Assimilation of Chinese Doppler Radar and Lightning Data Using WRF-GSI: A Case Study of Mesoscale Convective System

Yang, Yi; Wang, Ying; Zhu, Kefeng

doi:10.1155/2015/763919

Cited by 17 publications

(22 citation statements)

References 41 publications

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“…Therefore, the FY-4A lightning data lacks a detailed comparative evaluation with lightning data detected by other satellites in this study. It is necessary to conduct a more detailed assessment of the FY-4A LMI lightning data in the future [33]. Besides, in this lightning data assimilation method, a wet bias might be created or increased to cause the spurious severe convection produced, which indicates the lightning assimilation method could combine with other methods to adjust the hydrometeors towards smaller values at spurious regions to limit spurious convection effectively [19,51].…”

Section: Discussionmentioning

confidence: 99%

“…Some other researchers converted lightning data into proxy radar reflectivity and then assimilated the proxy reflectivity [27][28][29][30][31][32]. This scheme has been evaluated via different assimilation methods (DDFI [33]; EnKF [16]) in DA systems, and the results demonstrated that it can improve the accuracy of reflectivity prediction as well as short-term rainfall forecasts, especially in areas with missing radar data [34].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…However, each method had shortcomings; for example, cloud analysis provided stronger precipitation in short-term forecasts after assimilation than was observed [33]. The physical initialization method provided a better precipitation forecast, but the improvement was short-lived [32].…”

Section: Introductionmentioning

confidence: 98%

“…In our previous work, we attempted to assimilate lightning data utilizing radar proxy reflectivity transformed in Gridpoint Statistical Interpolation (GSI) code using diverse methods such as physical initialization and cloud analysis [32][33][34]. However, each method had shortcomings; for example, cloud analysis provided stronger precipitation in short-term forecasts after assimilation than was observed [33].…”

Section: Introductionmentioning

confidence: 99%

A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR

et al. 2017

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Abstract:Lightning network data, considered as a useful supplement to radar observations, are a good indicator of severe convection, and has high temporal and spatial resolution. In Numerical Weather Prediction (NWP) models, lightning data are a new source of data to improve the forecasting of convective systems. In this case study, lightning data assimilation is conducted by converting lightning data to water vapor mixing ratio via a simple smooth continuous function, with input variables of total flash rate and simulated graupel mixing ratio at 9 km gridded resolution. Relative humidity converted from the retrieved water vapor mixing ratio is assimilated into the background field utilizing the three-dimensional variational (3DVAR) method in WRFDA (the Weather Research and Forecasting model Data Assimilation system). The benefits of assimilating lightning data are demonstrated in a series of experiments using data from a strong convection event that affected Beijing, Tianjin, Hebei and Shandong Province, on 31 July 2007. A nested domain with resolutions of 9 km and 3 km is implemented. For this case, assimilating lightning data shows some improvements in predictions of both reflectivity and neighboring precipitation, and in the temperature, dew-point temperature and relative humidity profile after seven hours.

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“…In our previous work, we assimilated the cloud-to-ground or total flash rate into WRF via various methods. The cloud-to-ground flash rates were converted into proxy radar reflectivity basing on an empirical relationship in GSI (Girdpoint Statistics Interpolation) code, and then assimilated into the WRF model by physical initialization [28], WRF-GSI cloud analysis [29] and ensemble square root filter [30]. Total flash rates were converted into the proxy of relative humidity based on the relationship promoted by Fierro et al [26] and then assimilated via three-dimensional variational (3DVar) [31].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

2018

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Abstract:Lightning forecasting is a vital item in server convective system short-time forecasting. However, lightning parameterization in mesoscale numerical prediction models is still in its early stages of development. Several lightning parameterization schemes are implemented in the Weather Research and Forecasting (WRF) model. Data assimilation can provide a more accurate initial field, which could be useful for subsequent lightning forecasting. To evaluate its effect on lightning forecasting, a severe convective case that influenced Jiangsu and Anhui Province on 5 June 2009 is utilized and a series of experiments are conducted including assimilating radar reflectivity and lightning location network data via the three-dimensional variational (3DVar) method. Results show that data assimilation can effectively improve reflectivity forecasting and subsequent lightning forecasting. Lightning forecasting based on the PR92 lightning parameterization scheme, which is based on the convective cloud top height, offers a weaker magnitude forecast. The diagnostic method based on reflectivity and temperature has some spatial displacement. The potential forecast provided by lightning threat indexes produced an improvement in Anhui Province, while in other regions, it is located further east than the observation.

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Assimilation of Chinese Doppler Radar and Lightning Data Using WRF-GSI: A Case Study of Mesoscale Convective System

Cited by 17 publications

References 41 publications

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

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

A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR

Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

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