• A comparison of nonlinear and linear statistical downscaling for future climate. NOAA's Geophysical Fluid Dynamics Laboratory, Princeton, NJ. July 26, 2012.

 

• Using Global Climate Models and downscaled data in environmental studies. Department of Geography and Environmental Sustainability. University of Oklahoma. Norman, OK. Nov 1, 2013.

 

• Machine learning methods applied to statistical downscaling of precipitation. WSI – The Weather Company. Andover, MA. August, 2014.    

 

• Statistical Downscaling 201. University of Oklahoma- South Central Climate Science Center. Norman, OK. 2015.

 

• An Introduction to Statistical Downscaling. University of Oklahoma- South Central Climate Science Center. Norman, OK. 2015.

 

• Using Global Climate Models and Downscaled Data in Environmental Studies. Chesapeake biological Laboratory. University of Maryland – Center for Environmental Science. Solomons, MD. Sept 30th 2015.

 

• An Introduction to Statistical Downscaling. Short Course. Louisiana State University. Baton Rouge, LA. Feb 18th 2016

 

• Using Earth System Models and regional data in environmental studies. Baton Rouge, LA. Feb 19th 2016.    

 

• C.F. Gaitán. Regional differences in the value-added by statistical downscaling precipitation over North America using support vector regression. World Climate Research Programme ICRC-CORDEX Conference, Stockholm, Sweden. May 2016

• C.F. Gaitán. Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series for Montreal, Canada. World Climate Research Programme ICRC-CORDEX Conference, Stockholm, Sweden. May 2016.

• C.F. Gaitán. On the Use of Statistical Downscaling Time-invariant Transfer Functions an Their Effect on Historical and Future Climate Indices. Case study: Heat Wave Duration in Montreal, Canada. American Meteorological Society’s seventh Conference on Environment and Health. New Orleans, (oral presentation) 2016.

• C.F. Gaitán, B. Moore III & V. Balaji. Can We Obtain Viable Alternatives to Manning’s Equation Using Genetic Programming?. American Meteorological Society’s 30th Conference on Hydrology and 14th Conference on Artificial and Computational Intelligence and its Applications to Environmental Sciences. New Orleans (oral presentation) 2016.

• B. Muhling, C.F. Gaitán, D. Tommasi, V.S. Saba, C.A. Stock, and K.W. Dixon. Climate Change, Estuaries and Anadromous Fish Habitat in the Northeastern United States: models, downscaling and uncertainty. AGU-ASLO. New Orleans (poster presentation) 2016.

• N. Ruiz Castillo, C. Gaitán Ospina and R. McPherson. Projecting future change in Growing Degree Days of Winter Wheat. Session GC51D-1122.  AGU Fall meeting (poster presentation). San Francisco, 14-18 December 2015.

• E. Mullens, R. McPherson, D. Rosendahl, & C.F. Gaitán. Developing transportation-relevant historical trends and future climate projections for decision support in the South Central U.S. First International Conference on Surface Transportation System Resilience to Climate Change and Extreme Weather Events. Washington, D.C. Sept. 16-18, 2015.

• C.F. Gaitán. Statistical downscaling and the effect of the time-invariance assumption on growing season lengths, heat wave durations and other climate indices. STS 007. 60th World Statistics Conference –ISI 2015. Rio de Janeiro, Brazil. July 31st 2015.

• A. Bamzai, E. Mullens, D. Rosendahl, & C.F. Gaitán. Climate Scenarios and Interpreting Projections. North Central Climate Science Center Open Science Conference. Colorado State University. May 2015.

• K.W. Dixon, J. Lanzante, K. Hayhoe, A. Stonner & C.F. Gaitán. Evaluating Statistical Downscaling Performance Under Changing Climate Conditions. AGU Joint Assembly. Montreal, Canada. May 2015

• C.F. Gaitán. On time-invariant transfer functions and the application of statistically downscaled daily máximum temperatura to calculate historical and future heat waves. NOAA- Climate Prediction Apllications Science Workshop. Las Cruces, New Mexico. March 24-26, 2015.

• C.F. Gaitán. Statistical downscaling using machine learning. NOAA-GFDL Winter 2015 Poster Expo. Princeton, NJ. January 28th 2015.

• C.F. Gaitán. Quantifying the value of quantile mapping methods using a perfect-model approach. Oral presentation. Special Symposium on Model Post-processing and Downscaling. American Meteorological Society Annual Meeting. Phoenix, Arizona. January 2015.  

• C.F. Gaitan,  K. W. Dixon, V. Balaji and R. McPherson. Statistically downscaled North American precipitation using support vector regression and the Big Brother approach. 11th International conference on hydroinformatics. HIC 2014, 17-21 August 2014, New York, NY.

