Application of an Artificial Neural Network for a Direct Estimation of Atmospheric Instability from a Next-Generation Imager

Atmospheric instability information derived from satellites plays an important role in short-term weather forecasting,especially the forecasting of severe convective storms. For the next generation of weather satellites for Korea's multi-purpose geostationary satellite program, a new imaging instrument has been developed. Although this imaging instrument is not designed to perform full sounding missions and its capability is limited, its multi-spectral infrared channels provide information on vertical sounding. To take full advantage of the observation data from the much improved spatiotemporal resolution of the imager, the feasibility of an artificial neural network approach for the derivation of the atmospheric instability is investigated.The multi-layer perceptron model with a feed-forward and back-propagation training algorithm shows quite a sensitive response to the selection of the training dataset and model architecture. Through an extensive performance test with a carefully selected training dataset of 7197 independent profiles, the model architectures are selected to be 12, 5000, and 0.3 for the number of hidden nodes, number of epochs, and learning rate, respectively. The selected model gives a mean absolute error,RMSE, and correlation coefficient of 330 J kg^(-1), 420 J kg^(-1), and 0.9, respectively. The feasibility is further demonstrated via application of the model to real observation data from a similar instrument that has comparable observation channels with the planned imager.

Inter-comparison of the Infrared Channels of the Meteorological Imager Onboard COMS and Hyperspectral IASI Data

The successful launch and commissioning of the first geostationary meteorological satellite of Korea has the potential to enhance earth observation capability over the Asia Pacific region. Although the specifications of the payload, the meteorological imager(MI), have been verified during both ground and in-orbit tests, there is the possibility of variation and/or degradation of data quality due to many different reasons, such as the accumulation of contaminants, the aging of instrument components, and unexpected external disturbance. Thus, for better utilization of MI data, it is imperative to continuously monitor and maintain the data quality. As a part of such activity, this study presents an inter-calibration, based on the Global Space-based Inter-Calibration System(GSICS), between the MI data and the high quality hyperspectral data from the Infrared Atmospheric Sounding Interferometer(IASI) of the Metop-A satellite. Both sets of data, acquired for three years from April 2011 to March 2014, are processed to prepare the matchup dataset, which is spatially collocated, temporally concurrent,angularly coincident, and spectrally comparable. The results show that the MI data are stable within the specifications and show no significant degradation during the study period. However, the water vapor channel shows a rather large bias value of-0.77 K, with a root-mean-square difference(RMSD) of around 1.1 K, which is thought to be due to the shift in the spectral response function. The shortwave channel shows a maximum RMSD of around 1.39 K, mainly due to the coarse digitization at the lower temperature. The inter-comparison results are re-checked through a sensitivity analysis with different sets of threshold values used for the matchup dataset. Based on this, we confirm that the overall quality of the MI data meets the user requirements and maintains the expected performance, although the water vapor channel requires further investigation.