Abstract
Air temperature is a meteorological variable that influences the climate in the world. The availability of air temperature data is of concern in Brazil, particularly in the State of Mato Grosso do Sul (MS), since most weather stations are concentrated on the country's coast. Thus, the study aimed to develop models to estimate the average monthly and annual air temperatures (maximum and minimum) for the site of the State of MS. The linear multiple regression technique is adopted in this study. Temperature data from 1978 to 2018 were used, corresponding to 78 meteorological stations on the website of the State of MS. Geographical coordinates (latitude, longitude and altitude) were used as predictor variables for the models, and monthly and annual extreme temperatures (Tmax, Tmin) models were fitted. The regression models used in the study were statistically tested (α ≤ 0.01). The models of mean annual Tmin and mean annual Tmax obtained adjusted determination coefficients (R2adj) of 81.2% and 74.9%, respectively. The monthly average temperature models showed adjusted coefficients of determination between 0.69 and 0.90 for Tmax and from 0.71 to 0.86 for Tmin. Another method used to validate our results, the digital elevation model for the State of MS, obtained through a Shuttle Radar Topography Mission radar image. The obtained results fitted well with these of the annual and monthly models for extreme temperatures. The temperature models used in the study are duly suitable to predict air temperature in all sites in the State of MS.
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Data availability
The climate database is in the public domain and is available at: https://www.cemtec.ms.gov.br/ and http://portal.inemet.gov.br.
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The authors would like to thank their Universities for their support. Authors are grateful to INMET and DNCOS for making air temperature data available.
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de Souza, A., dos Santos, C.M., Ihaddadene, R. et al. Analysis of extreme monthly and annual air temperatures variability using regression model in Mato Grosso do Sul, Brazil. Model. Earth Syst. Environ. 8, 647–663 (2022). https://doi.org/10.1007/s40808-021-01096-6
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DOI: https://doi.org/10.1007/s40808-021-01096-6