Sue Ellen Haupt

Senior Scientist, Research Applications Laboratory

Information

Bio

Dr. Haupt is a Senior Scientist in the Research Applications Laboratory (RAL) of NCAR. Dr. Haupt leads projects in renewable energy prediction and coupling mesoscale models to microscale models. She is also a Contributing and Founding Director of the World Energy and Meteorology Council (WEMC) and a Fellow of the American Meteorological Society (AMS). She was recently awarded the European Academy of Wind Energy (EAWE) Scientific Award for 2022. Until June 2023, she served as Deputy Director of RAL. From 2011-2018, she directed the Weather Systems and Assessment Program of RAL. Before that, she headed the Department of Atmospheric and Oceanic Physics at the Applied Research Laboratory of The Pennsylvania State University where she remains an Adjunct Professor of Meteorology. She earned her Ph.D. in Atmospheric Science from the University of Michigan, M.S. in Mechanical Engineering from Worcester Polytechnic Institute, M.S. in Engineering Management from Western New England College, B.S. in Meteorology from Penn State, and did a postdoctoral fellowship with the Advanced Study Program of NCAR. She has also been on the faculty of the University of Colorado/Boulder; the U.S. Air Force Academy (visiting); University of Nevada, Reno; and Utah State University and previously worked for the New England Electric System and GCA Corporation.

Dr. Haupt is an expert in boundary layer meteorology, large scale atmospheric dynamics, applications of artificial intelligence in the environmental sciences, renewable energy, dynamical systems, numerical methods, and computational fluid dynamics. Her specialty is in applying novel numerical techniques to problems in the environmental sciences in both basic and applied research. She enjoys teaching and mentoring graduate students as well as developing and directing research programs and projects.

Dr. Haupt is currently Commissioner of the AMS Commission on the Weather, Water, Climate Enterprise. She recently served on AMS Council and the Energy committee and previously chaired the Committee on Artificial Intelligence Applications to Environmental Science of the AMS and has taught portions of short courses offered by that committee, as well as short courses related to meteorology and energy offered through WEMC. Additionally, Dr. Haupt is a member of the American Geophysical Union, Society of Women Engineers (served as faculty advisor for student groups for five years), Chi Epsilon Pi, and Phi Mu Epsilon. She co-authored Practical Genetic Algorithms (Wiley and Sons 1998, second edition 2004, Arabic edition 2011); is primary editor for Applications of Artificial Intelligence Methods in the Environmental Sciences (Springer, 2009); co-edited Weather Matters for Energy (2014); and has authored many book chapters, journal articles, conference papers, and technical reports.

Publications

Authored and Edited Books

1. Troccoli, A., L. Dubus, and S.E. Haupt, eds., 2014: Weather Matters for Energy, Springer, 528 pp. ISBN: 978-1-4614-9220-7.
2. Haupt, S.E., A. Pasini, and C. Marzban, Eds. 2009: Artificial Intelligence Methods in the Environmental Sciences, Springer, 424 pp.
3. Haupt, R.L., and S.E. Haupt, 2011: Practical Genetic Algorithms, Second Edition with CD, John Wiley and Sons, New York, NY, Arabic Edition.
4. Haupt, R.L., and S.E. Haupt, 2004: Practical Genetic Algorithms, Second Edition with CD, John Wiley and Sons, New York, NY, 255 pp.
5. Haupt, R.L., and S.E. Haupt, 1998: Practical Genetic Algorithms, John Wiley and Sons, New York, NY, 177 pp.

Book Chapters (all peer reviewed)

1. Haupt, S.E., R.M. Rauber, B. Carmichael, J.C. Knievel, and J.L. Cogan, 2019: Chapter 22: 100 Years of Progress in Applied Meteorology, Part 2: Basic Applications, in A Century of Progress in Atmospheric and Related Science: Celebrating the American Meteorological Society Centennial, AMS Monograph Series, Boston, MA. 24.1-24.35, DOI: 10.1175/AMSMONOGRAPHS-D-18-0012.1.
2. Haupt, S.E., S. Hanna, M. Askelson, M. Shepherd, M.A. Fragoment, N. Debbage, and B. Johnson, 2019: Chapter 23: 100 Years of Progress in Applied Meteorology, Part 2: Applications that Address Growing Populations, in A Century of Progress in Atmospheric and Related Science: Celebrating the American Meteorological Society Centennial, AMS Monograph Series, Boston, MA. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0007.1
3. Haupt, S.E., S. McIntosh, B. Kosovic, K. Miller, F. Chen, M. Shepherd, M. Williams, and S. Drobot, 2019: Chapter 24: 100 Years of Progress in Applied Meteorology, Part 3: Modern Applications, in A Century of Progress in Atmospheric and Related Science: Celebrating the American Meteorological Society Centennial, AMS Monograph Series, Boston, MA, 24.1-24.35, DOI: 10.1175/AMSMONOGRAPHS-D-18-0012.1.
4. Jimenez, P.A., J.A. Lee, B. Kosovic, and S.E. Haupt, 2019: Solar Resource Evaluation with Numerical Weather Prediction Models, in Green Energy and Technology: Solar Resources Mapping, A. Sanfilippo, L. Pomares, J. Polo, Ed, Springer, pp 199-2019. DOI: 10.1007/978-3-319-97484-2_7
5. Haupt, S.E., B. Kosovic, J.A. Lee, and P. Jimenez, 2019: Mesoscale Modeling of the Atmosphere, in Modeling and Simulation in Wind Plant Design and Analysis, P. Veers, Ed., IET Press, Volume 1, pp 65-116. Book DOI: 10.1049/PBPO125F, Chapter DOI: 10.1049/PBPO125F_ch3, e-ISBN: 9781785615221
6. Haupt, S.E., 2018: Short-Range Forecasting for Energy, in Weather and Climate Services for the Energy Industry, A. Troccoli, ed., Palgrave Macmillan, London, UK, pp. 97-108. https://link.springer.com/book/10.1007%2F978-3-319-68418-5
7. Troccoli, A., M. Bruno Soares, L. Dubus, S.E. Haupt, M. Dadeck Boulahya, and S. Dorling, 2018: Forging a Dialogue Between the Energy Industry and the Meteorological Community, in Weather and Climate Services for the Energy Industry, A. Troccoli, ed., Palgrave Macmillan, London, UK, pp. 65-84. https://link.springer.com/book/10.1007%2F978-3-319-68418-5
8. Dubus, L. A. Troccoli, S.E. Haupt, M.S. Boulahya, and S. Dorling, 2018: Lessons Learned Establishing a Dialogue Between the Energy Industry and the Meteorological Community and a Way Forward, in Weather and Climate Services for the Energy Industry, A. Troccoli, ed., Palgrave Macmillan, London, UK, pp. 179-190.
https://link.springer.com/book/10.1007%2F978-3-319-68418-5
9. Haupt, S.E., P. Jimenez, J.A. Lee, and B. Kosovic, 2017: Principles of Meteorology and Numerical Weather Prediction, in Renewable Energy Forecasting: From Models to Applications, G. Kariniotakis, Ed., Elsevier, London, UK, pp. 3-28.
10. Haupt, S.E. and D.J. Gagne, 2017: Use of Artificial Intelligence in Enabling Renewable Energy. In Nichols, G., S.E. Haupt, D.J. Gagne, A. Rucci, C. Toumey, G. Deshpande, P. Lanka, and S.A. Youngblood, 2017: State-of-the-art Report: Artificial Intelligence and Machine Learning for Defense Applications, HDIAD-SOAR-17-01, pp. 23-44. https://www.hdiac.org/wp-content/uploads/2017%20HDIAC%20SOAR%20-%20Artificial%20...
11. Haupt, S.E., W.P. Mahoney, and K. Parks, Wind Power Forecasting. In. Troccoli, A., L. Dubus, and S.E. Haupt, eds., 2014: Weather Matters for Energy, Springer, 295-318. ISBN: 978-1-4614-9220-7.
12. Schmehl, K.J., S.E. Haupt, and M. Pavolonis, 2012: A Genetic Algorithm Variational Approach to Data Assimilation and Application to Volcanic Emissions, in Data Assimilation and its Applications, M. Sharan and J.P. Issartel, eds., Springer, 676 pp.
13. Haupt, S.E., V. Lakshmanan, A. Pasini, C. Marzban, J. Williams, 2009: Environmental Science Models and Artificial Intelligence, , In Haupt, S.E., A. Pasini, and C. Marzban, Eds., Artificial Intelligence Methods in the Environmental Sciences, Springer, pp. 1-14.
14. Haupt, S.E., 2009: Introduction to Genetic Algorithms, , In Haupt, S.E., A. Pasini, and C. Marzban, Eds., Artificial Intelligence Methods in the Environmental Sciences, Springer, pp. 103-126.
15. Haupt, S.E., C.T. Allen, and G.S. Young, 2009: Addressing Air Quality Problems with Genetic Algorithms, In Haupt, S.E., A. Pasini, and C. Marzban, Eds., Artificial Intelligence Methods in the Environmental Sciences, Springer, pp. 269-296.
16. Haupt, S.E., 2000: Environmental Optimization: Applications of Genetic Algorithms, In Haupt, S.E., A. Pasini, and C. Marzban, Eds., Artificial Intelligence Methods in the Environmental Sciences, Springer, pp. 379-396.
17. Haupt, S.E., 2007: Genetic Algorithms and their Applications in Environmental Sciences, in Advanced Methods for Decision Making and Risk Management in Sustainability Science, J. Kropp, and J. Scheffran, eds., Nova Science Publishers, Inc. N.Y, 320pp.
18. Boyd, J.P. and S.E. Haupt, 1991: Polycnoidal waves: Spatially periodic generalizations of multiple solitons, in Nonlinear Topics in Ocean Physics: Proceedings of the International School of Physics “Enrico Fermi”, Ed. by A.R. Osborne, North-Holland, Amsterdam, 827-856.
19. Record, F.A., et al., (S.E. Haupt - chapter author), 1981: Acid Rain Information Book, Prepared for U.S. Department of Energy, Washington, D.C. by GCA/Technology Division.

