Search

High‐temporal‐resolution geostationary satellite infrared measurements are always used to capture and predict typical characteristics at the cloud top of rapidly developing strong storms at the pre‐convection or convection initiation (CI) stage. However, the large false alarm rate of CI nowcasting is difficult to avoid due to the complex and unpredictable trigger factors. Although the microwave measurement technique can observe thick clouds and even the precipitation within clouds due to the weaker atmospheric extinction effect on microwave, microwave data from polar‐orbiting satellites are rarely used to observe the CI due to their relatively low temporal resolution. In this study, we analyze several previously unknown CI characteristics over the East Asia region from 2016 to 2019 based on spatially and temporally matched Advanced Technology Microwave Sounder data. These typical CI samples are initially identified by using continuous infrared images from the Himawari‐8 geostationary satellite. The results show that there is a distinct cloud optical depth at the pre‐convection stage in the western (deep cloud clusters) and eastern (shallow cloud clusters) Tibetan Plateau (TP). The shallow precipitating cloud clusters of the CI over the eastern TP are possibly attributed to the favorable local dynamic and thermal conditions stem from the Asian monsoon. Another notable finding shows that the fast‐developing CI over the ocean has thick clouds compared with the samples over the land. Overall, the unique CI characteristics found from microwave observations in this study indicate that the future geostationary microwave sounder technologies will almost certainly provide some new findings and enhance early warning capabilities about convection. Key Points: This study uses microwave sounder data to explore some previously unknown convection initiation (CI) characteristicsMore relatively shallow cloud clusters at the pre‐convection stage are found based on microwave observations in the eastern Tibetan PlateauCompared with the samples over the land, strong CI that develops rapidly over the ocean has thicker clouds [ABSTRACT FROM AUTHOR]

Accurate and prior identification of local severe storm systems in pre-convection environments using geostationary satellite imagery measurements is a challenging task. Methodologies for "convective initiation" identification have already been developed and explored for operational nowcasting applications; however, warning of such convective systems using the new generation of geostationary satellite imagery measurements in pre-convection environments is still not well studied. In this investigation, the Random Forest (RF) machine learning algorithm is used to develop a predictive statistical model for tracking and identifying three different types of convective storm systems (weak, medium, and severe) over East Asia by combining spatially-temporally collocated Himawari-8 (H08) measurements and Numerical Weather Prediction (NWP) forecast data. The Global Precipitation Measurement (GPM) gridded product is used as a benchmark to train the predictive models based on a sample-balance technique which can adjust or balance the samples of three different convection types to avoid over-fitting any type of dataset. Variables such as brightness temperatures (BTs) from H08 water vapor absorption bands (6.2 μm, 6.9 μm and 7.3 μm) and Total Precipitable Water (TPW) from NWP show relatively high ranks in the predictive model training. These sensitive variables are closely associated with convectively dominated precipitation areas, indicating the importance of predictors from both H08 and NWP data. The final optimal RF model is achieved with an accuracy of 0.79 for classification of all convective storm systems, while the Probability of Detection (POD) of this model for severe and medium convections can reach 0.66 and 0.70, respectively. Two typical sudden convective storm cases in the warm season of 2018 tracked by this algorithm are described, and results indicate that the H08 and NWP based statistical model using the RF algorithm is capable of capturing local burst convective storm systems about 1–2 h earlier than the outbreak of heavy rainfall. [ABSTRACT FROM AUTHOR]