A simple lightning assimilation technique for improving retrospective WRF simulations. (2nd December 2016)

• Record Type: Journal Article
• Authors: Heath, Nicholas K.; Pleim, Jonathan E.; Gilliam, Robert C.; Kang, Daiwen
• Is Part Of: Journal of advances in modeling earth systems. Volume 8:Number 4(2016)
• Page Start: 1806
• Page End: 1824
• Publication Date: 2016-12-02
• Subjects: lightning assimilation -- WRF -- convective parameterization -- retrospective simulations; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1002/2016MS000735
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: 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. Key Points: Lightning assimilation was applied in the Kain‐Fritsch convective parameterization in WRF Lightning assimilation considerably improves mean bias, mean absolute error, and spatial correlation of WRF‐simulated warm‐season rainfall Lightning assimilation improves statistical measures of near‐surface meteorological variables