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

Authors :
Erdmann, Felix
Caumont, Olivier
Defer, Eric
Source :
Journal of Atmospheric & Oceanic Technology; Jan2022, Vol. 39 Issue 1, p3-30, 28p
Publication Year :
2022

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, which 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 flash-scale products. 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%–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. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
MACHINE learning
PIXELS

Details

Language :
English
ISSN :
07390572
Volume :
39
Issue :
1
Database :
Complementary Index
Journal :
Journal of Atmospheric & Oceanic Technology
Publication Type :
Academic Journal
Accession number :
154996811
Full Text :
https://doi.org/10.1175/JTECH-D-20-0160.1