Fusing Multisource A-Train Satellites and Reanalysis Data for a Comprehensive Deep Convective System Dataset

Abstract

Deep convective systems (DCSs) play a crucial role in global water cycles, energy distribution, and extreme weather events. This study aims to enhance the understanding of DCSs by creating a comprehensive dataset through the fusion of multisource A-Train satellite observations and reanalysis data. Fusing data from multiple active and passive sensors allows us to capture detailed vertical profiles and life stages of DCSs, overcoming the limitations of polar-orbiting satellites in tracking convection over time. Comparison with convection tracking data from geostationary satellites confirmed the reliability of lifecycle determination. Reanalysis data are included for each observed DCS samples, incorporating environmental conditions and aerosol data up to 36 h before the DCS occurrence. Using the fused dataset, we examined the radiative properties of DCSs across different stages of their lifecycle and the influence of environmental factors and aerosols on them. Radiative heating rates showed distinct variations, with mature stages exhibiting the highest shortwave heating and longwave cooling rates due to denser and higher cloud tops. Our findings reveal the role of humidity in increasing cloud top heights and the influence of vertical wind shear on convection development. Additionally, aerosol impacts were notable during the mature and dissipating stages, with higher concentrations linked to increased cloud top heights and lower temperatures. This comprehensive dataset advances the understanding of DCS dynamics, aiding in the improvement of predictive models for severe weather.

Keywords

• A-Train satellites
• cloud radiative effect
• data fusion
• deep convective system (DCS)