Machine Learning for Targeted Assimilation of Satellite Data

Abstract

Optimizing the utilization of huge data sets is a challenging problem for weather prediction. To a significant degree, prediction accuracy is determined by the data used in model initialization, assimilated from a variety of observational platforms. At present, the volume of weather data collected in a given day greatly exceeds the ability of assimilation systems to make use of it. Typically, data is ingested uniformly at the highest fixed resolution that enables the numerical weather prediction (NWP) model to deliver its prediction in a timely fashion. In order to make more efficient use of newly available high-resolution data sources, we seek to identify regions of interest (ROI) where increased data quality or volume is likely to significantly enhance weather prediction accuracy. In particular, we wish to improve the utilization of data from the recently launched Geostationary Operation Environmental Satellite (GOES)-16, which provides orders of magnitude more data than its predecessors. To achieve this, we demonstrate a method for locating tropical cyclones using only observations of precipitable water, which is evaluated using the Global Forecast System (GFS) weather prediction model. Most state of the art hurricane detection techniques rely on multiple feature sets, including wind speed, wind direction, temperature, and IR emissions, potentially from multiple data sources. In contrast, we demonstrate that this model is able to achieve comparable performance on historical tropical cyclone data sets, using only observations of precipitable water.