Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea

Abstract

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

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

“…The simulated precipitation is about half of the 118 mm/3 h observed, nevertheless the location of the precipitation spot is precise. This result is in line with many experiments showing the ability of LDA to correctly represent the position of intense convective events over Italy [51,53,54].…”

“…The application of LDA through the adjustment of water vapor profiles has been applied in different countries (among others [47][48][49][50]), showing a positive impact of LDA on the precipitation forecast at a short range. This method was also applied in the Mediterranean context [51][52][53][54], showing a notable potential for successful precipitation forecasts of convective events in the short term.…”

Impact of Water Vapor on the Development of a Supercell Over Eastern China

“…However, this lightning data assimilation method relies on convective parameterization scheme, and is not applicable to convective-scale assimilation. Based on a Nudging method, an empirical formula was proposed to increase the water vapor content at the location of lightning occurrence (Fierro et al, 2015(Fierro et al, , 2016Federico et al, 2019;Torcasio et al, 2021); although this is also an effective way to improve model's moisture field, however, uncertainties exist because of the empirical formula is not suitable for different climatic state and convective intensities. Results of our study indicate that, assimilating lightning data by transforming it to pseudo-water vapor within clouds is an effective approach to improve supercell prediction.…”

Prediction of Lightning Activity over Bangladesh Using Diagnostic and Explicit Lightning Parameterizations of WRF Model

“…This dataset of 10 km resolution is generated at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) located within NASA's Goddard Flight Center (Huffman 2019). The information from various low-Earth-orbit satellites (the GPM constellation satellites), holding different types of passive microwave radiometers (Torcasio et al 2021) are combined to estimate precipitation over the globe. The NASA/JAXA (Japan Aerospace Exploration Agency) GPM core observatory serves as the key satellite of the GPM constellation, which has the GPM Microwave Imager, radiometer, and a Ku/Ka-band dual frequency precipitation radar.…”