John R. Mecikalski, Ph.D.

Professor & Department Chair, Atmospheric and Earth Science


320 Sparkman Drive
National Space Science & Technology Center
Room 3040
Huntsville, AL 35899
Campus Map



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


  • 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


  • 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.

  • 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.