scholarly journals Observational constraints on warm cloud microphysical processes using machine learning and optimization techniques

2020 ◽  
Author(s):  
J. Christine Chiu ◽  
C. Kevin Yang ◽  
Peter J. van Leeuwen ◽  
Graham Feingold ◽  
Robert Wood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optimization Techniques ◽  
Observational Constraints ◽  
Warm Cloud ◽  
Cloud Microphysical Processes ◽  
Microphysical Processes

scholarly journals Efficient hyperparameter-tuned machine learning approach for estimation of supercapacitor performance attributes

2021 ◽  
Author(s):  
Syed Ishtiyaq Ahmed ◽  
Sreevatsan Radhakrishnan ◽  
Binoy B Nair ◽  
Rajagopalan Thiruvengadathan
Keyword(s):  
Machine Learning ◽  
Surface Area ◽  
Computational Models ◽  
Performance Metrics ◽  
Structural Features ◽  
Optimization Techniques ◽  
Storage Device ◽  
Micropore Surface Area ◽  
Supercapacitor Performance ◽  
Carbon Based

Abstract Recent years have witnessed the rise of supercapacitor as effective energy storage device. Specifically, carbon-based electrodes have been experimentally well studied and used in the fabrication of supercapacitors due to their excellent electrochemical properties. This work reports the development and utilization of highly tuned and efficient Machine Learning (ML) models that give insights into correlation between structural features of electrodes and supercapacitor performance metrics namely specific capacitance, power density and energy density. Artificial Neural Networks (ANN) and Random Forest (RF) models have been employed to predict the various in-operando performance metrics of carbon-based supercapacitors based on three input features such as mesopore surface area, micropore surface area and scan rate. Experimentally measured values of these parameters used for training and testing these two models have been extracted from a set of research papers reported in literature. The optimization techniques and various tuning methodologies adopted for identifying model hyperparameters are discussed in this paper. The authors demonstrate the importance of hyperparameter tuning and optimization in building accurate and reliable computational models.

scholarly journals Characterization and first results from LACIS-T: a moist-air wind tunnel to study aerosol–cloud–turbulence interactions

2020 ◽  
Vol 13 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
Dennis Niedermeier ◽  
Jens Voigtländer ◽  
Silvio Schmalfuß ◽  
Daniel Busch ◽  
Jörg Schumacher ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Spatial Scales ◽  
Experimental Studies ◽  
Field Measurements ◽  
Hygroscopic Growth ◽  
Moist Air ◽  
Aerosol Cloud ◽  
Cloud Microphysical Processes ◽  
First Results ◽  
Microphysical Processes

Abstract. The interactions between turbulence and cloud microphysical processes have been investigated primarily through numerical simulation and field measurements over the last 10 years. However, only in the laboratory we can be confident in our knowledge of initial and boundary conditions and are able to measure under statistically stationary and repeatable conditions. In the scope of this paper, we present a unique turbulent moist-air wind tunnel, called the Turbulent Leipzig Aerosol Cloud Interaction Simulator (LACIS-T) which has been developed at TROPOS in order to study cloud physical processes in general and interactions between turbulence and cloud microphysical processes in particular. The investigations take place under well-defined and reproducible turbulent and thermodynamic conditions covering the temperature range of warm, mixed-phase and cold clouds (25∘C>T>-40∘C). The continuous-flow design of the facility allows for the investigation of processes occurring on small temporal (up to a few seconds) and spatial scales (micrometer to meter scale) and with a Lagrangian perspective. The here-presented experimental studies using LACIS-T are accompanied and complemented by computational fluid dynamics (CFD) simulations which help us to design experiments as well as to interpret experimental results. In this paper, we will present the fundamental operating principle of LACIS-T, the numerical model, and results concerning the thermodynamic and flow conditions prevailing inside the wind tunnel, combining both characterization measurements and numerical simulations. Finally, the first results are depicted from deliquescence and hygroscopic growth as well as droplet activation and growth experiments. We observe clear indications of the effect of turbulence on the investigated microphysical processes.

scholarly journals Vertical redistribution of moisture and aerosol in orographic mixed-phase clouds

2020 ◽  
Author(s):  
Annette K. Miltenberger ◽  
Paul R. Field ◽  
Adrian H. Hill
Keyword(s):  
Air Temperature ◽  
Unified Model ◽  
Mixed Phase ◽  
Atmospheric Models ◽  
Microphysical Properties ◽  
Cloud Microphysical Processes ◽  
Microphysical Processes ◽  
Mixed Phase Clouds ◽  
Natural Laboratory

Abstract. Orographic wave clouds offer a natural laboratory to investigate cloud microphysical processes and their representation in atmospheric models. Wave clouds impact the larger-scale flow by the vertical redistribution of moisture and aerosol. Here we use detailed cloud microphysical observations from the ICE-L campaign to evaluate the recently developed Cloud Aerosol Interacting Microphysics (CASIM) module in the Met Office Unified Model (UM) with a particular focus on different parameterisations for heterogeneous freezing. Modelled and observed thermodynamic and microphysical properties agree very well (deviation of air temperature

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