In the past week we had an interesting experiment: putting the largest European Geo-scientific conference onto a virtual stage. In a short time frame, EGU managed to switch from a huge gathering of people in Vienna to an exchange of scientific ideas on digital channels. And taken together, they did a fabulous job. The channels ran smoothly, the feared chaos induced by trolls didn’t happen and ideas got exchanged quite friction-less. So is all well? Not quite. Under those circumstances it was close to the best what was possible, especially due to the limited time to put it up and running. Nevertheless, as we are part of climate science and the calls to limit travel gets louder, the question arise, how a digital conference might look like in cases in which there is enough time to prepare (so a year or more). So what happens and where are the dangers, when conferences get generally put online in the future.
EGU 2020 is done. It was an unusual one. Will it be ever the same again?
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.
When you have two probability distributions and want to know the difference between them, then you need a way to measure it. Over the years many metrics and distance measures have been developed and used, the most famous one is the Kullback-Leibler-Distance. In a paper in 2012 I had shown that a metric called Earth Mover’s Distance (EMD) shows considerable improvements in detecting differences between distributions. So it was a natural idea for me to try to make use of this measure, when we want to compare two distributions.
So given is a distribution by the model prediction, defined by the ensemble members, and an observation with a non-parametric distribution of its uncertainties. A nowadays standard tool for evaluation of ensemble prediction is CRPS. In this case it is evaluated at which percentile of the probability distribution the deterministic observation can be found. The paper now tries to make use of this tool and extends it by looking at uncertain observations. So effectively, what is done is to measure the distance between two distributions and by normalising it against a reference (e. g. the climate state) a metric distinguishing between a good and a bad prediction can be created.
So how does the EMD work? Well, it effectively measures how much work would be needed to transfer one distribution into another. So when you imagine a distribution as a sand pile, then it measures the minimal amount of fuel a machine would need to push the sand around until it creates the target distribution. This picture is also the one from which the EMD got its name. As a metric it measures the distance precisely and therefore allows to say, when you have two predictions, which one is closer to the observations.
But it is important here to mention, that there are problems with this view. Similar to CRPS, there exist literature, which describe that even with its properties, measures like EMD are potentially to kind to false sharp predictions compared to uniformed ones. In the CRPS case, the distance is squared, so that a longer transport of probability is necessary for a wrong prediction. In my paper I also show the results with this approach as IQD. A squared distance is much less intuitive than a linear one, it is harder to understand for scientists, why they should use this above the others, which leads to hesitant use of these kind of measures. Therefore, it will be necessary in the future to much better describe why the issues occur and develop new pictures to explain everyone, why squaring is the way to go. We also need new ways in general for verification in the future, but on this I will write more on the final post in this series.
Data verification is one of the corner stones in geoscience. Without knowing whether a prediction has been correct, it is not possible to claim that we can predict anything at all. Most of the verification bases nowadays on the assumption that observations are perfect, often without the acknowledgement of any uncertainties. Standard tools like contingency tables and correlations (the latter often used in some form in long-term predictions) makes it hard to take them into account (even when possible e. g. by sampling strategies).
Another problem is that having uncertainties for observations to work with is often not an easy task. An example are reanalysis data, which have long been only provided in form of one realisation. This led to the problem that while predictions were often available as ensembles, the observations to compare to were not. There are techniques available to use aggregated data and validate statistics of them, but the verification of most classical variables is still often done with certain observations. Currently the field is changing. Reanalysis start to become available in form of ensembles, so in the future we need new tools making use of these developments.
But also on the philosophical side there is more need to look into verification with uncertain observations. We know that the real world is not deterministic, we know that our instruments are imperfect and we are sure that these uncertainties matter. Why do we train our students in creating and measuring uncertainties, when we later on do not use them in our analysis? And yes, there is the issue that all observations are in their core models. We acknowledge that models are imperfect, otherwise we wouldn’t need ensembles for creating predictions. But why do we then not take care of the uncertainties due to the applications in those models when we create observations. Those models are certainly not much better (they are just applied on a different temporal and spatial scale. So we have to confront this issue in every step we take, we do that in data assimilation, so we have to do it in data verification as well.
Therefore, new developments in this field are essential. We need new tools to look into uncertain observations and make use of them. This paper is a small step into opening opportunities for future developments in this direction. It is certainly not a final solution and certainly not the first step. It is just another proposal of a tool to approach this challenge. We require in the future well understood and tested tools, which are applicable by the broader scientific community. How those might look like is currently open, also whether the tools presented here are of any wider use. In the paper I described two metrics, the EMD and the IQD, and developed a strategy to make verification tools with them. In the next post I will take a deeper look into the two metrics and shine a light on the opportunity they offer.