Lightning Data Assimilation Research Articles

Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.

Assessing the Impact of Lightning Data Assimilation in the WRF Model

Assessing the Impact of Lightning Data Assimilation in the WRF Model

Evaluation of Lightning Flash Rate Parameterizations in a Cloud‐Resolved WRF‐Chem Simulation of the 29–30 May 2012 Oklahoma Severe Supercell System Observed During DC3

AbstractEighteen lightning flash rate parameterization schemes (FRPSs) were investigated in a Weather Research and Forecasting model coupled with chemistry cloud‐resolved simulation of the 29–30 May 2012 supercell storm system observed during the Deep Convective Clouds and Chemistry (DC3) field campaign. Most of the observed storm's meteorological conditions were well represented when the model simulation included both convective damping and lightning data assimilation techniques. Newly‐developed FRPSs based on DC3 radar observations and Lightning Mapping Array data are implemented in the model, along with previously developed schemes from the literature. The schemes are based on relationships between lightning and various kinematic, structural, and microphysical thunderstorm characteristics (e.g., cloud top height, hydrometeors, reflectivity, and vertical velocity) available in the model. The results suggest the model‐simulated graupel and snow/ice hydrometeors require scaling factors to more closely represent proxy observations. The model‐simulated lightning flash trends and total flashes generated by each scheme over the simulation period are compared with observations from the central Oklahoma Lightning Mapping Array. For this supercell system, 13 of the 18 schemes overpredicted flashes by >100% with the group of FRPSs based on storm kinematics and structure (particularly updraft volume) performing slightly better than the hydrometeor‐based schemes. During the storm's first 4 hr, the upward cloud ice flux FRPS, which is based on the combination of vertical velocity and hydrometeors, well represents the observed total flashes and flash rate trend; while, the updraft volume scheme well represents the observed flash rate peak and subsequent sharp decline in flash rate.

Improving the lightning forecast with the WRF model and lightning data assimilation: Results of a two-seasons numerical experiment over Italy

We show, for the first time over Italy and over part of the central Mediterranean Basin, the impact of lightning data assimilation (LDA) on the strokes forecast for a long period. We use the Weather Research and Forecasting (WRF) model coupled with the Dynamic Lightning Scheme (DLS) at convection allowing horizontal resolution (3 km). We carried out a two-seasons experiment (summer 2020 and fall 2021) providing the forecast of lightning and precipitation for the next 6 h (nowcasting), considering two sub-periods (0-3 h and 3-6 h) for verification. The LDA is done through a nudging scheme that increases the water vapor mass in the mixed-phase region based on observed flash density rates and simulated graupel mixing ratio. No changes are made to the model run if spurious convection is predicted or no flashes are observed. LDA can trigger convection missed by the control forecast, without LDA, and/or can redistribute the strokes predicted to be more consistent with observations. LDA has a positive impact on strokes forecast, improving correct forecasts and reducing false alarms. This improvement is however confined to the first three-hours of forecast with negligible to negative impact for longer time ranges, in line with other studies. The improvement pattern is different in summer and fall, depending on the convection development.The analysis of the Fraction Skill Score shows the usefulness of the forecast for practical purposes, considering the current areas used by the Civil Protection Department to issue meteorological alerts for intense convective events over Italy. Finally, it is shown that the forecast at the short-range (0−3h) using LDA can improve the strokes forecast issued on the previous day, not using LDA, and the methodology of this paper can be applied to issue warnings and alerts as the storm is approaching.A brief examination of rainfall forecast shows positive impact of LDA at the short-range (0-3 h), with neutral impact for longer time ranges. The different impact of LDA on the strokes and precipitation forecasts is also highlighted.

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

AbstractThe accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period.

Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) pseudo-observations in AROME-France – proof of concept

Abstract. This study develops a lightning data assimilation (LDA) scheme for the regional, convection-permitting numerical weather prediction (NWP) model AROME-France. The LDA scheme intends to assimilate total lightning, i.e., cloud-to-ground (CG) and inter- and intra-cloud (IC), of the future Meteosat Third Generation (MTG) Lightning Imager (LI; MTG-LI). MTG-LI proxy data are created, and flash extent density (FED) fields are derived. An FED forward observation operator (FFO) is trained based on modeled, column-integrated graupel mass from 24 storm days in 2018. The FFO is successfully verified for 2 independent storm days. With the FFO, the LDA adapts a 1-dimensional Bayesian (1DBay) retrieval followed by a 3-dimensional variational (3DVar) assimilation approach that is currently run operationally in AROME-France for radar reflectivity data. The 1DBay retrieval derives relative humidity profiles from the background by comparing the FED observations to the FED inferred from the background. Retrieved relative humidity profiles are assimilated as sounding data. The evaluation of the LDA comprises different LDA experiments and four case studies. It is found that all LDA experiments can increase the background integrated water vapor (IWV) in regions where the observed FED exceeds the FED inferred from AROME-France outputs. In addition, IWV can be reduced where spurious FED is modeled. A qualitative analysis of 6 h accumulated rainfall fields reveals that the LDA is capable of locating and initiating some local precipitation fields better than a radar data assimilation (RDA) experiment. However, the LDA also leads to rainfall accumulations that are too high at some locations. Fractions skill scores (FSSs) of 6 h accumulated rainfall are overall similar for the developed LDA and RDA experiments. An approach aiming at mitigating effects due to differences in the optical extents of lightning flashes and the area of the corresponding cloud was developed and included in the LDA; however, it does not always improve the FSS.