Refereed Journal and Magazine Publications

1. Tye, M.R., M. Ge, J.H. Richter, E.D. Gutmann, A. Rugg, C.L. Bruyere, S.E. Haupt, F. Lehner, R. McCrary, A.J. Newman, and A. Wood, Evaluating an Earth system model from a water user perspective, submitted to EGUsphere. 2. Haupt, S.E., B. Kosovic, L.K. Berg, C.M. Kaul, M. Churchfield, J. Mirocha, D. Allaerts, T. Brummet, S. Davis, A. DeCastro, S. Dettling, C. Draxl, D.J. Gagne, P. Hawbecker, P. Jha, T. Juliano, W. Lassman, E. Quon, R.K. Rai, M. Robinson, W. Shaw, and R. Thedin, 2023: Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy, Wind Energy Science, https://doi.org/10.5194/wes-8-1251-2023. 3. Rao, Y., R. Redmon, K. Dale, S.E. Haupt, A. Hopkinson, A. Bostrom, S. Boukabara, T. Geenen, D.M. Hall, B.D. Smith, D. Niyogi, V. Ramaswamy, and E.A. Kihn, 2023: Developing Digital Twins for Earth Systems: Purpose, Requisits, and Benefits, submitted to AI for the Earth Systems, https://arxiv.org/abs/2306.11175. 4. Naegele, S., J.A. Lee, S.J. Greybush, G.S. Young, and S.E. Haupt, 2023: Identifying Wind Regimes near Kuwait using Self-Organizing Maps, submitted to J. Renewable & Sustainable Energy, accepted.. 5. Brotzge, J.A., D. Berchoff, D.L. Carlis, F.H. Carr, R.H. Carr, J.J. Gerth, B.D. Gross, T.M. Hamill, S.E. Haupt, N. Jacobs, A. McGovern, D.J. Stensrud, G. Szatkowski, I. Szunyogh, and X. Wang, 2023: Challenges and Opportunities in Numerical Weather Prediction, Bull. Amer. Meteorol. Soc., 04, E698–E705, https://doi.org/10.1175/BAMS-D-22-0172.1. 6. Balkinssoon, S., N. Fox, A. Lupo, S.E. Haupt, and S.G. Penny, 2023: Classification of tall tower meteorological variables and forecasting wind speeds in Columbia, Missouri, Renewable Energy, 217, 11912, 16 pp. https://doi.org/10.1016/j.renene.2023.119123. 7. Haupt, S.E., D.J. Gagne, W. Hsieh, V. Krasnopolsky, V. Lakshmanan, A. McGovern, C. Marzban, W. Moninger, P.Tissot, and J.K. Williams, 2022: The History and Practice of AI in the Environmental Sciences, Bull. Amer. Meteorol. Soc., E1351–E1370, https://doi.org/10.1175/BAMS-D-20-0234.1. 8. Juliano, T.W., B. Kosović, P.A. Jimenez, M. Eghdami, S.E. Haupt, and A. Martilli, 2022: “Gray Zone” Simulations using a Three-Dimensional Planetary Boundary Layer Parameterization in the Weather Research and Forecasting Model, Monthly Weather Review, 150(7), 1585-1619. https://doi.org/10.1175/MWR-D-21-0164.1. 9. McCandless, D.J. Gagne, T.C., B. Kosovic, S.E. Haupt, B. Yang, C. Becker, and J. Schreck, 2021: Machine Learning for Improving Surface Layer Flux Estimates, Boundary Layer Meteorology, https://doi.org/10.1007/s10546-022-00727-4. 10. Al-Rasheedi, M., M. Al-Khayat, C. Gueymard, S. E. Haupt, B. Kosović, A. Al-Qattan, and J. Lee, 2021: Performance analysis of a 10-MW wind farm in a hot, dusty desert environment. Part 1: Wind resource and power generation evaluation, Sustain. Energy Tech. Assess., 47, 101487. https://doi.org/10.1016/j.seta.2021.101487 11. Al-Khayat M., M. Al-Rasheedi, C. Gueymard, S. E. Haupt, B. Kosović, A. Al-Qattan, and J. Lee, 2021: Performance analysis of a 10-MW wind farm in a hot, dusty desert environment. Part 2: Combined dust and high-temperature effects on the operation of wind turbines, Sustain. Energy Tech. Assess., 47, 101461 (14 pp.) https://doi.org/10.1016/j.seta.2021.101461. 12. Balkinssoon, S., N. Fox, A. Lupo, S.E. Haupt, Y.C. Yi, P. Market, and S. Walsh, 2021: Determining chaotic characteristic and forecasting tall tower wind speeds in Missouri using Empirical Dynamical Modeling (EDM), Renewable Energy, 170, 1292-1307. https://doi.org/10.1016/j.renene.2021.01.108. 13. Boukabara, S.-A., V. M. Krasnopolsky, S. G. Penny, J. Q. Stewart, A. Mc- Govern, D. Hall, J. E. Ten Hoeve, J. Hickey, H.-L. A. Huang, J. Williams, K. Ide, P. Tissot, S. E. Haupt, K. S. Casey, N. Oza, A. Geer, E. S. Maddy, and R. N. Hoffman, 2021: Outlook for exploiting artificial intelligence in the earth and environmental sciences. Bull. Am. Meteorol. Soc., 102(4). https://doi.org/10.1175/BAMS-D-20-0031.1. 14. Haupt, S.E., W. Chapman, S.V. Adams, C. Kirkwood, J.S. Hosking, N.H. Robinson, S. Lerch, and A.C. Subramanian, 2020: Towards Implementing AI Post-processing in Weather and Climate: Proposed Actions from the Oxford 2019 Workshop. Philosophical Transactions of the Royal Meteorological Society A. 379: 20200091. https://doi.org/10.1098/rsta.2020.0091. 15. Carreno, I.L., M.T. Craig, M. Rossol, M. Ashfaq, F. Gatibeniz, S.E. Haupt, C. Ammann, C. Draxl, B.-M. Hodge, and C. Brancucci, 2020: Potential impacts of climate change on wind and solar electricity generation in Texas, Climate Change, 163, 745-766. https://doi.org/10.1007/s10584-020-02891-3. 16. Haupt, S.E., T. McCandless, S. Dettling, S. Alessandrini, G. Wiener, J. Lee, S. Linden, W. Petzke, T. Brummet, N. Nguyen, B. Kosovic, T. Hussain, and M. Al-Rasheedi, 2020: Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting, Energies, 13, 1979; doi:10.3390/en13081979. 17. Naegele, S.M., T.C. McCandless, S.J. Greybush, G.S. Young, S.E. Haupt, and M. Al-Rasheedi, 2020: Climatology of Wind Variability for the Kuwait Region, Renewable and Sustainable Energy Reviews,133, 110089, https://doi.org/10.1016/j.rser.2020.110089. 18. De Vos, M.G., W. Hazeleger, D. Bari, J. Behrens, S. Bendoukha, I. Garcia-Marti, R. van Haren, S.E. Haupt, R. Hut, F. Jansson, A. Mueller, P. Neilley, G. van den Oord, I. Pelupessy, P. Ruti, M.G. Schultz, and J. Walton, 2020: Weather and Climate Science in the Digital Era, Geoscience Communication, 3, 191-201. https://doi.org/10.5194/gc-3-191-2020 19. Kosović, B., S.E. Haupt, D. Adriaansen, S. Alessandrini, G. Wiener, L. Delle Monache, Y. Liu, S. Linden, T. Jensen, W. Cheng, M. Politovich, and P. Prestopnik, 2020: A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction, Energies, 13, 1372. https://doi.org/10.3390/en13061372. 