Applications of the adjoint technique to short-range ensemble forecasting of mesoscale convective systems

The feasibility of applying a mesoscale adjoint model to the creation of an ensemble for short-range simulations of mesoscale convective systems (MCSs) is explored. Because past studies show that forecasters routinely improve upon numerical guidance and can identify mesoscale-sized areas of forecast concern with a high level of skill, it is clear that forecasters have insights into the daily weather forecast problems that exceed what can be provided by a numerical weather prediction model. Using an adjoint model, one could develop a system in which a forecaster identifies the area of forecast concern and then designs a set of rapidly produced sensitivity experiments that evaluate the influence of key atmospheric parameters on the model forecast. The output from these sensitivity experiments is then used to create ensemble members for a short-range operational ensemble forecast, which is specifically designed to investigate the forecast concern of the day. This adjoint ensemble approach is tested for the 48-h period beginning 1200 UTC 27 May 1985, in which a long-lived MCS developed underneath a large-scale ridge. A mesoscale adjoint model is used to define the alterations to the model initial conditions necessary to evaluate the influences of key mesoscale structures, which the authors believe have a large influence on later convective development in the model. Results indicate that the adjoint technique is effective in creating the proper directional response in the model simulation. When compared to initial condition and model physics ensembles of this event, the adjoint ensemble produces more variance than the initial condition ensemble and almost as much variance as the model physics ensemble. However, the values of the equitable threat score and the ranked probability score are better for the adjoint ensemble between 6 and 24 h than for either of the other two ensembles. These results suggest that further exploration of ensembles that incorporate the experience and expertise possessed by forecasters is warranted.