Carlos A. Leitão Pires
Profile
Research Group: RG1 – Climate variability processes and extremes
Position at IDL: Researcher
Job: Full Professor
Email: clpires@fc.ul.pt
Telephone Number: (351) 21-750-0886
Room: 8.3.39
Academic Degree and field of specialization:
PhD in Meteorology, 1996, University of Paris VI – Pierre et Marie Curie, France, LMD (Mention: Très Honorable avec Félicitations du jury) University of Paris VI – Pierre et Marie Curie, France, LMD
CV: https://www.cienciavitae.pt/pt/D815-5148-0B3C
Fields of specialization: Statistical and Stochatical modelling of the climate system; Application of Machine Learning Tecnhiques to Climatic Data; Data assimilation and Inverse Problems in Meteorology and Oceanography; Drought predictability; Tsunami modelling.
Scientific Informations
ORCID: http://orcid.org/0000-0002-1700-6607
SCOPUS ID: https://www.scopus.com/feedback/author-affiliation/review.uri?afwFlowId=1571416762822
Researcher ID: https://publons.com/researcher/2684825/carlos-a-pires/
Research Gate: https://www.researchgate.net/profile/Carlos_Pires11
Google Scholar: https://scholar.google.pt/citations?user=aILj_HEAAAAJ
Website: http://idl.campus.ciencias.ulisboa.pt/profiles/carlos-pires/
Scientific Interests
Main Interests:
Statistical and stochatic diagnostics and modelling of Earth Science data with the focus on non-Gaussian distributed and nonlinear statistics
Application of Machine Learning and Blind Source Decomposition techniques to climatic data
Data assimilation in meteorology and oceanography focusing in the development of algorithms to assimilate data affected by non-Gaussian distributed errors
Statistical modelling and predictability of droughts
Tsunami inverse problems
Predictability of the Climate system by using Dynamical System tools
Top 10 Publications:
Pires C.A., Hannachi A. (2017). Independent Component Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality. Complexity, Vol. 2017, Article ID 3076810, 23 pages. https://doi.org/10.1155/2017/3076810
Martins, D. S., Paredes, P., Raziei, T., Pires, C., Cadima, J. and Pereira, L. S. (2016), Assessing reference evapotranspiration estimation from reanalysis weather products. An application to the Iberian Peninsula. Int. J. Climatol.. doi:10.1002/joc.4852
http://onlinelibrary.wiley.com/doi/10.1002/joc.4852/abstract
Pires C.A., Ribeiro A. (2016) Separation of the atmospheric variability into non-Gaussian multidimensional sources by projection pursuit techniques. Climate Dynamics DOI: 10.1007/s00382-016-3112-9 https://link.springer.com/article/10.1007/s00382-016-3112-9
Ribeiro A. Pires C.A. (2016) Seasonal drought predictability in Portugal using statistical-dynamical techniques. Journal of Physics and Chemistry of the Earth 94 (2016) 155-166.http://dx.doi.org/10.1016/j.pce.2015.04.003
Pires C.A. Perdigão RAP (2015) Non-Gaussian interaction information: estimation, optimization and diagnostic application of triadic wave resonance, Nonlin. Processes Geophys., 22, 87-108. doi:10.5194/npg-22-87-2015. https://www.nonlin-processes-geophys.net/22/87/2015/
Pires, C.A.; Perdigão, R.A.P. Minimum Mutual Information and Non-Gaussianity Through the Maximum Entropy Method: Theory and Properties. Entropy 2012, 14, 1103-1126. http://www.mdpi.com/1099-4300/14/6/1103
Bocquet, M., Pires C.A., Wu, L. 2010 Beyond Gaussian statistical modeling in geophysical data assimilation (Review). Monthly Weather Review. Vol. 138. 2997-3023.http://journals.ametsoc.org/doi/abs/10.1175/2010MWR3164.1
Pires, C.A., O. Talagrand, M. Bocquet, 2010 Diagnosis and Impacts of non-Gaussianity of Innovations in Data Assimilation. Physica D. Nonlinear Phenomena, Vol. 239, (17), 1701-1717. doi:10.1016/j.physd.2010.05.006 http://www.sciencedirect.com/science/article/pii/S0167278910001508?via%3Dihub
Pires, C.A. and Miranda P., 2001. Tsunami Waveform Inversion by Adjoint Methods. Journal of Geophysical Research – Oceans,106, No. C9, 19773-19796. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2000JC000334
Pires, C.A., Vautard, R., Talagrand, O., 1996. On Extending the limits of Variational Assimilation in Nonlinear Chaotic Systems. Tellus, 48A, p. 96-121. https://www.tandfonline.com/doi/abs/10.3402/tellusa.v48i1.11634
Projects (10 most relevant):
Principal Investigator:
Avaliação da Predictabilidade e hibridação de Previsões sazonais de seca na Europa Ocidental -PHDROUGHT – PTDC/GEO-MET/3476/2012
Seasonal Predictability and Downscaling over the Atlantic-European Region – POCI/CTE-ATM/62475/2004
Técnicas de Melhoramento da Previsão Meteorológica – POCTI/CTA/11050/2001
Participation:
Project ROADMAP (“Next Generation Climate Science in Europe for Oceans”) financed by the EU call ‘JPI Climate & JPI Oceans 2019′.
