{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:56:17Z","timestamp":1775145377669,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models have been developed to tackle different issues in society, but there is still a gap in applications to applied physics. Here, different machine learning models are evaluated for precipitation prediction over South America. Currently, numerical weather prediction models are unable to precisely reproduce the precipitation patterns in South America due to many factors such as the lack of region-specific parametrizations and data availability. The results are compared to the general circulation atmospheric model currently used operationally in the National Institute for Space Research (INPE: Instituto Nacional de Pesquisas Espaciais), Brazil. Machine learning models are able to produce predictions with errors under 2 mm in most of the continent in comparison to satellite-observed precipitation patterns for different climate seasons, and also outperform INPE\u2019s model for some regions (e.g., reduction of errors from 8 to 2 mm in central South America in winter). Another advantage is the computational performance from machine learning models, running faster with much lower computer resources than models based on differential equations currently used in operational centers. Therefore, it is important to consider machine learning models for precipitation forecasts in operational centers as a way to improve forecast quality and to reduce computation costs.<\/jats:p>","DOI":"10.3390\/rs13132468","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T11:01:38Z","timestamp":1624532498000},"page":"2468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Machine Learning for Climate Precipitation Prediction Modeling over South America"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0769-9750","authenticated-orcid":false,"given":"Juliana Aparecida","family":"Anochi","sequence":"first","affiliation":[{"name":"National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9645-7528","authenticated-orcid":false,"given":"Vin\u00edcius Albuquerque","family":"de Almeida","sequence":"additional","affiliation":[{"name":"Laboratory for Applied Meteorology, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4968-5330","authenticated-orcid":false,"given":"Haroldo Fraga","family":"de Campos Velho","sequence":"additional","affiliation":[{"name":"National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1590\/S0102-77862010000200004","article-title":"Regimes de precipita\u00e7\u00e3o na Am\u00e9rica do Sul: Uma revis\u00e3o bibliogr\u00e1fica","volume":"25","author":"Reboita","year":"2010","journal-title":"Rev. Bras. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/485913","article-title":"Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model","volume":"2013","author":"Krasnopolsky","year":"2013","journal-title":"Adv. Artif. Neural Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zanna, L., and Bolton, T. (2020). Data-Driven Equation Discovery of Ocean Mesoscale Closures. Geophys. Res. Lett., 47.","DOI":"10.1029\/2020GL088376"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dijkstra, H., Petersik, P., Hern\u00e1ndez-Garc\u00eda, E., and L\u00f3pez, C. (2019). The Application of Machine Learning Techniques to Improve El Ni\u00f1o Prediction Skill. Front. Phys., 7.","DOI":"10.3389\/fphy.2019.00153"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Krasnopolsky, V., and Lin, Y. (2012). A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental US. Adv. Meteorol., 2012.","DOI":"10.1155\/2012\/649450"},{"key":"ref_6","unstructured":"(2021, February 20). NOAA Artificial Intelligence Strategy: Analytics for Next-Generation Earth Science, Available online: https:\/\/nrc.noaa.gov\/."},{"key":"ref_7","unstructured":"(2021, February 20). AI and Machine Learning at ECMWF. Available online: https:\/\/www.ecmwf.int\/en\/newsletter\/163\/news\/ai-and-machine-learning-ecmwf\/."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/16000870.2019.1696142","article-title":"Probabilistic thunderstorm forecasting by blending multiple ensembles","volume":"72","author":"Bouttier","year":"2020","journal-title":"Tellus Ser. A Dyn. Meteorol. Oceanogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7574","DOI":"10.1175\/JCLI-D-12-00009.1","article-title":"Limitations of Seasonal Predictability for Summer Climate over East Asia and the Northwestern Pacific","volume":"25","author":"Kosaka","year":"2012","journal-title":"J. Clim."