Dr. Poterjoy presented a seminar on “Progress and Lessons Learned During a Six-Month Collaboration with NOAA NSSL on Convective-Scale Data Assimilation”.
The nonlinear evolution of model forecast errors in convective weather regimes presents a major obstacle for predicting severe weather events. Ensemble forecasts provide an effective means of characterizing this uncertainty, which is a crucial step towards assimilating observations in weather models. In practice, these errors are assumed to follow a parametric form such as a Gaussian distribution. While this strategy works effectively for many applications in atmospheric science, it is inappropriate when the underlying dynamics produce highly skewed, bounded, or even bimodal error distributions—all of which are typical when clouds are present.
In this presentation, I will discuss recent efforts developing and testing a data assimilation technique that avoids many of the assumptions made by current methods. This talk will summarize progress made during a six-month visit to NSSL, collaborating with scientists through the NOAA Warn-on-Forecast program. Using severe weather outbreaks selected from the 2016 season, the new technique will be compared with the conventional ensemble Kalman filter data assimilation method applied in the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) framework.
A recording of the presentation is available on the anonymous ftp site: ftp://ftp.aoml.noaa.gov/hrd/pub/blog/seminars/2017/Poterjoy_HRD_Seminar_20170330.mp4