Multivariate nonlinear analysis of climatological variables and its effect on local temperature.

Authors

DOI:

https://doi.org/10.53591/easi.v3i1.0532

Keywords:

Multiobjective, climatological variables, statistical prediction

Abstract

Atmospheric temperature describes the specific heat content of the air at particular places and times. In this sense, the presented work proposes a multivariable system that takes data sets of different climatological variables, with the aim of regulating the temperature level. The diversity in climatological variables significantly affects precipitation, humidity, wind speed and temperature. Thus, a study has been carried out on these variables in terms of nonlinear dynamics. The aim of the work is to obtain a better understanding of the dynamics of local climatological variables. On the other hand, due to the fact that the time series analyzed are small, the analysis becomes complex, at the moment of joining all the variables in conjunction, and processing them by means of multivariate statistical prediction methodologies. The results have shown the mean values in the different variables with which a temperature is maintained between  and , which shows possible work with multi-objective optimization of the obtained model.

Author Biographies

Gloria Vanegas Zabala, Instituto Superior Tecnológico Bolívar, Ambato 180102, Ecuador

Master Universitario en Robótica y Automatización (2013, Departamento de Ingeniería de Sistemas y Automática, Universidad Carlos III de Madrid: Madrid, Madrid, ES). Doctora en Automática, Robótica e Informática Industrial (2024, Departamento de Ingeniería de Sistemas y Automática. Universitat Politècnica de València: Valencia, Valencia, ES ). Ingeniera en Electrónica y Computación (2008, Facultad de Informática y Electrónica, Escuela Superior Politécnica de Chimborazo: Riobamba, Chimborazo, EC )

Franklin Samaniego, Universidad Nacional de Chimborazo, Riobamba 060108, Ecuador

PhD. in Automation, Robotics and Industrial Informatics, Universitat Politècnica de València, Valencia - Spain, Master's Degree in Computer Science and Technology, Universidad Carlos III - Madrid - Spain, Master's Degree in Robotics and Automation, Universidad Carlos III - Madrid - Spain. Engineer in Electronics and Computer Engineering, Technologist in Applied Informatics. UPV Researcher 2015-2019, University Professor.

Cecilia Urquizo-Alvarez, Instituto Superior Tecnológico Bolívar, Ambato 180102, Ecuador

Technologist in computer network assembly and maintenance

Instituto Superior Tecnológico Bolívar

References

Adwan, I., Milad, A., Memon, Z. A., Widyatmoko, I., Zanuri, N. A., Memon, N. A., & Yusoff, N. I. M. (2021). Asphalt pavement temperature prediction models: A review. In Applied Sciences (Switzerland) (Vol. 11, Issue 9). MDPI AG. https://doi.org/10.3390/app11093794

Arnell, N. W., Brown, S., Gosling, S. N., Gottschalk, P., Hinkel, J., Huntingford, C., Lloyd-Hughes, B., Lowe, J. A., Nicholls, R. J., Osborn, T. J., Osborne, T. M., Rose, G. A., Smith, P., Wheeler, T. R., & Zelazowski, P. (2016). The impacts of climate change across the globe: A multi-sectoral assessment. Climatic Change, 134(3), 457–474. https://doi.org/10.1007/s10584-014-1281-2

Arnell, N. W., Lowe, J. A., Challinor, A. J., & Osborn, T. J. (2019). Global and regional impacts of climate change at different levels of global temperature increase. Climatic Change, 155(3), 377–391. https://doi.org/10.1007/s10584-019-02464-z

Barange, Bahri, Beveridge, MCM, Cochrane, KL, Funge-Smith, Poulain, & eds. (2018). Impacts of climate change on fisheries and aquaculture. In United Nations’ Food and Agriculture Organization (Vol. 12, Issue 4, pp. 628–635).