Study of the Intense Meteorological Event Occurred in September 2022 over the Marche Region with WRF Model: Impact of Lightning Data Assimilation on Rainfall and Lightning Prediction

A destructive V-shaped thunderstorm occurred over the Marche Region, in Central Italy, on 15 September 2022. Twelve people died during the event, and damage to properties was extensive because the small Misa River flooded the area. The synoptic-scale conditions that caused this disastrous event are analysed and go back to the presence of tropical cyclone Danielle in the eastern Atlantic. The performance of the weather research and forecasting (WRF) model using lightning data assimilation (LDA) is studied in this case by comparing the forecast with the control forecast without lightning data assimilation. The forecast performance is evaluated for precipitation and lightning. The case was characterised by four intense 3-h (3 h) periods. The forecasts of these four 3-h phases are analysed in a very short-term forecast (VSF) approach, in which a 3 h data assimilation phase is followed by a 3 h forecast. A homemade 3D-Var is used for lightning data assimilation with two different configurations: ANL, in which the lightning is assimilated until the start of the forecasting period, and ANL-1H, which assimilates lightning until 1 h before the 3 h forecasting period. A sensitivity test for the number of analyses used is also discussed. Results show that LDA has a significant and positive impact on the precipitation and lightning forecast for this case.

Combined assimilation of radar and lightning data for the short-term forecast of severe convection system

In this study, a lightning data assimilation (LDA) method adjusting dynamical fields was examined in the rapid cycle with Weather Research and Forecasting (WRF) model and WRF Data Assimilation (WRFDA). This method retrieves pseudo vertical velocity profiles from total lightning observations and promotes wind convergence over lightning regions. This newly developed LDA scheme is compared with radar data assimilation (RDA) that has been routinely applied in severe storm nowcasting models. Depending on whether the radar or lightning data is assimilated, four experiments were designed to evaluate the positive impacts of dynamical adjustment from LDA. Using a typical severe mesoscale convection system occurred in Beijing, we found that the effect of RDA and LDA are different. The assimilation of radar radial velocity mainly improves the forecast in a longer time, while assimilation of pseudo-vertical-velocity from lightning data mainly corrects intensity and location of the forecasted precipitation. When both radar and lightning data are assimilated, the small-scale wind convergence are promoted and contributes to the intensified updrafts. Thermal and water vapor fields are also adjusted indirectly. Consequently, the convective precipitation is significantly improved and the positive impacts from the combined assimilation scheme persisted over a longer period (at least 3 h). It can be concluded that a combined assimilation of convective data from multiple sources such as radar and lightning data enhance prominently the accuracy of short-term convection forecasts.

A Method for Assimilating Pseudo Dewpoint Temperature as a Function of GLM Flash Extent Density in GSI‐Based EnKF Data Assimilation System—A Proof of Concept Study

AbstractIn this study, a new lightning data assimilation (LDA) scheme using Geostationary Lightning Mapper (GLM) flash extent density (FED) is developed and implemented in the National Severe Storms Laboratory Warn‐on‐Forecast System (WoFS). The new LDA scheme first assigns a pseudo relative humidity between the cloud base and a specific layer based on the FED value. Then at each model layer, the pseudo relative humidity is converted to pseudo dewpoint temperature according to the corresponding air temperature. Some sensitivity experiments are performed to investigate how to assign and use GLM/FED in an optimum way. The impact of assimilating this pseudo dewpoint temperature on a short‐term severe weather forecast is preliminarily assessed in this proof‐of‐concept study. A high‐impact weather event in Kansas on 24 May 2021 is used to evaluate the performance of the new scheme on analyses and subsequent short‐term forecasts. The results show that the assimilation of additional FED‐based dewpoint temperature observations along with radar, satellite radiance, and cloud water can improve short‐term (3‐hr) forecast skill in terms of quantitative and qualitative verifications against the observations. The improvement is primarily due to the direct and indirect adjustment of dynamic and thermodynamic conditions through the LDA process. More specifically, the assimilation of FED‐based dewpoint temperature, in addition to the other observations currently used in WoFS, tends to enhance the ingredients required for thunderstorm formation, namely moisture, instability, and lifting mechanism.

