| 23 Jul 2025
Predicting thunderstorm risk probability at very short time range using deep learning
Abstract. Forecasting electrical activity within the atmosphere remains one of the most challenging predictions, especially due to the chaotic nature of thunderstorms. Lightning strikes are precisely located and occur very quickly, which makes this task particularly difficult. Additionally, these phenomena pose a significant risk to aviation, as they statistically strike each aircraft more than once per year. Over the years, several techniques have been employed for very short-term lightning forecasting (lower than one hour 5 and every five minutes), such as observation-based methods and, more recently, deep learning methods. Previous studies often face difficulties in accurately forecasting lightning probability, and even with AI-driven methods, it is still difficult to obtain calibrated outputs. To address this limitation, we propose a methodology that successfully predicts lightning risk using Convolutional Neural Networks (CNNs) with attention mechanisms. The network is fed with satellite observations and Numerical Weather Prediction (NWP) outputs formatted as a spatio-temporal sequence. Results show a F1 10 score of 0.65 for 5-minute predictions and 0.5 for 30-minute predictions with a very low Expected Calibration Error (ECE) of less than 10 %. Thanks to the well-calibrated outputs, risk probability maps can be plotted, showing areas with strong to low chances of having electrical activity.
Review of “Predicting thunderstorm risk probability at very short time range using deep learning”
The preprint proposes a deep learning methodology for very short-term (5-60 minutes) probabilistic forecasting of lightning risk, motivated by aviation safety within the ALBATROS project. It adapts the ED-DRAP neural network, incorporating spatio-temporal sequences from satellite (GOES-16 ABI brightness temperature and GLM lightning groups) and NWP (GFS lifted index and relative humidity) data over a region centered on the Gulf of Mexico and Florida. A key focus is on achieving well-calibrated outputs through a combined cross-entropy and Dice loss function, enabling interpretable risk probability maps. Results report F1 scores of 0.65 at 5 minutes and 0.5 at 30 minutes, with ECE below 10%. In general, the manuscript is well-structured, the approach is innovative in emphasizing calibration for probabilistic lightning nowcasting without radar data, and the topic is highly relevant for natural hazards research, particularly in aviation and thunderstorm impacts. However, I have some concerns regarding the scope, comparisons, and generalizability.
My main concern is the limited scope and potential lack of generalizability of the dataset and results. The data is restricted to winter mornings (00:00-05:00 UTC, December-February) from 2020-2023, covering only 154 days with a balanced split of stormy and non-stormy periods. While this controls for variability, it may not capture seasonal, diurnal, or regional differences in thunderstorm dynamics (e.g., summer afternoons or other global hotspots). The study area is narrowed to a subset of CONUS, but no sensitivity analysis is provided for other regions. A discussion on how these choices affect broader applicability, perhaps with preliminary tests on extended data, would strengthen the contribution.
My second concern is the benchmarking and novelty assessment. The model is compared to ConvLSTM, PredRNN, persistence, and U-Net, showing superior F1 and calibration scores. However, it lacks direct comparison to recent lightning-specific DL models from the literature, such as those in Brodehl et al. (2022), Geng et al. (2021), or Leinonen et al. (2023), which also use satellite/radar data for nowcasting. While the intentional exclusion of radar data is well-justified for enhancing applicability to aircraft flight paths where radar coverage may be limited or absent, discussing how the proposed method might compare to radar-inclusive baselines would better contextualize its advantages and limitations.
Other comments
L90-95: Clarify why the smaller area (red rectangle in Fig. 1) was chosen beyond computation cost, does it represent typical thunderstorm regimes?
Fig. 2: Add coordinate axes (latitude/longitude) to subfigure (b) to match (a) for consistency and better spatial context.
L164-165: The effective training/testing area is further cropped to 256x256 pixels (17.3°N–37.7°N, 93°W–72°W) from the subselected red rectangle; consider adding this cropped boundary as an inner rectangle in Fig. 1 for clarity.
L175-180: The input sequence (6 timesteps) is justified by a comparative study, but I suggest including a table or figure summarizing F1 scores for 2/4/6/8 timesteps to support this.
L305-310: The example in Fig. 9 misses only 5% lightning, but it’s not clear which threshold is used in this case.