Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning

Journal of Advances in Modeling Earth Systems

Ebrahim Nabizadeh¹  Ashesh Chattopadhyay¹  Pedram Hassanzadeh¹ 

[1] Department of Mechanical Engineering Rice University Houston TX USA;

关键词: extreme weather events;    deep learning;    analog forecasting;    weather prediction;    data‐driven modeling;   

DOI  :  10.1029/2019MS001958

来源: DOAJ

【 摘 要 】

Abstract Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings.

【 授权许可】

Unknown