• C.F. Gaitan,  V. Balaji and Berrien Moore III. Rediscovering Manning's equation using genetic programming. 11th International conference on hydroinformatics. HIC 2014, 17-21 August 2014, New York, NY.

• C.F. Gaitan, K. W. Dixon, V. Balaji and R. McPherson. SVR and Bayesian Neural Networks applied to statistical downscaling of precipitation. Oral presentation. 12th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences. AMS 94th Annual Meeting, 2-6 February 2014, Atlanta, GA.

• C.F. Gaitan, K. W. Dixon,  R. McPherson, B. Moore III, V. Balaji and A. Radhakrishnan. Statistical downscaling of daily precipitation and the stationarity assumption. Poster presentation. 28th Conference on Hydrology. AMS 94th Annual Meeting, 2-6 February 2014, Atlanta, GA.

• C.F. Gaitan, K. W. Dixon, V. Balaji and R. McPherson. Statistical downscaling of daily precipitation using machine-learning algorithms. Poster presentation. 2013 Climate Informatics Workshop. Boulder, CO. Sept. 2013.

• C.F. Gaitan, W.Hsieh and A.Cannon. Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada. Oral presentation 2013 CMOS-CGU-CWRA. Saskatoon, Canada. May 2013.

• C.F. Gaitan, W. Hsieh, A. Cannon. A comparison of nonlinear and linear statistical downscaling for future climate.  Oral presentation. 2012 CMOS congress. Montreal, B.C. May  2012.

• C.F. Gaitan, W. Hsieh, A. Cannon. A comparison of nonlinear and linear statistical downscaling for future climate.  Oral presentation. AMS annual meeting.  New Orleans, USA. January  2012.

• W. W. Hsieh, C.F. Gaitan, , A. J. Cannon. Machine learning methods in climate and weather research. The Third Santa Fe Conference on Global and Regional Climate Change, Santa Fe, NM, Oct 31-Nov 3, 2011.

• C.F. Gaitan, W. Hsieh, A. Cannon. Validation of statistical downscaling methods in terms of weather and climate. Surface temperatures in Southern Ontario and Quebec. Poster presentation. World Climate Research Programme. Open Science Conference "Climate Research in Service to Society". Denver, USA. October 2011.

• C.F. Gaitan et al.. Statistical downscaling of surface temperature in different Koppen-Geiger climate zones in Equatorial South America. Poster presentation. World Climate Research Programme. Open Science Conference "Climate Research in Service to Society". Denver, USA. October 2011

• C.F. Gaitan, W. Hsieh, A. Cannon. Validation of statistical downscaling methods in terms of weather and climate. Surface temperature in Southern Ontario and Quebec.  Oral presentation. 2011 CMOS congress. Victoria, B.C. June 2011.   

• C.F. Gaitan, W. Hsieh, A. Cannon and P. Gachon. Evaluation of STARDEX indices calculated from linear and nonlinear statistical downscaled daily temperatures.  91st AMS annual meeting. Seattle, WA. Jan 2011.

• C.F. Gaitan, M. Hassan. Empirical models for suspended sediment dynamics. Oral presentation. 2010 Hydrologic Synthesis Capstone Symposium. Vancouver, B.C. August 2010.

• C. Gaitan, W.Hsieh, P. Gachon, A. Cannon and K. Rasouli. Statistical Downscaling of Temperature Data Sets: A comparison of Methods Based on the STARDEX Indices. Canadian Water Resources Association. Oral Presentation. 63rd National Conference. Vancouver, B.C. June 2010.

• C. Gaitan Ospina, W.W. Hsieh, A.J. Cannon and P. Gachon. Nonlinear statistical downscaling of maximum and minimum temperature. Case study: Vancouver Island, Canada. Oral Presentation. 2010 CMOS and CGU congress. Ottawa, Ontario. June 2010.

• K. Rasouli, W.W.Hsieh, A.J. Cannon and C.F.Gaitan. Streamflow forecasting by machine learning methods utilizing numerical weather prediction and climate indices. 2010 CMOS and CGU congress. Ottawa, Ontario. June 2010.

• C. Gaitan, N. Obregon and M. Vanegas. Long Term Rainfall Predictive Model by Using ANN and Precipitation Data Sets Gathered at Multiple Raingauges of the Northwestern Coast of South-America.  Geophysical Research Abstracts, Vol. 9, 10896, 2007. 

• C.F. Gaitan, N. Obregon, D.E. Torres. Spatiotemporal Rainfall Predictive Model using ANN and Storm Data Sets. Case Study: Bogotá City, Colombia - 10th International Meeting on Statistical Climatology (10IMSC) Abstract Book Final. August 2007.

• C.F. Gaitan, A. Rivera. Relation between hydraulic offer and macroclimatic variables for the Cauca Department, Colombia. XVII National Seminar of Hydraulics and Hydrology - Sociedad Colombiana de Ingenieros.   Oral presentation. 2006.