Satellite data can often suffer from a lack of context. In order to remedy that lack, a new technique employed in satellite data analysis has been developed in which data is grouped and categorized. In my ongoing work, I work to pare out individual clouds from the CloudSat database of cloudy reflectivity. With knowledge of the size and shape of individual clouds, we can develop simple theories for the behavior and scaling of clouds. The grouping techniques I have developed have allowed me to create a thorough catalog of the size and shape of tropical deep convective clouds.
Currently, we’re working on using GPM (and lots of other NASA satellites) to better understand precipitation across the tropics.
Igel, M. R. and J. A. Biello: A Reconstructed Total Tropical Precipitation Framework (2019). npj Climate and Atmospheric Science. (2) 32. DOI: 10.1038/s41612-019-0090-8. IB
Bourgeois, Q., A.M.L. Ekman, M. R. Igel, and R. Krejci (2016): Ubiquity and impact of Thin Mid-Level Clouds in the tropics. Nature Communications. 7, doi:10.1038/ncomms12432. BEIK
Igel, M. R., and S. C. van den Heever (2015): Deep-Convective Morphology as Observed from CloudSat. Atmospheric Chemistry and Physics Discussion. 15, 15977-16017, doi: 10.5194/acpd-15-15977-2015. IH
Igel, M. R., and S. C. van den Heever (2015): The Relative Influence of Environmental Characteristics on Deep Convective Morphology as Observed by CloudSat. Journal of Geophysical Research. 120, doi: 10.1002/2014JDO22690. IH
Igel, M. R., A. J. Drager, and S. C. van den Heever (2014): A CloudSat Cloud-Object Partitioning Technique and Assessment and Integration of Deep Convective Anvil Sensitivities to Sea Surface Temperature. Journal of Geophysical Research. 119, 10-515-10,535, doi: 10.1002/2014JD021717. IDH