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Leo_Carro

Leopoldo Carro Calvo


Education: Ph.D. in Telecommunications, Universidad de Alcalá (UAH), Spain


Current position: Researcher (Juan De La Cierva 2014) (2016-2018).


Scientific Interests: Soft-Computing - Artificial Intelligence - Applications to stratospheric and tropospheric research.


e-mail: leocarro@ucm.es


Former affiliations

  1. Postdoctoral Researcher. KIT - Institute of Meteorology and Climate Research - Troposphere Researche, Karlsruhe, Germany, 2015-2016.

  2. Postdoctoral Researcher. UAH - Departmento fo signal theory and communications, Madrid, Spain, 2013-2014.

  3. Doctoral Researcher. UAH - Departmento fo signal theory and communications, Madrid, Spain, 2011-2013.

  4. Doctoral Researcher. JLU - Institut für Geographie , Giessen, Germany, November 2010 - March 2011.

  5. PhD Student. University of Alcalá, 2010-2013.

Research Activity


Publications: Peer-reviewed papers (SCI)

    2017

  1. Carro-Calvo L., Ordóñez C., García-Herrera R., Schnell J.L. (2017): Spatial clustering and meteorological drivers of summer ozone in Europe. Atmospheric Environment, doi:10.1016/j.atmosenv.2017.08.050.

  2. Carro-Calvo L., Casanova-Mateo C., Sanz-Justo J., Casanova-Roqueb J.L., Salcedo-Sanz S. (2017): Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data. Atmosfera, 30 (1), 1-10, doi:10.20937/ATM.2017.30.01.01.

  3. Andrés-Pérez E., González-Juárez D., Martin-Burgos M.J., Carro-Calvo L., Salcedo-Sanz S. (2017): Influence of the number and location of design parameters in the aerodynamic shape optimization of a transonic aerofoil and a wing through evolutionary algorithms and support vector machines. Engineering Optimization, 49 (2), 181-198, doi:10.1080/0305215X.2016.1165568.


  4. 2016

  5. L. Carro-Calvo, C. Hoose, M. Stengel and S. Salcedo-Sanz, (2016): Cloud Glaciation Temperature estimation from passive remote sensing data with Evolutionary Computing . Journal of Geophysical Research – Atmospheres, vol. 121, pp. 13591-13608, doi:10.1002/2016JD025552.

  6. S Salcedo-Sanz, RC Deo, L Carro-Calvo, B Saavedra-Moreno (2016): Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms, Theoretical and Applied Climatology, vol 125, 13-25, doi:10.1007/s00704-015-1480-4.


  7. 2015

  8. B Saavedra‐Moreno, A Iglesia, J Magdalena‐Saiz, L Carro‐Calvo , L Durán, S Salcedo‐Sanz (2015): Surface wind speed reconstruction from synoptic pressure fields: machine learning versus weather regimes classification techniques, Wind Energy, vol 18, n 9, 1531-1544, doi: 10.1002/we.1774.

  9. S Salcedo-Sanz, JC Nieto Borge, L Carro-Calvo, L Cuadra, K Hessner, E Alexandre (2015): Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface, Ocean Engineering, vol 101, 244-253, doi:10.1016/j.oceaneng.2015.04.041.


  10. 2014

  11. S Salcedo-Sanz, D Gallo-Marazuela, A Pastor-Sánchez, L Carro-Calvo, A Portilla-Figueras, L Prieto (2014): Offshore wind farm design with the Coral Reefs Optimization algorithm, Renewable Energy, vol 63, 109-115, doi:10.1016/j.renene.2013.09.004.

  12. Sancho Salcedo-Sanz, D Gallo-Marazuela, A Pastor-Sánchez,L Carro-Calvo, Antonio Portilla-Figueras, Luis Prieto(2014): Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe, Expert Systems with Applications, vol 40, n 16, 6292-6297, doi:10.1016/j.eswa.2013.05.054.


  13. 2013

  14. L Carro-Calvo, S Salcedo-Sanz, J Luterbacher (2013): Neural computation in paleoclimatology: General methodology and a case study, Neurocomputing , vol 113, 262-268, doi:10.1016/j.neucom.2012.12.045.

  15. B Saavedra-Moreno, S Salcedo-Sanz, L Carro-Calvo, J Gascón-Moreno, S Jiménez-Fernández, L Prieto(2013): Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms, Journal of Wind Engineering and Industrial Aerodynamics , vol 116, 49-60, doi:10.1016/j.jweia.2013.03.005.

