Lightning over the Boreal Zone: Skill Assessment for Various
Land-Atmosphere Model Configurations and Lightning Indices
Abstract
Current lightning predictions are uncertain because they either rely on
empirical diagnostic relationships based on the present climate or use
coarse-scale climate scenario simulations in which deep convection is
parameterized. Previous studies demonstrated that simulations with
convection-permitting resolutions (km-scale) improve lightning
predictions compared to coarser-grid simulations using convection
parameterization for different geographical locations but not over the
boreal zone.
In this study, lightning simulations with
the NASA Unified-Weather Research and Forecasting (NU-WRF) model are
evaluated over a 955x540 km2 domain including the Great Slave Lake in
Canada for six lightning seasons. The simulations are performed at
convection-parameterized (9 km) and convection-permitting (3 km)
resolution using the Goddard 4ICE and the Thompson microphysics (MP)
schemes. Four lightning indices are evaluated against observations from
the Canadian Lightning Detection Network (CLDN), in terms of
spatiotemporal frequency distribution, spatial pattern, daily
climatology, and an event-based overall skill assessment. Concerning the
model configuration, regardless of the spatial resolution, the Thompson
scheme is superior to the Goddard 4ICE scheme in predicting the daily
climatology but worse in predicting the spatial patterns of lightning
occurrence. Several evaluation metrics indicate the benefit of working
at a convection-permitting resolution. The relative performance of the
different lightning indices depends on the evaluation criteria. Finally,
this study demonstrates issues of the models to reproduce the observed
spatial pattern of lightning well, which might be related to an
insufficient representation of land surface heterogeneity in the study
area.