Developing an optimized parameterization scheme for deriving a lightning threat product from a global model for all seasons over India

Abstract

This study marks the utilization of medium-range forecasts of cloud-to-ground (CG) lightning threats over India across all seasons from a global model. CG flashes are derived from two lightning parameterization schemes: Price and Rind (PR92) scheme and Lopez Scheme and is evaluated against earth network lightning sensor data. Both methods with existing storm detection criteria initially produced a lightning with overestimated counts and large spatial extent. Hence a Revised PR92-Lopez Blended (RPLB) scheme is developed by redefining the storm detection points in each scheme and combined them by giving separate weights for land and ocean through a regression-based approach. RPLB gives an improved skill in spatial and frequency distribution, and reduced false alarms with respect to the individual schemes up to a five-day lead time. The estimated CG flashes are then categorized into threat levels ranging up to extreme for effective use in the early warning and decision support systems.

Keywords: Cloud-to-Ground lightning threat; Lightning parameterization; Lopez scheme; PR92 scheme.

Fig. 1

Climatology of total lightning flash density (flashes km^− 2 year^− 1) from LIS and OTD combined during May 1995 to December 2014. The states/union territories are marked on the map.

Fig. 2

Spatial distribution of 24 h accumulated lightning flash counts from (a, d, g, j) LLN, and estimated lightning using (b, e, h, k) PR92 scheme and Rev_PR92 scheme (c, f, i, l) for the lightning cases from different seasons – (a, b, c) winter, (d, e, f) pre-monsoon, (g, h, i) monsoon, and (j, k, l) post-monsoon.

Fig. 3

Same as Fig. 2 but for Lopez scheme.

Fig. 4

Schematic of revised schemes (a) PR92 and (b) Lopez.

Fig. 5

Frequency distribution of lightning flash counts across the seasons observed from LLN (bars), and estimated from Rev_PR92 (blue circles), Rev_Lopez (red circles), and RPLB (green circles) schemes averaged over the cases selected from different seasons. The different colors of bars represent different bins.

Fig. 6

Fractions skill score calculated for pre-monsoon (a–e), monsoon (f–j), post-monsoon (k–o) and winter (p–t) over the active lightning regions from Rev_PR92 (green), Rev_Lopez (blue) and RPLB (red) schemes for the thresholds: 1 (a, f, k, p), 10 (b, g, l, q), 50 (c, h, m, r),100 (d, i, n, s), and 200 (e, j, o, t). The error bars show the extent of FSS values for all the cases and the mean shown with a solid line with markers.

Fig. 7

Spatial distribution of 24-h accumulated CG flash counts from LLN (a–d), RPLB scheme (e–h) and the corresponding threat levels (i–l) for the cases from different seasons viz., winter: 24 Jan 2023 (a, e, i), pre-monsoon: 07 Apr 2023 (b, f, j), monsoon: 28 Jul 2023 (c, g, k) and post-monsoon: 22 Nov 2023 (d, h, l).

Fig. 8

Spatial distribution of 24-h accumulated lightning flash counts valid on 07th Apr 2023 from (a) LLN, and forecast from RPLB for lead time (b) 1-day, (c) 3-day, and (d) 5-day. The bottom panel shows the accumulated CG flash counts from LLN (e) along with the estimated threat levels from RPLB with lead time (f) 1-day, (g) 3-day and (h) 5-day.