A Temporal Bias Correction using a Machine Learning Attention model

arXiv:2402.14169v1 Announce Type: new
Abstract: Climate models are biased with respect to real world observations and usually need to be calibrated prior to impact studies. The suite of statistical methods that enable such calibrations is called bias correction (BC). However, current BC methods struggle to adjust for temporal biases, because they disregard the dependence between consecutive time-points. As a result, climate statistics with long-range temporal properties, such as heatwave duration and frequency, cannot be corrected accurately, making it more difficult …

abstract arxiv attention bias biases climate climate models cs.lg current impact machine machine learning physics.ao-ph prior statistical struggle studies temporal type world