WRF-LTNGDA: A lightning data assimilation technique implemented in the WRF model for improving precipitation forecasts

Giannaros, Theodore M.; Kotroni, Vassiliki; Lagouvardos, Konstantinos

doi:10.1016/j.envsoft.2015.11.017

Cited by 50 publications

(52 citation statements)

References 41 publications

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“…Our results show that the POD improves for all precipitation thresholds when lightning data assimilation is used and the percentage of improvement is slightly better than that reported in Giannaros et al (2016) for the lower thresholds (below 10 mm day −1 ). Even if we cannot give a definitive answer to this issue, because of the many important differences between this study and that of Giannaros et al (2016), the lightning data assimilation technique has a role.…”

Section: Discussionmentioning

confidence: 37%

“…Recently, Giannaros et al (2016) presented the WRF-LTNGDA, a lightning data assimilation technique implemented in WRF. They presented the results for eight cases in Greece.…”

Section: Discussionmentioning

confidence: 99%

“…They enabled lightning to control the cumulus parameterization scheme activation. Recently, Giannaros et al (2016) implemented a similar approach in the Weather Research and Forecasting (WRF) model, showing the positive and statistically robust impact of the lightning data assimilation on the 24 h rainfall forecast for eight convective events over Greece. Fierro et al (2013) and Qie et al (2014) introduced two lightning data assimilation schemes for the WRF model intervening on the mixing ratios of the hydrometeors (water vapour in the case of Fierro et al, 2013, and ice crystals, graupel and snow in Qie et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several studies have shown the positive impact that lightning assimilation has on the weather forecast and especially on the precipitation forecast (Alexander et al, 1999;Chang et al, 2001;Papadopulos et al, 2005;Mansell et al, 2007;Fierro et al, 2013;Giannaros et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…However, Giannaros et al (2016) applied the methodology to eight convective cases that occurred in Greece from 2010 to 2013. Considering a larger number of cases allowed them to statistically test the improvement of the precipitation forecast through lightning data assimilation.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation

Federico

Petracca

Panegrossi

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature, and graupel mixing ratio. The methodology is set up to improve the shortterm (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies.The methodology is applied to 20 cases that occurred in fall 2012, which were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of rain gauges over Italy, which is the target area of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the precipitation forecast at the short range (3 h) and over a wide area (Italy).Results for the 27 October case study show how the methodology works and its positive impact on the 3 h precipitation forecast. In particular, the model represents better convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of convection by lightning data assimilation improves the precipitation forecast at fine scales (meso-β).The application of the methodology to 20 cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, with the exception of the bias. The probability of detection (POD) increases by 3-5 % for the 3 h forecast and by more than 5 % for daily precipitation, depending on the precipitation threshold considered.Score differences between simulations with or without data assimilation are significant at 95 % level for most scores and thresholds considered, showing the positive and statistically robust impact of the lightning data assimilation on the precipitation forecast.

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Section: Discussionmentioning

confidence: 37%

“…Recently, Giannaros et al (2016) presented the WRF-LTNGDA, a lightning data assimilation technique implemented in WRF. They presented the results for eight cases in Greece.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation

Federico

Petracca

Panegrossi

et al. 2017

Nat. Hazards Earth Syst. Sci.

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A simple lightning assimilation technique for improving retrospective WRF simulations

Heath

Pleim

Gilliam

et al. 2016

J Adv Model Earth Syst

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Convective rainfall is often a large source of error in retrospective modeling applications. In particular, positive rainfall biases commonly exist during summer months due to overactive convective parameterizations. In this study, lightning assimilation was applied in the Kain-Fritsch (KF) convective scheme to improve retrospective simulations using the Weather Research and Forecasting (WRF) model. The assimilation method has a straightforward approach: force KF deep convection where lightning is observed and, optionally, suppress deep convection where lightning is absent. WRF simulations were made with and without lightning assimilation over the continental United States for July 2012, July 2013, and January 2013. The simulations were evaluated against NCEP stage-IV precipitation data and MADIS near-surface meteorological observations. In general, the use of lightning assimilation considerably improves the simulation of summertime rainfall. For example, the July 2012 monthly averaged bias of 6 h accumulated rainfall is reduced from 0.54 to 0.07 mm and the spatial correlation is increased from 0.21 to 0.43 when lightning assimilation is used. Statistical measures of near-surface meteorological variables also are improved. Consistent improvements also are seen for the July 2013 case. These results suggest that this lightning assimilation technique has the potential to substantially improve simulation of warm-season rainfall in retrospective WRF applications.

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Improving the simulation of convective dust storms in regional‐to‐global models

Foroutan

Pleim

2017

J Adv Model Earth Syst

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Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional-to-global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies.

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WRF-LTNGDA: A lightning data assimilation technique implemented in the WRF model for improving precipitation forecasts

Cited by 50 publications

References 41 publications

Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation

Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation

A simple lightning assimilation technique for improving retrospective WRF simulations

Improving the simulation of convective dust storms in regional‐to‐global models

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