Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Abstract

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite -R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

Ensemble data assimilation of total column ozone using a coupled meteorology–chemistry model and its impact on the structure of Typhoon Nabi (2005)

“…The MLEF employs a cost function derived using a Gaussian probability density function and produces both the analysis and the background error covariance (Zupanski, 2005). It is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization using the Hessian preconditioning (Zupanski, 2005;Zupanski et al, 2007bZupanski et al, , 2008, and has been employed in many studies including uncertainty analysis, parameter estimation and data assimilation (e.g., Zupanski et al, 2007a;Lokupitiya et al, 2008;Kim et al, 2010;Apodaca et al, 2014;Tran et al, 2014;Park et al, 2015).…”

“…The optimal value of parameter ε is obtained by implicitly assuming lognormal probability density function errors for a multiplicative bias correction in Eq. (6) (e.g., Apodaca et al, 2014) as…”

“…Although we use the reference pressure at the model top as 10 hPa, which is the highest value we could use in the current model version, there are still considerable amounts of O 3 in the stratosphere that could not be included in the calculation of the model guess (e.g., background). Since this creates a negative bias in the mean observation error, we introduce a multiplicative bias correction ε to preserve positive-definiteness of the bias-corrected guess (Apodaca et al, 2014) as…”

An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

“…Studies employing data assimilation (Alexander et al 1999;Pessi and Bussinger 2009;Fierro et al 2012) have utilized nudging techniques to improve forecasting and analysis for some extreme events. More recently, scientists have investigated the utility of lightning observations for identifying convective events at several spatial scales for which they conclude that the assimilation of lightning favorably impacts several model variables as well as initial conditions (Apodaca et al 2014;Fierro et al 2014).…”

A Survey and Perspectives on Mathematical Models for Quantitative Precipitation Estimation Using Lightning

“…Lately, researchers have investigated the utility of lightning observations for identifying convective events at several different spatial scales for which they conclude that the assimilation of lightning favorably impacts several model variables as well as initial conditions [54,55].…”