Exploratory Analysis of Spatiotemporal Patterns in Meteorological Data
Abstract
Meteorological studies of atmospheric variables impact different sectors of society, such as agriculture and health. Meteorological analysis is fundamental for atmospheric sciences, meteorological services, and international cooperation, while data quality and control are vital for environmental analysis and monitoring. This paper presents an open tool aimed at exploratory analysis of data from the Brazilian National Institute of Meteorology. A case study was carried out, involving the exploratory analysis of data from a measuring tower in the city of São Paulo. It was possible to develop a tool capable of working with data from several towers spread across Brazil, providing simplified plot views of the measured variables.
Keywords:
Data visualization, Metorological data, Time series
References
Akkem, Y., Biswas, S. K., and Varanasi, A. (2023). Smart farming using artificial intelligence: A review. Engineering Applications of Artificial Intelligence, 120:105899. DOI: 10.1016/j.engappai.2023.105899.
Anderson, K. (2005). Predicting the weather: Victorians and the science of meteorology. University of Chicago Press.
Buono, P., Aris, A., Plaisant, C., Khella, A., and Shneiderman, B. (2005). Interactive pattern search in time series. In Erbacher, R. F., Roberts, J. C., Grohn, M. T., and Borner, K., editors, Visualization and Data Analysis 2005, volume 5669, pages 175 – 186. International Society for Optics and Photonics, SPIE. DOI: 10.1117/12.587537.
Ceravolo, R., Coletta, G., Miraglia, G., and Palma, F. (2021). Statistical correlation between environmental time series and data from long-term monitoring of buildings. Mechanical Systems and Signal Processing, 152:107460. DOI: 10.1016/j.ymssp.2020.107460.
Choi, K., Yi, J., Park, C., and Yoon, S. (2021). Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines. IEEE Access, 9:120043–120065. DOI: 10.1109/ACCESS.2021.3107975.
Daipha, P. (2019). Masters of uncertainty: Weather forecasters and the quest for ground truth. University of Chicago Press.
Defrise, P. (1958). Petterssen, s.-introduction to meteorology. Ciel et Terre, Vol. 74, p. 472, 74:472.
Harrison, G. (2014). Meteorological measurements and instrumentation. John Wiley & Sons.
Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
Ravindra, K., Rattan, P., Mor, S., and Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. Environment International, 132. DOI: 10.1016/j.envint.2019.104987.
Tredennick, A. T., Hooker, G., Ellner, S. P., and Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology, 102(6):e03336. DOI: 10.1002/ecy.3336.
Vuckovic, M. and Schmidt, J. (2020). Visual analytics approach to comprehensive meteorological time-series analysis. Data, 5(4). DOI: 10.3390/data5040094.
Zhai, Z., Martínez, J. F., Beltran, V., and Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170:105256. DOI: 10.1016/j.compag.2020.105256.
Anderson, K. (2005). Predicting the weather: Victorians and the science of meteorology. University of Chicago Press.
Buono, P., Aris, A., Plaisant, C., Khella, A., and Shneiderman, B. (2005). Interactive pattern search in time series. In Erbacher, R. F., Roberts, J. C., Grohn, M. T., and Borner, K., editors, Visualization and Data Analysis 2005, volume 5669, pages 175 – 186. International Society for Optics and Photonics, SPIE. DOI: 10.1117/12.587537.
Ceravolo, R., Coletta, G., Miraglia, G., and Palma, F. (2021). Statistical correlation between environmental time series and data from long-term monitoring of buildings. Mechanical Systems and Signal Processing, 152:107460. DOI: 10.1016/j.ymssp.2020.107460.
Choi, K., Yi, J., Park, C., and Yoon, S. (2021). Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines. IEEE Access, 9:120043–120065. DOI: 10.1109/ACCESS.2021.3107975.
Daipha, P. (2019). Masters of uncertainty: Weather forecasters and the quest for ground truth. University of Chicago Press.
Defrise, P. (1958). Petterssen, s.-introduction to meteorology. Ciel et Terre, Vol. 74, p. 472, 74:472.
Harrison, G. (2014). Meteorological measurements and instrumentation. John Wiley & Sons.
Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
Ravindra, K., Rattan, P., Mor, S., and Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. Environment International, 132. DOI: 10.1016/j.envint.2019.104987.
Tredennick, A. T., Hooker, G., Ellner, S. P., and Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology, 102(6):e03336. DOI: 10.1002/ecy.3336.
Vuckovic, M. and Schmidt, J. (2020). Visual analytics approach to comprehensive meteorological time-series analysis. Data, 5(4). DOI: 10.3390/data5040094.
Zhai, Z., Martínez, J. F., Beltran, V., and Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170:105256. DOI: 10.1016/j.compag.2020.105256.
Published
2025-09-29
How to Cite
QUILLE, Rosa Virginia Encinas; ALMEIDA, Felipe Valencia de; MONTEIRO, Danielle; CORRÊA, Pedro Luiz Pizzigatti.
Exploratory Analysis of Spatiotemporal Patterns in Meteorological Data. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 89-96.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2025.248219.
