Prof. Frédéric Fabry

 

Remote Sensing and Precipitation Prediction                                                                                                                                                                     

Office: Burnside Hall 810
Tel.: (514) 398-3652
Fax.: (514) 398-6115
Website
frederic.fabry [at] mcgill.ca (Email)


Research interests

Short-term (0-3 hr) prediction of disrupting atmospheric phenomena is one of the rare areas of meteorology that has not seen any significant progress in the past few decades. As a result, the most publicly visible forecast busts in recent years occurred from missed warnings of severe weather. Short-term prediction is also a task where we do not yet largely rely on numerical forecasting. There are many reasons for this: severe atmospheric phenomena evolve quickly; their numerical prediction requires considerable processing as well as proper initial conditions at high resolution; these initial conditions, coming largely from remote sensing, remain currently incomplete especially on properties that drive storm evolution such as temperature, humidity, and winds.

To overcome these problems, progress must occur on many fronts. We must improve the quantity and quality of the data to be assimilated in models. Radar data assimilation is also more complex than that of other data, and radar-specific ideas should be explored. It is also possible that direct detection and non-numerical forecasting may remain the only practical prediction approach in the foreseeable future, and we need to develop new ideas on that front too.

My research interests hence include:

Improving the remote sensing of atmospheric properties

On the technical side, we must improve the quantity, quality, and variety of data available to initialize models properly. This includes:

  1. The improved exploitation of a radar-based technique to estimate the index of refraction of air near the surface (Fabry et al. 1997; Feng et al. 2016). This provides valuable information especially on near-surface humidity at both small scales around a radar and at larger scales when considering constraints from many radars (Feng and Fabry 2018);
  2. The development of new signal processing techniques to extend the coverage of radar data and provide more constraints to initialize numerical models, or to understand problems with existing approaches (Fabry et al. 2013);
  3. Experimentation with new remote sensing techniques such as a scanning microwave radiometer to constrain humidity over larger areas than traditional instruments (Fabry and Meunier 2009; Themens and Fabry 2014);
  4. The meteorological analysis of data from our sensors. We operate a variety of instruments that we use to study atmospheric processes and their radar signatures such as riming (Vogel and Fabry 2018) or that we combine with other sensors to extract more information and evaluate model performance (e.g., Radhakrishna et al. 2015).

Data assimilation at the convective scale

Data assimilation is a well-established yet evolving technique to combine optimally the information from past forecasts and the constraints from new measurements to obtain the best estimate of initial conditions for generating useful forecasts. But radar data assimilation proves difficult: information is better on the storms’ consequences (precipitation, storm flows) than on its causes (temperature, humidity, environmental winds); it is also limited about fields that will affect the storms’ future (e.g., inflow properties). Extracting and properly constraining those properties is critical yet currently poorly done; we are hence investigating approaches to improve that task.

Nowcasting of precipitation and severe weather

Until now, for very short-term forecasts (known as “nowcasts”), nothing performs better than using radar data and extrapolating them in time. These short-term forecasts, are used for a variety of applications such as aviation weather and flood forecasting. Most approaches rely on decade-old ideas. We seek to improve these approaches by better exploiting the large radar data sets now available (Fabry et al. 2017) as well as the newly available frequent satellite information and model guidance.

To learn more

For details on publications made by my students, co-workers, and I, consult the Google scholar citations.

I also wrote a textbook, Radar Meteorology – Principles and Practice that introduces readers to radar and how to use it in both the operational meteorology and research context.

You can also consult our group’s web site for more details on our activities: www.radar.mcgill.ca.

References

  • Fabry, F., C. Augros, and A. Bellon, 2013. The case of sharp velocity transitions in high vertical wind shear when measuring Doppler velocities with narrow Nyquist intervals. Journal of Atmospheric and Oceanic Technology, 30, 389–394, doi: 10.1175/JTECH-D-12-00151.1.
  • Fabry, F., C. Frush, A. Kilambi, and I. Zawadzki, 1997. On the extraction of near-surface index of refraction using radar phase measurements from ground targets. Journal of Atmospheric and Oceanic Technology, 14, 978–987, doi: 10.1175/1520-0469(1995)052<0838:LTROOT>2.0.CO;2.
  • Fabry, F., and V. Meunier, 2009. Conceptualisation and design of a "mesoscale radiometer". Proceedings, 8th International Symposium on Tropospheric Profiling, Delft, Netherlands, 18-22 October 2009, paper S06-P03.
  • Fabry, F., V. Meunier, B.Puigdomènech Treserras, A. Cournoyer, and B. Nelson, 2017: On the climatological use of radar data composites: Possibilities and challenges. Bulletin of the American Meteorological Society, 98, 2135–2148, doi: 10.1175/BAMS-D-15-00256.1.
  • Feng, Y.-C., and F. Fabry, 2018. Quantifying the information of radar-estimated refractivity by dual-polarimetric and multiple elevation data. Journal of Atmospheric and Oceanic Technology (in press).
  • Feng, Y.-C., F. Fabry, and T. M. Weckwerth, 2016. Improving radar refractivity retrieval by considering the change in the refractivity profile and the varying altitudes of ground targets. Journal of Atmospheric and Oceanic Technology, 33, 989–1004, doi: 10.1175/JTECH-D-15-0224.1.
  • Radhakrishna, B., F. Fabry, J. J. Braun, and T. Van Hove, 2015.  Precipitable water from GPS over the continental United States: diurnal cycle, intercomparisons with NARR, and link with convection initiation. Journal of Climate, 28, 2584–2599.
  • Themens, D., and F. Fabry, 2014. Why scanning instruments are a necessity for constraining temperature and humidity fields in the lower atmosphere. Journal of Atmospheric and Oceanic Technology, 31, 2462–2481, doi: 10.1175/JTECH-D-14-00017.1.
  • Vogel, J.M., and F. Fabry, 2018: Attempts to observe polarimetric signatures of riming in stratiform precipitation. Journal of Applied Meteorology and Climatology, 57, 457–476, doi: 10.1175/JAMC-D-16-0370.1.

Currently teaching

ATOC 309 (Winter 2019)


Some recent publications

  • Besson, L., O. Caumont, L. Goulet, S. Bastin, L. Menut, F. Fabry, and J. Parent du Châtelet, 2017. Comparison of real-time refractivity measurement by radar with automatic weather stations, AROME-WMED and WRF forecasts simulations during the SOP1 of HyMeX campaign. Quarterly Journal of the Royal Meteorological Society, 142, 138–152, doi: 10.1002/qj.2799.
  • Fabry, F., V. Meunier, B.Puigdomènech Treserras, A. Cournoyer, and B. Nelson, 2017: On the climatological use of radar data composites: Possibilities and challenges. Bulletin of the American Meteorological Society, 98, 2135–2148,  doi: 10.1175/BAMS-D-15-00256.1.
  • Boulanger, Y., F. Fabry, A. Kilambi, D. S. Pureswaran, B. R. Sturtevant, and R. Saint-Amant, 2017: The use of weather surveillance radar coupled with high-resolution three dimensional weather data to monitor a spruce budworm mass exodus flight. Agricultural and Forest Meteorology, 234-235, 127–135, doi:10.1016/j.agrformet.2016.12.018.

For a complete list of publications for all our faculty, please visit our publications page.