Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method—A Case Study of a Mesoscale Convective System

Gan, Ruhui; Yang, Yi; Xie, Qian; Lin, Erliang; Wang, Ying; Liu, Peng

doi:10.1007/s13351-021-0092-4

Cited by 9 publications

(7 citation statements)

References 53 publications

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“…Fierro et al [43,44] proposed a method for LDA based on the adjustment of the water vapor field of NWP; this method can also be used in a 3D-Var framework through the assimilation of pseudoprofiles of water vapor [45,46]. 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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Gan et al and Liu et al applied lightning to several severe convective events in eastern China. The results showed that assimilating lightning data effectively improves the forecasting of convective precipitation [26,28].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Yang

et al. 2021

Remote Sensing

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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.

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“…With the advancement of lightning detection technologies both at ground level and via satellite in the past decades, observed lightning flashes with coverage from regional to global scales are available and can be used for lightning assimilation (LTA). A robust LTA can improve convective simulations in meteorological models for retrospective atmo-D. Kang et al: Lightning assimilation in the WRF model spheric simulations (e.g., Heath et al, 2016;Marchand and Fuelberg, 2015) or help generate better initial fields for realtime weather forecasting (e.g., Lagouvardos et al, 2013;Giannaros et al, 2016;Fierro et al, 2012Fierro et al, , 2015 by pinpointing where deep convection occurred and altering the meteorology in what is generally referred to as a hot start (Gan et al, 2021). In addition, lightning also profoundly impacts the chemical composition of the troposphere by generating and releasing nitrogen oxides (LNO x ) that can significantly alter ground-level ozone (O 3 ) concentrations in some regions (Kang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales

et al. 2022

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Abstract. The lightning assimilation (LTA) technique in the Kain–Fritsch convective parameterization in the Weather Research and Forecasting (WRF) model has been updated and applied to continental and hemispheric simulations using lightning flash data obtained from the National Lightning Detection Network (NLDN) and the World Wide Lightning Location Network (WWLLN), respectively. The LTA technique uses lightning data to trigger the Kain–Fritsch convective parameterization via realistic temperature and moisture perturbations. The impact of different values for cumulus parameters associated with the Kain–Fritsch scheme on simulations with and without LTA were evaluated for both the continental and the hemispheric simulations. Comparisons to gauge-based rainfall products and near-surface meteorological observations indicated that the LTA improved the model's performance for most variables. The simulated precipitation with LTA, using WWLLN lightning flashes in the hemispheric applications, was significantly improved over the simulations without LTA when compared to precipitation from satellite observations in the equatorial regions. The simulations without LTA showed significant sensitivity to the cumulus parameters (i.e., user-toggled switches) for monthly precipitation that was as large as 40 % during convective seasons for monthly mean daily precipitation. With LTA, the differences in simulated precipitation due to the different cumulus parameters were minimized. The horizontal grid spacing of the modeling domain strongly influenced the LTA technique and the predicted total precipitation, especially in the coarser scales used for the hemispheric simulation. The user-definable cumulus parameters and domain resolution manifested the complexity of convective process modeling both with and without LTA. These results revealed sensitivities to domain resolution, geographic heterogeneity, and the source and quality of the lightning dataset.

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Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method—A Case Study of a Mesoscale Convective System

Cited by 9 publications

References 53 publications

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

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

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

Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales

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