Uncertainty quantification through the climate analogue identification process by ClimaDist

Analogue has been a widely-used concept in atmospheric science, particularly useful in weather forecasting and climate-related studies. The underlying idea is straightforward. An analogue is identified by determining its level of similarity to a reference weather or climate condition, traditionally, via computing a Euclidean distance. Recently, a deep-learning based framework, called ClimaDist, was proposed for climate analogue identification, found to outperform traditional Euclidean distance metrics. Despite the promising performance, similarly to many deep-learning models, it is challenging to estimate the uncertainty of the analogue searching process undertaken by ClimaDist. This hinders its applicability to real-world operations, especially for those requiring decision making.

To address this challenge, this study extends the capabilities of ClimaDist through incorporating a uncertainty quantification method, together with explainable AI (XAI) techniques. Specifically, the Evidential Deep Learning (EDL) approach is applied to the analogue searching process undertaken by the ClimaDist. This enables effective quantification of the uncertainty associated with data and model, respectively, while exploring their relationship with overall model performance. Two distinct scenarios are applied to these two models using data that were seen and unseen during the training processing. 

An experiment has been designed to verify the proposed approach using ERA5 data over a square domain centred at the Nettebach (Germany) covering the geographic range of 55°N to 47°N and 3°E to 11°E. Two ClimaDist models, one with the best validation performance and the other one best training performance, respectively, are used for comparison. These models are assessed based on the similarity of the found analogues and via under two distinct scenarios –with input data seen and unseen during the training process, respectively. Preliminary results suggest that the integration of uncertainty quantification enhances the interpretability and reliability of analogue identification, enabling improved downstream applications. Specifically, high model uncertainty can be highlighted by the proposed approach while fully unseen data is used as input. This not only provides valuable insight in knowing the capacity of the underlying model but also allows the optimization of resource usage.