74 citations as recorded by crossref.

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3. Evaluating the impact of initial conditions on lightning simulation over Rajasthan: A synoptic and modelling approach using the WRF model F. Nima K K et al. https://doi.org/10.1016/j.atmosres.2025.108546
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5. WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions X. Feng et al. https://doi.org/10.5194/gmd-14-3741-2021
6. Lightning forecasting in Bangladesh based on the lightning potential index and the electric potential K. Rabbani et al. https://doi.org/10.1016/j.atmosres.2021.105973
7. Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification N. Nikolov et al. https://doi.org/10.3390/fire7040111
8. European NOx emissions in WRF-Chem derived from OMI: impacts on summertime surface ozone A. Visser et al. https://doi.org/10.5194/acp-19-11821-2019
9. Understanding the impact of ice nucleation on lightning and rainfall: A case study A. Hazra et al. https://doi.org/10.1016/j.atmosres.2022.106350
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12. Wet Scavenging in WRF‐Chem Simulations of Parameterized Convection for a Severe Storm During the DC3 Field Campaign Y. Li et al. https://doi.org/10.1029/2019JD030484
13. Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model T. Giannaros et al. https://doi.org/10.1016/j.atmosres.2014.12.009
14. Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 1: An overview M. Huang et al. https://doi.org/10.5194/acp-21-11013-2021
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16. Performance evaluation of an operational lightning forecasting system in Europe T. Giannaros et al. https://doi.org/10.1007/s11069-016-2555-y
17. Improving Lake-Breeze Simulation with WRF Nested LES and Lake Model over a Large Shallow Lake X. Zhang et al. https://doi.org/10.1175/JAMC-D-18-0282.1
18. Implications of Sea Breezes on Air Quality Monitoring in a Coastal Urban Environment: Evidence From High Resolution Modeling of NO2 and O3 B. Wang et al. https://doi.org/10.1029/2022JD037860
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20. Using cloud ice flux to parametrise large-scale lightning D. Finney et al. https://doi.org/10.5194/acp-14-12665-2014
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22. Lightning Over Central Canada: Skill Assessment for Various Land‐Atmosphere Model Configurations and Lightning Indices Over a Boreal Study Area J. Mortelmans et al. https://doi.org/10.1029/2022JD037236
23. Seasonality in power law scaling of convective and stratiform rainfall with lightning intensity over Indian Monsoon regions B. Choudhury et al. https://doi.org/10.1016/j.atmosres.2020.105265
24. Reactive nitrogen in and around the northeastern and mid-Atlantic US: sources, sinks, and connections with ozone M. Huang et al. https://doi.org/10.5194/acp-25-1449-2025
25. 基于气象大模型的门控时空融合网络闪电预报方法 怡. 李 et al. https://doi.org/10.1360/SSTe-2025-0001
26. The Impact of High-Resolution SRTM Topography and Corine Land Cover on Lightning Calculations in WRF A. de Meij et al. https://doi.org/10.3390/atmos13071050
27. The impact of lightning on tropospheric ozone chemistry using a new global lightning parametrisation D. Finney et al. https://doi.org/10.5194/acp-16-7507-2016
28. Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods Y. Wang et al. https://doi.org/10.3390/atmos9030099
29. Seasonal and Regional Distribution of Lightning Fraction Over Indian Subcontinent R. Ghosh et al. https://doi.org/10.1029/2022EA002728
30. Prediction of lightning activity using WRF-ELEC model: Impact of initial and boundary conditions M. Gharaylou et al. https://doi.org/10.1016/j.jastp.2020.105438
31. Relationship between observed lightning and simulated hydrometeors over Bangladesh A. Azam et al. https://doi.org/10.1007/s00703-026-01157-4
32. Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations B. Blaylock & J. Horel https://doi.org/10.1175/WAF-D-19-0141.1
33. Constraining the budget of NOx and volatile organic compounds at a remote tropical island using multi-platform observations and WRF-Chem model simulations C. Poraicu et al. https://doi.org/10.5194/acp-25-6903-2025
34. Significant contribution of lightning NO to summertime surface O3 on the Tibetan Plateau M. Li et al. https://doi.org/10.1016/j.scitotenv.2022.154639
35. Simulation of premonsoon thunderclouds over two climatic regimes: evaluation and dichotomous detection R. Biswasharma et al. https://doi.org/10.1016/j.atmosres.2025.108027
36. Attention-Based Dual-Source Spatiotemporal Neural Network for Lightning Forecast T. Lin et al. https://doi.org/10.1109/ACCESS.2019.2950328
37. Sensitivity analysis of weather research and forecasting (WRF) model output variables to the thunderstorm lifecycle and its application H. Huang et al. https://doi.org/10.1007/s11069-022-05455-z
38. Distinct aerosol effects on cloud-to-ground lightning in the plateau and basin regions of Sichuan, Southwest China P. Zhao et al. https://doi.org/10.5194/acp-20-13379-2020
39. Evaluation and Usefulness of Lightning Forecasts Made with Lightning Parameterization Schemes Coupled with the WRF Model G. Vani K. et al. https://doi.org/10.1175/WAF-D-21-0080.1
40. Modeling lightning density using cloud top parameters A. Karagiannidis et al. https://doi.org/10.1016/j.atmosres.2019.02.013
