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Implementation of various non-linear similarity functions for stable atmospheric surface layer in the WRF modeling system: An evaluation for three contrasting nights in CASES-99 dataset

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ATMOSPHERIC RESEARCH 2025年 315卷

作者： Namdev, Prabhakar Sharan, Maithili Mishra, Saroj K. Indian Inst Technol Delhi Ctr Atmospher Sci New Delhi 110016 India

Accurate representation of the stable atmospheric surface layer (ASL) in numerical models has been a challenging task due to the occurrence of different physical processes such as radiative transport, turbulent mixing, and the coupling of vegetation with the atmosphere and underlying soil, as well as aspects of land use heterogeneity, etc. The Monin-Obukhov similarity theory has been widely used to parameterize surface turbulent fluxes in numerical models, which utilize similarity functions to account for the effect of atmospheric stability. Over the years, researchers have proposed various forms of similarity functions, depending on different field experiment datasets around the globe. This study incorporates some of the well-established non-linear similarity functions under stable stratification in the revised MM5 surface layer scheme in the Weather Research and Forecasting (WRF) Model version 4.2.2 and hence develop a scheme that has various functional forms of similarity functions for computing surface turbulent fluxes under stable stratification. The proposed scheme has been evaluated in simulating near-surface atmospheric variables and surface turbulent fluxes over the domain centered around the location of the Ranchi dataset site (23.412oN, 85.440oE;India) and the CASES-99 experiment site (37.38oN, 96.44oW;Kansas, USA) during the months of January 2009 and October 1999, respectively. The modified scheme is also evaluated for the three contrasting nights representative of intermittently turbulent, fully turbulent, and radiative ASL based on the CASES-99 dataset. In general, all the newly installed similarity functions are found to be consistent in predicting surface turbulent fluxes as well as near-surface atmospheric variables with respect to the default surface layer scheme and the observed data derived from the flux towers over the two domains. However, this study reveals that all the similarity functions are found to be inconsistent during the fully turbul

关键词： Monin-Obukhov similarity theory Surface layer parameterization similarity functions Surface turbulent fluxes WRF Model

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On the Extent of Applicability of Various Non-linear similarity functions for Computation of Surface Fluxes under Stable Conditions in Numerical Models

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BOUNDARY-LAYER METEOROLOGY 2024年第6期190卷 29-29页

作者： Namdev, Prabhakar Sharan, Maithili Mishra, Saroj K. Indian Inst Technol Delhi Ctr Atmospher Sci New Delhi 110016 India

In this study, a systematic mathematical analysis has been presented for the extent of applicability of various non-linear similarity functions for momentum (phi(m)) and heat (phi(h)) under stable conditions to compute surface turbulent fluxes in numerical models. The investigation is carried out for equal and unequal momentum (z0) and heat (z(h)) roughness lengths. The study reveals that phi(m) and phi(h) utilized in the National Centre for Atmospheric Research Community Atmosphere Model version 5 (NCAR-CAM5) (Holtslag et al. in Mon Weather Rev 118:1561-1575, 1990) have several restrictions on their applicability in moderately to strongly stable cases. If the ratios of z0 and z(h) to the height (z) from the surface (i.e., z(0)/z and z(h)/z) lie in the range (0.2,1), the functions are valid for a limited range of zeta (stability parameter) in strong stable conditions (zeta > 1);however, when z(0)/z <= 0.2 and z(h)/z <= 0.2, the validity of functions is unrestricted. In terms of bulk Richardson number (Ri(B)), the functions are valid for a limited range of moderately to strongly stable conditions. These theoretically derived upper limits have also been validated using observations from the UK Meteorological Office's Cardington and Cooperative Atmosphere-Surface Exchange Study-99 datasets. On the other hand, similarity functions based on Cheng and Brutsaert (Boundary-Layer Meteorol 114:519-538, 2005), Grachev et al. (Boundary-Layer Meteorol 124:315-333, 2007), Srivastava et al. (Meteorol Appl 27, 2020), and Gryanik et al. (J Atmos Sci 77:2687-2716, 2020) are found to be theoretically valid for all values of zeta and Ri(B). The efforts have also been made to implement these functions in the Weather Research and Forecasting as well as global scale models.

关键词： Monin-Obukhov similarity theory similarity functions Surface layer parameterization Surface turbulent fluxes Numerical models

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RENEWABLE ENERGY 2020年 150卷 907-917页

作者： Han, Xingxing Liu, Deyou Xu, Chang Shen, Wen Zhong Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China HEDA Wind Power Technol Ltd Co Shanghai Peoples R China Tech Univ Denmark Dept Wind Energy Lyngby Denmark

