A Novel Approach for the Detection of Developing Thunderstorm Cells

Abstract

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network.

High-Resolution Lightning Detection and Possible Relationship with Rainfall Events over the Central Mediterranean Area

“…In recent years, research interest in lightning has increased thanks to improved monitoring technology, which makes a huge amount of high-precision data available on an increasing temporal range. Interest is mainly related to scientific aspects, nature and the development of the phenomenon itself [1,2], associated hazards [3][4][5][6][7], the relationship between weather and climate features [8][9][10], and innovation in detection approaches [11,12]. In fact, cloud-ground lightning causes damage to buildings, infrastructures, and facilities, wildfires, and even casualties [1].…”

A Semi-Lagrangian Method for Detecting and Tracking Deep Convective Clouds in Geostationary Satellite Observations

“…Two additional combinations of channels are used to detect areas of deep convective cloud anvil. The water vapour difference (WVD) combination (figure 3b) of the upper troposphere WV channel minus the lower troposphere WV channel has been shown to provide a high detection rate for deep convective clouds (Müller et al, 2018(Müller et al, , 2019. In clear sky or low cloud conditions, WVD shows the temperature difference between the upper and lower troposphere of generally around -20 K. However, in the presence of high, thick clouds the 6.2 µm has an additional contribution from stratospheric water vapour produces a warm, and in extreme cases positive WVD value (Schmetz et al, 1997).…”

Case Study of a Retrieval Method of 3D Proxy Reflectivity from FY-4A Lightning Data and Its Impact on the Assimilation and Forecasting for Severe Rainfall Storms

“…Therefore, lightning data can be used to monitor the occurrence and evolution of mesoscale convective systems [7]. Moreover, lightning detection has the advantages of high-precision, long-distance coverage, and is relatively unaffected by terrain [8][9][10][11]. With the accumulation of high-quality lightning data, the application of lightning assimilation techniques in NWP models, aiming to improve the forecasting skills of convective precipitation, has been a major scientific topic recently [12][13][14][15].…”