Performance Improvement for FY-2E Convection Monitoring Using a Spatial-response Matched Filter Method
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Abstract
In China, severe convective weather system often causes sudden disasters, and its occurrence time and falling area is difficult to be forecasted. The improvement of convection monitoring and forecasting closely depends on the advance of the monitoring capability. Geostationary satellites can provide a large range, full day cloud information, so they may be the most practical tools for monitoring the convection. In these years, the convective cloud identification method is mainly based on infrared channels of satellites using brightness temperature threshold. The accuracy of the brightness temperature is crucial for convective cloud identification, which depends on not only the satellite calibration, but also the satellite instruments sensitivity, especially for the mesoscale and small-scale targets. Based on the observation performance and principle of FY-2E meteorological satellite, a spatial-response matched filter (SRMF) method for FY-2E is set up and applied for convection observations in the thermal infrared band, the MTSAT/JAMI is used as the reference standard of the revised method, and the spatial-response of VISSR and JAMI is matched with the same infrared band. Accordingly, the infrared channel brightness temperature is corrected. Furthermore, some typical convection examples are selected during summer of 2013 and 2014 for statistics, the SRMF performance is evaluated focusing on convection spatial distribution, development process and convective cloud inner structure.Results indicate recovered images after SRMF processing show more sensitivity of convective cloud identification. For small-scale convective core in the mesoscale cloud and very short time convection, it has significant improvement for reducing effect from the high temperature background smoothing, and also it enhances the ability to reveal the mesoscale and finer scales cloud. Besides, after SRMF processing, the convection distribution statistical error is reduced, improving the missing problem for the short time and small scope convective cloud. The SRMF method also enhances the characterization ability for the convection development potential. These results all indicate that the SRMF is more suitable for convection nowcasting, such a progress is believed to be beneficial to convection monitoring and forecasting. In the future, monitoring operational work on the convective weather, for small-scale and mesoscale clouds, this SRMF technique could be applied to reduce the overall observation error, and then improve the spatial resolution for the deep convective cloud top identification. The method can also be extended to the detail inner structure of tropical cyclones.
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