scholarly journals Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

2019 ◽  
Author(s):  
Zhonghua Zheng ◽  
Nicole Riemer ◽  
Matthew West ◽  
Valentine G. Anantharaj
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Atmospheric Models ◽  
Mixing State ◽  
Aerosol Mixing State

scholarly journals A Review of the Representation of Aerosol Mixing State in Atmospheric Models

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 168 ◽  
Author(s):  
Robin Stevens ◽  
Ashu Dastoor
Keyword(s):  
Optical Properties ◽  
Climate Models ◽  
Research Effort ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Heterogeneous Chemistry ◽  
Computationally Efficient ◽  
Atmospheric Models ◽  
Mixing State ◽  
Aerosol Mixing State

Aerosol mixing state significantly affects concentrations of cloud condensation nuclei (CCN), wet removal rates, thermodynamic properties, heterogeneous chemistry, and aerosol optical properties, with implications for human health and climate. Over the last two decades, significant research effort has gone into finding computationally-efficient methods for representing the most important aspects of aerosol mixing state in air pollution, weather prediction, and climate models. In this review, we summarize the interactions between mixing-state and aerosol hygroscopicity, optical properties, equilibrium thermodynamics and heterogeneous chemistry. We focus on the effects of simplified assumptions of aerosol mixing state on CCN concentrations, wet deposition, and aerosol absorption. We also summarize previous approaches for representing aerosol mixing state in atmospheric models, and we make recommendations regarding the representation of aerosol mixing state in future modelling studies.

scholarly journals Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics

Atmosphere ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 15 ◽  
Author(s):  
Michael Hughes ◽  
John Kodros ◽  
Jeffrey Pierce ◽  
Matthew West ◽  
Nicole Riemer
Keyword(s):  
Machine Learning ◽  
Global Distribution ◽  
Mixing State ◽  
Aerosol Mixing State

scholarly journals On the estimation of boundary layer heights: A machine learning approach

2020 ◽  
Author(s):  
Raghavendra Krishnamurthy ◽  
Rob K. Newsom ◽  
Larry K. Berg ◽  
Heng Xiao ◽  
Po-Lun Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Boundary Layer ◽  
Great Plains ◽  
Absolute Error ◽  
Learning Approaches ◽  
Atmospheric Models ◽  
Near Surface ◽  
Boundary Layer Height ◽  
Learning Framework ◽  
Using Data

Abstract. The planetary boundary-layer height (zi) is a key parameter used in atmospheric models for estimating the exchange of heat, momentum and moisture between the surface and the free troposphere. Near-surface atmospheric and subsurface properties (such as soil temperature, relative humidity etc.) are known to have an impact on zi. Nevertheless, precise relationships between these surface properties and zi are less well known and not easily discernable from the long-term data. Machine learning approaches, such as Random Forest, which use a multi-regression framework, help to decipher some of the physical processes linking surface-based characteristics to zi. In this study, a multi-year dataset from 2016 to 2019 at the Southern Great Plains site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with over 20 different surface meteorological measurements as inputs to a RF model. The model is trained using radiosonde-derived zi values spanning the period from 2016 through 2018, and then evaluated using data from 2019. Results from 2019 showed significantly better agreement with the radiosonde compared to estimates derived from a thresholding technique using Doppler lidars. Noteworthy improvements in daytime zi estimates was observed using the RF model, where a 50 % improvement in mean absolute error compared to lidar-only zi estimates and provided an R2 of greater than 85 %. We also explore the effect of zi uncertainty on convective velocity scaling and present preliminary comparisons between the RF model and zi estimates derived from atmospheric models.

scholarly journals On the estimation of boundary layer heights: a machine learning approach

2021 ◽  
Vol 14 (6) ◽  
pp. 4403-4424
Author(s):  
Raghavendra Krishnamurthy ◽  
Rob K. Newsom ◽  
Larry K. Berg ◽  
Heng Xiao ◽  
Po-Lun Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Boundary Layer ◽  
Great Plains ◽  
Absolute Error ◽  
Learning Approaches ◽  
Atmospheric Models ◽  
Near Surface ◽  
Boundary Layer Height ◽  
Learning Framework ◽  
Using Data

Abstract. The planetary boundary layer height (zi) is a key parameter used in atmospheric models for estimating the exchange of heat, momentum, and moisture between the surface and the free troposphere. Near-surface atmospheric and subsurface properties (such as soil temperature, relative humidity, etc.) are known to have an impact on zi. Nevertheless, precise relationships between these surface properties and zi are less well known and not easily discernible from the multi-year dataset. Machine learning approaches, such as random forest (RF), which use a multi-regression framework, help to decipher some of the physical processes linking surface-based characteristics to zi. In this study, a 4-year dataset from 2016 to 2019 at the Southern Great Plains site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with over 20 different surface meteorological measurements as inputs to a RF model. The model is trained using radiosonde-derived zi values spanning the period from 2016 through 2018 and then evaluated using data from 2019. Results from 2019 showed significantly better agreement with the radiosonde compared to estimates derived from a thresholding technique using Doppler lidars only. Noteworthy improvements in daytime zi estimates were observed using the RF model, with a 50 % improvement in mean absolute error and an R2 of greater than 85 % compared to the Tucker method zi. We also explore the effect of zi uncertainty on convective velocity scaling and present preliminary comparisons between the RF model and zi estimates derived from atmospheric models.

scholarly journals Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling

2020 ◽  
Author(s):  
Zhonghua Zheng ◽  
Jeffrey Curtis ◽  
Yu Yao ◽  
Jessica Gasparik ◽  
Valentine Anantharaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
System Modeling ◽  
Global Scale ◽  
Earth System ◽  
Mixing State ◽  
Earth System Modeling ◽  
Submicron Aerosol ◽  
Aerosol Mixing State

scholarly journals Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling

2020 ◽  
Author(s):  
Zhonghua Zheng ◽  
Jeffrey H. Curtis ◽  
Yu Yao ◽  
Jessica T. Gasparik ◽  
Valentine G. Anantharaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
System Modeling ◽  
Global Scale ◽  
Earth System ◽  
Mixing State ◽  
Earth System Modeling ◽  
Submicron Aerosol ◽  
Aerosol Mixing State

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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