Neural network aided fast pointing information determination approach for occultation payloads from in-flight measurements: Algorithm design and assessment

摘要

Pointing information is decisive to solving precise profile retrieval issues from occultation measurements. Research regarding stratospheric O-3 hole in Antarctic and surface O-3 pollution would significantly benefit from massive occultation measurements. A neural network aided pointing information determination approach, in terms of tangent heights, is proposed to address issues requiring fast and easy-to-use determined tangent heights. The geometrical triangular iteration (GTI) algorithm in this work is based on N-2 absorption microwindows, and several treatments (e.g., tangential stride generator and triangular-net optimization) are adopted. In addition, LSTM is employed to reduce time consumption and increase accuracy. Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) in-flight measurements are used to assess this approach. The comparison between the proposed algorithm and eight official products indicates a promising performance. Correlation coefficient for each orbit is greater than 0.99. The processing time is about 16.6 min per orbit with an average cost of similar to 0.06. The introduction of LSTM technique demonstrates an approximate 28.49% better result, with less computation time. It costed less than 30 s to determine eight orbit tangent heights. In general, although minor issues remain, this LSTM-aided GTI algorithm is applicable in industry. (C) 2019 COSPAR. Published by Elsevier Ltd.