edu.ucar.opensky::articles:22026 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T15:50:12.311316 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7s185d3 https://doi.org/10.5065/D65D8PWK https Related Dataset #1 NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive Continuous assimilation of lightning data using time-lagged ensembles for a convection-allowing numerical weather prediction model 2018-09-16T00:00:00Z publication https://n2t.org/ark:/85065/d7s185d3 https://doi.org/10.1029/2018JD028494 Wang, Haoliang author Liu, Yubao author Zhao, T. author Liu, Yuewei author Xu, Mei author Shen, Si author Jiang, Y. author Yang, H. author Feng, S. author UCAR/NCAR - Library publisher In this study, a lightning data assimilation method based on the time-lagged ensembles for predicting severe convection is presented. With the lightning data assimilation scheme, the background error covariances are computed using time-lagged ensembles, which consist of deterministic forecasts from eight forecast cycles initialized every 3hr. Pseudo-observations of graupel mixing ratio (q(g)) are retrieved from total lightning rates by utilizing empirical vertical profiles obtained from the simulation results of the previous forecast cycles, and the corresponding observation errors are estimated according to the uncertainties in the lightning observations and the empirical vertical profiles of q(g). The increments of the model state variables are computed with the Kalman gain matrices and are continuously ingested into the Weather Research and Forecasting model via nudging terms acting on the prognostic equations over each time step during model integration. The effect of the lightning data assimilation scheme on convection analysis and forecast was assessed through a case study of a severe convective event, which took place in the Guangdong of China. Assimilating lightning data recovered many of the observed convective cells, suppressed the spurious convection, and corrected the displacement errors of the convective systems. Quantitative verifications indicate that forecast skills were improved mainly in the convective regions with the impact of assimilating lightning data on stratiform regions being overall less effective. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2018 American Geophysical Union. None eng geoscientificInformation