20. McCandless, T., S. Dettling, and S.E. Haupt, 2020: Comparison of Implicit vs Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction, Energies, 13 (682), 14 pp. doi:10.3390/en13030689. 21. Tye, M.R., S.E. Haupt, E. Gilleland, C. Kalb, T. Jensen, and B. Brown, 2020: Assessing the evidence for weather regimes governing solar power generation in Kuwait, Energies, 12 (4409), 17 pp. doi:10.3390/en12234409. 22. Siems-Anderson, A.R., C.L. Walker, G. Wiener, W.P. Mahoney III, and S.E. Haupt, 2019: An Adaptive Big Data Weather System for Surface Transportation, Transportation Research Interdisciplinary Perspectives, 3, 100071. http://dx.doi.org/10.1016/j.trip.2019.100071 23. Haupt, S.E., M.C. Casado, M. Davidson, J. Dobschinski, P. Du, M. Lange, T. Miller, C. Mohlen, A. Motley, R. Pestana, J. Zack, 2019: Use of Probabilistic Forecasts in Theory and Practice, IEEE Power and Energy Magazine, November/December, pp. 46-57. DOI: 10.1109/MPE.2019.2932639. 24. Haupt, S.E., B. Kosovic, W. Shaw, L. Berg, M. Churchfield, J. Cline, C. Draxl, B. Ennis, E. Koo, R. Kotamarthi, L. Mazzaro, J. Mirocha, P. Moriarty, D. Munoz-Esparza, E. Quon, R.K. Rai, M. Robinson, G. Sever, 2019: On Bridging a Modeling Scale Gap: Mesoscale to Microscale Coupling for Wind Energy, Bulletin of the American Meteorological Society, 100, 2533-2549. https://doi.org/10.1175/BAMS-D-18-0033.1. 25. Amaro e Silva, R.., S.E. Haupt, and M.C. Brito, 2019: A regime-based approach for integrating wind information with spatio-temporal solar forecasting, Renewable and Sustainable Energy, 11, 056102, https://doi.org/10.1063/1.5098763. 26. McCandless, T.C. and S.E. Haupt, 2019: The Super-Turbine Wind Power Conversion Paradox: Using Machine Learning to Reduce Errors Caused by Jensen’s Inequality, Wind Energy Science, 4, 343-353. https://doi.org/10.5194/wes-4-343-2019. 27. Gagne II, D.J., S.E. Haupt, D.W. Nychka, and G. Thompson, 2019: Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms, Monthly Weather Review, 147, 2827-2845. DOI: 10.1175/MWR-D-18-0316.1. 28. Rai, R.K., L.K. Berg, B. Kosovic, S.E. Haupt, J.D. Mirocha, B. Ennis, and C. Draxl, 2019: Evaluation of the Impact of Horizontal Grid Spacing in Terra Incognita on Coupled Mesoscale-microscale Simulations using the WRF Framework, Monthly Wea. Rev, 147, 1007-1027. https://journals.ametsoc.org/doi/abs/10.1175/MWR-D-18-0282.1. 29. Craig, M.T., S. Cohen, J. Macknick, C. Draxl, O.J. Guerra, M. Sengupta, S.E. Haupt, B.-M. Hodge, and C. Brancucci, 2018: A Review of the Potential Impacts of Climate Change on Bulk Power System Planning and Operations in the United States, Renewable and Sustainable Energy, DOI: 10.1016/j.rser.2018.09.022 30. Mirocha, J.D., M.J. Churchfield, D. Munoz-Esparaza, R. Rai, Y. Feng, B. Kosovic, S.E. Haupt, B. Brown, B.L. Ennis, C. Draxl, J.S. Rodrigo, W.J. Shaw, L.K. Berg, P. Moriarty, R. Linn, R.V. Kotamarthi, R. Balakrishnan, J. Cline, M. Robinson, and S. Ananthan, 2018: Large-Eddy Simulation sensitivities to variations of configuration and forcing parameters in canonical boundary layer flows for wind energy applications, Wind Energy Science, 3, 589-613. https://doi.org/10.5194/wes-3-589-2018 31. Haupt, S.E., B. Kosovic, T. Jensen, J. Lazo, J. Lee, P. Jimenz, J. Cowie, G. Wiener, T. McCandless, M. Rogers, S. Miller, M. Sangupta, Y. Xue, L. Hinkelman, P. Kalb, J. Heiser, 2018: Building the Sun4Cast System: Improvements in Solar Power Forecasting, Bulletin of the American Meteorological Society, Jan. 2018, 121-135. doi: 10.1175/BAMS-D-16-0221.1 32. Dobschinski, J. R. Bessa, P. Du, K. Geisler, S.E. Haupt, M. Lange, C. Mohrlen, D. Nakafuji, J. T. Rodriguez, 2017: Uncertainty Forecasting in a Nutshell, IEEE Power and Engineering Systems, 15 (6), 40-49. DOI: 10.1109/MPE.2017.2729100 33. Haupt, S.E., S. Dettling, J. Williams, J. Pearson, T. Jensen, T. Brummet, B. Kosovic, G. Wiener, T. McCandless, and C. Burghardt, 2017: Impact of Distributed PV on Demand Load Forecasts, Solar Energy, 157, 542-551. https://doi.org/10.1016/j.solener.2017.08.049 34. McGovern A, Elmore KL, Gagne DJ II, Haupt SE, Karstens CD, Lagerquist R, et al, 2017: Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bulletin of the American Meteorological Society Oct; 98 (10): 2073-90. https://doi.org/10.1175/BAMS-D-16-0123.1 35. Gagne II, D.J., A. McGovern, S.E. Haupt, R. Sobash, J.K. Williams, and M. Xue, 2017: Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles, Weather and Forecasting, 32, 1819-1840. https://doi.org/10.1175/WAF-D-17-0010.1 36. Reikard, G., S.E. Haupt, and T. Jensen, 2017: Forecasting ground-level irradiance over short horizons: Meteorological and time Series Models, Renewable Energy, 112, 474-485. https://doi.org/10.1016/j.renene.2017.05.019 37. Gagne II, D.J., A. McGovern, S.E. Haupt, and J.K. Williams, 2017: Evaluation of Statistical Learning Configurations for Gridded Solar Irradiance Forecasting, Solar Energy, 150, 383-393. https://doi.org/10.1016/j.solener.2017.04.031 38. Bieringer, P.E., G.S. Young, L.M. Rodriguez, A.J. Annunzio, F. Vandenberghe, and S.E. Haupt, 2017: Paradigms and commonalities in atmospheric source term estimation methods, Atmospheric Environment, 156, 102-112. http://dx.doi.org/10.1016/j.atmosenv.2017.02.011 39. Haupt, S.E. and B. Kosovic, 2017: Variable Generation Power Forecasting as a Big Data Problem, IEEE Transactions on Sustainable Energy, 8 (2), pp. 725-732. DOI: 10.1109/TSTE.2016.2604679. 