Lightning data assimilation in the WRF-ARW model for short-term rainfall forecasts of three severe storm cases in Italy

Prat, Albert Comellas; Federico, Stefano; Torcasio, Rosa Claudia; Fierro, Alex O.; Dietrich, Stefano

doi:10.1016/j.atmosres.2020.105246

Cited by 12 publications

(14 citation statements)

References 38 publications

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“…While the aim of the paper is to show the impact of LDA over the sea, the forecast was also verified over land with the IMERG dataset. Results show a positive impact of LDA over the Italian peninsula, in agreement with the results already found by the same authors in past studies with WRF and RAMS@ISAC numerical weather prediction models [8,30,55].…”

Section: Discussionsupporting

confidence: 92%

“…The idea behind this setting is to maximize the impact of LDA on the forecast. Comellas Prat et al [30], using a similar WRF setting, showed that choosing A = 0.85 (B = 0.17, i.e., A + B = 1.02) resulted in worse performance compared to A = 0.95 (B = 0.07), the latter giving more weight to LDA. We performed similar tests for some cases of November 2019 (not shown), and the results reveal that A = 0.95 (B = 0.0.7) gives a better precipitation forecast compared to A = 0.85 (B = 0.17).…”

Section: Model Setup and Lightning Data Assimilation Proceduresmentioning

confidence: 99%

“…In this work, WRF was set up with 42 vertical levels, while in a similar study [30], we used 60 vertical levels. Increasing the number of levels would likely lead to a better forecast but would also increase the computational cost.…”

Section: Model Setup and Lightning Data Assimilation Proceduresmentioning

confidence: 99%

“…For the above reasons, LDA is suitable for improving the forecast of intense convective weather events in rapid update cycle (RUC) data assimilation systems. Broadly speaking, LDA can be divided into two classes: LDA in models parameterizing convection (horizontal resolution > 3-5 km) and LDA in models explicitly resolving the convection (horizontal resolution < 3-5 km) [18,29,30].…”

Section: Introductionmentioning

confidence: 99%

“…Federico et al [8] implemented the method of Fierro et al [29] in the regional atmospheric modeling system (RAMS) model and obtained a substantial improvement of the quantitative precipitation forecast (QPF) for 20 cases over Italy that occurred during Hydrological Mediterranean Experiment-Special Observing Period 1 (HyMeX-SOP1) [43]. Recently, Comellas Prat et al [30], using the WRF model, applied the method of Fierro et al [29] to evaluate the impact of LDA for three case studies of severe weather that occurred over Italy. The three cases span a range of convective precipitation from a long-lived, wide-area intense-precipitation event to a heavy and localized rainfall case.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Model Setup and Lightning Data Assimilation Proceduresmentioning

confidence: 99%

Section: Model Setup and Lightning Data Assimilation Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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WRF data assimilation of weather stations and lightning data for a convective event in northern Italy

Maggioni,

Manzoni,

Perotto

et al. 2023

Bull. of Atmos. Sci.& Technol.

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The present work shows the relevance of assimilating mesoscale observations and lightning data in the Weather Research and Forecasting (WRF) model, to simulate a strong convective event in northern Italy, poorly forecasted by available weather models even a few hours before the event itself. The data assimilation was conducted by testing the 3D-VAR and 4D-VAR assimilation algorithms implemented in the WRF data assimilation (WRFDA) suite, with different configurations and different assimilation windows. An extensive sensibility test has been operated to properly analyze the effect that the assimilation of a single station has on the model outcomes. Input data were taken from two networks of more than 1000 citizen-science meteorological stations, available in northern Italy, and from lightning flashes derived from Earth Networks Total Lightning Network, assimilated using the atmospheric water vapor as a proxy variable. Rain forecasts over an area in the north of Milan were compared to the station's measurements in the same area; POD, FAR, and CSI categorical statistics have been calculated. Results showed a positive improvement in the forecasted rain amounts with the ingestion of mesoscale weather data into 3D-VAR and 4D-VAR algorithms, more pronounced using 4D-VAR with a more frequent input data integration. A few improvements were reported by the 3D-VAR, with the lightning data assimilation, probably caused by the absence of the model's spin-up time with this configuration. An ideal simulation, which increased the water vapor of the air mass 2 h before the convective event, reported a positive enhancement of the rain amounts. The tests conducted on a single convective event are nevertheless encouraging, because they show a positive improvement of forecast with the assimilation of near-ground weather data and tropospheric water vapor 1 or 2 h before the beginning of the convection activity.

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Lightning forecasting in Bangladesh based on the lightning potential index and the electric potential

Rabbani

Islam

Fierro

et al. 2022

Atmospheric Research

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Lightning data assimilation in the WRF-ARW model for short-term rainfall forecasts of three severe storm cases in Italy

Cited by 12 publications

References 38 publications

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

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

WRF data assimilation of weather stations and lightning data for a convective event in northern Italy

Lightning forecasting in Bangladesh based on the lightning potential index and the electric potential

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