Machine Learning Enables Real-Time Proactive Quality Control: A Proof-Of-Concept Study

Abstract

To improve the forecast accuracy of numerical weather prediction, it is essential to obtain better initial conditions by combining simulations and available observations via data assimilation. It has been known that a part of observations degrade the forecast accuracy. Detecting and discarding such detrimental observations via proactive quality control (PQC) could improve the forecast accuracy. However, conventional methods for diagnosing observation impacts require future observations as a reference state and PQC cannot be real-time in general. This study proposes using machine learning (ML) trained by a time series of analyses to obtain a reference state without future observations and enable real-time ML-based PQC. This study presents proof-of-concept using a low-dimensional dynamical system. The results indicate that ML-based and model-based estimates of observation impacts are generally consistent. Furthermore, ML-based real-time PQC successfully improves the forecast accuracy compared to a baseline experiment without PQC.