40. Cheng, W.Y.Y., Y. Liu, A. Bourgeois, Y. Wu, and S.E. Haupt, 2017: Short-term Wind Forecast of a Data Assimilation/Weather Forecasting System with Wind Turbine Anemometer Measurement Assimilation, Renewable Energy, 107, 340-351. doi.org/10.1016/j.renene.2017.02.014 41. Lee, J.A., S.E. Haupt, P.A. Jimenez, T.C. McCandless, M.A. Rogers, and S.D. Miller, 2017: Solar Irradiance Nowcasting Case Studies near Sacramento, Weather and Forecasting, 56, 85-108. DOI: http://dx.doi.org/10.1175/JAMC-D-16-0183.1 42. Jimenez, P.A., S. Alessandrini, S.E. Haupt, A. Deng, B. Kosovic, J.A. Lee, and L. Delle Monache, 2016: Role of Unresolved Clouds on Short-Range Global Horizontal Irradiance Predictability, Monthly Weather Review, 144, 3099-3107. DOI: 10.1175/MWR-D-16-0104.1 43. Lee, J.A., S.E. Haupt, and G.S. Young, 2016: Down-Selecting NWP Multi-Physics Ensembles with Hierarchical Cluster Analysis, Journal of Climatology and Weather Forecasting, 4 (1), 10000156 (16 pp). http://dx.doi.org/10.4172/2332-2594.1000156 44. Petrozziello, A., G. Cervone, P. Franzese, S.E. Haupt, R. Cerulli, 2016: Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms, Applied Artificial Intelligence, 15 pp. http://dx.doi.org/10.1080/08839514.2017.1300005 45. McCandless, T.C., G.S. Young, S.E. Haupt, and L.M Hinkelman, 2016: Regime-Dependent Short-Range Solar Irradiance Forecasting, Journal of Applied Meteorology and Climatology, 55, 1599-1613. 46. Jimenez, P.A., J.P. Hacker, J. Dudhia, S.E. Haupt, J.A. Ruiz-Arias, C.A. Gueymard, G. Thompson, T. Eidhammer, and A.J. Deng, 2016: WRF-Solar: An Augmented NWP Model for Solar Power Prediction, Bull. Amer. Met. Soc, July 2016, 1249-1264. DOI:10.1175/BAMS-D-14-00279.1. 47. McCandless, T.C., S.E. Haupt, and G.S. Young, 2016: A Regime-Dependent Artificial Neural Network Technique for Short-Range Solar Irradiance Forecasting, Applied Energy, 89, 351-359. 48. Haupt, S.E., J. Copeland, W.Y.Y. Cheng, C. Amman, Y. Zhang, and P. Sullivan, 2016: Quantifying the Wind and Solar Power Resource and their Inter-annual Variability over the United States under Current and Projected Future Climate, Journal of Applied Meteorology and Climatology, 55, pp. 345-363. DOI: 10.1175/JAMC-D-15-0011.1 49. Tuohy, A., J. Zack, S. Haupt, J. Sharp, M. Ahlstrom, S. Dise, E. Grimit, C. Mohlren, M. Lange, M. Garcia Casado, J. Black, M. Marquis, C. Collier, 2015: Solar Forecasting – Method, Challenges, and Performance, IEEE Power and Energy Magazine, 13 (6), 50-59. DOI:10.1109/MPE.2015.2461351 50. McCandless, T.C., S.E. Haupt, and G.S. Young, 2015: A Model Tree Approach to Forecasting Solar Irradiance Variability, Solar Energy, 120, 514-524. DOI:10.1016/j.solener.2015.07.0200038-092X 51. Haupt, S.E. and W.P. Mahoney, 2015: Wind Power Forecasting, IEEE Spectrum, Nov 2015, pp. 46-52. 52. Orwig, K.D., M. Ahlstrom, V. Banunarayanan, J. Sharp, J.M. Wilczak, J. Freedman, S.E. Haupt, J. Cline, O. Bartholomie, H. Hamman, B.-M. Hodge, C. Finley, D. Nakafuji, J. Peterson, D. Maggio, and M. Marquis, 2014: Recent Trends in Variable Generation Forecasting and Its Value to the Power System, IEEE Transactions on Renewable Energy, 6 (3), 924-933. doi:10.1109/TSTE.2014.2366118. 53. Breen, B.P., K.J. Schmehl, G.S. Young, J.A. Lee, S.E. Haupt, and D.R. Stauffer, 2014: Uncertainty in Contaminant Concentration Fields Resulting from Planetary Boundary Layer Depth Uncertainty, J. Applied Meteorology and Climatology, 53 (11), 2610-2626. 54. Haupt, S.E. and L. Delle Monache, 2014: Understanding Ensemble Prediction: How Probabilistic Wind Power Prediction can Help in Optimizing Operations, WindTech, 10 (6), pp. 27-29. 55. Haupt, S.E., A.J. Annunzio, and K.J. Schmehl, 2013: Evolving Dispersion Realizations of Atmospheric Flow, Boundary Layer Meteorology, 149 (2), 197-217. 56. Troccoli, A., P. Audinet, P. Bonelli, S. Boulahya, C. Buontempo, P. Coppin, L. Dubus, J.A. Dutton, J. Ebinger, D. Griggs, S.-E. Gryning, D. Gunasekera, M. Harrison, S.E. Haupt, T. Lee, P. Mailier, P.-P. Mathieu, R. Schaeffer, M. Schroedter-Homscheidt, R. Zhu, and J. Zillman, 2013: Promoting New Links Between Energy and Meteorology, Bulletin of the American Meteorological Society, 94, ES36-ES40. 57. Haupt, R.L and S.E. Haupt, 2013: Wondering While Wandering, IEEE Antenna and Propagation Magazine, 55 (1), 215-216. 58. Annunzio, A.J., G.S. Young, S.E. Haupt, 2012: Combining methods from entity and field frameworks to determine the source information for a contaminant. Atmospheric Environment, 46, 580-589, 10.1016/j.atmosenv.2011.04.080. 59. Mahoney, W.P., K. Parks, G. Wiener, Y. Liu, B. Myers, J. Sun, L. Delle Monache, D. Johnson, T. Hopson, and S.E. Haupt, 2012: A Wind Power Forecasting System to Optimize Grid Integration, special issue of IEEE Transactions on Sustainable Energy on Applications of Wind Energy to Power Systems, 3 (4), 670-682. https://doi.org/10.1175/BAMS-D-12-00044.1. 60. Lee, J.A., W.C. Kolczynski, T.C. McCandless, and S.E. Haupt, 2012: Objective Techniques for Configuring and Down-Selecting an NWP Ensemble for Low-Level Wind Predictions, Monthly Weather Review, 140, 2270-2286. 61. Lei, L., D.R. Stauffer, S.E. Haupt, and G.S. Young, 2012: A Hybrid Ensemble Kalman Filter Approach to Data Assimilation in the Lorenz System, Tellus, 64, 18484, 14 pp. http://dx.doi.org/10.3402/tellusa.v64i0.18484. 62. Annunzio, A.J., G.S. Young, and S.E. Haupt, 2012: Utilizing State Estimation to Determine the Source Information for a Contaminant, Atmospheric Environment, 46, 580-589. 63. Schmehl, K.J., S.E. Haupt, and M. Pavolonis, 2012: A Genetic Algorithm Variational Approach to Data Assimilation and Application to Volcanic Emissions, Pure and Applied Geophysics, Invited submission to Special Issue on Data Assimilation and its Applications, DOI 10.1007/s00024-011-0385-0, 169, 519-537. 64. Kolczynski, W.C., D.R. Stauffer, S.E. Haupt, N.S. Altman and A. Deng, 2011: Investigation of ensemble variance as a measure of true forecast variance, Mon. Wea. Rev, 139, 3954-3963. DOI: 10.1175/MWR-D-10-05081.1 65. McCandless, T.M., S.E. Haupt, and G.S. Young, 2011: Statistical Guidance Methods for Predicting Snowfall Accumulation in the Northeast United States, National Weather Digest, 35 (2), 14 pp. 66. Zajaczkowski, F.J., S.E. Haupt, and K.J. Schmehl, 2011: A Preliminary Study of Assimilating Numerical Weather Prediction Data into Computational Fluid Dynamics Models for Wind Prediction, Journal of Wind Engineering and Industrial Aerodynamics, 99, 320-329, doi:10.1016/j.jweia.2011.01.023. (awarded ARL Research Publication Award 2012) 67. Rodriguez, L.M., S.E. Haupt, and G.S. Young, 2011: Impact of Sensor Characteristics on Source Characterization for Dispersion Modeling, Measurement, 44, 802-814. 