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.
Highlights
Quantitative precipitation forecast (QPF) is an outstanding mission of the forecaster community and represents, especially for convective events, a very difficult task, because of the multitude of temporal/spatial scales involved and the fast evolution and space variability of rainfall [1–3]
The storms are often associated with mid-latitude cyclonic systems or wave troughs, with or without secondary cyclogenesis, or with deep moist convection development produced by mesoscale convective systems (MCSs) [13–16]; when an MCS is located over the same area for several hours, large amounts of precipitation can accumulate in less than a day [10,11,17,18]
Several dangerous events are highly localized in space and time, the impact of the main synoptic patterns associated with these rainfall events [22,23] needs to be considered, since a large-scale analysis allows for improving the predictability of rainfall-related phenomena
Summary
Quantitative precipitation forecast (QPF) is an outstanding mission of the forecaster community and represents, especially for convective events, a very difficult task, because of the multitude of temporal/spatial scales involved and the fast evolution and space variability of rainfall [1–3]. Lightning data assimilation (LDA) has been widely used in the last two decades to improve precipitation forecasts, at the short and mid-range (0–24 h) because of some important properties of lightning observations: (a) lightning is located precisely in space and time in areas of deep convection (the spatial error of lightning is less than 200 m for the dataset used in this paper and lightning is detected instantaneously); (b) lightning data are easy to transfer and do not require broadband connections; (c) lightning data are available in almost real time because the time interval between lightning detection and data availability is of the order of a few minutes; (d) lightning can be detected in remote areas beyond the radar coverage or in complex orographic regions, where the availability of other sources of data is scarce.
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