An explainable machine learning technique to forecast lightning density over North-Eastern India

IF 1.8 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Joyjit Mandal , Chandrani Chatterjee , Saurabh Das
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引用次数: 0

Abstract

Increasing lightning fatalities over India is a concerning subject. Especially, it is pretty crucial over North-Eastern part of the country where lightning is extremely frequent. Given the complex nature of the problem, machine learning can be an excellent option in such forecasting scenarios. However, such dynamic processes seek proper transparency of the model. The current work attempts to devise a model for short range prediction (one month ahead) of lightning density based on primary atmospheric parameters from satellite data with a lead time of one month over North –Eastern and Eastern part of the country. Random Forest regression seems to outperform other models explored, with a R2 of 0.86 and an MAE of 0.0071. The interpretation of the model output using SHAP index reveals that 2 m temperature at previous two months and CAPE and K-index at previous month has a positive impact on the output of the model whereas, instantaneous surface heat flux of previous month and two month prior K-index has an inhibiting effect on model's output. The use of machine learning techniques for atmospheric predictions without the shed of the black box can be of importance to the scientific community. Such studies especially over lightning prone tropical regions can be crucial in meteorological forecasting applications.

预测印度东北部闪电密度的可解释机器学习技术
印度雷电造成的死亡人数不断增加是一个令人担忧的问题。特别是在雷电极为频繁的印度东北部地区,这一点尤为重要。鉴于问题的复杂性,机器学习在此类预测场景中是一个极佳的选择。然而,这种动态过程需要模型具有适当的透明度。目前的工作试图根据卫星数据中的主要大气参数,设计一个短程(提前一个月)闪电密度预测模型,预测时间为该国东北部和东部地区一个月。随机森林回归模型的 R2 为 0.86,MAE 为 0.0071,似乎优于其他模型。利用 SHAP 指数对模型输出的解释表明,前两个月的 2 米气温、前一个月的 CAPE 和 K 指数对模型输出有积极影响,而前一个月的瞬时地表热通量和前两个月的 K 指数对模型输出有抑制作用。使用机器学习技术进行大气预测,而不使用黑箱,对科学界具有重要意义。此类研究,尤其是对易发生雷电的热带地区的研究,在气象预报应用中至关重要。
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来源期刊
Journal of Atmospheric and Solar-Terrestrial Physics
Journal of Atmospheric and Solar-Terrestrial Physics 地学-地球化学与地球物理
CiteScore
4.10
自引率
5.30%
发文量
95
审稿时长
6 months
期刊介绍: The Journal of Atmospheric and Solar-Terrestrial Physics (JASTP) is an international journal concerned with the inter-disciplinary science of the Earth''s atmospheric and space environment, especially the highly varied and highly variable physical phenomena that occur in this natural laboratory and the processes that couple them. The journal covers the physical processes operating in the troposphere, stratosphere, mesosphere, thermosphere, ionosphere, magnetosphere, the Sun, interplanetary medium, and heliosphere. Phenomena occurring in other "spheres", solar influences on climate, and supporting laboratory measurements are also considered. The journal deals especially with the coupling between the different regions. Solar flares, coronal mass ejections, and other energetic events on the Sun create interesting and important perturbations in the near-Earth space environment. The physics of such "space weather" is central to the Journal of Atmospheric and Solar-Terrestrial Physics and the journal welcomes papers that lead in the direction of a predictive understanding of the coupled system. Regarding the upper atmosphere, the subjects of aeronomy, geomagnetism and geoelectricity, auroral phenomena, radio wave propagation, and plasma instabilities, are examples within the broad field of solar-terrestrial physics which emphasise the energy exchange between the solar wind, the magnetospheric and ionospheric plasmas, and the neutral gas. In the lower atmosphere, topics covered range from mesoscale to global scale dynamics, to atmospheric electricity, lightning and its effects, and to anthropogenic changes.
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