Most of the k - epsilon closures for modeling stratified surface layer are derived from the classical similarity functions and might fail in complex terrain due to limitations of the classical similarity functions. Despite the classical similarity functions to limit the flux Richardson number R-f, we present new similarity functions estimated from field measurement in full range of R-f and propose a k - e model using the new similarity functions to improve predictions of stratified surface layer flows in complex terrain. Measurements show that the classical similarity functions are partially valid in complex terrain and the wind shear under strongly stable conditions is constrained at large R-f. According to numerical studies in two areas of complex terrain, models using the classical similarity functions and the new similarity functions both present good predictions of convective airflows in complex terrain. The new similarity functions are shown to significantly improve the k - epsilon model in predicting stably stratified airflows in complex terrain by constraining the wind shear at large R-f, while the classical similarity functions without limiting the wind shear lead to significantly misestimating the wind speedup factor under stable conditions. Using the proposed model to predict flows in wind farm could benefit wind resource estimation and wind power forecasting. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： similarity functions Wind speedup factor Flux Richardson number k-e closure Wind farm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115482-115482页

作者： Khojamli, Halime Razmara, Jafar Univ Tabriz Dept Comp Sci Tabriz Iran

Today, recommender systems play a vital role in the acceleration of searches by internet users to find what they are interested in. Among the strategies proposed for recommender systems, collaborative filtering has received due attention regarding its simplicity and efficiency. The key factor for the success of this strategy returns to the similarity calculation methods that affect the accuracy of its recommendations. Regarding the large volume of articles published in the field of collaborative filtering for the development of recommender systems, it is necessary to provide a comprehensive review of the similarity functions and their efficiency presented in the field. Of course, several surveys have already been published to investigate the similarity functions proposed for collaborative filtering, but these articles either have looked briefly at these functions or reviewed a few numbers of them. In this study, the effort was to provide a comprehensive study on the similarity functions proposed for collaborative filtering with a special focus on the rating-based and neighbor-based approaches. After a brief explanation of each similarity function, some popular evaluation metrics were used for their evaluation using the MovieLens datasets. The comparative evaluation results presented in this article provide a highly useful reference for researchers in this field to choose their appropriate similarity function.

关键词： Recommender systems Collaborative filtering Neighborhood-based approach similarity functions similarity measures

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期36卷 4931-4944页

作者： Rodriguez-Gonzalez, Ansel Y. Martinez-Trinidad, Jose F. Carrasco-Ochoa, Jesus A. Ruiz-Shulcloper, Jose Alvarado-Mentado, Matias Mexican Natl Res Council CONACyT Mexico City DF Mexico CICESE UT3 Andador 10 109 Tepic Nayarit Mexico INAOE Dept Comp Sci Luis Enrique Erro 1 Tonantzintla 72840 Puebla Mexico Univ Informat Sci UCI Carretera San Antonio de los BanosKm 2 1-2 Havana Cuba Ctr Res & Adv Studies CINVESTAV Dept Comp Sci Inst Politecn Nacl 2508 Mexico City 07300 DF Mexico

There are many problems were the objects under study are described by mixed data (numerical and non numerical features) and similarity functions different from the exact matching are usually employed to compare them. Some algorithms for mining frequent patterns allow the use of Boolean similarity functions different from exact matching. However, they do not allow the use of non Boolean similarity functions. Transforming a non Boolean similarity function into a Boolean one, and then applying the previous algorithms for mining frequent patterns, could lead to loss some patterns, and even more to generate some other patterns which indeed should not be considered as frequent similar patterns. In this paper, we extend the similar frequent pattern mining by allowing the use of non Boolean similarity functions. Several properties for pruning the search space of frequent similar patterns and a data structure that allows computing the frequency of patterns candidates, are proposed. Also, three algorithms for mining frequent patterns using non Boolean similarity functions are proposed. Experimental results show the efficiency and efficacy of the algorithms. The proposed algorithms obtain better patterns for classification than those patterns obtained by traditional frequent pattern miners, and miners using Boolean similarity functions.

关键词： Data mining frequent patterns similarity functions Mixed data

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Learning with similarity functions: a Tensor-Based Framework

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COGNITIVE COMPUTATION 2019年第1期11卷 31-49页

作者： Ragusa, Edoardo Gastaldo, Paolo Zunino, Rodolfo Cambria, Erik Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit DITEN Genoa Italy Nanyang Technol Univ Sch Comp Sci & Engn Nanyang Singapore

Machine learning algorithms are typically designed to deal with data represented as vectors. Several major applications, however, involve multi-way data, such as video sequences and multi-sensory arrays. In those cases, tensors endow a more consistent way to capture multi-modal relations, which may be lost by a conventional remapping of original data into a vector representation. This paper presents a tensor-oriented machine learning framework, and shows that the theory of learning with similarity functions provides an effective paradigm to support this framework. The proposed approach adopts a specific similarity function, which defines a measure of similarity between a pair of tensors. The performance of the tensor-based framework is evaluated on a set of complex, real-world, pattern-recognition problems. Experimental results confirm the effectiveness of the framework, which compares favorably with state-of-the-art machine learning methodologies that can accept tensors as inputs. Indeed, a formal analysis proves that the framework is more efficient than state-of-the-art methodologies also in terms of computational cost. The paper thus provides two main outcomes: (1) a theoretical framework that enables the use of tensor-oriented similarity notions and (2) a cognitively inspired notion of similarity that leads to computationally efficient predictors.

