Using Machine Learning to Improve Weather Forecasting for Naval Operations

Abstract

Forecasting the weather over the oceans of the Earth is a necessary but difficult requirement of US Navy operations around the globe. Before computer science formally existed as a discipline, meteorologists were using the first computers to explore the possibilities of Numerical Weather Prediction (NWP) to support this forecasting. The foundation of NWP is a fluid dynamics model of the atmosphere interacting with a multitude of physical processes that drive the spatiotemporal vector fields of wind and scalar fields of pressure, temperature, and water vapor mixing ratio. Because the spatial domain of the model is essentially the entire Earth and practical forecasting operates on a cadence of updating every six hours, the computational constraints are formidable and require a number of compromises in formulating the model. The Naval Research Laboratory (NRL) has over a few decades developed the Navy’s primary NWP model called the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS). COAMPS has effectively supported naval operations at the Fleet Numerical Meteorology and Oceanography Operations Center (FNMOC) for the past 20 years but is going to be replaced by a more advanced model called Neptune in the next few years. As a part of this process, NRL has a project to understand how Machine Learning (ML) might be used to improve the forecasting accuracy and computational efficiency of this new system. In particular, there has recently been significant progress in applying ML to model turbulence in fluid dynamics. Since proper representation of turbulence in NWP models has been identified as a weakness, the opportunity of using ML to improve weather forecasting has focused on this aspect of the problem. To support a data-driven ML approach to improving the NWP model, a series of Large Eddy Simulations (LES) have been performed at spatial resolutions not possible with operational NWP models to provide “ground truth” data for how turbulence behaves in the atmosphere. The goal is to learn both the relevant combinations of state variables and the partial differential equations of the spatial dynamics for insertion into the NWP model. This approach requires data and model compression to find useful state variables (or feature vectors) via unsupervised learning since not all quantities available in the LES data can be represented by the low resolution NWP model. Given a suitable space of feature vectors, a supervised learning step finds the partial differential equations for the NWP model that match the dynamics of the LES data. The talk will emphasize the broader issues of how ML can be used in the scientific domain when complex modeling choices can be better resolved with a data-driven approach.