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.
Highlights
Thunderstorms are an important component of the climate system as they can impact the atmospheric environment around them; they are capable of redistributing moisture, heat, and wind patterns (Price, 2013)
Thereafter, an assessment between the lightning data (LIGHT) and no data assimilation (NODA) simulations through the calculation of root mean square (RMS) errors of the lightning observations is shown
The aim of the study was to evaluate if lightning data assimilation can be useful in mesoscale, regional, and global applications at a coarse resolution in which convection cannot be explicitly resolved
Summary
Thunderstorms are an important component of the climate system as they can impact the atmospheric environment around them; they are capable of redistributing moisture, heat, and wind patterns (Price, 2013). In the vast majority of these studies, dynamical relaxation, or nudging techniques were applied. Even though these studies highlighted the importance of utilizing lightning observations to improve the representation of convection in models, they had less emphasis on improving environmental conditions. Motivated by the initial success of nudging techniques in cloud-resolving model applications, the objective of this study is to investigate if lightning observations can be useful in mesoscale, regional, and global applications at a coarse resolution, in which convection cannot be resolved explicitly. We would like to evaluate the impact of lightning observations on the environment around storms, with potential implications for data assimilation, reanalysis, and climate studies. There should be a fraction of lightning information that can spread into larger scales
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