关键词： Tensor data similarity functions Tensor-based learning Intelligent embedded systems

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Learning with similarity functions: A novel design for the extreme learning machine

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NEUROCOMPUTING 2017年 261卷 37-49页

作者： Gastaldo, P. Bisio, F. Gianoglio, C. Ragusa, E. Zunino, R. Univ Genoa DITEN Dept Elect Elect Telecommun Engn & Naval Architec Genoa Italy

The paper addresses the role of randomization in the training process of a learning machine, and analyses the affinities between two well-known schemes, namely, Extreme Learning Machines (ELMs) and the learning framework using similarity functions. These paradigms share a common approach to inductive learning, which combines an explicit remapping of data with a linear separator;however, they seem to exploit different strategies in the design of the mapping layer. The paper shows that, in fact, the theory of learning with similarity functions can stimulate a novel interpretation of the ELM paradigm, thus leading to a common framework. New insights into the ELM model are obtained, and the ELM strategy for the setup of the neurons' parameters can be significantly improved. Experimental results confirm that the novel method improves over conventional approaches, especially in the trade-off between classification accuracy and machine complexity (i.e., the dimensionality of the remapped space). This, in turn, supports the reliability of the unified framework envisioned in this paper. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Extreme learning machine similarity functions Single-layer feedforward neural networks

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Performance Evaluation of Different similarity functions and Classification Methods using Web Based Hindi Language Question Answering System 2

Performance Evaluation of Different Similarity Functions and...

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2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC)

作者： Devi, Rajni Dua, Mohit Banasthali Vidyapith Dept Comp Engn Banasthali Rajasthan India Natl Inst Technol Dept Comp Engn Kurukshetra Haryana India

Question Answering (QA) system is an approach to extract the correct answer for the query asked by the user in its own language. The work discussed is implemented for Hindi Language objective type questions and answers. The paper implements the comparison of nine different similarity functions and two classification methods used to retrieve the desired information. The results revealconclude that Smith Waterman outperforms the other similarity functions in perforamnce evaluation. The K-Nearest Neighbor(K-NN) algorithm gives 97%, 95.6% and Neasret Neighbor (NN) algorithm gives 93.3%,95% for two differtent test data sets, respectively. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Hindi Question Answering System Machine Learning Data Mining similarity functions Text similarity measure classification method Nearest Neighbor K-Nearest Neighbor

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Comparison of Different similarity functions on Hindi QA System

Comparison of Different Similarity Functions on Hindi QA Sys...

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International Conference on Information and Communication Technology for Sustainable Development (ICT4SD)

作者： Sneha, Bagde Mohit, Dua Singh, Virk Zorawar Banasthali Vidyapith Dept Comp Sci & Engn Banasthali India Natl Inst Technol Dept Comp Engn Kurukshetra Haryana India

ISBN: (纸本)9789811001291;9789811001277

This paper discusses a comparative analysis of different similarity measures for Hindi question answering system using machine learning approach from information retrieval and classification perspectives. Many machine learning tasks require similarity functions that evaluate likeness between examinations. similarity computations are particularly important for clustering that depends on precise estimate of the distance between data points. This framework is considered for data matching for multiphrase words and misspelled words.

关键词： Hindi question answering system Machine learning Data mining similarity functions Text similarity measure N-gram approach Jaccard coefficient similarity Euclidean similarity measure Jaro-Wrinkler

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Accuracy of Class Prediction using similarity functions in PAM

Accuracy of Class Prediction using Similarity Functions in P...

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IEEE International Conference on Industrial Technology (ICET)

作者： Muck, Isaac B. Hnatyshin, Vasil Thayasivam, Umashanger Rowan Univ Dept Math Glassboro NJ 08062 USA Rowan Univ Dept Comp Sci Glassboro NJ 08062 USA

ISBN: (纸本)9781467380751

Clustering have been proven to be an effective technique for finding data instances with similar characteristics. Such algorithms are based on the notion of distance between data points, often computed using Euclidean metric. That is why, clustering algorithms are mostly applicable to the data sets comprising of numerical values. However, the real life data often consist of features which are categorical in nature. For example, to identify abnormal behavior or a cyberattack in a network, we usually examine packet headers which contain categorical values such as source and destination IP addresses, source and destination port numbers, upper layer protocols, etc. Euclidean metric is not applicable to such data sets because it cannot compute the distance between categorical variables. To address this problem, similarity functions have been designed to determine the relationship between given categorical values. similarity defines how closely related the objects are to one another. Often similarity could be thought of as opposite to distance where similar objects have high value, while dissimilar objects have low or zero value. In this paper we explored accuracy of various similarity functions using the Partitioning Around Medoids (PAM) clustering algorithm. We tested similarity functions on several data sets to determine their ability to correctly predict the class labels. We also examined the applicability of various similarity functions to different types of data sets.

关键词： Classification Clustering Partitional Clustering similarity functions Network Security Anomaly Detection

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