68. McCandless, T.M., S.E. Haupt, and G.S. Young, 2011: The Effects of Imputing Missing Data on Ensemble Temperature Forecasts, Journal of Computers, 6 (2), 162-171. 69. Haupt, S.E., R.L. Haupt, and G.S. Young, 2011: A Mixed Integer Genetic Algorithm used in Chem-Bio Defense Applications, Journal of Soft Computing, 15 (1), 51-59. DOI 10.1007/s00500-009-0516-z. 70. Haupt, S.E., F.J. Zajaczkowski, and L.J. Peltier, 2011: Detached Eddy Simulation of Atmospheric Flow about a Surface Mounted Cube at High Reynolds Number, J. Fluids Engineering, 133, DOI: 10.1115/1.4003649, 8 pp. 71. Kuroki, Y., G.S. Young, and S.E. Haupt, 2010: UAV Navigation by an Expert System for Contaminant Mapping with a Genetic Algorithm, Expert Systems with Applications, 37, Issue 6, 4687-4697. (doi:10.1016/j.eswa.2009.12.039) 72. Haupt, S.E., R.F. Kunz, L.J. Peltier, and J.J. Dreyer, 2010: CFD Coupled with a Thermal Impact Model for Building Design, Journal of Computers, 5, No. 10, 1552-1559. 73. Peltier, L.J., S.E. Haupt, J.C. Wyngaard, D.R. Stauffer, A. Deng, and J.A. Lee, K.J. Long, and A.A. Annunzio, 2010: Parameterization of NWP Uncertainty for Dispersion Modeling, Journal of Applied Meteorology and Climatology, 49, 1604-1614. 74. Long, K.J., S.E. Haupt, and G.S. Young, 2010: Assessing Sensitivity of Source Term Estimation. Atmospheric Environment, 44, 1558-1567. 75. Lee, J.A., L.J. Peltier, S.E. Haupt, J.C. Wyngaard, D.R. Stauffer, and A. Deng, 2009: Improving SCIPUFF Dispersion Forecasts with NWP Ensembles, Journal of Applied Meteorology and Climatology, 48, 2305-2319. 76. Haupt, R.L., S.E. Haupt, and D. Aten, 2009: Evaluating the Radar Cross Section of Maritime Radar Reflectors using Computational Electromagnetics, ACES Journal, 24, 403-426. 77. Kolczynski, W.C., D.R. Stauffer, S.E. Haupt, and A. Deng, 2009: Ensemble Variance Calibration for Representing Meteorological Uncertainty for Atmospheric Transport and Dispersion Modeling, Journal of Applied Meteorology and Climatology, 48, 2001-2021. DOI: 10.1175/2009JAMC2059.1 78. Long, K.J., F.J. Zajaczkowski, S.E. Haupt, and L.J. Peltier, 2009: Modeling a Hypothetical Chlorine Release on a College Campus, Journal of Computers, 40, 881-890. 79. Haupt, S.E., A. Beyer-Lout, K.J. Long, and G.S. Young, 2009: Assimilating Concentration Observations for Transport and Dispersion Modeling in a Meandering Wind Field, Atmospheric Environment, 43, 1329-1338. 80. Greybush, S.J., S.E. Haupt, G.S. Young, 2008: The regime dependence of optimally weighted ensemble model consensus forecasts of surface temperature, Weather and Forecasting, 23, 1146-1161. 81. Haupt, S.E., G.S. Young, and C.T. Allen, 2007: A Genetic Algorithm Method to Assimilate Sensor Data for a Toxic Contaminant Release, Journal of Computers, 2, 85-93. 82. Allen, C.T., S.E. Haupt, and G.S. Young, 2007: Source Characterization with a Receptor/Dispersion Model Coupled with A Genetic Algorithm, Journal of Applied Meteorology and Climatology, 46, 273-287. 83. Allen, C.T., G.S. Young, and S.E. Haupt, 2007: Improving Pollutant Source Characterization by Optimizing Meteorological Data with a Genetic Algorithm, Atmospheric Environment, 41, 2283-2289. 84. Haupt, S.E. G.S. Young, and C.T. Allen, 2006: Validation Of A Receptor/Dispersion Model Coupled With A Genetic Algorithm, Journal of Applied Meteorology, 45, 476–490. 85. Haupt, S.E., 2006: A Quadratic Empirical Model Formulation for Dynamical Systems using a Genetic Algorithm,, Computers and Mathematics with Applications, 51, 431-440. 86. Haupt, S.E. and C. Truong, 2006: Program Development Grants, SWE Magazine, Fall Issue, 2006, 52-56. 87. Haupt, S.E., 2005: A Demonstration of Coupled Receptor/Dispersion Modeling with a Genetic Algorithm, Atmospheric Environment, 39, 7181-7189. 88. Haupt, S.E., 2005: Why are there fewer women in engineering? IEEE AP Magazine, Ethics Column contribution, April 2005 issue. 89. Haupt, S.E. and R.L. Haupt, 2005: IEEE Aerospace Conference Junior Engineering and Science Conference, Aerospace Systems Magazine, January 2005. 90. Haupt, S.E. and Gregory, J. D., 2004: Measuring the Mark: Planning and Assessment Boosts K-12 Outreach, SWE Magazine, Fall 2004. 91. Haupt, R.L. and S.E. Haupt, 2000: Optimum Population Size and Mutation Rate for a Simple Real Genetic Algorithm that Optimizes Array Factors, Applied Computational Electromagnetics Society Journal, 15, No. 2, pp. 94-102. 92. Haupt, R.L. and S.E. Haupt, 2000: Creativity with a Genetic Algorithm, IEEE Potentials, April/May 2000, pp. 26-29. 93. Branstator, G. and S.E. Haupt, 1998: An Empirical Model of Barotropic Atmospheric Dynamics and its response to Tropical Forcing, Journal of Climate, 11, 2645-2667. 94. Curry, J.H., S.E. Haupt, and M.N. Limber, 1995: Truncated Systems, Initialization, and the Slow Manifold, Tellus, 47A, 145-161. 95. Haupt, R.L., and S.E. Haupt, 1994: Introduction to Multigrid using MATLAB, Computer Applications in Engineering Education, 1, 421-431. 96. Haupt, S.E., J.C. McWilliams, and J. J. Tribbia, 1993: Modons in Shear Flow, Journal of the Atmospheric Sciences, 50, 1181-1198. 97. Haupt, S.E. and J.P. Boyd, 1991: Double Cnoidal Waves of the Korteweg de Vries Equation: Solution by the Spectral Boundary Value Approach, Physica D, 50, 117-134. 98. Haupt, S.E. and J.P. Boyd, 1988: Modeling Nonlinear Resonance: A Modification to the Stokes' Perturbation Expansion, Wave Motion, 10, 83-98.

