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Min Wang, Shudao Zhou, Zhong Yang, and Zhanhua Liu

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

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Sylvio Luiz Mantelli Neto, Aldo von Wangenheim, Enio Bueno Pereira, and Eros Comunello

) 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

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T. Narayana Rao, N. V. P. Kirankumar, B. Radhakrishna, D. Narayana Rao, and K. Nakamura

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

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Ali Behrangi, Koulin Hsu, Bisher Imam, and Soroosh Sorooshian

; 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

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Jiarong Shi and Liu Yang

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

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Zongsheng Zheng, Chenyu Hu, Zhaorong Liu, Jianbo Hao, Qian Hou, and Xiaoyi Jiang

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

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Pawel Netzel and Tomasz Stepinski

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

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Yoeri M. Dijkstra and Henk M. Schuttelaars

; 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

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Hugo Carrão, Andrew Singleton, Gustavo Naumann, Paulo Barbosa, and Jürgen V. Vogt

monitor groundwater levels and surface water supplies ( Mishra et al. 2009 ; Mishra and Singh 2011 ). The magnitude of negative SPI values corresponds to percentiles p ( x ) of a probability distribution that are frequently used as threshold levels (triggers) to classify meteorological drought intensity (e.g., McKee et al. 1993 ; Agnew 2000 ; Svoboda et al. 2002 ; Steinemann 2003 ). Several classification systems of meteorological drought intensity based on fixed threshold levels of the SPI have

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Stefanie M. Herrmann and Karen I. Mohr

regimes requires a robust and spatially accurate representation of their seasonal climatology to track changes not only in total annual rainfall but also in the seasonal character of rainfall regimes, which is of crucial importance to rain-fed agriculture and pasture development in the semiarid and subhumid zones. There are a number of local- and regional-scale seasonality classifications (e.g., Keen and Tyson 1973 ; Garbutt et al. 1981 ; Foeken 1994 ; Nicholson 1996 ; Phillips and McIntyre 2000

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