The Spring Experiment within the Hazardous Weather Testbed kicked off today. The Experimental Forecast Program has a running list of objectives (there just is too much to explore) so lets hit the high points of producing convective outlooks:
1. Can we merge human and ensemble forecasts to produce shorter time scale forecasts (in this case create a long period (20 hour) forecast and turn that into multiple 3 hour periods)?
What we are testing involves making the best use of the models and incorporating what the forecasters are good at. The models are terrible in getting severe weather at the right times and in the right places. Challenges in predictability (i.e. small scale errors in observations, errors in how we represent model processes, etc) add up to prevent being truly spot on in the forecasts. But forecasters, after years of experience and training, can be great at pattern recognition and referencing climatology about severe storms and severe storm processes. Merging of the two can thus help make better forecasts as we demonstrated last year. We call this technique Temporal Disaggregation (TD). It just means we take the ensemble forecast and apply it to the area outlined by forecasters. It worked well and compared favorably to what forecasters could draw independently ... in other words: what we drew in fine grained forecasts matched well what the model probabilities would be if we corrected the location!
As far as time, we learned that if the periods were long enough we could account for poor time forecasts. So the next big question is:
2. Can we make good short period forecasts? What resources can we take advantage of to get around the predictability challenge?
So our TD technique will produce a first guess of the forecasts for the 3 shorter periods (18-21, 21-00, 00-03 UTC). From these first guesses, we will use updated ensemble data to update these forecasts. Here we have more models to use with the latest data (through data assimilation). One of the challenges an operational forecaster would face is: Can this be done timely and accurately while providing good risk information? Can the experimental models be used reliably? Can we create good proxies or variables that we can extract from the model that relate either directly or indirectly to severe weather?
We have the CAPS 8 member 4-km grid spacing, radar data assimilation ensemble (SSEF), the 7 member SSEO, and 10 member AFWA. We also have the NSSL mesoscale ensemble (NME) run at 18km grid spacing with 36 members but updated hourly with 3 cycles of forecasts out to 03 UTC. This latter ensemble uses the ensemble Kalman filter to assimilate surface data, aircraft and satellite observations.
A big part of the experiment will be spent evaluating the models, the techniques, and the forecasts themselves. We hope to highlight the model capability at this higher temporal resolution. We will also make it a point to identify good metrics that reliably identify good forecasts as compared to what forecasters will also deem as good. In this way we put the metrics to the test and discuss the strengths and weaknesses of them.
Today was mostly about showing all the tools and models at our disposal while making a forecast for thunderstorms with hail in the North Carolina and Kentucky area. The SSEF, SSEO, and AFWA convection allowing models were in moderate agreement about producing a few strong storms. The uncertainty was moderately high (only half of the corresponding members of each ensemble produced these stronger storms), but confidence was high that a few of them would be capable of producing hail. Sure enough we put our forecast for 21-00 UTC for the hail and the first and biggest report occurred at 20:55 UTC, with the rest in NC occurring thereafter.
I think for the next post I can mention some of the high resolution variables that we use as proxies for severe weather. And future posts will discuss some other experimental products that we get to see including the UKMet offices' Unified Model.
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