In this comprehensive study, we have delved into the intricate realm of lightning forecasting, a captivating yet challenging pursuit due to the elusive nature of storm electrification. Our focus rested on evaluating the skill of lightning forecasts of NCMRWF Regional Ensemble Prediction System (NEPS-R), based on the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Based on the lightning hotspots in the observed seasonal mean lightning flash count, study regions were identified over East-Northeast India (ENEI) and Peninsular India (PI) during pre-monsoon season and Central and East-Northeast India (CENEI) for monsoon season during the year 2022. Probabilistic and deterministic skill of NEPS-R in lightning prediction using rank histogram, Relative Operating Characteristic (ROC), Brier Skill Score (BSS), Continuous Ranked Probability Skill Score (CRPSS), reliability diagrams, Probability of Detection (POD), False Alarm ratio (FAR), Equitable Threat Score (ETS), and Fractions Skill Score (FSS) were investigated in the present study. Rank histograms brought out the negative bias, which increases with increasing lead time over all the study regions. While ROC and BSS indicate that forecast is skillful on day-1 over ENEI for all the thresholds (> 1, > 5 and > 10) and over PI on day-2 and day-3, CRPSS has a contrasting trend with most skillful forecast over CENEI region. Reliability diagrams indicate under-forecasting for lower probabilities and over-forecasting for higher probabilities for all the thresholds for all the study areas. The overconfidence in the forecast could be at least partially attributed to the sampling error caused by a small ensemble size of NEPS-R, which also displayed similar trend of skill as BSS. Higher POD values in the CENEI region have surpassed those in ENEI and PI. Lower FAR and higher ETS are found over PI region compared to ENEI and CENEI. Furthermore, FSS (threshold > 1) indicates that CENEI region attains better skill (> 0.5) at a neighborhood size of 36 km in day-1 forecast compared to ENEI (68 km) and PI (84 km). The skill scores do emphasize the model’s ability in predicting the dominant large scale features of monsoon. This is reflected in the better skill of CENEI region during monsoon as compared to ENEI and PI regions during the pre-monsoon season, where the model is falling short in predicting the prominent local scale features. Also, the model’s inadequacy in proper representation of microphysics and kinematics does affect the lightning prediction capability of the model which could be addressed with improved model initial conditions and parameterization schemes and finer horizontal resolution.

在这项综合研究中，我们深入研究了闪电预报的复杂领域，由于风暴带电的难以捉摸的性质，这是一项令人着迷但具有挑战性的追求。我们的重点是评估基于英国气象局全球和区域集合预报系统 (MOGREPS) 的 NCMRWF 区域集合预报系统 (NEPS-R) 的闪电预报技能。根据观测到的季节性平均闪电次数中的闪电热点，确定了季风季节前印度东北部（ENEI）和印度半岛（PI）以及季风季节印度中部和东北部（CENEI）的研究区域2022 年期间。NEPS-R 在闪电预测中的概率和确定性技能，使用排名直方图、相对操作特性 (ROC)、Brier 技能评分 (BSS)、连续排名概率技能评分 (CRPSS)、可靠性图、检测概率 (本研究调查了 POD）、误报率（FAR）、公平威胁评分（ETS）和分数技能评分（FSS）。排名直方图显示了负偏差，该偏差随着所有研究区域的前置时间的增加而增加。虽然 ROC 和 BSS 表明，在第 1 天，所有阈值（> 1、> 5 和 > 10）的预测均优于 ENEI，并且第 2 天和第 3 天的 PI 均优于 PI，但 CRPSS 与最熟练的预测形成鲜明对比的趋势CENEI 地区上空。可靠性图表明，对于所有研究区域的所有阈值，较低概率的预测不足，而较高概率的预测过高。预测的过度自信至少可以部分归因于 NEPS-R 较小的集合规模引起的抽样误差，NEPS-R 也表现出与 BSS 类似的技能趋势。CENEI 地区较高的 POD 值已超过 ENEI 和 PI。与 ENEI 和 CENEI 相比，PI 区域的 FAR 较低，ETS 较高。此外，FSS（阈值 > 1）表明，与 ENEI（68 公里）和 PI（84 公里）相比，CENEI 区域在第 1 天的预测中在 36 公里的邻域大小上获得了更好的技能（> 0.5）。技能得分确实强调了模型预测季风主要大尺度特征的能力。这反映在季风期间 CENEI 地区的技能优于季风前季节的 ENEI 和 PI 地区，后者的模型在预测显着的局部尺度特征方面存在不足。此外，该模型在微观物理和运动学方面的不足确实影响了模型的闪电预测能力，这可以通过改进模型初始条件和参数化方案以及更精细的水平分辨率来解决。