  16. N Kirchner-Bossi , L Prieto , R García-Herrera , L Carro-Calvo , S Salcedo-Sanz (2013): Multi-decadal variability in a centennial reconstruction of daily wind. Applied Energy, vol 105, 30-46, DOI:10.1016/j.apenergy.2012.11.072

  17. J Gascón-Moreno, E G Ortiz-García, S Salcedo-Sanz, L Carro-Calvo , B Saavedra-Moreno, A Portilla-Figueras(2013): Evolutionary optimization of multi-parametric kernel\ epsilon-SVMr for forecasting problems, Soft Computing, vol 17, n 2, 213-221, DOI: 10.1007/s00500-012-0886-5.


  18. 2012

  19. L Carro-Calvo, S Salcedo-Sanz, L Prieto, N Kirchner-Bossi, A Portilla-Figueras, S Jiménez-Fernández (2012): Wind speed reconstruction from synoptic pressure patterns using an evolutionary algorithm. Applied Energy, 89, 347-354, doi:10.1016/j.apenergy.2011.07.044.


  20. 2011

  21. L Carro-Calvo , S Salcedo-Sanz, N Kirchner-Bossi, A Portilla-Figueras, L Prieto, R Garcia-Herrera, E Hernández-Martín (2011): Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing. Energy, 36, 1571-1581, doi:10.1016/j.energy.2011.01.001

Conferences:

L. Carro-Calvo, C. Hoose, S. Salcedo-Sanz, M. Stengel, “Analyzing the cloud glaciation point using satellite data, "International Radiation Simposium" (IRS2016), Auckland, New Zealand, April, 2016.

B. Saavedra-Moreno, S. Salcedo-Sanz, L. Carro-Calvo, J. A. Portilla-Figueras and J. Magdalena-Saiz, “Reconstruction of wind speed based on synoptic pressure values and Support Vector regression,"The 14th International Conference on Intelligent Data Engineering and Automated Learning" (IDEAL 2013), Heifei, China, October, 2013.

S. Salcedo-Sanz, L. Carro-Calvo, J. A. Portilla-Figueras, L. Cuadra and D. Camacho “Fuzzy clustering with grouping genetic algorithms," The 14th International Conference on Intelligent Data Engineering and Automated Learning" (IDEAL 2013), Heifei, China, October, 2013.

L. Carro-Calvo, S. Salcedo-Sanz, A. Portilla-Figueras, S. Jiménez-Fernández, L. Cuadra and E. Alexandre-Cortizo, “A novel pointer-encoding genetic programming algorithm for classification problems,” 15th Applied Stochastic Models and Data Analysis International Conference, Mataró, Spain, 2013.

S. Jiménez-Fernández, S. Salcedo-Sanz, G. Gómez-Prada, L. Carro-Calvo, J. Maellas-Benito, “Sizing a Hybrid Photovoltaic-Hydrogen System for Remote Telecommunication Stand-alone Facilities using Evolutionary Algorithms,” International Conference on Intelligent Systems Design and Applications (ISDA), Córdoba, Spain, 2011.

P. A. Gutierrez, S. Salcedo-Sanz, C. Hervás, L. Carro-Calvo, J. Sánchez-Monedero and L. Prieto, “Evaluating nominal and ordinal classifiers for wind speed prediction From synoptic pressure patterns,” International Conference on Intelligent Systems Design and Applications (ISDA), Córdoba, Spain, 2011.


Participation in Funded Research Projects

Development of new systems for wind resource characterization, new wind farm sites and wind prediction (ETSWIND), Solute Ltd 2012-2014. PI: Dr. Sancho Salcedo Sanz

Analysis and estimation of wind series in wind farms. Ministerio / Industria, Energía y Turismo (Programa Avanza Competitividad) 2010-2011. PI: Dr. Sancho Salcedo Sanz

Soft-computing algorithms for weather-types classification. Iberdrola Renovables 2009-2010. PI: Dr. Sancho Salcedo Sanz

Optimization and prediction in urban systems using Soft-computing techniques: improvements in pollution measurement networks and WiFi ubiquitious deployment. Comunidad de Madrid / Universidad de Alcalá 2009. PI: Dr. Sancho Salcedo Sanz

Definition of an air quality index for Madrid city. Madrid city council, Department of air quality and pollution control and evaluation 2009. PI: Dr. Sancho Salcedo Sanz

Desarrollo de heurísticos híbridos basados en redes neuronales, computación evolutiva y SVMs para la predicción de generación de energía eléctrica en plantas eólicas y fotovoltáicas 2008. PI: Dr. Sancho Salcedo Sanz