Towards ensemble assimilation of all-sky hyperspectral satellite observations using principal component analysis

Satellite-based hyperspectral radiometers usually have thousands of infrared channels which contain atmospheric state information with higher vertical resolution compared to observations from traditional sensors. However, the large numbers of channels can lead to computational burden in satellite data retrieval and assimilation. Furthermore, most of the channels are highly correlated and the pieces of independent information contained in the hyperspectral observations are usually much smaller than the number of channels. Principal Component Analysis (PCA) was used in this research to compress the observational information content contained in the Atmosphere Infrared Sounder (AIRS) channels to a few leading principal components (PCs). The corresponding PC scores were then assimilated into a PCA-based ensemble Kalman filter (EnKF) system. In this proof-of-concept study based on simulated observations, hyperspectral brightness temperatures were simulated using the atmospheric state vectors from convection-permitting ensemble simulations of Hurricane Harvey (2017) as input to the Community Radiative Transfer Model (CRTM). The PCs were derived from a pre-existing training dataset of brightness temperatures calculated from convection-permitting simulation over a large domain in the Indian Ocean representing generic atmospheric conditions over tropical oceans. The EnKF increments from assimilating many individual measurements in the brightness temperature space were compared to the EnKF increments from assimilating significantly fewer numbers of leading PCs. Results showed that assimilating about 10 to 20 leading PCs could yield increments that were nearly indistinguishable to that from assimilating hyperspectral measurements from orders of magnitude larger number of hyperspectral channels.