Model Solutions Galore

This week we've been experiencing broader risk areas in the Spring Forecasting Experiment than previous weeks. The instability has recovered across much of the Great Plains, and the persistent southwesterly flow at upper levels due to a trough in the west has sent steep lapse rates over a wide area. While the trough is still somewhat too far west for the greatest flow to coincide with a broad area of instability, deep-layer shear has been sufficient for severe storms to occur somewhere each day. Determining the exact location is a daily difficulty for our SFE participants.

When we have large areas to consider in conjunction with the huge amount of NWP data we have from the CLUE, deterministic CAMs, and operational large-scale guidance, the number of different scenarios can be overwhelming. Particularly this week, there are multiple solutions for how the day's weather could evolve, according to the NWP. Mesoscale details from prior convection have also played a large role both today and yesterday in making our forecasts, and the variation in the CAM guidance reflects the reliance on those small-scale details. One member's outflow boundary is likely not in the same place as another's, and it's up to participants to determine which solution we think will verify. As an example of the different solutions we saw yesterday, here's a snapshot of five ensemble members whose configuration differs only in the microphysics scheme they're using. Observations are in the lower right hand panel:

Participants and facilitators had difficulty both today and yesterday pinpointing the most likely scenario. While we could forecast roughly where we anticipated the mesoscale boundaries to set up, the subsequent evolution was very complex. Participants today remarked that they had new appreciation for the forecaster's job, as the difficulty in integrating all of this information into one forecast product became readily apparent today. When the model solutions are so varied, it emphasizes that this is a low-confidence event. We were fairly sure something would happen today and tomorrow - we just didn't know exactly where.

Processing this data into something forecasters can easily and quickly use is one of the great research-to-operations challenges faced by the community. Though we didn't consider every single piece of guidance available to us today and yesterday, we nonetheless ran over our scheduled forecast time on the total severe desk. Since this is an experiment, we can be somewhat flexible with our schedule. However, operational forecasters don't have that luxury. Though we were considering more area than a typical WFO (in fact, today we considered how at least four separate areas of the CONUS would evolve), the wealth of data provided by observations, the CLUE, and the other NWP is time-consuming to analyze.

How can we distill the plethora of information provided by the ensembles into useful tools for forecasters? Researchers are experimenting with summary fields such as ensemble means and maximums of any member, but the variability between members contains valuable information. High agreement between the members of an ensemble suggests high confidence (though ensembles currently have a tendency toward underdispersion - when the ensemble doesn't have enough differences between the members). Multiple solutions and timings suggests less certainty. Different placements of features can be washed out by mean fields or neighborhood probabilities. How to convey ensemble information is one of the great challenges our field must overcome if we wish to use the wealth of data at our fingertips.