Global monitoring of deep convection using passive microwave observations

In this study, we present the DEEPSTORM (DEEP moiSt aTmospheric cOnvection from micRowave radioMeter) algorithm, able to retrieve ice water path (IWP) and to detect deep moist atmospheric convection (DC) from 80°S to 80°N using observations from four spaceborne passive microwave radiometers. DEEPSTORM is based on a machine learning approach and is fitted against observations from the CPR (Cloud Profiling Radar) spaceborne radar on-board CloudSat. IWP predictions show an average root mean square error of 0.27 kg/m² and a correlation index of 0.87. DC occurrence is detected with a probability of 59% and a false alarm rate of 24%. The prediction accuracy of IWP and DC is significantly better when the IWP exceeds 0.5 kg/m² showing that DEEPSTORM is well suited to detect and characterise the strongest DC events. Overall DC detection is more accurate in the tropics than in mid-latitudes while the IWP retrieval works better in the mid-latitudes. Two examples illustrating the potential of DEEPSTORM are presented: the IWP is retrieved during Hurricane Matthew in 2016, and a climatology of DC occurrences between September 2016 and December 2016 is presented. This work will allow building a quasi-worldwide and 20-year long database of DC occurrence and intensity.