On the visualisation, verification and recalibration of ternary probabilistic forecasts

TL;DR

This paper introduces a geometric approach to visualizing and verifying ternary probabilistic forecasts using a triangle representation, enhancing interpretability and calibration analysis.

Contribution

It presents a novel geometric interpretation of ternary forecasts, including a new colour scheme and Ternary Reliability Diagram for verification and calibration.

Findings

Enhanced visualization conveying all forecast information

New interpretation of verification scores as distances in a triangle

Application to seasonal precipitation forecasts in South America

Abstract

We develop a geometrical interpretation of ternary probabilistic forecasts in which forecasts and observations are regarded as points inside a triangle. Within the triangle, we define a continuous colour palette in which hue and colour saturation are defined with reference to the observed climatology. In contrast to current methods, forecast maps created with this colour scheme convey all of the information present in each ternary forecast. The geometrical interpretation is then extended to verification under quadratic scoring rules (of which the Brier Score and the Ranked Probability Score are well--known examples). Each scoring rule defines an associated triangle in which the square roots of the score, the reliability, the uncertainty and the resolution all have natural interpretations as root--mean--square distances. This leads to our proposal for a Ternary Reliability Diagram in which data relating to verification and calibration can be summarised. We illustrate these ideas with data relating to seasonal forecasting of precipitation in South America, including an example of nonlinear forecast calibration. Codes implementing these ideas have been produced using the statistical software package R and are available from the authors.