In the final post on this background-series I want to write about the necessity for new ideas in verification. Verification is essential in geo- and climate science, as it gives validity to our work of predicting the future, whether it is on the short or long timescale. Especially in long-term prediction we have the huge challenge to verify our predictions on a low number of cases. We are happy when we got our 30+ events to identify our skill, but we have to find ways to make quality statements on potentially much lower number of cases. When we e.g. investigate El Niño events over the satellite period, we might have a time series bellow 10 time steps at hand and come to a dead end with classical verification techniques. Contingency tables require much more cases, because otherwise potential uncertainties become so huge that they cannot be controlled. Correlation measures are also highly dependent on many cases. Everything below 30 is not really acceptable, which is shown by quite high thresholds to reach significance. Still, most of long term prediction evaluation rely on such methods.
An alternative idea has been proposed by DelSole and Tippett, which I had first seen at the S2S2D-Conference in 2018. In this case we do not investigate a whole time series at once, as we would do for correlations, but single events. This allows to evaluate the effect of every single time step on the verification and give therefore new information beside the information on the whole time series.
I have shown in the new paper, that this approach allows also a paradigm shift in evaluating forecasts. While we looked beforehand in many approaches at a situation, where the evaluation of a year depends on the evaluation on other years, by counting the successes of each single year makes a prediction evaluation much more valuable. We do often not ask how good a forecast is, but whether it is better than another forecast. And we want to know at the time of forecasting, how likely it is that a forecast is better than another. But this information is not given by many standard verification techniques, as they take into account the value of difference between two forecasts at each time step. This is certainly important information, but limits our view in essential questions of our evaluation. Theoretically, it is often possible, that one single year can decide whether one forecast is better than another. Or more extreme: When in correlation one forecast is really bad in one year, but is better in all other years, it can still be dominated by the other forecast. These consequences have to be taken into account when we verify our models with these techniques.
As such, it is important to collect new ideas about how we want to verify and quantify the quality with its uncertainties of the new challenges, which are posed to us. This new paper applies new approaches in many of these departments, but there is certainly quite some room for new ideas in this important field for the future.