Comparison of the WRF-FDDA-Based Radar Reflectivity and Lightning Data Assimilation for Short-Term Precipitation and Lightning Forecasts of Severe Convection

This work evaluates and compares the performance of the radar reflectivity and lightning data assimilation schemes implemented in weather research and forecasting-four-dimensional data assimilation (WRF-FDDA) for short-term precipitation and lightning forecasts. All six mesoscale convective systems (MCSs) with a duration greater than seven hours that occurred in the Guangdong Province of China during June 2020 were included in the experiments. The results show that both the radar reflectivity data assimilation and lightning data assimilation improved the analyses and short-term forecasts of the precipitation and lightning of the MCSs. On average, for precipitation forecasts, the experiments with radar reflectivity data assimilation performed better than those with lightning data assimilation; however, for lightning forecasts, the experiments with lightning data assimilation performed better in the analysis period and 1 h forecast, and for some cases, the superiority lasted to three forecast hours. This highlights the potential of lightning data assimilation in short-term lightning forecasting.

Is an NWP-Based Nowcasting System Suitable for Aviation Operations?

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories.

A satellite lightning observation operator for storm-scale numerical weather prediction

Abstract. This study aims at simulating satellite-measured lightning observations with numerical weather prediction (NWP) system variables. A total of eight parameters, calculated with the AROME-France NWP system variables, were selected from a literature review to be used as proxies for satellite lightning observations. Two different proxy types emerged from this literature review: microphysical and dynamical proxies. Here, we investigate which ones are best related to satellite lightning and calibrate an empirical relationship between the best parameters and lightning data. To obtain those relationships, we fit machine learning regression models to our data. In this study, pseudo flash extent accumulation (FEA) observations are used because no actual geostationary lightning observations are available yet over France, and non-geostationary satellite lightning data represent a sample that is too small for our study. The performances of each proxy and machine learning regression model are evaluated by computing fractions skill scores (FSSs) with respect to observed FEA and proxy-based FEA. The present study suggests that microphysical proxies are more suited than the dynamical ones to model satellite lightning observations with the AROME-France NWP system. The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study, but no proxy combination yielded better results than microphysical proxies alone. Finally, different accumulation periods of the FEA had little influence, i.e. similar FSS, on the regression model's ability to reproduce the observed FEA. In future studies, the microphysical-based relationship will be used as an observation operator to perform satellite lightning data assimilation in storm-scale NWP systems and applied to NWP forecasts to simulate satellite lightning data.

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

In this study, the impact of water vapor on the improvement of precipitation simulation in a supercell event over eastern China is investigated by assimilating lightning data into the weather research and forecast (WRF) three-dimensional variational system. The results indicate that the vertical distribution of vapor plays a crucial role in convection precipitation simulation. The WRF alone fails to capture the vertical distribution of vapor, especially at higher altitudes, and vapor simulation is often weak in WRF. Assimilation of lightning data enhances the range and intensity of precipitation forecasts; assimilation of pseudo water vapor transformed from lightning data improves the convection intensification and updraft height of supercells by rectifying the vertical moisture profile. Lightning data assimilation can efficiently compensate for the paucity of observations during supercell growth, and the benefits of lightning data assimilation are concentrated in areas where lightning data is accessible. Further, the importance of microscale water vapor variations, which can be well represented by assimilating pseudo-water vapor, in representing the supercell’s range and intensity is highlighted.

Improving Forecast of Severe Oceanic Mesoscale Convective Systems Using FY-4A Lightning Data Assimilation with WRF-FDDA

The Fengyun-4A (FY-4A) geostationary satellite carries the Lightning Mapping Imager that measures total lightning rate of convective systems from space at high spatial and temporal resolutions. In this study, the performance of FY-4A lightning data assimilation (LDA) on the forecast of non-typhoon oceanic mesoscale convective systems (MCSs) is investigated by using an LDA method implemented in the Weather Research and Forecasting-Four Dimensional Data Assimilation (WRF-FDDA). With the LDA scheme, three-dimensional graupel mixing ratio fields retrieved from the FY-4A lightning data and the corresponding latent heating rates are assimilated into the Weather Research and Forecasting model via nudging terms. Two oceanic MCS cases over the South China Sea were selected to perform the study. The subjective evaluation results demonstrate that most of the oceanic convective cells missed by the control experiments are recovered in the analysis period by assimilating FY-4A lightning data, due to the promoted updrafts by latent-heat nudging, the more accurate and faster simulations of the cold pools, and the associated gust-fronts at the observed lightning locations. The cold pools and gust-fronts generated during the analysis period helped to maintain the development of the MCSs, and reduced the morphology and displacement errors of the simulations in the short-term forecast periods. The quantitative evaluation indicates that the most effective periods of the LDA for simulation enhancement were at the analysis time and the nowcasting (0–2 h forecast) periods.

