John R. Mecikalski, Ph.D. Professor & Department Chair, Atmospheric and Earth Science Contact 320 Sparkman DriveNational Space Science & Technology CenterRoom 3040Huntsville, AL 35899 Campus Map email@example.com Biography John's interest in weather started in elementary school, and this fed his curiosity to collect weather observations in Wisconsin for ~10 years. chasing storms, modeling convective clouds, and understanding how to use satellite data provides the basis of his enthusiasm in a relatively wide variety of research he performs today at UAH. Curriculum Vitae Personal Website Education Ph.D., Atmospheric Science, University of Wisconsin, 1999 M.S., M.S., Atmospheric Science, University of Wisconsin, 1991 B.S., Atmospheric Science, University of Wisconsin, 1998 Expertise Dynamics of scale interaction within convectively driven weather systems Tropical meteorology Data assimilation and numerical modeling Regional energy budget estimations Assessing the atmospheric boundary layer and convection using remote sensing technologies Recent Publications Mecikalski, J. R., T. N. Sandmal, E. M. Murillo, C. R. Homeyer, K. M. Bedka, J. M. Apke, and C. P. Jewett, 2021: A random forest model to assess predictor importance and nowcast severe storms using high-resolution radar–GOES satellite–lightning observations. Mon. Wea. Rev., In review. Apke, J. M., J. R. Mecikalski, 2021: On the origin of rotation derived from super rapid scan satellite imagyer at the cloud tops of severe deep convection. Mon. Wea. Rev., In press. Henderson, D. S., J. A. Otkin, and J. R. Mecikalski, 2021: Characteristics of convection initiation in high-resolution numerical weather prediction models: Evaluation using geostationary satellite-based forecast interest fields. Mon. Wea. Rev., In press. Mishra, V., J. F. Cruise, and J. R. Mecikalski, 2020: Assimilation of coupled microwave/thermal infrared soil moisture profiles into a crop model for robust maize yield estimates over Southeast United States. European J. Agronomy, 123, 126208. Li, X., J. R. Mecikalski, and T. J. Lang, 2020: A study on assimilation of CYGNSS wind speed data for tropical convection during 2018 January MJO. Remote Sensing, 12, 1243; doi:10.3390/rs12081243 Li, X., J. R. Mecikalski, J. Srikkishen, B. Zavodsky, and W. A Petersen, 2019: Assimilation of GPM rain-rate products with GSI data assimilation system for heavy and light precipitation events. J. Advances in Modeling Earth Systems,12, e2019MS001618. https://doi.org/10.1029/2019MS001618 Apke, J. M., J. R. Mecikalski, K. M. Bedka, E. W. McCaul, C. R. Homeyer, and C. P. Jewett, 2018: Investigating the relationship between deep convection updraft characteristics and satellite based super rapid scan mesoscale atmospheric motion vector derived flow. Mon. Wea. Rev., 146, 3461. Posselt, D. J., Li, X., Tushaus, S. A., Mecikalski, J. R. (in press). Assimilation of dual-polarization radar observations in mixed- and ice- phase regions of convective storms: Information content and forward model error. Mon. Wea. Rev. Gravelle, C. M., Mecikalski, J. R., Bedka, K. M., Line, W. E., Petersen, R. A., Sieglaff, J. M., Stano, G. T., Goodman, S. J. (in press). Using GOES-R demonstration products to "bridge the gap" between severe weather watches and warnings: An example for the 20 May 2013 Moore, OK tornado outbreak. Bull. Amer. Meteorol. Soc.