Scales of uncertainty

The latest fuss in meteorological circles has focused rather intently on the Storm of Christmas, then the day after, and now the day after the day after Christmas storm. The storm has been "delayed" by 2 days over the course of the last week, and has shifted position numerous times in the forecast (gonna miss far to the east, bounce back to the west for a hit). We call this behavior uncertainty.

So what is uncertainty? "not confirmed", "still undecided", a "lack in confidence"

In modeling terms, the forecast solution has not yet converged. Our current capabilities, however, are quite robust at short range, meaning we expect the solution (over many initialization cycles) to converge. This time period is usually between 48 and 72 hours depending on the modeling system, the scale of the features that need to be resolved well, and the overall scale of the dynamical system which we seek to predict.

Over the years, despite many advances in satellite data, data assimilation systems, and numerical models and their increasingly sophisticated methods ... we still need to wait for the disturbances to enter the well instrumented radiosonde network*. Alas this too can be deceiving as was the case for the Surprise snowstorm of January 2000. You see a classic nor'easter can have origins from the Pacific Northwest which travels in amplified flow towards the gulf coast. The system itself may never leave the well instrumented but the warm gulf, cool temperatures aloft, and strong wind shear may foster convection ... thunderstorms ... or organized noise. This noise can then feedback from the large scale to the small scale ... outside the network and remain outside the network as the coastal low forms and deepens as it travels up the coast.

So, you can see how a "converged" solution of where the storm will be, can help us have confidence on predicting the other aspects of the storms. But uncertainty remains in where the precipitation will be, what precipitation type will fall, and for how long.

This uncertainty about snowfall placement and amount is very similar to the summer forecasting of thunderstorms. We may have a converged solution of the larger scale details but the smaller scale details can have a large effect on where, when, which storms will form and how severe they might be.

This where the art of forecasting kicks in. Where the analog experience of forecasters contributes. Knowing how they were fooled last time, or how they picked up on certain observed details which caused them to do better than the models. The forecaster has the ability to understand these scales of uncertainty. Only in the last few years have methods been developed, like the Ensemble Kalman Filter, which can show us where the uncertainty is for a specified region at 3,5,7 day lead time. In fact the Winter Weather Reconaissance program is designed to use these methods, then collect data in the uncertain regions to see how that can change the forecast and its uncertainty!