Poster Session P12.12 Statistical Analysis on Severe Convective Weather combining satellite, conventional observation and NCEP data

Thursday, 30 October 2008
Madison Ballroom (Hilton DeSoto)
Yaping Zhu, Institute of Aviation Meteorology, Beijing, , China; and J. Liu, Z. Cheng, and Y. Li

Handout (204.7 kB)

Severe convective weather plays a key role in many fields of atmospheric studies. Hazards related to thunderstorms in southeast China bring on the heavy flood and cost many tens of millions of dollars annually in lost time. Many research studies in severe weather are explored based on conventional sounding reports, satellite images, and model data. The main purpose of this study is to explore a new way that will combine three sources data and give a statistical analysis on dynamic, thermodynamic, and environmental parameters correlation with severe weather in southeast China.

The present work uses a combination of three different data sources: three hourly surface conventional observations, hourly satellite images, six hourly NCEP grid data. After synoptic stations correlating with severe weather from three hourly conventional observations are selected, the nearly simultaneous satellite pixels, and NCEP grid data are matched in longitude and latitude. The severe weather reports include synoptic codes 17-19, 89-99 which are from weather region 57 in southeast China.

Some dynamic, thermodynamic, and environmental parameters are selected and calculated from three sources data which can depict development and evolution of severe convective weather. The parameter from the surface sounding reports is depression of the dew point which describes the temperature and moisture conditions in the surface air mass. The characteristics based on satellite pixels include the brightness temperatures (or reflections) of five channels and the brightness temperature differences between the infrared channels (IR1-IR2, IR1-WV, IR1-NIR, WV-NIR) from GOES-9. All these satellite information are from the top of convective clouds correlating with severe weather and have the ability to improve the analysis and prediction the development of deep convective clouds. The features selected and calculated from NCEP grid data include a thermodynamic parameter θse which is considered as a conservative characteristic of the energy transformation in the atmosphere, three instability indices CAPE, CIN, SI, RSH, which reveal the potential instability of convective environments and have been identified as being suitable for destabilisation and development of convective storms.

All the parameters above are statistically analyzed and the results show that combination with the depression of the dew point from surface reports and IR1-WV based on satellite pixels can discriminate the severe convective clouds and clear sky in the two-dimensional characteristic scatter figures. The high, thick, cold clouds (such as convective storms), with cloud tops possibly extending into the lower stratosphere, the 10.7μm TB is colder than that at 6.5μm, resulting in a positive TB difference between these bands, at the same time the depressions of the dew point of the low level air mass are near to saturation and the differences are very small. The combination TB of IR1 with θse or CIN depicts the thermodynamic and dynamic characteristics a conditionally unstable atmosphere or increasing the conditional instability of large quantities of air in the unstable environment. In fact, the characteristics from satellite images just describe the deep convective clouds whose tops penetrate to high level, it is hard to monitor and predict the initiation of severe convective weather correlating with small scale structure only using satellite remote sensing. At the different development processes of storms, especially in the period of initiation or for small scale cells, more effective methods combing other ways or parameters are proposed and the quantity criteria combing surface observations, satellite data and environment data are studied in the future work. And the cloud-cell dynamic, geometric features and motion features (such as rates of TB decrease, rate of TB isotherm expansion) will be added in development periods combing other parameters.

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