Fully Refereed Conference Papers

1. Haupt, S.E., T.C. McCandless, J.C. Lee, B. Kosovic, S. Alessandrini, S. Dettling, T. Hussain, J. Al-Rasheedi, 2020: Combining Physical Modeling with Artificial Intelligence for Solar Power Forecasting, 47th IEEE Photovoltaic Specialists Conference (PVSC), Calgary, OR, pp. 2051-2053, doi: 10.1109/PVSC45281.2020.9300434.
2. Haupt, S.E., L. Berg, M. Churchfield, B. Kosovic, W. Shaw, J. Mirocha, 2019: Mesocale to Microscale Coupling for Wind Energy Applications: Addressing the Challenges, J. Physics Conference Series (2020) 012076 1452, NAWEA/WindTech Conference, Amherst, MA, October 15, 2019. doi:10.1088/1742-6596/1452/1/012076
3. Kosovic, B., P. Jimenez, T.W. Juliano, A. Martilli, M. Eghdami, A.P. Barros, and S.E. Haupt, 2019: Three-Dimensional Planetary Boundary Layer Parameterization for High-Resolution Mesoscale Simulations, J. Physics Conference Series (2020) 1452 012080, NAWEA/WindTech Conference, Amherst, MA. doi:10.1088/1742-6596/1452/1/012080
4. Haupt, S.E., J. Cowie, S. Linden, T.C. McCandless, B. Kosovic, and S. Alessandrini, 2018: Machine Learning for Applied Weather Prediction, IEEE eScience International Conference, Amsterdam, Netherlands, October 24.
5. Haupt, S.E. and B. Kosovic, 2015: Big Data and Machine Learning for Applied Weather Forecasts: Forecasting Solar Power for Utility Operations, IEEE Symposium Series on Computational Intelligence, Capetown, South Africa, December 9.
6. Jayaraman, B., J. Brasseur, T.C. McCandless, and S.E. Haupt, 2014: Nonequilibrium behavior of the daytime atmospheric boundary layer from LES. In Bulletin of the American Physical Society, 67th Annual Meeting of the APS Division of Fluid Dynamics, Vol. 59, Nov 2014.
7. Stewart, S.W., S.E. Haupt, and J.A. Cole, 2011: Addressing Wind Resource Potential in the Built Environment, ASME 5th International Conference on Energy Sustainability, Washington, DC, Aug. 7-10.
8. Haupt, S.E., G. Wiener, Y. Liu, B. Myers, J. Sun, D. Johnson, and W. Mahoney, 2011: A Wind Power Forecasting System to Optimize Power Integration, ASME 5th International Conference on Energy Sustainability, Washington, DC, Aug. 7-10.
9. Haupt, S.E., G. Wiener, J. Sun, S. Linden, B. Myers, Y. Liu, W. Mahoney, and D. Johnson, 2011: Forecasting Wind Power Ramps, 13th International Conference on Wind Engineering, Amsterdam, Netherlands, July 13, 2011.
10. Haupt, S.E., F.J. Zajaczkowski, K.J. Long, and A.J. Annunzio, 2010: Assimilating NWP data into CFD Models for Wind Prediction, Fifth International Symposium on Computational Wind Engineering, Chapel Hill, NC, May 23-27.
11. Haupt, S.E., K.J. Long, A.J. Annunzio, and L.M. Rodriguez, 2010: Predicting Realizations versus Averages: Applying Assimilation to Improve Dispersion Modeling for Security Analysis, Fifth International Symposium on Computational Wind Engineering, Chapel Hill, NC, May 23-27.
12. Haupt, S.E., G.S. Young, and A.J. Annunzio, 2010: Inverting Surface Observations: Convective and Stable Boundary Layer Depth Estimation Methods, Fifth International Symposium on Computational Wind Engineering, Chapel Hill, NC, May 23-27.
13. Zajaczkowski, F.J., S.E. Haupt, and K.J. Long, 2010: Wind Turbine Siting by Using Mesoscale Model Data Assimilation and Computational Fluid Dynamics, 48th AIAA Aerospace Sciences Meeting, Orlando, FL, 4-7 Jan.
14. Haupt, S.E., G.S. Young, K.J. Long, A. Beyer-Lout, and A. Annunzio, 2008: Data Fusion and Prediction for CBRN Transport and Dispersion for Security, 2008 IEEE Aerospace Conference with AIAA, Big Sky, MT, March 1-8.
15. Haupt, S.E., R.L. Haupt, and G.S. Young, 2007: Using Genetic Algorithms in Chem-Bio Defense Applications, ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security (BLISS-2007), Edinburgh, UK, Aug. 9-10.
16. Haupt, S.E., G.S. Young, K.J. Long, and A. Beyer, 2007: Data Requirements from Evolvable Sensor Networks for Homeland Security Problems, NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2007), Edinburgh, UK, Aug. 5-9.
17. Wilson, R.P., S.E. Haupt, L.J. Peltier, and R.F. Kunz, 2006: Detached Eddy Simulation of a Surface Mounted Cube at High Reynolds Number. Proceedings of the ASME Joint U.S. - European Fluids Engineering Summer Meeting, Miami, FL. July 17-20.
18. Haupt, S.E., R.F. Kunz, L.J. Peltier, J.J. Dreyer, and H.J. Gibeling, 2006: Impact of Heat Transfer on Contaminant Dispersion in a Public Building. Proceedings of the ASME Joint U.S. - European Fluids Engineering Summer Meeting, Miami, FL. July 17-20.
19. Haupt, S.E., C.T. Allen, and G.S. Young, 2006: A Genetic Algorithm Method for Sensor Data Assimilation and Source Characterization, World Congress on Computational Intelligence, Vancouver, Canada, July 16-20.
20. Haupt, S.E., 2003: Empirical Modeling of Unsteady Flows, Proceedings of the Fluid Engineering Division, 4th ASME-JSME Joint Fluids Engineering Conference Honolulu, HI, Paper FEDSM2003-45761.
21. Haupt, S.E. and C.E. Hailey, 2002: Empirical Modeling of Observed Microchannel Flow, Proceedings of the IEEE Aerospace Conference, Big Sky, MT.
22. Haupt, S.E. and R.L. Haupt, 1998: Genetic Algorithms in Complex Systems, Proceedings of the IEEE Aerospace Conference, 1998, Snowmass, CO.
23. Haupt, S.E. and R.L. Haupt, 1997: Phase Only Adaptive Nulling with a Genetic Algorithm, Proceedings of the IEEE Aerospace Conference, 1997 Snowmass, CO.
24. Haupt, S.E., 1996: Modon Stability, Abstract volume of the Mathematical Geophysics Symposium, Santa Fe, NM.
25. Haupt, S.E., R.L. Haupt, and J.C. Adams, 1991: Multigrid analysis of four-point-probe measurements of tapered resistive sheets, Fourth Copper Mountain Conference on Multigrid Techniques.
26. Haupt, R.L. and S.E. Haupt, 1991: Multigrid with MATLAB, Fourth Copper Mountain Conference on Multigrid Techniques.
27. Haupt, S.E. and R.L. Haupt, 1990: Multigrid analysis of four-point-probe measurements of tapered resistive sheets, 1990 National Radio Science Meeting, Boulder, CO.

