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- Title
Assessment of Cloud Retrieval for IASI 1D-Var Cloudy-Sky Assimilation and Improvement with an ANN Approach.
- Authors
LEE, AHREUM; BYUNG-JU SOHN; PAVELIN, ED; YOONJAE KIM; HYUN-SUK KANG; SAUNDERS, ROGER; YOUNG-CHAN NOH
- Abstract
The Unified Model (UM) data assimilation system incorporates a 1D-Var analysis of cloud variables for assimilating hyperspectral infrared radiances. For the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation, a first guess of cloud top pressure (CTP) and cloud fraction (CF) is estimated using the minimum residual (MR) method, which simultaneously obtains CTP and CF by minimizing radiance difference between observation and model simulation. In this study, we examined how those MR-based cloud retrievals behave, using ‘‘optimum’’ CTP and CF that yield the best 1D-Var analysis results. It is noted that theMRmethod tends to overestimate cloud top height while underestimating cloud fraction, compared to the optimum results, necessitating an improved cloud retrieval. An artificial neural network (ANN) approach was taken to estimate CTP as close as possible to the optimum value, based on the hypothesis that CTP and CF closer to the optimum values will bring in better 1D-Var results. The ANN-based cloud retrievals indicated that CTP and CF biases shown in the MR method are much reduced, giving better 1D-Var analysis results. Furthermore, the computational time can be substantially reduced by theANNmethod, compared to theMR method. The evaluation of theANNmethod in a global weather forecasting system demonstrated that it helps to use more temperature channels in the assimilation, although its impact on UM forecasts was found to be near neutral. It is suggested that the neutral impact may be improved when error covariances for the cloudy sky are employed in the UM assimilation system.
- Subjects
ARTIFICIAL neural networks; WEATHER forecasting; RADIANCE
- Publication
Weather & Forecasting, 2020, Vol 35, Issue 4, p1363
- ISSN
0882-8156
- Publication type
Article
- DOI
10.1175/WAF-D-19-0218.1