Setting the Standard: Recommended Practices for Data Preprocessing in Data-Driven Climate Prediction

TL;DR

This paper establishes standardized data preprocessing protocols for AI/ML climate prediction models, emphasizing their importance for improving forecast accuracy, robustness, and transparency across various timescales.

Contribution

It provides comprehensive guidelines and case studies on data preprocessing techniques tailored for climate prediction models, addressing a critical gap in current practices.

Findings

Preprocessing significantly affects climate prediction outcomes.

Standardized anomaly creation improves model robustness.

Proper handling of non-stationarity and extremes enhances forecast reliability.

Abstract

Artificial intelligence (AI) - and specifically machine learning (ML) - applications for climate prediction across timescales are proliferating quickly. The emergence of these methods prompts a revisit to the impact of data preprocessing, a topic familiar to the climate community, as more traditional statistical models work with relatively small sample sizes. Indeed, the skill and confidence in the forecasts produced by data-driven models are directly influenced by the quality of the datasets and how they are treated during model development, thus yielding the colloquialism, "garbage in, garbage out." As such, this article establishes protocols for the proper preprocessing of input data for AI/ML models designed for climate prediction (i.e., subseasonal to decadal and longer). The three aims are to: (1) educate researchers, developers, and end users on the effects that preprocessing has on climate predictions; (2) provide recommended practices for data preprocessing for such applications; and (3) empower end users to decipher whether the models they are using are properly designed for their objectives. Specific topics covered in this article include the creation of (standardized) anomalies, dealing with non-stationarity and the spatiotemporally correlated nature of climate data, and handling of extreme values and variables with potentially complex distributions. Case studies will illustrate how using different preprocessing techniques can produce different predictions from the same model, which can create confusion and decrease confidence in the overall process. Ultimately, implementing the recommended practices set forth in this article will enhance the robustness and transparency of AI/ML in climate prediction studies.