What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations

Schulte, Richard M.; Lebsock, Matthew D.; Haynes, John M.

doi:https://doi.org/10.5194/amt-16-3531-2023

Articles | Volume 16, issue 14

https://doi.org/10.5194/amt-16-3531-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-16-3531-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 14

Research article

|

25 Jul 2023

Research article |

| 25 Jul 2023

What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations

Richard M. Schulte, Matthew D. Lebsock, and John M. Haynes

Data sets

Data Products, CloudSat DPC CloudSat Data Processing Center https://www.cloudsat.cira.colostate.edu/data-products

Model code and software

A-Train Subadiabatic Model Richard Schulte https://doi.org/10.5281/zenodo.7706791

Short summary

In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.