The chaotic nature of the atmosphere system is especially significant in seasonal
forecasting, in which a little deviation of the same initial conditions may yield completely different forecasts. These forecasts, still possible because large-scale variability can be studied in terms of atmospheric predictors, force meteorologists to resort to ensemble forecasts.
do not consist of a single prediction but a set of N possible realizations
(also named ensemble members
) which, ideally, account for the forecast error. Of course, the goodness of these forecasts is no longer determined solely by their accuracy, which at the same time (and as they are probabilistic) requires another definition than deterministic forecast.
To assess these attributes, we will explore the use of a powerful tool for visualization and verification: rank histograms. A rank histogram, also known as verification rank histograms or Talagrand diagrams, serves as an assessment tool to evaluate the reliability of an ensemble prediction system.
While the ensemble aims to represent the set of all possible outcomes (mimicking the distribution of observations), inherent system flaws often hinder this representation. Rank histograms play a crucial role in identifying such flaws. Let’s first see how a rank histogram is constructed; we will follow these steps:
After this, we can already analyze our rank histogram. We can expect four types of rank histograms: flat, U-shaped, dome-shaped and asymmetric; and each of them has a different interpretation
, as explained below.