SHARE – Seamless High-resolution Atmosphere-ocean Research. RECI/GEO-MET/0380/2012.
`Développements méthodologiques pour pour l’assimilation des observations: Validation a posteriori, diagnostics d’optimalité et de Gaussianité’ from the Programme National: `Les Enveloppes Fluides et l’Environnement’ , INSU, 2007. http://www.insu.cnrs.fr/lefe/projets-retenus-en-2007
Gestão do risco de secas: Identificação, monotorização, caracterização, predição e mitigação. PTDC/AGR-AAM/71649/2006.
BRIEF – Building Regional Ensemble Forecasts. PTDC/CTE-ATM/73607/2006.
CLIVAR – Variabilidade e Mudança Climática: Padrões e Impactos à Escala Regional. POCTI/CTA/39607/2001.
Books & Chapters (up to 10):
Predictabilidade Sazonal de Secas. Avaliação ao nível regional e agrícola. Editores: Carlos A. L. Pires e Luis Santos Pereira. Edições ISA Press. 978-972-8669-63-8. pp (VIII+332). https://www.researchgate.net/publication/287948935_Drought_Seasonal_Predictability_in_Portuguese_Predictabilidade_Sazonal_de_Secas_-_Avaliacao_ao_nivel_regional_e_agricola
Gestão do Risco em Secas, Métodos, tecnologias e Desafios. Editores: Luis Santos Pereira, João Tiago Mexia e Carlos A. L. Pires, Edições Colibri, CEER, 978-989-689-066-7. pp (VIII+344).Pires, C.A. 2013. “Compreender o Clima, uma aventura pelos Paradigmas da Modelação”, in: Matemática do Planeta Terra, pp 99-139. Editores: Fernando Pestana da Costa, João Teixeira Pinto e Jorge Buescu, IST Press, ISBN: 978-989-8481-26-9. PDF
Pires, C.A. , Sousa, J., 2010. “Desenvolvimento, Calibração e Validação de Modelos de Markov a classes de seca, condicionados por regimes de circulação atmosférica de grande escala”, in: Gestão do Risco em Secas, Métodos, tecnologias e Desafios, pp 209-224. Editores: Luis Santos Pereira, João Tiago Mexia e Carlos A. L. Pires, Edições Colibri, CEER, 978-989-689-066-7. PDF
Pires, C. and P.M.A. Miranda, 2005: “Adjoint Inversion of the source parameters of near-shore tsunamigenic earthquakes” – K. Satake (ed.), Tsunamis: Case Studies and Recent Developments, pp 241-258.PDF
P Miranda, FES Coelho, António Rodrigues Tomé, Maria Antónia Valente, Anabela Carvalho, Carlos Pires, Henrique Oliveira Pires, Vanda Cabrinha Pires, Carlos Ramalho, 2002. “20th Century Portuguese Climate and Climate Scenarios” in: Climate Change in Portugal. Scenarios, Impacts and Adaptation Measures—SIAM Project, pp 23-83. Eds: Santos FD, Forbes K, Moita R. Lisbon: Gradiva Publishers. ISBN 972-662-843-1. PDF
Vautard, R., Pires, C., 1997. Applications of Singular Spectrum Analysis to Climatological Time Series, em T. Subba Rao, M. B. Priestley, O. Lessi (Editores) Applications of Time-Series Analysis in Astronomy and Meteorology. Chapman and Hall, p388-398. https://www.crcpress.com/Applications-of-Time-Series-Analysis-in-Astronomy-and-Meteorology/Rao-Priestly-Lessi/p/book/9780412638008
Conferences (last 15 presentations):
Pires C, Hannachi A (2019) Bi-spectral Analysis of the El-Niño index and its stochastic modeling. Geophysical Research Abstracts
Vol. 21, EGU2019-15232-1, 2019, EGU General Assembly 2019
Pires C, Hannachi A (2018) Building Statistically Independent subspace sources of the Sea Surface Temperature Variability, Geophysical Research Abstracts, vol. 20, EGU2018-8975, EGU General Assembly 2018, 8-13 April 2018, Vienna, Austria
Ribeiro, A. F. S., Russo, A., Gouveia, C.M., Páscoa, P., C. Pires, R. M., Trigo (2018). Joint probability of droughts and wheat yield anomalies in Iberia, MEDCLIVAR 2018 Conference. Belgrade, Serbia. (Poster communication)
Ribeiro, A. F. S., Russo, A., Gouveia, C.M., Páscoa, P., C. Pires (2018). Joint probability of droughts and wheat yield anomalies in Iberia, Workshop IPMA 2018 “A previsão numérica do tempo em Portugal: estado da arte e novos desafios” Lisbon, Portugal. (Poster communication)
Pires, C.A.L. (2017) Independent Subspace Analysis of the monthly variability of the sea surface temperature field. Vol. 19, EGU2017-17135, 2017. EGU General Assembly. http://meetingorganizer.copernicus.org/EGU2017/EGU2017-17135.pdf
RAP Perdigão, J Hall, CAL Pires, G Blöschl (2017). Nonlinear Synergistic Emergence and Predictability in Complex Systems: Theory and Hydro-Climatic Applications. EGU General Assembly Conference Abstracts 19, 8858. http://meetingorganizer.copernicus.org/EGU2017/EGU2017-8858.pdf
D.S. Martins, A.A. Paulo, C. Pires & L.S. Pereira (2017). Long-term variation of PDSI and SPI computed with reanalysis products. 10th WORLD CONGRESS on Water Resources and Environment “Panta Rhei”, 5-9 July 2017. Athens, Greece.
