Wednesday, 25 January 2017
4E (Washington State Convention Center )
Cloud properties such as Cloud Top Pressure (CTP) and Cloud Fraction (CF) are significant for utilizing cloud-contaminated radiances in Infrared Atmospheric Sounding Interferometer (IASI) cloudy sky data assimilation. In this study, we assess two retrieval algorithms for the cloud variables. One is minimum residual method (Eyre and Menzel, 1989), which retrieves CTP and CF simultaneously by minimizing difference between observed and forward-modelled cloudy radiances and the other one is the cloud detection method (McNally and Watts, 2003) getting CTP based on clear channels information; the difference between observed radiances and forward-modelled clear radiances is less than 1K. In doing so, we simulated cloudy IASI radiances under various cloud conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) 137-level short-range forecast datasets by using the Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV) radiative transfer model version 11.2. And those obtained radiances are used as inputs to these cloud parameters retrieval algorithms. By comparing reference CTP and CF with retrieved CTP and CF, it was identified that the minimum residual method retrieves them quite well, with a correlation coefficient of 0.79. However, the method also yields multiple solutions in some cases as it is difficult to distinguish forward-modelled radiances between high cloud top with small CF and low cloud top with large CF. Despite higher correlation coefficient (0.86), the cloud detection method makes CTP larger than corresponding reference CTP in some large CTP cases. These retrieved lower cloud tops can cause problems in cloudy sky data assimilation as they provide cloud-contaminated information between the reference and retrieved cloud tops. We further investigated which cloud situations make these algorithms performance better. In this presentation we discuss a possible way to improve this technique in numerical weather prediction (NWP) model.
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