A Geostationary Lightning Pseudo-Observation Generator Utilizing Low-Frequency Ground-Based Lightning Observations

Abstract

Coincident Geostationary Lightning Mapper (GLM) and National Lightning Detection Network (NLDN) observations are used to build a generator of realistic lightning optical signal in the perspective to simulate Lightning Imager (LI) signal from European NLDN-like observations. Characteristics of GLM and NLDN flashes are used to train different machine learning (ML) models, that predict simulated pseudo-GLM flash extent, flash duration, and event number per flash (targets) from several NLDN flash characteristics. Comparing statistics of observed GLM targets and simulated pseudo-GLM targets, the most suitable ML-based target generators are identified. The simulated targets are then further processed to obtain pseudo-GLM events and flashes. In the perspective of lightning data assimilation, Flash Extent Density (FED) is derived from both observed and simulated GLM data. The best generators simulate accumulated hourly FED sums with a bias of 2% to the observation, while cumulated absolute differences remain of about 22 %. A visual comparison reveals that hourly simulated FED features local maxima at the similar geolocations as the FED derived from GLM observations. However, the simulated FED often exceeds the observed FED in regions of convective cores and high flash rates. The accumulated hourly area with FED>0 flashes per 5 km×5 km pixel simulated by some pseudo-GLM generators differs by only 7% to 8% from the observed values. The recommended generator uses a linear Support Vector Regressor (linSVR) to create pseudo-GLM FED. It provides the best balance between target simulation, hourly FED sum, and hourly electrified area.

Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) observations in AROME-France – Proof of Concept

“…This work first generated MTG-LI data that are used to create the FED observations (Erdmann et al, 2022). Then, an observation operator for FED is developed based on a linear, climatological relationship between observed FED and the column integrated AROME-France graupel mass above the −5 • C isotherm (m g ; as suggested by The observation operator is then used to compare AROME-France-derived background FED (AROME_FED) to the FED observations.…”

“…Lightning dataThis work adapts the GEO lightning pseudo-observation generator as developed byErdmann et al (2022). It was trained using low frequency (LF) ground-based National Lightning Detection Network (NLDN) records collected over the South-East US.…”

An observation operator for geostationary lightning imager data assimilation in the French storm-scale numerical weather prediction system AROME

“…This assumption is based on related studies on the LIS instrument onboard the International Space Station (ISS-LIS) and the GLM (Bateman et al, 2021;Peterson et al, 2017). Erdmann et al (2022) developed a method to generate GLM pseudo-observations using lightning data from the National Lightning Detection Network (NLDN), consisting of ground sensors in the contiguous US. As the MTG-LI and GLM instruments are expected to provide similar observation and data structure, and an intercomparison study between the NLDN and The simulation of synthetic observations is performed in two steps.…”

“…As the MTG-LI and GLM instruments are expected to provide similar observation and data structure, and an intercomparison study between the NLDN and The simulation of synthetic observations is performed in two steps. First, GLM (or MTG-LI in our case) flash characteristics, such as flash duration or flash extent, are simulated using ML supervised models (Erdmann et al, 2022). The ML models have been trained with flash characteristics of coincident NLDN and GLM flashes from a database of 10 lightning active days in the South-East of the USA, spread over a 6 months period.…”

“…The objective of the present study is to prepare the assimilation of lightning satellite data in storm-scale NWP systems by designing a suitable observation operator in the scope of development of a new EnVar assimilation system for the French regional NWP system AROME-France. The lightning data employed here are generated from ground-based lightning data from the Météorage network (Erdmann et al, 2022) to mimic the future MTG-LI data. This paper is organized as follows.…”