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mariotti, A., Ruti, P.M., and Rixen, M. (2018). Progress in subseasonal to seasonal prediction through a joint weather and climate community effort. NPJ Clim. Atmos. Sci., 1.","DOI":"10.1038\/s41612-018-0014-z"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Weisheimer, A., and Palmer, T.N. (2014). On the reliability of seasonal climate forecasts. J. R. Soc. Interface, 11.","DOI":"10.1098\/rsif.2013.1162"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e2018JD02937","DOI":"10.1029\/2018JD029375","article-title":"Subseasonal to seasonal prediction of weather to climate with application to tropical cyclones","volume":"125","author":"Robertson","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1007\/s00024-019-02386-y","article-title":"Two Geoscience Applications by Optimal Neural Network Architecture","volume":"177","author":"Anochi","year":"2020","journal-title":"Pure Appl. Geophys."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Anochi, J., and Campos Velho, H. (2014, January 9\u201312). Optimization of feedforward neural network by Multiple Particle Collision Algorithm. Proceedings of the 2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI), Orlando, FL, USA.","DOI":"10.1109\/FOCI.2014.7007817"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Anochi, J. (2015). Previs\u00e3o Clim\u00e1tica De Precipita\u00e7\u00e3o Por Redes Neurais Autoconfiguradas. [Ph.D. Thesis, Instituto Nacional de Pesquisas Espaciais].","DOI":"10.5902\/2179460X19968"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.engappai.2006.06.005","article-title":"Optimizing feedforward artificial neural network architecture","volume":"20","author":"Benardos","year":"2007","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1007\/s00521-010-0504-3","article-title":"Metaheuristics for the feedforward artificial neural network (ANN) architecture optimization problem","volume":"20","author":"Carvalho","year":"2011","journal-title":"Neural Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Litzinger, S., Klos, A., and Schiffmann, W. (2019, January 17\u201319). Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm. Proceedings of the Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Munich, Germany.","DOI":"10.1007\/978-3-030-30484-3_32"},{"key":"ref_19","first-page":"160","article-title":"Aplicaci\u00f3n de los algoritmos evoluci\u00f3n diferencial y colisi\u00f3n de part\u00edculas al diagn\u00f3stico de fallos en sistemas industriales","volume":"33","author":"Santiago","year":"2012","journal-title":"Investig. Oper."},{"key":"ref_20","unstructured":"Sambatti, S., Anochi, J., Luz, E., Carvalho, A., Shiguemori, E., and Campos Velho, H. (2012, January 1\u20135). Automatic configuration for neural network applied to atmospheric temperature profile identification. Proceedings of the 3rd International Conference on International Conference on Engineering Optimization, Rio de Janeiro, Brazil."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez Torres, R., Campos Velho, H., and Chiwiacowsky, L. (2018). Rotation-based multi-particle collision algorithm with Hooke-Jeeves approach applied to the structural damage identification. Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering, Springer.","DOI":"10.1007\/978-3-319-96433-1_5"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez Torres, R., Luz, E., and Campos Velho, H. (2015). Multi-Particle Collision Algorithm for Solving an Inverse Radiative Problem. Integral Methods in Science and Engineering, Birkhauser.","DOI":"10.1007\/978-3-319-16727-5_26"},{"key":"ref_23","unstructured":"Penha Neto, G., Campos Velho, H., and Shiguemori, E. (July, January 29). UAV autonomous navigation by image processing with uncertainty trajectory estimation. Proceedings of the International Symposium on Uncertainty Quantification and Stochastic Modeling, Rouen, France."},{"key":"ref_24","unstructured":"Anochi, J., Hern\u00e1ndez Torres, R., and Campos Velho, H. (July, January 29). Climate precipitation prediction with uncertainty quantification by self-configuring neural network. Proceedings of the International Symposium on Uncertainty Quantification and Stochastic Modeling, Rouen, France."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cintra, R., Campos Velho, H., Anochi, J., and Cocke, S. (2016, January 13\u201316). Data assimilation by artificial neural networks for the global FSU atmospheric model: Surface pressure. Proceedings of the 2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015, Curitiba, Brazil.","DOI":"10.1109\/LA-CCI.2015.7435937"},{"key":"ref_26","first-page":"657","article-title":"A new stochastic optimization algorithm based on particle collisions","volume":"92","author":"Sacco","year":"2005","journal-title":"Trans. Am. Nucl. