Lightning data assimilation with comprehensively nudging water contents at cloud-resolving scale using WRF model

Chen, Zhixiong; Qie, Xiushu; Liu, Dongxia; Xiong, Yujiu

doi:10.1016/j.atmosres.2019.02.001

Cited by 25 publications

(19 citation statements)

References 36 publications

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“…Chen et al [42] proposed a data assimilation scheme, based on the functions of Fierro et al [29] and Qie et al [41], which takes into account the dynamical and thermodynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

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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“…Chen et al [42] proposed a data assimilation scheme, based on the functions of Fierro et al [29] and Qie et al [41], which takes into account the dynamical and thermodynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…While both RA and RALN successfully forecast the rain band at the second forecast hour, the precipitation system from RALN is more organized and located slightly to the south of that from RA (closer to the 400‐m elevation contour, especially for the southwestern section), which is in better agreement with the observed rain band location. It should be noted that the lightning‐alone experiment LN might perform better if a moisture adjustment scheme had been implemented (e.g., Chen et al, 2019; Fierro et al, 2019; Hu et al, 2020). The moisture adjustment was not applied in the current study because our focus here is on the role of kinematic pseudo observations and the optimal combination of humidity pseudo observations derived from radar reflectivity and lightning data deserves a more detailed and separate study, which is being explored and will be reported in a follow‐on study.…”

Section: Real Case Studiesmentioning

confidence: 99%

“…The NIC theory lays the physical foundation for assimilating lightning observations. For example, the state variables retrieved from lightning flash rates via empirical relationships, such as latent heat (Alexander et al, 1999; Pessi & Businger, 2009), specific humidity (Fierro et al, 2012, 2014, 2015, 2016, 2019; Hu et al, 2020; Mansell et al, 2007; Papadopoulos et al, 2005; Zhang et al, 2017), hydrometer mass (Chen et al, 2019; Kong et al, 2020; Mansell, 2014; Qie et al, 2014; Wang et al, 2017), and temperature (Marchand & Fuelberg, 2014), are assimilated to trigger convection at locations where lightning is observed. Generally, these lightning DA methods are very similar to the cloud analysis schemes used to assimilate radar reflectivity observations that force convection by adjusting the microphysical or thermodynamic state variables (e.g., Hu et al, 2006; Zhao & Xue, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it is particularly challenging for weather radar to acquire observations over mountainous regions, where emitted radar beams suffer from full or partial blocking from terrain. Thus, efforts have been made to combine radar data assimilation (DA) with other data sources, such as lightning observations provided by traditional ground‐based lightning detection networks (Chen et al, 2019; Fierro et al, 2012, 2014). In recent years, lightning imagers aboard geostationary satellites have become available, such as the Geostationary Lightning Imager (GLM) on the GOES‐R satellite (Goodman et al, 2013) and the Lightning Mapping Imager (LMI) on the FY‐4A satellite (Yang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A Method to Update Model Kinematic States by Assimilating Satellite‐Observed Total Lightning Data to Improve Convective Analysis and Forecasting

