Current Research

In short, my current research aims to provide guidance for the next version of the NSSL’s Warn-on-Forecast system (WoFS). I am primarily focused on trying to improve storm-scale analyses and short-term forecasts by advancing and developing novel data assimilation and modeling techniques. More specifically, I am:

  • working with other scientists at NSSL/CIWRO to develop a 1-km version of WoFS (WoFS-1km)
  • investigating the impact of correcting storm displacement errors on analyses and forecasts of severe convective weather using my version of the feature-alignment technique (FAT) prior to each data assimilation cycling step,
  • researching and developing stochastic physics perturbation methods and their potential beneficial impacts for WoFS,
  • verifying WoFS analyses and forecasts using novel techniques, and
  • helping explore ways to best assimilate temporally-frequent (every ~1 min) phased-array radar (PAR) volumetric data onto a 1-km grid domain using an ensemble Kalman filter (EnKF).

Research Interests

My research interests primarily focus on…

  • ensemble, storm-scale (< 4 km) analyses and short-term (< 6 h) forecasts of severe convective weather
  • data assimilation techniques/methods
  • storm-scale predictability
  • forecast verification using traditional, spatial, and object-based techniques
  • collection of severe storm observations using mobile mesonets, radiosondes, and research radars

Past Research

For my M.S. degree, I worked with Dr. Michael Coniglio to assess the impact of assimilating radar data on forecasts of convection using both traditional and spatial verification techniques (Stratman et al. 2013). For my Ph.D. degree, I worked with Dr. Keith Brewster to explore the impacts of various data assimilation strategies on analyses and forecasts of the 24 May 2011 Oklahoma tornado outbreak. These experiments included assimilating CASA radar data, applying incremental analysis updating and cycling data assimilation techniques, and using more sophisticated microphysical parameterization schemes (Stratman and Brewster 2017). In addition, I evaluated forecast skill using grid-, neighborhood-, and object-based verification techniques.

As an NRC Postdoctoral Research Associate at NSSL, I worked with Dr. Corey Potvin on improving analyses and forecasts of idealized supercells by correcting for storm displacement errors during the data assimilation process using a new version of the feature alignment technique (FAT) I developed (Stratman et al. 2018). The FAT was advanced and tested in multiple-storm scenarios in Stratman and Potvin 2022. In addition to advancing the FAT work, I worked with Dr. Nusrat Yussouf and others in assessing the impact of assimilating NSSL’s phased-array radar (PAR) data at different temporal frequencies on short-term forecasts of severe convection using a data assimilation and forecast system similar to the WoF system (WoFS) (Stratman et al. 2020).

Publications

  • Stratman, D. R., N. Yussouf, C. A. Kerr, B. C. Matilla, J. R. Lawson, and Y. Wang, 2024: Testing Stochastic and Perturbed Parameter Methods in an Experimental 1-km Warn-on-Forecast System Using NSSL’s Phased-Array Radar Observations. Mon. Wea. Rev., 152, 433–454, https://doi.org/10.1175/MWR-D-23-0095.1.
  • Heinselman, P. L., and Coauthors, 2023: Warn-on-Forecast System: From Vision to Reality. Wea. Forecasting, 39, 75–95, https://doi.org/10.1175/WAF-D-23-0147.1.
  • Kerr, C. A., B. C. Matilla, Y. Wang, D. R. Stratman, T. A. Jones, and N. Yussouf, 2022: Results from a Realtime Next-Generation 1-km Warn-on-Forecast System Prototype. Wea. Forecasting, 38, 307–319, https://doi.org/10.1175/WAF-D-22-0080.1.
  • Wang, Y., N. Yussouf, C. A. Kerr, D. R. Stratman, B. C. Matilla, 2022: An Experimental 1-km Warn-on-Forecast System for Hazardous Weather Events, Mon. Wea. Rev., 150, Early Online Release, https://doi.org/10.1175/MWR-D-22-0094.1.
  • Stratman, D. R. and C. K. Potvin, 2022: Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios, Mon. Wea. Rev., 150, 2033-2054, https://doi.org/10.1175/MWR-D-21-0289.1.
  • Putnam, B. J., Y. Jung, N. Yussouf, D. Stratman, T. Supinie, M. Xue, C. Kuster, and J. Labriola, 2021: The Impact of Assimilating ZDR Observations on Storm-Scale Ensemble Forecasts of the 31 May 2013 Oklahoma Storm Event. Mon. Wea. Rev., 149, 1919-1942, https://doi.org/10.1175/MWR-D-20-0261.1.
  • Weber, M., K. Hondl, N. Yussouf, Y. Jung, D. Stratman, and Coauthors, 2021: Towards the Next Generation Operational Meteorological Radar. Bull. Amer. Meteor. Soc., 102, E1357–E1383, https://doi.org/10.1175/BAMS-D-20-0067.1.
  • Stratman, D. R., N. Yussouf, Y. Jung, T. A. Supinie, M. Xue, P. S. Skinner, and B. J. Putnam, 2020: Optimal temporal frequency of NSSL phased-array radar observations for an experimental Warn-on-Forecast system. Wea. Forecasting, 35, 193–214, https://doi.org/10.1175/WAF-D-19-0165.1.
  • Stratman, D. R., C. K. Potvin, and L. J. Wicker, 2018: Correcting storm displacement errors in ensembles using the feature alignment technique (FAT). Mon. Wea. Rev., 146, 2125–2145, https://doi.org/10.1175/MWR-D-17-0357.1.
  • Stratman, D. R. and K. A. Brewster, 2017: Sensitivities of 1-km forecasts of 24 May 2011 tornadic supercells to microphysics parameterizations. Mon. Wea. Rev., 145, 2697–2721, https://doi.org/10.1175/MWR-D-16-0282.1.
  • Gallo, B. T., A. J. Clark, I. Jirak, J. S. Kain, S. J. Weiss, M. Coniglio, K. Knopfmeier, J. Correia, C. J. Melick, C. D. Karstens, E. Iyer, A. R. Dean, M. Xue, F. Kong, Y. Jung, F. Shen, K. W. Thomas, K. Brewster, D. Stratman, G. W. Carbin, W. Line, R. Adams-Selin, and S. Willington, 2017: Breaking New Ground in Severe Weather Prediction: The 2015 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Wea. Forecasting, 32, 1541–1568, https://doi.org/10.1175/WAF-D-16-0178.1.
  • Stratman, D. R., M. C. Coniglio, S. E. Koch, M. Xue, 2013: Use of multiple verification methods to evaluate forecasts of convection from hot- and cold-start convection-allowing models. Wea. Forecasting, 28, 119–138, https://doi.org/10.1175/WAF-D-12-00022.1.