Abstract
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.
Highlights
Severe convective weather systems are usually accompanied by short-lived heavy rainfall, thunderstorms, strong winds, tornadoes, and/or hailstorms on the order of a dozen to three hundred kilometers horizontally [1]
Operational numerical weather prediction (NWP) model data is used to predict the occurrence of severe convective weather systems [3]
Severe convective storm systems can be well tracked or observed by geostationary (GEO) weather satellites and/or ground-based weather radars in their initial stages [5], which are always adopted as convective initiation (CI) products in nowcasting applications
Summary
Severe convective weather systems are usually accompanied by short-lived heavy rainfall, thunderstorms, strong winds, tornadoes, and/or hailstorms on the order of a dozen to three hundred kilometers horizontally [1]. In the most recent decade, with the rapid improvement of space-based imaging sensors, Laing et al [11] found that the presence of high altitude cirrus clouds can significantly impact the accuracy of convective storm system identification They proposed a new marker of 233 K (BT at 10.5–12.5 μm band from the European GEO meteorological Satellite-7, and Meteosat-7) for judging convection. In this study, based on H08/AHI data, the RF learning algorithm is used to develop a near real-time (NRT) tracking and warning predictive model for convective storm systems. Seven months of continuous H08 and GFS NWP data (from April to October 2016) are used here to build a robust and efficient convective storm prediction model (SWIPE) with RF algorithm This period covers the typical summer precipitation season over China. The GPM IMERG product is used as the truth for training the SWIPE prediction model based on its high quality
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