Statistical Properties of Cloud Movement and Life-cycle Evolution from Satellite Observations

Abstract:

Using the ForTraCC tracking algorithm and geostationary infrared observations, our group examined the trajectories and evolution of precipitating clouds from a decade of satellite data. This analysis captured millions of storms across the globe, yielding robust statistics on evolution. While the majority of storms are short-lived and small, as a whole their movements trace out features of the global atmospheric circulation. Even on short time scales, the spatiotemporal pattern of phenomena such as the ITCZ and the midlatitude storm tracks become apparent. Like their spatial structure, the evolution of individual storm properties over their life cycle can also be aggregated over extended time periods to understand how long storms live and the mechanisms of their development, both on regional and global scales. The location and intensity of storms are predicted to shift even under conservative climate change scenarios. Thus, it is important to identify the present-day structure, statistics, and development properties of bulk atmospheric of storm clouds that contribute to the water cycle. In our presentation, we will show (1) the climatological structure, (2) statistical distributions of their properties, and (3) the life cycle characteristics of aggregated storms on regional and global scales. By utilizing long-term, multi-satellite data, we will present the most comprehensive study of satellite-based storm spatial structure and evolution.

To accomplish this, we take a multi-sensor approach by integrating satellite observations to gain deeper insights into how other properties evolve over the lifecycle of the storms, both globally and across specific atmospheric features. Using infrared data, we found storm properties to be non-linear across different seasons and regions, but still highly regular; building on this work, we will examine their evolution by merging observations from precipitation relevant satellites, such as Cloudsat, TRMM, and GPM. Additionally, we will show our statistical distribution model of storm properties and compare with theoretical distributions. This observation-based work will compliment existing modeling studies of the hydrological cycle and atmospheric circulation.