Many other conference papers and invited talks - see CV

Publications and Presentations (84)
Title	Source	Date
Impact of tropical and extratropical cyclones on future U.S. Offshore wind energy	Bulletin of the American Meteorological Society	2024-08-01
Identifying wind regimes near Kuwait using self-organizing maps	Journal of Renewable and Sustainable Energy	2024-03-06
Classification of tall tower meteorological variables and forecasting wind speeds in Columbia, Missouri	Renewable Energy	2023-11-01
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy	Wind Energy Science	2023-08-16
Challenges and opportunities in numerical weather prediction	Bulletin of the American Meteorological Society	2023-03-01
Machine learning for improving surface-layer-flux estimates	Boundary-Layer Meteorology	2022-11-01
Enhancing forecast value with artificial intelligence	NOAA UFS Seminar Series 2022	2022-10-13
“Gray Zone” simulations using a three-dimensional planetary boundary layer parameterization in the Weather Research and Forecasting Model	Monthly Weather Review	2022-07-01
The history and practice of AI in the environmental sciences	Bulletin of the American Meteorological Society	2022-05-01
Performance analysis of a 10-MW wind farm in a hot and dusty desert environment. Part 1: Wind resource and power generation evaluation	Sustainable Energy Technologies and Assessments	2021-10-01
Performance analysis of a 10-MW wind farm in a hot and dusty desert environment. Part 2: Combined dust and high-temperature effects on the operation of wind turbines	Sustainable Energy Technologies and Assessments	2021-10-01
Determining chaotic characteristics and forecasting tall tower wind speeds in Missouri using empirical dynamical modeling (EDM)	Renewable Energy	2021-06-01
Outlook for exploiting artificial intelligence in the earth and environmental sciences	Bulletin of the American Meteorological Society	2021-05-01
Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop	Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences	2021-04-01
Climatology of wind variability for the Shagaya region in Kuwait	Renewable and Sustainable Energy Reviews	2020-11-01
Combining artificial intelligence with physics-based methods for probabilistic renewable energy forecasting	Energies	2020-04-02
A comprehensive wind power forecasting system integrating artificial intelligence and numerical weather prediction	Energies	2020-03-16
Comparison of implicit vs. explicit regime identification in machine learning methods for solar irradiance prediction	Energies	2020-02-05
On bridging a modeling scale gap: Mesoscale to microscale coupling for wind energy	Bulletin of the American Meteorological Society	2019-12-01
Assessing evidence for weather regimes governing solar power generation in Kuwait	Energies	2019-11-20
The use of probabilistic forecasts: Applying them in theory and practice	IEEE Power and Energy Magazine	2019-11-01
A regime-based approach for integrating wind information in spatio-temporal solar forecasting models	Journal of Renewable and Sustainable Energy	2019-09-01
Interpretable deep learning for spatial analysis of severe hailstorms	Monthly Weather Review	2019-08-01
Evaluation of the impact of horizontal grid spacing in terra incognita on coupled mesoscale–microscale simulations using the WRF framework	Monthly Weather Review	2019-03-01
A review of the potential impacts of climate change on bulk power system planning and operations in the United States	Renewable and Sustainable Energy Reviews	2018-12-01
Generation of spatial weather fields with generative adversarial networks	4th Conference on Stochastic Weather Generators (SWGEN 2018)	2018-10-02
100 years of progress in applied meteorology. Part I: Basic applications	Meteorological Monographs	2018-01-01
Building the Sun4Cast System: Improvements in solar power forecasting	Bulletin of the American Meteorological Society	2018-01-01
Uncertainty forecasting in a nutshell: Prediction models designed to prevent significant errors	IEEE Power and Energy Magazine	2017-11-01
Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models	Renewable Energy	2017-11-01
Blending distributed photovoltaic and demand load forecasts	Solar Energy	2017-11-01
Using artificial intelligence to improve real-time decision-making for high-impact weather	Bulletin of the American Meteorological Society	2017-10-01
Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles	Weather and Forecasting	2017-10-01
Evaluation of statistical learning configurations for gridded solar irradiance forecasting	Solar Energy	2017-07-01
Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation	Renewable Energy	2017-07-01
Paradigms and commonalities in atmospheric source term estimation methods	Atmospheric Environment	2017-05-01
Source reconstruction of atmospheric releases with limited meteorological observations using genetic algorithms	Applied Artificial Intelligence	2017-02-07
Solar irradiance nowcasting case studies near Sacramento	Journal of Applied Meteorology and Climatology	2017-01-01
Applications of meteorology for energy	University of Connecticut Seminar 2016	2016-11-04
The role of unresolved clouds on short-range global horizontal irradiance predictability	Monthly Weather Review	2016-09-01
WRF-Solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction	Bulletin of the American Meteorological Society	2016-07-01
Regime-dependent short-range solar irradiance forecasting	Journal of Applied Meteorology and Climatology	2016-07-01
The Sun4Cast® Solar Power Forecasting System: The Result of the Public-Private-Academic Partnership to Advance Solar Power Forecasting	NCAR Technical Note	2016-06-16
Metrics for Evaluation of Solar Energy Forecasts	NCAR Technical Note	2016-06-14
A regime-dependent artificial neural network technique for short-range solar irradiance forecasting	Renewable Energy	2016-04-01
Down-selecting numerical weather prediction multi-physics ensembles with hierarchical cluster analysis	Journal of Climatology and Weather Forecasting	2016-02-29
A method to assess the wind and solar resource and to quantify interannual variability over the United States under current and projected future climate	Journal of Applied Meteorology and Climatology	2016-02-01
A model tree approach to forecasting solar irradiance variability	Solar Energy	2015-10-01
The SunCast Solar Power Forecasting Decision Support System [presentation]	American Solar Energy Society Conference	2015-07-28
Integrating distributed PV forecasts with load forecasts [presentation]	American Solar Energy Society Conference	2015-07-28
Recent trends in variable generation forecasting and its value to the power system	IEEE Transactions on Sustainable Energy	2015-07-01
The SunCast Solar Power Forecasting System [presentation]	International Conference on Energy and Meteorology	2015-06-23
Solar metrics [presentation]	3rd International Conference on Energy and Meteorology	2015-06-23
Short-range weather forecasting (hours to days) for energy applications [presentation]	3rd International Conference on Energy and Meteorology Pre-Conference Seminar	2015-06-22
NCAR's recent advances in wind power forecasting [presentation]	Meeting of the North American Wind Energy Association	2015-06-11
Counting on solar production: Advances in forecasting [presentation]	Utility Solar Conference of the Solar Electric Power Association	2015-04-28
Solar power forecasting: SunCast and GRAFS [presentation]	Utility Variable Generation Integration Group Forecasting Workshop	2015-02-19
Introduction to probabilistic forecasting [presentation]	UVIG Variable Generation Forecasting Workshop 2015	2015-02-18
The Suncast Solar Power Forecasting System [presentation]	95th AMS Annual Meeting	2015-01-07
Solar metrics: The relationship between forecast system component behavior and the overall score [presentation]	Sixth Conference on Weather, Climate, and the New Energy Economy	2015-01-06
Uncertainty in contaminant concentration fields resulting from atmospheric boundary layer depth uncertainty	Journal of Applied Meteorology and Climatology	2014-11-01
Evolving turbulence realizations of atmospheric flow	Boundary-Layer Meteorology	2013-11-01
Promoting new links between energy and meteorology	Bulletin of the American Meteorological Society	2013-04-01
A Multi-Entity Field Approximation to determine the source location of multiple atmospheric contaminant releases	Atmospheric Environment	2012-12-01
A wind power forecasting system to optimize grid integration	IEEE Transactions on Sustainable Energy	2012-10-01
An objective methodology for configuring and down-selecting an NWP Ensemble for low-level wind prediction	Monthly Weather Review	2012-07-01
A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: Application in the Lorenz system	Tellus Series A: Dynamic Meteorology and Oceanography	2012-05-29
A genetic algorithm variational approach to data assimilation and application to volcanic emissions	Pure and Applied Geophysics	2012-03-01
Combining methods from entity and field frameworks to determine the source information for a contaminant	Atmospheric Environment	2012-01-01
Addressing wind resource potential in the built environment	Proceedings of the ASME 5th International Conference on Energy Sustainability 2011	2012-01-01
Utilizing state estimation to determine the source location for a contaminant	Atmospheric Environment	2012-01-01
Investigation of ensemble variance as a measure of true forecast variance	Monthly Weather Review	2011-12-01
Statistical guidance methods for predicting snowfall accumulation in the Northeast United States	National Weather Digest	2011-08-31
Forecasting wind power ramps [presentation]	13th International Conference on Wind Engineering	2011-08-31
A wind power forecasting system to optimize power integration [presentation]	5th ASME/JSME Engineering Conference	2011-08-07
Impact of sensor characteristics on source characterization for dispersion modeling	Measurement	2011-06-01
A consensus forecasting approach for improved turbine hub height wind speed predictions	WINDPOWER 2011 Conference & Exhibition	2011-05-24
A consensus forecasting approach for improved turbine hub height wind speed predictions	AWEA Windpower Conference & Exhibition 2011	2011-05-24
A preliminary study of assimilating numerical weather prediction data into computational fluid dynamics models for wind prediction	Journal of Wind Engineering and Industrial Aerodynamics	2011-04-01
Detached eddy simulation of atmospheric flow about a surface mounted cube at high Reynolds number	Journal of Fluids Engineering	2011-03-01
The effects of imputing missing data on ensemble temperature forecasts	Journal of Computers	2011-02-01
A mixed integer genetic algorithm used in chem-bio defense applications	Soft Computing: A Fusion of Foundations, Methodologies and Applications	2011-01-01
Eigenvalue matching, a traveling salesman, and a genetic algorithm	CGD Division Briefings and Research Reports	1996-04-29
Modons in shear flow	Journal of the Atmospheric Sciences	1993-05-01

