Validation of Automatic Cb Observations for METAR Messages without Ground Truth

Abstract

AbstractIn aviation meteorology, METAR messages are used to disseminate the existence of cumulonimbus (Cb) clouds. METAR messages are traditionally constructed manually from human observations, but there is a growing trend toward automation of this process. At the Finnish Meteorological Institute (FMI), METAR messages incorporate an operational automatic detection of Cb based solely on weather radar data, when manual observations are not available. However, the verification of this automatic Cb detection is challenging, as good ground truth data are not often available; even human observations are not perfect as Cb clouds can be obscured by other clouds, for example. Therefore, statistical estimation of the relevant verification measures from imperfect observations using latent class analysis (LCA) was explored. In addition to radar-based products and human observations, the convective rainfall rate from EUMETSAT’s Nowcasting Satellite Application Facility and lightning products from the Finnish lightning network were used for determining the existence of Cb clouds. Results suggest that LCA gives reasonable estimates of verification measures and, based on these estimates, the Cb detection system at FMI gives results comparable to human observations.