41. Lightning NO2 simulation over the contiguous US and its effects on satellite NO2 retrievals Q. Zhu et al. https://doi.org/10.5194/acp-19-13067-2019
42. Estimates of lightning NOx production based on high-resolution OMI NO2 retrievals over the continental US X. Zhang et al. https://doi.org/10.5194/amt-13-1709-2020
43. Prediction of lightning activity over Bangladesh using diagnostic and explicit lightning parameterizations of WRF model M. Paramanik et al. https://doi.org/10.1007/s11069-023-06355-6
44. Global association of aerosol with flash density of intense lightning O. Altaratz et al. https://doi.org/10.1088/1748-9326/aa922b
45. Examining relationships between cloud‐resolving model parameters and total flash rates to generate lightning density maps C. Bovalo et al. https://doi.org/10.1002/qj.3494
46. Evaluation of Lightning Prediction by an Electrification and Discharge Model in Long-Term Forecasting Experiments L. Xu et al. https://doi.org/10.1155/2022/4583030
47. Evaluating different lightning parameterization schemes to simulate lightning flash counts over Maharashtra, India G. Mohan et al. https://doi.org/10.1016/j.atmosres.2021.105532
48. WRF-ELEC lightning validation with IITM-LLN: Performance across the Indian subcontinent B. Choudhury et al. https://doi.org/10.1007/s12040-025-02534-0
49. Seasonal prediction of lightning activity in North Western Venezuela: Large-scale versus local drivers Á. Muñoz et al. https://doi.org/10.1016/j.atmosres.2015.12.018
50. LightNet+: A dual-source lightning forecasting network with bi-direction spatiotemporal transformation X. Zhou et al. https://doi.org/10.1007/s10489-021-03089-5
51. Performance Evaluation of an Explicit Lightning Forecasting System S. Dafis et al. https://doi.org/10.1029/2017JD027930
52. Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method J. He & T. Loboda https://doi.org/10.1088/1748-9326/abbc3b
53. Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) pseudo-observations in AROME-France – proof of concept F. Erdmann et al. https://doi.org/10.5194/nhess-23-2821-2023
54. A satellite lightning observation operator for storm-scale numerical weather prediction P. Combarnous et al. https://doi.org/10.5194/nhess-22-2943-2022
55. Simulating lightning into the RAMS model: implementation and preliminary results S. Federico et al. https://doi.org/10.5194/nhess-14-2933-2014
56. CORDEX-WRF v1.3: development of a module for the Weather Research and Forecasting (WRF) model to support the CORDEX community L. Fita et al. https://doi.org/10.5194/gmd-12-1029-2019
57. HRUnet: A Deep Learning Model Designed for Lightning Forecasting at High Spatial Resolution J. Wu et al. https://doi.org/10.1109/TGRS.2026.3668102
58. Reviews and perspectives of high impact atmospheric processes in the Mediterranean S. Michaelides et al. https://doi.org/10.1016/j.atmosres.2017.11.022
59. Improvements to the WRF-Chem 3.5.1 model for quasi-hemispheric simulations of aerosols and ozone in the Arctic L. Marelle et al. https://doi.org/10.5194/gmd-10-3661-2017
60. Box Model Intercomparison of Cloud Chemistry M. Barth et al. https://doi.org/10.1029/2021JD035486
61. A gated spatiotemporal fusion network for lightning forecasting based on weather foundation models Y. Li et al. https://doi.org/10.1007/s11430-025-1638-8
62. Quantification of the effect of modeled lightning NO2 on UV–visible air mass factors J. Laughner & R. Cohen https://doi.org/10.5194/amt-10-4403-2017
63. Association of lightning occurrence with precipitation cloud column structure at a fixed position Y. Cui et al. https://doi.org/10.1016/j.atmosres.2021.105989
64. Response of lightning NOx emissions and ozone production to climate change: Insights from the Atmospheric Chemistry and Climate Model Intercomparison Project D. Finney et al. https://doi.org/10.1002/2016GL068825
65. Assessment of Updated Fuel‐Based Emissions Inventories Over the Contiguous United States Using TROPOMI NO2 Retrievals M. Li et al. https://doi.org/10.1029/2021JD035484
66. The impacts of shipping emissions on lightning: roles of aerosol-radiation-interactions and aerosol-cloud-interactions R. Sun et al. https://doi.org/10.1088/1748-9326/ad2aba
67. The POLARCAT Model Intercomparison Project (POLMIP): overview and evaluation with observations L. Emmons et al. https://doi.org/10.5194/acp-15-6721-2015
68. Improving the lightning forecast with the WRF model and lightning data assimilation: Results of a two-seasons numerical experiment over Italy S. Federico et al. https://doi.org/10.1016/j.atmosres.2024.107382
69. Interpretable artificial intelligence models for predicting lightning prone to inducing forest fires S. Song et al. https://doi.org/10.1016/j.jastp.2024.106408
70. Anak Krakatau triggers volcanic freezer in the upper troposphere A. Prata et al. https://doi.org/10.1038/s41598-020-60465-w
71. Impact of Lightning NOx Emissions on Atmospheric Composition and Meteorology in Africa and Europe L. Menut et al. https://doi.org/10.3390/atmos11101128
72. Unveiling India’s lightning patterns through satellite-based climatology and numerical modelling of severe weather events: A review U. Mondal et al. https://doi.org/10.1007/s12040-024-02491-0
73. A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard A. Thomas & S. Noble https://doi.org/10.3389/feart.2024.1376605
74. A Year-Long Total Lightning Forecast over Italy with a Dynamic Lightning Scheme and WRF S. Federico et al. https://doi.org/10.3390/rs14143244