A Geostationary Lightning Pseudo-Observation Generator Utilizing Low-Frequency Ground-Based Lightning Observations

Abstract Coincident Geostationary Lightning Mapper (GLM) and National Lightning Detection Network (NLDN) observations are used to build a generator of realistic lightning optical signal in the perspective to simulate Lightning Imager (LI) signal from European NLDN-like observations. Characteristics of GLM and NLDN flashes are used to train different machine-learning (ML) models, which predict simulated pseudo-GLM flash extent, flash duration, and event number per flash (targets) from several NLDN flash characteristics. Comparing statistics of observed GLM targets and simulated pseudo-GLM targets, the most suitable ML-based target generators are identified. The simulated targets are then further processed to obtain pseudo-GLM events and flash-scale products. In the perspective of lightning data assimilation, flash extent density (FED) is derived from both observed and simulated GLM data. The best generators simulate accumulated hourly FED sums with a bias of 2% to the observation while cumulated absolute differences remain of about 22%. A visual comparison reveals that hourly simulated FED features local maxima at the similar geolocations as the FED derived from GLM observations. However, the simulated FED often exceeds the observed FED in regions of convective cores and high flash rates. The accumulated hourly area with FED > 0 flashes per 5 km × 5 km pixel simulated by some pseudo-GLM generators differs by only 7%–8% from the observed values. The recommended generator uses a linear support vector regressor (linSVR) to create pseudo-GLM FED. It provides the best balance between target simulation, hourly FED sum, and hourly electrified area.

Assessment of Storm‐Scale Real Time Assimilation of GOES‐16 GLM Lightning‐Derived Water Vapor Mass on Short Term Precipitation Forecasts During the 2020 Spring Forecast Experiment

AbstractThis study assesses the impact of assimilating pseudo‐observations for water vapor mass derived from the Geostationary Operational Environmental Satellites GOES‐16 lightning data on short‐term quantitative precipitation forecasts (QPFs) over the contiguous United States (CONUS), with an emphasis given to regions characterized by an overall poor radar coverage. The GOES‐16/17 provide nearly uniform and high temporal resolution total lightning observations over most of the Americas. To leverage this information for convective scale weather forecasts, a three‐dimensional variational data assimilation (DA) package developed by the National Severe Storm Laboratory was employed to assimilate these data. To mimic operational settings, the Weather Research and Forecasting Model configuration used in NOAA Global Systems Laboratory's High‐Resolution Rapid Refresh Model version 4 was utilized. During the 2020 NOAA Hazardous Weather Testbed Spring Forecasting Experiment, four experiments were run in real time during a 29‐day period over CONUS and surrounding territories to assess the added value of GOES‐16 lightning over conventional radar data. Overall, the lightning DA (LDA) showed benefit in improving QPFs up to 6 hr, with the best improvements seen during the first 3 hr. Owing to notably larger lightning activity over the eastern CONUS, the most noticeable impacts from the LDA were seen there. Case‐by‐case analysis revealed that positive impacts from the LDA were seen over areas characterized by both good (eastern two thirds of CONUS) and poor radar coverage (western one third of CONUS). Highlighting the inherent difficulties in developing an optimal observation operator based on moisture for LDA applications, the GLM‐based DA experiments systematically overestimated rainfall over the eastern two thirds of CONUS while underestimating it over the western one third of CONUS.

Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.

Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.

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

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.

Evaluating the Performance of Lightning Data Assimilation from BLNET Observations in a 4DVAR-Based Weather Nowcasting Model for a High-Impact Weather over Beijing

The Beijing Broadband Lightning Network (BLNET) was successfully set up in North China and had yielded a considerable detection capability of total lightning (intracloud and cloud to ground) over the regions with complex underlying (plains, mountains, and oceans). This study set up a basic framework for the operational application of assimilating total lightning activities from BLNET and assesses the potential benefits in cloud-scale, very short-term forecast (nowcasting) by modulating the vertical velocity using the 4DVar technique. Nowcast statistics aggregated over 11 cycles show that the nowcasting performances with the assimilation of BLNET lightning datasets outperform RAD and the assimilation of GLD360 (Global Lightning) datasets. The assimilation of BLNET data improves the model's dynamical states in the analysis by enhancing the convergence and updraft in and near the convective system. To better implement of assimilating real-time lightning data, this study also conducts sensitivity experiments to investigate the impact of the horizontal length scale of a distance-weighted interpolation, binning time intervals, and different vertical profile or distance weights prior to the DA. The results indicate that the best forecast performance for assimilating BLNET lightning datasets is obtained in a 4DVar cycle when the lightning accumulation interval is 3 min, the radius of horizontal interpolation is 5 × 5, and the statistically vertical velocity profile and the distance weights obtained from cumulus cloud.