Assessment of Cloud Retrieval for IASI 1D-Var Cloudy-Sky Assimilation and Improvement with an ANN Approach
| Autor: | Byung-Ju Sohn, Yoonjae Kim, Young-Chan Noh, Ed Pavelin, Hyun-Suk Kang, Roger Saunders, Ahreum Lee |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric sounding
Atmospheric Science 010504 meteorology & atmospheric sciences business.industry media_common.quotation_subject 0207 environmental engineering Hyperspectral imaging Cloud computing Assimilation (biology) 02 engineering and technology Unified Model 01 natural sciences Data assimilation Sky Environmental science 020701 environmental engineering business 0105 earth and related environmental sciences media_common Remote sensing |
| Zdroj: | Weather and Forecasting. 35:1363-1380 |
| ISSN: | 1520-0434 0882-8156 |
| DOI: | 10.1175/waf-d-19-0218.1 |
| Popis: | 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 the MR method 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 the ANN method, compared to the MR method. The evaluation of the ANN method 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. |
| Databáze: | OpenAIRE |
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