et al. 2020

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This study assesses the benefit of convective-scale data assimilation (DA) for model initialization using well-known functional relationships between lightning flash rate and vertical velocity. Based on the relationships, a lightning DA scheme to update model kinematic states was implemented in the Weather Research and Forecasting Data Assimilation (WRFDA) three-dimensional variational (3DVar) system. This scheme combines total lightning observations with model-based prescribed vertical velocity profiles to retrieve kinematic information through a DA scheme. With the availability of space-borne lightning imagers in recent years, total lightning observations from the Lightning Mapping Imager (LMI) on board the FY-4A geostationary satellite were assimilated in combination with radar DA. A detailed analysis of the impact of the lightning DA scheme on convective precipitation forecasting was conducted using a squall line case over Beijing on 13 July 2017. The assimilation of LMI data provides added benefits to the assimilation of radar radial winds by reducing wind errors and strengthening convergence along the squall line in the analysis. Although the microphysical states are identical due to the assimilation of reflectivity, the lightning DA scheme helps in promoting updraft developments at lightning observation locations, which improves the representation of mixed-phase convection. The quantitative verification of short-term convective forecasts indicated that the lightning DA adds value to the radar DA by improving the precipitation forecast skill. The lightning DA scheme was evaluated further for a heavy rainfall case in 2018 over the Beijing metropolitan area and revealed overall similar forecast improvements for composite reflectivity and accumulated rainfall. Plain Language Summary Lightning flashes are closely related to the upward air motions in thunderstorms and hence are indicative of strong wind convergence. Currently, lightning imagers on board the geostationary satellites provide increased availability of lightning data over broad regions and can improve weather forecasting accuracy. This paper describes how the space-borne lightning observations could be employed to update model kinematic states and improve convective precipitation forecasting.

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“…Water vapor mixing ratio was adjusted by a nudging function where lightning was observed but deep convections were not simulated, and by means of modifying the microphysics parameterization scheme, the locations of the convective systems were improved on 0-3h different model forecasts (WRF [12][13][14]; RAMS [20,21]). Through substituting the water mass by mixing ratios of selected ice species in nudging function, lightning data were also used to adjust the density of ice-phase particles in the mixed-phase region in WRF, and the convective precipitation was improved clearly in active lightning regions [22,23]. Besides, lightning data were used to discern the graupel-dominant regions, and its assimilation helped improve the short-term lightning and precipitation forecasts [24].…”

Section: Introductionmentioning

confidence: 99%

Case Study of a Retrieval Method of 3D Proxy Reflectivity from FY-4A Lightning Data and Its Impact on the Assimilation and Forecasting for Severe Rainfall Storms

et al. 2020

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As the first Geostationary Satellite with the LMI (Lightning Mapping Imager) instrument aboard running over the eastern hemisphere, FY-4A (Feng-Yun-4A) can better indicate severe convection and compensate for the limitations of radar observation in temporal and spatial resolution. In order to realize the application of FY-4A lightning data in numerical weather prediction (NWP) models, a logarithmic relationship between FY-4A lightning density and maximum radar reflectivity is presented to convert FY-4A lightning data into maximum FY-4A proxy reflectivity. Then, according to the profiles of radar reflectivity, the maximum FY-4A proxy reflectivity is extended to 3D FY-4A proxy reflectivity. Finally, the 3D FY-4A proxy reflectivity is assimilated in RMAPS-ST (Rapid-refresh Multi-scale Analysis and Prediction System—Short Term) to compare with radar assimilation. Four groups of continuous cycling data assimilation and forecasting experiments are carried out for a severe rainfall case. The results demonstrate that cycling assimilation of 3D FY-4A proxy reflectivity can adjust the moisture condition effectively, and indirectly affects the temperature and wind fields, then makes the thermal and dynamic analysis more reasonable. The Fractions Skill Scores (FSSs) show that the rainfall forecasts are improved significantly within 6 h by assimilating 3D FY-4A proxy reflectivity, which is similar to the parallel experiments in assimilating radar reflectivity. In addition, other cycling data assimilation experiments are carried out in mountainous areas without radar data. The improvement of precipitation forecasts in mountainous areas further proves that the application of assimilating 3D FY-4A proxy reflectivity can be considered a useful substitute where observed radar data are missing. Through the two severe rainfall cases, this method could be framed as an example of how to use lightning for data assimilation.

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Lightning data assimilation with comprehensively nudging water contents at cloud-resolving scale using WRF model

Cited by 25 publications

References 36 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

A Method to Update Model Kinematic States by Assimilating Satellite‐Observed Total Lightning Data to Improve Convective Analysis and Forecasting

Case Study of a Retrieval Method of 3D Proxy Reflectivity from FY-4A Lightning Data and Its Impact on the Assimilation and Forecasting for Severe Rainfall Storms

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