Deep convective cloud systems are central to the global water and energy cycle, and yet their representation in climate models remains challenging. This study explores the potential of machine learning to classify and characterize cloud structures inside cloud systems using radiometric measurements from the planned C²OMODO (Convective Core Observation through MicrOwave Derivative in the trOpics) mission. The relationships between cloud structure (anvil, stratiform, convective, and deep convective) and geophysical variables such as ice water path as well as integrated vertical ice momentum are investigated using a gradient boosting algorithm both for classification and regression purposes. The gradient boosting classification method achieves an overall accuracy (True Positive) above 70%. Retrievals of the geophysical variables yield R² ranging from 0.60 to 0.99. Furthermore, it is shown that applying a prior classification of the scenes improves the performances of the retrieval. This study highlights the potential of the forthcoming C²OMODO mission in advancing our understanding of convective systems and paves the way for in-depth studies on alternative or refined classification schemes and inputs, which could deliver even better results.

Convective systems are responsible for energy transfers between the lower and upper atmospheric layers and play a fundamental role in the water cycle, weather and climate evolution. With a tandem of high resolution and wide swath all-sky atmospheric sounders, the C2OMODO concept allows the estimation of vertical dynamics within deep convective systems.

Convective systems are responsible for energy transfers between the lower and upper atmospheric layers and play a fundamental role in the water cycle, weather and climate evolution. With a tandem of high resolution and wide swath all-sky atmospheric sounders, the C2OMODO concept allows the estimation of vertical dynamics within deep convective systems.

In the continuity of the existing SAPHIR microwave radiometer on-board Megha-Tropiques, in operation since 2011, a new generation atmospheric sounder is currently under study. SAPHIR-NG is designed to provide enhanced observations of clear sky water vapor and profiles of hydrometeors. The scientific objectives and requirements, as well as the instrumental implementation are described.

SAPHIR-NG is a new generation atmospheric sounder concept based on the existing SAPHIR instrument, onboard Megha-Tropiques, which has been operating since 2011. Its improvements with respect to SAPHIR are presented hereafter, both in terms of scientific objectives and instrumental architecture.

We present a new closure study between radiosonde and microwave satellite humidity measurements. The radiosonde data are from the Global Climate Observing System Reference Upper-Air Network. The satellite data are from the radiometers: MHS, Advanced Technology Microwave Sounder, and Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiométrie. Like previous studies, we find the satellite data to be “colder” than simulated radiosonde data. But the mean bias value (0.4 K) is smaller than previously reported and, according to our analysis, not significant. The error budget suggests an uncertainty of 0.52-1.06 K. We also show that the improvement in closure can be attributed to improvements in the intercomparison methodology.