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CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network1. Introduction Cloud classification is very important for weather forecasts since it directly determines weather activities such as precipitation, snow, hail, and lightning. According to the shape, structure, characteristics, and height, clouds can be divided into 3 groups (high, middle, and low clouds), 10 general types, and 29 categories. They are characterized by their many kinds, rapid changes, similarity, and easy integration with the sky background. Therefore, it is difficult to classify
1. Introduction Cloud classification is very important for weather forecasts since it directly determines weather activities such as precipitation, snow, hail, and lightning. According to the shape, structure, characteristics, and height, clouds can be divided into 3 groups (high, middle, and low clouds), 10 general types, and 29 categories. They are characterized by their many kinds, rapid changes, similarity, and easy integration with the sky background. Therefore, it is difficult to classify
) is not a trivial task. It involves aspects of human perception, atmospheric sciences, mathematics, computer artificial intelligence, etc., in the design of an “artifact” called an intelligent agent (IA; Russell and Norvig 2003 , chapter 2). The existing image analysis artifacts used as SOS do not match the qualitative performance of SO, and to find better solutions, improvements to the classification techniques must be made. SO observations are normalized by the World Meteorological
) is not a trivial task. It involves aspects of human perception, atmospheric sciences, mathematics, computer artificial intelligence, etc., in the design of an “artifact” called an intelligent agent (IA; Russell and Norvig 2003 , chapter 2). The existing image analysis artifacts used as SOS do not match the qualitative performance of SO, and to find better solutions, improvements to the classification techniques must be made. SO observations are normalized by the World Meteorological
1. Introduction Classification of tropical precipitating systems is highly essential because the cloud dynamical processes and their impact on the atmospheric circulation are distinctly different for different precipitating systems. For instance, the latent heating profile of an environment associated with deep convective rain is positive throughout the troposphere, while the stratiform profile is dominated by heating above the freezing level (due to condensation and vapor deposition) and
1. Introduction Classification of tropical precipitating systems is highly essential because the cloud dynamical processes and their impact on the atmospheric circulation are distinctly different for different precipitating systems. For instance, the latent heating profile of an environment associated with deep convective rain is positive throughout the troposphere, while the stratiform profile is dominated by heating above the freezing level (due to condensation and vapor deposition) and
1. Introduction There are many different climate types across the world and they have distinctive influences on several fields such as agriculture and energy usage of most residential and commercial buildings ( Briggs et al. 2003a ). Given sufficient climate data, climate classification will provide valuable guidelines to agriculture and building design. There are many climate classification methods based on different meteorological variables and indices. A proper and reasonable classification
1. Introduction There are many different climate types across the world and they have distinctive influences on several fields such as agriculture and energy usage of most residential and commercial buildings ( Briggs et al. 2003a ). Given sufficient climate data, climate classification will provide valuable guidelines to agriculture and building design. There are many climate classification methods based on different meteorological variables and indices. A proper and reasonable classification
methods when applied to downstream image classification tasks. Several past studies have applied self- and unsupervised methods to satellite cloud imagery: Kurihana et al. (2022a , b) used a convolutional autoencoder, Yuan (2019) used an information maximizing generative adversarial network, and Denby (2020) used a contrastive method that involves comparing vector embeddings of image triplets ( Jean et al. 2019 ). Recently, new self-supervised deep learning schemes have been developed that have
methods when applied to downstream image classification tasks. Several past studies have applied self- and unsupervised methods to satellite cloud imagery: Kurihana et al. (2022a , b) used a convolutional autoencoder, Yuan (2019) used an information maximizing generative adversarial network, and Denby (2020) used a contrastive method that involves comparing vector embeddings of image triplets ( Jean et al. 2019 ). Recently, new self-supervised deep learning schemes have been developed that have
