A preceding CI (PreCI) cloud-growth identification algorithm was developed based on 5-km spatial and 30-min temporal resolution infrared channel (10.3-11.3μm) and a radar-based CI survey in April-August of 2010. PreCI was defined at a time when cloud tops rise above the freezing level with a cooling rate of at least 8K/30min in this and next times. Based on the mosaics of composite radar reflectivity in the same period, more than 3000 convective episodes (>35 dBZ and last more than 30 min) were identified. The location and timing of their CI (the first detection of 35 dBZ radar echoes of each convective episode) were recorded. Verification of this satellite-based PreCI identification algorithm against the radar-based CI showed that the algorithm had a 60%-70% hitting probability. In other words, 60-70% of the PreCI signal identified by the satellite-based algorithm developed into CI as detected by radar.
Results showed that the PreCI locations well collocated with elevated terrain especially those that were surrounded by abundant water resource or valleys. The similarity between the terrain shape and the morphology of the clouds around CI can be clearly seen on 1.25-km-resolution imagery at visible channel (0.55-0.90μm). The appearance of the first cloud corresponding to each PreCI signal was also identified at a time when the cloud top started to have a cooling rate of at least 4K/30min before reaching the freezing level. The first cloud formed at almost the same location as that of PreCI and the subsequently radar-based CI, but with a lead time of 30 min and 1h respectively before PreCI and CI.
To obtain more general features of PreCI, the authors have just finished a survey of PreCI of isolated convection during the warm seasons of 2008-2012 using the above mentioned algorithm. The spatial and temporal distributions of the identified PreCI has been obtained. Further analyses on other features of the PreCI are underway. This study may help to have a better knowledge of the evolution of cumulus clouds from formation to warm cloud process to cold cloud process and also may help forecasters to gain more lead time in forecasting convective episodes.