P. Paredes, D.S. Martins, J. Cadima, C. Pires & L.S. Pereira. (2017) Accuracy of daily PM-ETo estimations with ERA-Interim reanalysis products. 10th WORLD CONGRESS on Water Resources and Environment “Panta Rhei”, 5-9 July 2017. Athens, Greece.
2016
Pires, C.A.L., RAP Perdigão RAP. (2016) Non-Gaussian Information-Theoretical Analytics for Diagnostic and Inference of Hydroclimatic Extremes. Vol. 18, EGU2016-17069, 2016. EGU General Assembly. http://meetingorganizer.copernicus.org/EGU2016/EGU2016-17069-1.pdf
Pires, C.A.L., Ribeiro A. (2016) Separation of the low-frequency atmospheric variability into non-Gaussian multidimensional sources by Independent Subspace Analysis. Vol. 18, EGU2016-9069, 2016. EGU General Assembly. http://meetingorganizer.copernicus.org/EGU2016/EGU2016-9069-1.pdf
Pires, C.A.L. (2016) Estatística fora do paradigma Gaussiano: exemplos, métodos e aplicações ao clima. Encontro Nacional da Sociedade Portuguesa de Matemática. Escola Superior de Tecnologia de Barreiro do Instituto Politécnico de Setúbal, 11-13 julho 2016.
Pires C.A., Trigo RM, Perdigão RAP. Triadic Non-Gaussian teleconnections in the Sea Surface Temperature Field: a source of interannual predictability coming from triadic wave resonances. Geophysical Research Abstracts. Vol. 17, EGU2015-8013, 2015. EGU General Assembly 2015
Ribeiro AR, Pires, CA. Seasonal statistical-dynamical forecasts of droughts over Western Iberia. Geophysical Research Abstracts Vol. 17, EGU2015-6336, 2015. EGU General Assembly 2015
2014
Pires CAL (2014) Statistical climatology tools based on non-Gaussian diagnostics
and information theory, Proceedings of the International Conference MEME-2014: Mathematics and Engineering in Marine and Earth problems, Talk T111, pp 62-67. Aveiro, Portugal, 22-25 July 2014. http://c2.glocos.org/public/conferences/18/Book_of_Abstracts.pdf
Martins D, Pires CAL, Pereira LS, Paulo A (2014) Trend Analysis of Drought Extremes in Iberian Peninsula Using SPI. Talk in the special session SS1: Statistical approaches to drought risks and extreme precipitation assessment. International Conference on EcoHidrology, EcoHCC-2014, 10-12 September 2014, Tomar, Portugal.
Ribeiro A, Pires CAL (2014) Seasonal drought predictability in western Iberia using statistical-dynamical techniques. Talk in the special session SS1: Statistical approaches to drought risks and extreme precipitation assessment. International Conference on EcoHidrology, EcoHCC-2014, 10-12 September 2014, Tomar, Portugal.
Pires CA, Perdigão RAP, 2014. Triadic Non-Gaussian low-frequency Teleconnections in the Atmosphere and Ocean. Geophysical Research Abstracts. Vol. 16, EGU2014-14203, 2014 EGU General Assembly 2014. http://meetingorganizer.copernicus.org/EGU2014/EGU2014-14203.pdf
Ribeiro A, Pires CA. 2013. Potencial da predictabilidade sazonal de secas na Ibéria ocidental. Proceedings da 8ª ALEGG, 438-442. 8ª ALEGG, Évora, Portugal, 29-31 de Janeiro 2014. Proceeding Drought Iberia.pdf
Carlos Pires, Informação mútua não-Gaussiana entre modos de variabilidade lenta da Atmosfera e Oceano. Proceedings da 8ª ALEGG, 438-442. 8ª ALEGG, Évora, Portugal, 29-31 de Janeiro 2014. Proceedings Carlos Pires ALEGG.pdf