; Behrangi et al. 2009b ; Kurino 1997 ; among others). Using the self-organizing feature map (SOFM; Kohonen 1984 ) classification method and multispectral data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat second generation (MSG) satellite, Behrangi et al. (2009a) developed a multispectral precipitation estimation algorithm (PERSIANN-MSA). The algorithm classifies input features into a predetermined number of clusters, calculates mean rain rate for each
; Behrangi et al. 2009b ; Kurino 1997 ; among others). Using the self-organizing feature map (SOFM; Kohonen 1984 ) classification method and multispectral data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat second generation (MSG) satellite, Behrangi et al. (2009a) developed a multispectral precipitation estimation algorithm (PERSIANN-MSA). The algorithm classifies input features into a predetermined number of clusters, calculates mean rain rate for each
Supervised Classification of Sound Speed Profiles via Dictionary Learningthe possibility of generating optimal sparse representations, LDs have also been applied to clustering ( Sprechmann and Sapiro 2010 ) and classification tasks ( Tang et al. 2019 ; Suo et al. 2014 ). In this approach, specific dictionaries are trained to retain most of the meaningful information of each class. Then, testing data are classified by selecting the dictionary yielding the sparse representation that generates the lowest error ( Ramirez et al. 2010 ; Zhao et al. 2018 ). Here
the possibility of generating optimal sparse representations, LDs have also been applied to clustering ( Sprechmann and Sapiro 2010 ) and classification tasks ( Tang et al. 2019 ; Suo et al. 2014 ). In this approach, specific dictionaries are trained to retain most of the meaningful information of each class. Then, testing data are classified by selecting the dictionary yielding the sparse representation that generates the lowest error ( Ramirez et al. 2010 ; Zhao et al. 2018 ). Here
; Prandle 2011 ; MacCready and Geyer 2010 ; Geyer and MacCready 2014 ). The main result of HR66 is their estuarine classification diagram. While other classification schemes have appeared more recently (e.g., Scott 1993 ; Guha and Lawrence 2013 ; Geyer and MacCready 2014 ; Dijkstra and Schuttelaars 2021 ), the Hansen and Rattray classification diagram remains at the basis of estuarine theory. However, we identified that, while their model development is correct, there are several inconsistencies
; Prandle 2011 ; MacCready and Geyer 2010 ; Geyer and MacCready 2014 ). The main result of HR66 is their estuarine classification diagram. While other classification schemes have appeared more recently (e.g., Scott 1993 ; Guha and Lawrence 2013 ; Geyer and MacCready 2014 ; Dijkstra and Schuttelaars 2021 ), the Hansen and Rattray classification diagram remains at the basis of estuarine theory. However, we identified that, while their model development is correct, there are several inconsistencies
Deep Learning for Typhoon Intensity Classification Using Satellite Cloud Imagesmethods to automatically classify intensity-based typhoons. Researchers have already applied machine learning (ML) algorithms in the field of marine meteorology, such as support vector machine (SVM), extreme learning machine (ELM), and back propagation (BP) to analyze meteorological cloud images ( L. Li et al. 2015 ; P. Li et al. 2015 ; Xia et al. 2015 ). Traditional ML algorithms usually achieve unsatisfactory classification results from sophisticated intensity-associated typhoon information
methods to automatically classify intensity-based typhoons. Researchers have already applied machine learning (ML) algorithms in the field of marine meteorology, such as support vector machine (SVM), extreme learning machine (ELM), and back propagation (BP) to analyze meteorological cloud images ( L. Li et al. 2015 ; P. Li et al. 2015 ; Xia et al. 2015 ). Traditional ML algorithms usually achieve unsatisfactory classification results from sophisticated intensity-associated typhoon information
1. Introduction Global climate classification schemes aim to identify distinct climate types and map their geographical extents. By discretizing a multitude of local climates (LCs) into a manageable number of climate types (CTs; a list of all acronyms is given in Table 1 ), classification simplifies the spatial variability of climates into a form that is more meaningful and easier to analyze. Thus, climate classification provides intuitive and valuable insight into the relationships between
1. Introduction Global climate classification schemes aim to identify distinct climate types and map their geographical extents. By discretizing a multitude of local climates (LCs) into a manageable number of climate types (CTs; a list of all acronyms is given in Table 1 ), classification simplifies the spatial variability of climates into a form that is more meaningful and easier to analyze. Thus, climate classification provides intuitive and valuable insight into the relationships between
