Inteligencia artificial para modelar y comprender fenómenos meteorológicos y climáticos en la Provincia De Bolívar, Ecuador

Deysi Guanga; Henry Vallejo; Galuth García; Danilo Barrerno

Inteligencia artificial para modelar y comprender fenómenos meteorológicos y climáticos en la Provincia De Bolívar, Ecuador

Autores/as

Deysi Guanga Universidad Estatal de Bolívar
Henry Vallejo Universidad Estatal de Bolívar
Galuth García Universidad Estatal de Bolívar
Danilo Barrerno Universidad Estatal de Bolívar

Palabras clave:

Aprendizaje Automático, Cambio Climático, Inteligencia Artificial, Modelado Climático

Resumen

En los últimos años, la inteligencia artificial (IA) ha tenido un profundo impacto en varios campos, incluidas las ciencias del sistema terrestre, al mejorar el pronóstico del tiempo, la emulación de modelos, la estimación de parámetros y la predicción de eventos climáticos. Este artículo revisa cómo se utiliza la IA para analizar eventos climáticos, como inundaciones y sequías, destacando la importancia de crear modelos de IA precisos, transparentes y confiables en la provincia de Bolívar, Ecuador. Se revisan las técnicas de aprendizaje automático (ML) y aprendizaje profundo (DL) para la detección de eventos como inundaciones y sequías, la predicción de su impacto y la atribución de sus causas. A través de la contextualización de datos climáticos históricos de la región, se demuestra el potencial de la IA para mejorar los sistemas de alerta temprana y la toma de decisiones. Se discuten también los desafíos asociados a la calidad de los datos, la interpretabilidad de los modelos y su integración operativa. Finalmente, se presentan ejemplos de visualización de datos de temperatura y humedad para la provincia de Bolívar como resultado del proyecto de investigación desarrollado por la UEB, subrayando la necesidad de soluciones de IA que sean precisas, confiables y adaptadas a las necesidades locales para fortalecer la resiliencia ante los crecientes riesgos climáticos.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Aligo, E. A., Gallus, W. A., & Segal, M. (2009). On the impact of WRF model vertical grid resolution on midwest summer rainfall forecasts. Weather and Forecasting, 24(2), 575–594. https://doi.org/10.1175/2008WAF2007101.1

Boukabara, S. A., Krasnopolsky, V., Stewart, J. Q., Maddy, E. S., Shahroudi, N., & Hoffman, R. N. (2019). Leveraging modern artificial intelligence for remote sensing and NWP benefits and challenges. Bulletin of the American Meteorological Society, 100(12), ES473–ES491. https://doi.org/10.1175/BAMS-D-18-0324.1

Brenowitz, N. D., & Bretherton, C. S. (2018). Prognostic Validation of a Neural Network Unified Physics Parameterization. Geophysical Research Letters, 45(12), 6289–6298. https://doi.org/10.1029/2018GL078510

Capecchi, V., Antonini, A., Benedetti, R., Fibbi, L., Melani, S., Rovai, L., Ricchi, A., & Cerrai, D. (2021). Assimilating x-and s-band radar data for a heavy precipitation event in italy. Water, 13(13). https://doi.org/10.3390/W13131727

Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P. (2020). Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning. Journal of Advances in Modeling Earth Systems, 12(2). https://doi.org/10.1029/2019MS001958

Chen, K., Wang, P., Yang, X., Zhang, N., & Wang, D. (2020). A model output deep learning method for grid temperature forecasts in Tianjin Area. Applied Sciences, 10(17). https://doi.org/10.3390/APP10175808

Clark, P., Roberts, N., Lean, H., Ballard, S. P., & Charlton-Perez, C. (2016). Convection-permitting models: A step-change in rainfall forecasting. Meteorological Applications, 23(2), 165–181. https://doi.org/10.1002/MET.1538

Cohen, J., Coumou, D., Hwang, J., Mackey, L., Orenstein, P., Totz, S., & Tziperman, E. (2019). S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts. Wiley Interdisciplinary Reviews: Climate Change, 10(2). https://doi.org/10.1002/WCC.567

Cullen, M. J. P., Davies, T., Mawson, M. H., James, J. A., Coulter, S. C., & Malcolm, A. (1997). An overview of numerical methods for the next generation u.k. nwp and climate model. Atmosphere - Ocean, 35, 425–444. https://doi.org/10.1080/07055900.1997.9687359

Dewitte, S., Cornelis, J. P., Müller, R., & Munteanu, A. (2021). Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction. Remote Sensing, 13(16), 3209. https://doi.org/10.3390/RS13163209

Dueben, P. D., & Bauer, P. (2018). Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development, 11(10), 3999–4009. https://doi.org/10.5194/GMD-11-3999-2018

Gonzalez-Calabuig, M., Cortes-Andres, J., Williams, T. K. E., Zhang, M., Pellicer-Valero, O. J., Fernandez-Torres, M. A., & Camps-Valls, G. (2024). The AIDE Toolbox: Artificial intelligence for disentangling extreme events. IEEE Geoscience and Remote Sensing Magazine, 12(2), 113–118. https://doi.org/10.1109/MGRS.2024.3382544

Hanna, E., Cropper, T. E., Hall, R. J., & Cappelen, J. (2016). Greenland Blocking Index 1851–2015: a regional climate change signal. International Journal of Climatology, 36(15), 4847–4861. https://doi.org/10.1002/JOC.4673

