Impact of Radar Reflectivity and Lightning Data Assimilation on the Rainfall Forecast and Predictability of a Summer Convective Thunderstorm in Southern Italy

Abstract

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events.

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

“…Convective-scale observations such as Doppler Weather Radar (DWR) and lightning flashes indicate the presence or absence of deep, moist and mixed-phase convection, which is required to modify the representation of localized mesoscale features in the model in order to improve the initial conditions and thereby the improved short-term forecasts (Kong et al, 2018;Lee et al, 2010;Liu et al, 2019;Wang et al, 2013). Several studies revealed that there are considerable improvements in short-term forecasts of severe weather phenomena such as thunderstorms and tropical cyclones by assimilating the radar data into the model initial conditions (Federico et al, 2021;Fierro et al, 2019;Gastaldo et al, 2018;Gustafsson et al, 2018;Jones et al, 2016;Lai et al, 2020;Lilly, 1990;Schenkman et al, 2011;Sun et al, 2014;Yussouf et al, 2015). Weather radars are an essential data source for improving the accuracy of short-range precipitation forecasts (Lee et al, 2020;Schwitalla & Wulfmeyer, 2014;Sokol et al, 2021b).…”

“…However, not all convective systems produce lightning, thus, the Lightning Detection Network (LDN) cannot observe those systems whereas they can still be seen in radar reflectivity records. Moreover, flashes may be absent or sparse in the early phases of developing convection (Federico et al ., 2021). Recently, several attempts have been made to incorporate lightning data into NWP models (Federico et al ., 2021; Fierro et al ., 2012; Liu et al ., 2021; Mansell et al ., 2007; Papadopoulos et al ., 2005; Pessi & Businger, 2009).…”

“…Moreover, flashes may be absent or sparse in the early phases of developing convection (Federico et al ., 2021). Recently, several attempts have been made to incorporate lightning data into NWP models (Federico et al ., 2021; Fierro et al ., 2012; Liu et al ., 2021; Mansell et al ., 2007; Papadopoulos et al ., 2005; Pessi & Businger, 2009). Papadopoulos et al ., 2005 used lightning observations to locate the convective regions, nudged the model‐simulated water vapour profiles towards the observed profiles and noticed a significant improvement in the predicted rainfall during the assimilation period.…”

“…Several studies revealed that there are considerable improvements in short‐term forecasts of severe weather phenomena such as thunderstorms and tropical cyclones by assimilating the radar data into the model initial conditions (Federico et al ., 2021; Fierro et al ., 2019; Gastaldo et al ., 2018; Gustafsson et al ., 2018; Jones et al ., 2016; Lai et al ., 2020; Lilly, 1990; Schenkman et al ., 2011; Sun et al ., 2014; Yussouf et al ., 2015). Weather radars are an essential data source for improving the accuracy of short‐range precipitation forecasts (Lee et al ., 2020; Schwitalla & Wulfmeyer, 2014; Sokol et al ., 2021b).…”