Bracken, C., Holman, K. D., Rajagopalan, B., & Moradkhani, H. (2018). A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis. Water Resources Research, 54(1), 243–255. https://doi.org/10.1002/2017WR020403

Busuioc, A., Giorgi, F., Bi, X., & Ionita, M. (2006). Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania. Theoretical and Applied Climatology, 86(1–4), 101–123. https://doi.org/10.1007/s00704-005-0210-8

Favre, A. C., Adlouni, S. El, Perreault, L., Thiémonge, N., & Bobée, B. (2004). Multivariate hydrological frequency analysis using copulas. Water Resources Research, 40(1). https://doi.org/10.1029/2003WR002456

Goyal, A. (2023). Exploring the Impact of Climate Change: A Comprehensive Dataset on Temperature. https://www.kaggle.com/datasets/goyaladi/climate-insights-dataset/data

Jin, Y.-H., Kawamura, A., Jinno, K., & Berndtsson, R. (2005). Nonlinear multivariable analysis of SOI and local precipitation and temperature. Nonlinear Processes in Geophysics, 12, 67–74. https://doi.org/https://doi.org/10.5194/npg-12-67-2005

Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., & others. (2018). Global Warming of 1.5◦ C: Special Report. Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/sr15/

Miao, Y., Zhang, C., Zhang, X., & Zhang, L. (2023). A Multivariable Convolutional Neural Network for Forecasting Synoptic-Scale Sea Surface Temperature Anomalies in the South China Sea. Weather and Forecasting, 38(6), 849–863. https://doi.org/10.1175/WAF-D-22-0094.1

Olson, B., & Kleiber, W. (2017). Approximate Bayesian computation methods for daily spatiotemporal precipitation occurrence simulation. Water Resources Research, 53(4), 3352–3372. https://doi.org/10.1002/2016WR019741

O’Neill, B. C., M Done, J., Gettelman, A., Lawrence, P., Lehner, F., Lamarque, J.-F., Lin, L., J Monaghan, A., Oleson, K., Ren, X., & others. (2018). The benefits of reduced anthropogenic climate change (BRACE): a synthesis. Climatic Change, 146, 287–301. https://doi.org/https://doi.org/10.1007/s10584-017-2009-x

Renard, B., Thyer, M., McInerney, D., Kavetski, D., Leonard, M., & Westra, S. (2022). A Hidden Climate Indices Modeling Framework for Multivariable Space-Time Data. Water Resources Research, 58(1). https://doi.org/10.1029/2021WR030007

Sesana, E., Gagnon, A. S., Ciantelli, C., Cassar, J. A., & Hughes, J. J. (2021). Climate change impacts on cultural heritage: A literature review. In Wiley Interdisciplinary Reviews: Climate Change (Vol. 12, Issue 4). John Wiley and Sons Inc. https://doi.org/10.1002/wcc.710

Solazzo, E., Bianconi, R., Hogrefe, C., Curci, G., Tuccella, P., Alyuz, U., Balzarini, A., Baro, R., Bellasio, R., Bieser, J., Brandt, J., Christensen, J. H., Colette, A., Francis, X., Fraser, A., Garcia Vivanco, M., Jiménez-Guerrero, P., Im, U., Manders, A., … Galmarini, S. (2017). Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: Multivariable temporal and spatial breakdown. Atmospheric Chemistry and Physics, 17(4), 3001–3054. https://doi.org/10.5194/acp-17-3001-2017

Tukimat, N. N. A., Harun, S., & Tadza, M. Y. M. (2019). The potential of canonical correlation analysis in multivariable screening of climate model. IOP Conference Series: Earth and Environmental Science, 365(1). https://doi.org/10.1088/1755-1315/365/1/012025

Zscheischler, J., Westra, S., Van Den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., & others. (2018). Future climate risk from compound events. Nature Climate Change, 8(6), 469–477. https://doi.org/https://doi.org/10.1038/s41558-018-0156-3

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Published

2024-07-31

How to Cite

Vanegas Zabala, G., Samaniego, F., & Urquizo-Alvarez, C. E. (2024). Multivariate nonlinear analysis of climatological variables and its effect on local temperature. EASI: Engineering and Applied Sciences in Industry, 3(1), 1–7. https://doi.org/10.53591/easi.v3i1.0532

Issue

Vol. 3 No. 1 (2024): Maintenance and sustainability enhancing industrial efficiency (January-June Issue)

Section

Research articles

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Copyright (c) 2024 Gloria Vanegas Zabala, Franklin Samaniego, Cecilia Urquizo-Alvarez

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