He, S., Li, X., DelSole, T., Ravikumar, P., & Banerjee, A. (2021). Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. 35th AAAI Conference on Artificial Intelligence, 1, 169–177. https://doi.org/10.1609/aaai.v35i1.16090

Höhlein, K., Kern, M., Hewson, T., & Westermann, R. (2020). A comparative study of convolutional neural network models for wind field downscaling. Meteorological Applications, 27(6). https://doi.org/10.1002/MET.1961

Huang, X. Y., Xiao, Q., Barker, D. M., Zhang, X., Michalakes, J., Huang, W., ... & Kuo, Y. H. (2009). Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Monthly Weather Review, 137(1), 299–314. https://doi.org/10.1175/2008MWR2577.1

Kim, K. S., Lee, J. B., Roh, M. Il, Han, K. M., & Lee, G. H. (2020). Prediction of ocean weather based on denoising autoencoder and convolutional LSTM. Journal of Marine Science and Engineering, 8(10), 1–24. https://doi.org/10.3390/JMSE8100805

Kirkwood, C., Economou, T., Odbert, H., & Pugeault, N. (2021). A framework for probabilistic weather forecast post-processing across models and lead times using machine learning. Philosophical Transactions of the Royal Society A, 379(2194). https://doi.org/10.1098/RSTA.2020.0099

Kurth, T., Subramanian, S., Harrington, P., Pathak, J., Mardani, M., Hall, D., ... & Anandkumar, A. (2023). FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. PASC '23. https://doi.org/10.1145/3592979.3593412

Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., ... & Battaglia, P. (2023). Learning skillful medium-range global weather forecasting. Science, 382(6677), 1416–1422. https://doi.org/10.1126/SCIENCE.ADI2336

Li, L. (2019). Geographically weighted machine learning and downscaling for high-resolution spatiotemporal estimations of wind speed. Remote Sensing, 11(11). https://doi.org/10.3390/RS11111378

Maiello, I., Gentile, S., Ferretti, R., Baldini, L., Roberto, N., Picciotti, E., ... & Marzano, F. (2017). Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event. Hydrology and Earth System Sciences, 21(11), 5459–5476. https://doi.org/10.5194/HESS-21-5459-2017

Meehl, G. A., Goddard, L., Boer, G., Burgman, R., Branstator, G., Cassou, C., ... & Yeager, S. (2014). Decadal climate prediction an update from the trenches. Bulletin of the American Meteorological Society, 95(2), 243–267. https://doi.org/10.1175/BAMS-D-12-00241.1

Moraux, A., Dewitte, S., Cornelis, B., & Munteanu, A. (2019). Deep learning for precipitation estimation from satellite and rain gauges measurements. Remote Sensing, 11(21). https://doi.org/10.3390/RS11212463

O’Gorman, P. A., & Dwyer, J. G. (2018). Using Machine Learning to Parameterize Moist Convection. Journal of Advances in Modeling Earth Systems, 10(10), 2548–2563. https://doi.org/10.1029/2018MS001351

Palmer, T., & Stevens, B. (2019). The scientific challenge of understanding and estimating climate change. PNAS, 116(49), 34390–34395. https://doi.org/10.1073/PNAS.1906691116

Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020). WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting. Journal of Advances in Modeling Earth Systems, 12(11). https://doi.org/10.1029/2020MS002203

Ricchi, A., Bonaldo, D., Cioni, G., Carniel, S., & Miglietta, M. M. (2021). Simulation of a flash-flood event over the Adriatic Sea with a high-resolution coupled system. Scientific Reports, 11(1). https://doi.org/10.1038/S41598-021-88476-1

Rosenfeld, D., & Lensky, I. M. (1998). Satellite-Based Insights into Precipitation Formation Processes. Bulletin of the American Meteorological Society, 79(11), 2457–2476. https://doi.org/10.1175/1520-0477(1998)079<2457:SBIIPF>2.0.CO;2

Scher, S. (2018). Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning. Geophysical Research Letters, 45(22), 12,616-12,622. https://doi.org/10.1029/2018GL080704

Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., ... & Stadtler, S. (2021). Can deep learning beat numerical weather prediction? Philosophical Transactions of the Royal Society A, 379(2194). https://doi.org/10.1098/RSTA.2020.0097

Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040–53065. https://doi.org/10.1109/ACCESS.2019.2912200

Urbich, I., Bendix, J., & Müller, R. (2020). Development of a seamless forecast for solar radiation using anaklim++. Remote Sensing, 12(21), 1–19. https://doi.org/10.3390/RS12213672

Vicente, S. M., & Li, S. (n.d.). Weather and Climate Extreme Events in a Changing Climate. coordinating Lead Authors. https://doi.org/10.1017/9781009157896.013

Villares, M., Moratorio, S., Abrigo, M., & Tomasoni, M. (n.d.). Informe IA y Cambio Climático Oportunidades y Desafíos. www.sustentabilidadsf.com.ar

Weyn, J. A., Durran, D. R., & Caruana, R. (2020). Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere. Journal of Advances in Modeling Earth Systems, 12(9). https://doi.org/10.1029/2020MS002109

Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D., Zhang, T., ... & Popescu, I. (2018). Crowdsourcing Methods for Data Collection in Geophysics. Reviews of Geophysics, 56(4), 698–740. https://doi.org/10.1029/2018RG000616

Zheng, G., Li, X., Zhang, R. H., & Liu, B. (2020). Purely satellite data-driven deep learning forecast of complicated tropical instability waves. Science Advances, 6(29). https://doi.org/10.1126/SCIADV.ABA1482