well as not considering the scattering caused by the clouds. We explore the effects that scattering has on the emission spectra by modelling a suite of hot Jupiter atmospheres with varying cloud single scattering albedos and temperature profiles. We examine from simple isothermal cases to more complex thermal structures and physically driven cloud modelling. We show that scattering can produce spectral signatures in the emission spectrum even for isothermal atmospheres. We identify the problems that arise from fitting JWST spectra when the spectral shape is dominated by the scattering from the clouds. Finally, we propose a novel method of fitting the single scattering albedo of the cloud in emission retrievals, this technique does not require any prior knowledge of the cloud chemical or physical properties. We show that this technique can retrieve the wavelength dependent shape of the single scattering albedo while accurately modelling the chemistry in the atmosphere.  </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>

Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations

Existing roads require periodic evaluation in order to ensure safe transportation. Estimations of the available sight distance (ASD) are fundamental to make sure motorists have sufficient visibility to perform basic driving tasks. Mobile LiDAR Systems (MLS) can provide these evaluations with accurate three-dimensional models of the road and surroundings. Similarly, Geographic Information System (GIS) tools have been employed to obtain ASD. Due to the fact that widespread GIS formats used to store digital surface models handle elevation as an attribute of location, the presented methodology has separated the representation of ground and aboveground elements. The road geometry and surrounding ground are stored in digital terrain models (DTM). Correspondingly, abutting vegetation, manmade structures, road assets and other roadside elements are stored in 3D objects and placed on top of the DTM. Both the DTM and 3D objects are accurately obtained from a denoised and classified LiDAR point cloud. Based on the consideration that roadside utilities and most manmade structures are well-defined geometric elements, some visual obstructions are extracted and/or replaced with 3D objects from online warehouses. Different evaluations carried out with this method highlight the tradeoff between the accuracy of the estimations, performance and geometric complexity as well as the benefits of the individual consideration of road assets.

POINT CLOUD MODELLING BASED ON THE TUNNEL AXIS AND BLOCK ESTIMATION FOR MONITORING THE BADALING TUNNEL, CHINA

Recent years have witnessed a growing investigation of terrestrial laser scanning (TLS) for monitoring the deformation of tunnels. TLS provides the ability to obtain a more accurate and complete description of the tunnel surfaces, allowing the determination of the mechanism and magnitude of tunnel deformation, because the entire surface of the tunnel is more concretely modelled rather than being represented by a number of points. This paper models and analyses the point clouds from TLS to detect the possible deformation of a newly built tunnel. In the application of monitoring the Badaling Tunnel for the Winter Olympics 2020 in Beijing, China, the proposed method includes the following components: the tunnel axis is automatically estimated based on a 3D quadratic form estimation; all of the point clouds are segmented into axis-based blocks; and representative points, solved by a singular value decomposition (SVD) method, are estimated to describe the tunnel surface and establish the correspondence of data between days. The deformations are detected in the form of the distance discrepancies of representative points and verified by the measurements using total station.

Software Engineering Research Gaps in the Cloud

This paper takes a systematic review methodology to unearth the reason for a slow adoption of cloud computing by businesses, despite the user interests and cloud advancements gained recently. The key finding is that the IT industry has taken different modelling approaches to engineer and deliver the cloud services based on the goals of different key cloud players, thereby raising various adoption challenges and concerns. In this context, there is a need for rethinking Software Engineering concepts. This motivates us to question whether the existing Software Engineering theories and modelling principles are sufficient for the new cloud computing paradigm. Due to the paucity of a comprehensive review in literature, the main aim of this review article is to identify such research gaps and insufficiencies in Software Engineering, and to provide recommendations for bridging these gaps. In this work, the systemic review of the state of the art of cloud computing has resulted in identifying four major cloud modelling gaps that require prime attention. The paper discusses these gaps and identifies the key Software Engineering challenges prevalent in addressing each of these gaps. Finally, the author proposes five topmost research recommendations specifically designed for overcoming these gaps/challenges in order to facilitate a sustainable cloud adoption. Overall, the author’s findings have established the need to rethink Software Engineering theories for arriving at a multilateral or distributed cloud modelling approach. With such rethinking, the proposed cloud design automatically incorporates cloud user-roles, interoperability, intelligent automation and trusted cloud infrastructure strategies for achieving a sustainable cloud framework of the future.