Curriculum Vitae

Job Duties

Help oversee science and personnel of the Research Applications Laboratory, National Center for Atmospheric Research. Oversee programs in use of atmospheric science in aviation, security, hydrology, forecasting, renewable energy, surface transportation,wildland fire management, artificial intelligence methods used for decision support, and beyond. Manage projects. Develop programs.

Professional Interests

Wind and Renewable Energy, Boundary Layer Meteorology, Turbulence, Dispersion, Dynamical Systems, Fluid Dynamics, Dynamic Meteorology, Climate, Numerical Modeling, Artificial Intelligence, Genetic Algorithms, Spectral Methods, Inverse Methods, Computational Fluid Dynamics, Geophysical Fluid Dynamics, Coherent Structures, Physical Oceanography, Nonlinear Wave Mechanics

Education

Ph.D., Atmospheric and Oceanic Science, The University of Michigan.
M.S., Mechanical Engineering, Worcester Polytechnic Institute.
M.S., Engineering Management, Western New England College.
B.S., Meteorology and Marine Science Certificate, The Pennsylvania State University.

Selected Publications

Practical Genetic Algorithms, 2nd Editon, with CD, R.L. Haupt and S.E. Haupt, John Wiley & Sons, 2004, 253pp.
(see Amazon.com or Wiley homepage for more info)

Practical Genetic Algorithms, R.L. Haupt and S.E. Haupt, John Wiley & Sons, 1998, 177pp.

Applications of Artificial Intelligence Methods in Environmental Science, ed. S.E. Haupt, C. Marzban, and A. Pasini, 2009. Springer. (S.E. Haupt author of 4 chapters plus primary editor)

Troccoli, A., L. Dubus, and S.E. Haupt, eds., 2014: Weather Matters for Energy, Springer, 528pp.

Full_curvit_2023B.doc

Education

Degree	School Name	Country	Field of Study
BS	Pennsylvania State University-Main Campus	United States of America	Atmospheric Science/Meteorology
BS	Pennsylvania State University-Main Campus	United States of America	Atmospheric Science/Meteorology
PhD	University of Michigan-Ann Arbor	United States of America	Atmospheric Science/Meteorology
PhD	University of Michigan-Ann Arbor	United States of America	Atmospheric Science/Meteorology
MS	Western New England College	United States of America	Business Administration/Management
MS	Worcester Polytechnic Institute	United States of America	Mechanical Engineering

Interests

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Authored and Edited Books

Book Chapters (all peer reviewed)

Refereed Journal and Magazine Publications

Fully Refereed Conference Papers

Job Duties

Professional Interests

Education

Selected Publications

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