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A robust method for diagnosing climate extremes in Mosul City.

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Bibliographic Details
Title: A robust method for diagnosing climate extremes in Mosul City.
Authors: Al-Hayali, Marwan Moysar1 (AUTHOR) marwan.22csp62@student.uomosul.edu.iq, AL-Talib, Bashar A.1 (AUTHOR) bashar.altalib@uomosul.edu.iq
Source: AIP Conference Proceedings. 2025, Vol. 3395 Issue 1, p1-12. 12p.
Subject Terms: *CLIMATE extremes, *SEASONAL temperature variations, *DIMENSIONAL reduction algorithms, *RANDOM variables, *CLIMATE change, *CITIES & towns, *METEOROLOGICAL databases
Geographic Terms: MOSUL (Iraq)
Abstract: This paper aims to robustly analyze climate data for the city of Mosul during the seasons of the year from 2013 to 2022, focusing on the minimum temperature variable. Modern methods that have not been used previously are facilitated to detect climate fluctuations, adapt them to the study data, and explore the general and extreme climates that have not been discussed earlier. The purpose is determining the creep of climatic seasons such as summer and winter, being the most extreme seasons of spring and autumn, and the impact of noise variables on climatic fluctuations. Variable clustering techniques have been used to discover the latent components according to the directional group model. The "K+1" noise cluster strategy has been used to identify highly noisy variables. A wide format of data is proposed: P > N, when the number of variables is greater than the number of observations. Observations represent years (10), and variables represent days (365) per year for all months (12). This arrangement has proven to be suitable for the variable clustering technique for high-dimensional data. The results show that there are strong correlations in minimum temperatures in transitional seasons as well as in periods of climatic. This indicates the effect of the seasons on the minimum temperatures in the city of Mosul during the period from 2013 to 2022. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
Description
Abstract:This paper aims to robustly analyze climate data for the city of Mosul during the seasons of the year from 2013 to 2022, focusing on the minimum temperature variable. Modern methods that have not been used previously are facilitated to detect climate fluctuations, adapt them to the study data, and explore the general and extreme climates that have not been discussed earlier. The purpose is determining the creep of climatic seasons such as summer and winter, being the most extreme seasons of spring and autumn, and the impact of noise variables on climatic fluctuations. Variable clustering techniques have been used to discover the latent components according to the directional group model. The "K+1" noise cluster strategy has been used to identify highly noisy variables. A wide format of data is proposed: P > N, when the number of variables is greater than the number of observations. Observations represent years (10), and variables represent days (365) per year for all months (12). This arrangement has proven to be suitable for the variable clustering technique for high-dimensional data. The results show that there are strong correlations in minimum temperatures in transitional seasons as well as in periods of climatic. This indicates the effect of the seasons on the minimum temperatures in the city of Mosul during the period from 2013 to 2022. [ABSTRACT FROM AUTHOR]
ISSN:0094243X
DOI:10.1063/5.0301798