In this study, a new lightning data assimilation (LDA) scheme using Geostationary Lightning Mapper (GLM) flash extent density (FED) is developed and implemented in the National Severe Storms Laboratory (NSSL) Warn-on-Forecast System (WoFS). The new LDA scheme first retrieves the pseudo relative humidity between the cloud base and a specific layer based on the FED value. Then on each model layer, the pseudo relative humidity is converted to dewpoint temperature according to the corresponding air temperature. Some sensitivity experiments are performed to investigate how to derive and use GLM/FED in a best possible way. The impact of assimilating this derived pseudo dewpoint temperature on short-term severe weather forecast is preliminarily assessed in this proof-of-concept study. A high-impact weather event over Kansas on 24 May 2021 is used to evaluate the performance of the new scheme on analyses and subsequent short-term forecasts. The results show that the assimilation of additional FED derived dewpoint temperature observations along with radar, satellite radiance and cloud water can improve short-term (3-h) forecast skill. The improvement is primarily due to the direct and indirect adjustment of dynamic and thermodynamic conditions through the LDA process. More specifically, the assimilation of FED-derived dewpoint temperature, in addition to the other observations currently used in WoFS, tends to enhance the ingredients required for a thunderstorm to occur, namely moisture, instability and lifting mechanism.