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.pnucene.2005.10.004","article-title":"Two stochastic optimization algorithms applied to nuclear reactor core design","volume":"48","author":"Sacco","year":"2006","journal-title":"Prog. Nucl. Energy"},{"key":"ref_28","unstructured":"Sacco, W., Filho, H., and Pereira, C. (2007). Cost-Based Optimization of a Nuclear Reactor Core Design: A Preliminary Model, Available online: https:\/\/inis.iaea.org\/search\/searchsinglerecord.aspx?recordsFor=SingleRecord&RN=39107793."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.pnucene.2007.09.004","article-title":"A Metropolis Algorithm applied to a Nuclear Power Plant Auxiliary Feedwater System surveillance tests policy optimization","volume":"50","author":"Sacco","year":"2008","journal-title":"Prog. Nucl. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_31","first-page":"3","article-title":"A new multi-particle collision algorithm for optimization in a high performance environment","volume":"1","author":"Luz","year":"2008","journal-title":"J. Comput. Interdiscip. Sci."},{"key":"ref_32","unstructured":"Luz, E., Becceneri, J., and Campos Velho, H. (2011, January 16\u201320). Multiple Particle Collision Algorithm applied to radiative transference and pollutant localization inverse problems. Proceedings of the IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, Anchorage, AK, USA."},{"key":"ref_33","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1175\/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2","article-title":"The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present)","volume":"4","author":"Adler","year":"2003","journal-title":"J. Hydrometeorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1175\/1520-0477(1996)077<0437:TNYRP>2.0.CO;2","article-title":"The NCEP\/NCAR 40-year reanalysis project","volume":"77","author":"Kalnay","year":"1996","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1175\/WAF-D-16-0062.1","article-title":"The Brazilian Global Atmospheric Model (BAM): Performance for tropical rainfall forecasting and sensitivity to convective scheme and horizontal resolution","volume":"31","author":"Figueroa","year":"2016","journal-title":"Weather. Forecast."},{"key":"ref_37","unstructured":"Kubota, P. (2012). Variability of Storage Energy in the Soil-Canopy System and Its Impact on the Definition of Precipitation Standard in South America. [Ph.D. Thesis, Instituto Nacional de Pesquisas Espaciais]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1175\/JAS3446.1","article-title":"A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description","volume":"62","author":"Morrison","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1175\/2008MWR2556.1","article-title":"Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes","volume":"137","author":"Morrison","year":"2009","journal-title":"Mon. Weather. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Iacono, M., Delamere, J., Mlawer, E., Shephard, M., Clough, S., and Collins, W. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2008JD009944"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.1175\/2008JCLI2557.1","article-title":"The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the community atmosphere model","volume":"22","author":"Park","year":"2009","journal-title":"J. Clim."},{"key":"ref_42","unstructured":"(2021, January 12). Progn\u00f3stico Clim\u00e1tico de Ver\u00e3o, Available online: https:\/\/portal.inmet.gov.br\/notasTecnicas#."},{"key":"ref_43","unstructured":"(2021, January 12). Progn\u00f3stico Clim\u00e1tico de Outono, Available online: https:\/\/portal.inmet.gov.br\/notasTecnicas#."},{"key":"ref_44","unstructured":"(2021, January 12). Progn\u00f3stico Clim\u00e1tico de Inverno, Available online: https:\/\/portal.inmet.gov.br\/notasTecnicas#."},{"key":"ref_45","unstructured":"(2021, January 12). Progn\u00f3stico Clim\u00e1tico de Primavera, Available online: https:\/\/portal.inmet.gov.br\/notasTecnicas#."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2468\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:23:08Z","timestamp":1760163788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":45,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132468"],"URL":"https:\/\/doi.org\/10.3390\/rs13132468","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,24]]}}}