Abstract
This study is motivated by the great importance of Cbs for aviation safety. The study investigates the role of Numerical Weather Prediction (NWP) filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of about 30 different experiments, covering Central Europe. These experiments compile different stability filter settings as well as the use of different channels for the InfraRed (IR) brightness temperatures (BT). As stability filters, parameters from Numerical Weather Prediction (NWP) are used. The application of the stability filters restricts the detection of Cbs to regions with a labile atmosphere. Various NWP filter settings are investigated in the experiments. The brightness temperature information results from the infrared (IR) Spinning Enhanced Visible and InfraRed Image (SEVIRI) instrument on-board of the Meteosat Second Generation satellite and enables the detection of very cold and high clouds close to the tropopause. Various satellite channels and BT thresholds are applied in the different experiments. The satellite only approaches (no NWP filtering) result in the detection of Cbs with a relative high probability of detection, but unfortunately combined with a large False Alarm Rate (FAR), leading to a Critical Success Index (CSI) below 60% for the investigated summer period in 2016. The false alarms result from other types of very cold and high clouds. It is shown that the false alarms can be significantly decreased by application of an appropriate NWP stability filter, leading to the increase of CSI to about 70% for 2016. CSI is increased from about 70 to about 75% by application of NWP filtering for the other investigated summer period in 2017. A brief review and reflection of the literature clarify that the function of the NWP filter can not be replaced by MSG IR spectroscopy. Thus, NWP filtering is strongly recommended to increase the quality of satellite based Cb detection. Further, it has been shown that the well established convective available potential energy (CAPE) and the convection index (KO) work well as a stability filter.
Highlights
Cumulonimbus clouds (Cbs) originate from rapid vertical updraft of humid and warm air enforced by constraint forces caused e.g., by mountains, heating or cold fronts
The results show that applying an Numerical Weather Prediction (NWP) filtering can be used to optimize the relation of POD and False Alarm Rate (FAR) and to improve the critical success index Critical Success Index (CSI)
The performed experiments show that the satellite based detection of Cbs can be significantly improved by application of an appropriate NWP stability filter
Summary
Cumulonimbus clouds (Cbs) originate from rapid vertical updraft of humid and warm air enforced by constraint forces caused e.g., by mountains, heating or cold fronts. The fast cooling of the air with rising altitude leads to optically thick and very cold clouds, referred to as cumulonimbus clouds (Cbs), which are usually accompanied by lightning, heavy precipitation, hail, and turbulence. And reliable prediction of Cb clouds is of great importance for weather forecasts and warnings, in particular for aviation, as Cb clouds (thunderstorms) constitute one of the most important natural risks for aircraft accidents. Observational data plays a pivotal role for the accurate detection and short term forecast of Cbs. Over ocean and a vast number of countries, which are not well equipped with precipitation RADARs, satellites are in addition to global ground based lightning networks the only observational source for the detection of Cbs, e.g., [1]. From a satellite perspective they are characterized by very cold brightness temperatures corresponding to high cloud top heights (CTH)
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