# Experimental Study of Cloud-to-Ground Lightning Nowcasting with Multisource Data Based on a Video Prediction Method

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Data

#### 2.1. Lightning Data

#### 2.2. WRF Model Prediction Products

## 3. Method

#### 3.1. Preprocessing of Lightning Data

#### 3.2. Training Set and Test Set

#### 3.3. Neural Network Structure

_{max}) and CAPE. Since the number of variables of the lightning occurrence frequency and the WRF model products are different and cannot be merged to form a spatiotemporal sequence in the time dimension, a two-dimensional convolutional network called CNN Net is first used to convolve the WRF model products to perform feature extraction and compress the number of variables to 1. Since the CNN Net performs feature extraction for WRF model products separately, the model products do not contain a temporal dimension, and the north–south and east–west dimension is also 256 × 256, so the dimensions of WRF model products are [256, 256, 13], namely [rows, columns, variables]. The convolved results are merged with the lightning occurrence frequency in the time dimension, which contains the starting forecast moment from 3 h in the past to 3 h in the future, forming a spatiotemporal sequence of dimensions [6, 256, 256, 1]. Subsequently, ConvLSTM Net is used to extract features in time and space and then perform spatiotemporal sequence prediction to achieve hourly lightning occurrence area nowcasting for the next 0 to 3 h. The network structure and data flow are detailed in Figure 2.

#### 3.3.1. 2D and 3D Convolution Layers

#### 3.3.2. ConvLSTM

**t**represents the step of the network;

**σ**is the sigmoid function with the output range of [0, 1];

**tan h**represents the hyperbolic tangent function with the output range of [−1, 1];

**W**and

**b**are the weights to be trained and bias, respectively.

#### 3.4. Network Training

#### 3.5. Controlled Experimental Design

**F**and cloud top height

**H**[15]. The cloud top height is determined based on the thresholds of radar echo (20 dBZ) and temperature (0 °C), and the forecast results of this parameterization scheme are included in the WRF model. The PR92 scheme contains both land and ocean scenarios, and the forecast equation on land is

## 4. Forecast Results

#### 4.1. Nowcasting Results and Scoring Test

#### 4.2. Case Study

_{max}products by the numerical model (Figure 5b,c). However, in the border area of Jiangsu and Anhui, both CLSTM-LFN and CLSTM-LFN-O produced false forecasts (blue lines in Figure 5) due to the middle values of CAPE from numerical model products (Figure 5j–l) and a small amount of lightning at the initial forecast moment.

## 5. Variable Importance Analysis

_{max}, decreases more rapidly with increasing forecast time. Generally, a larger CAPE tends to produce stronger convective activities [59], and several studies have shown that there is a strong correlation between CAPE values and thunderstorms [60,61], which provides an indication for the occurrence of lightning in atmospheric circulation. With increasing forecast time, the deviation of microphysical variables forecasted by numerical model products gradually increases, causing its relative importance to decrease rapidly. However, the type, content and spatial distribution of ice-phase particles have a certain correlation with the location of lightning [62,63,64], which still provides a reference for the occurrence of lightning at the microscopic level and is an indispensable variable in the process of the nowcasting model.

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The WRF model domain (

**a**) and the study region range (

**b**); the colors represent the height of the terrain. The abbreviations for Shandong, Shaanxi, Anhui, Sichuan, Hubei, Guizhou, Jiangxi, Fujian, Yunnan, Guangxi, Zhejiang and Jiangsu are SD, SX, AH, SC, HuB, GZ, JX, FJ, YN, GX, ZJ and JS, respectively.

**Figure 2.**CLSTM-LFN structure and data flow. The numbers in square brackets represent the variable dimensions, and t and t + 1 represent the starting forecast moment and 1 h after the starting forecast moment, respectively.

**Figure 5.**24 August 2020 15:00 to 24 August 2020 17:00 CLSTM-LFN forecast results (

**a**–

**c**), CLSTM-LFN-O forecast results (

**d**–

**f**) and WRF model prediction products for Rmax (

**g**–

**i**) and CAPE (

**j**–

**l**). Green shading in a-f represents lightning forecast results, and black dots represent lightning observations.

**Figure 6.**Performance diagram of 24 August 2020 15:00 to 24 August 2020 17:00 CLSTM-LFN forecast results (green circles) and CLSTM-LFN-O forecast results (yellow circles). The magenta lines represent the TS scores, the black dashed line represents bias scores and the number in the circle represents the forecast time.

**Figure 7.**7 August 2020 03:00 to 7 August 2020 05:00 CLSTM-LFN forecast results (

**a**–

**c**), CLSTM-LFN-O forecast results (

**d**–

**f**) and WRF model products for CAPE (

**g**–

**i**). Green shading represents lightning nowcasting results, and black dots represent lightning observations.

**Figure 8.**Performance diagram of 7 August 2020 03:00 to 7 August 2020 05:00 CLSTM-LFN forecast results (green circles) and CLSTM-LFN-O forecast results (yellow circles). The magenta lines represent the TS scores, the black dashed line represents bias scores and the number in the circle represents the forecast time.

**Figure 9.**19 August 2020 06:00 to 19 August 2020 08:00 CLSTM-LFN forecast results (

**a**–

**c**) and WRF model products for R

_{max}(

**d**) and CAPE (

**e**).

**Figure 10.**Relative importance of historical lightning occurrence frequency and WRF model products at different forecast times (color bar). The blue line represents the ${r}_{\mathrm{relative}}^{t}$.

**Figure 11.**Relative importance of each variable of the WRF model products (Exp_WRF_sequence) at different forecast times.

Variables | Description | Units |
---|---|---|

W_{max} | maximum vertical velocity component of wind | m/s |

helicity | storm relative helicity | m^{2}/s^{2} |

RAINNC | accumulated total grid scale precipitation | mm |

QVAPOR | water vapor mixing ratio | g/kg |

QCLOUD | cloud water mixing ratio | g/kg |

QRAIN | rain water mixing ratio | g/kg |

QICE | ice mixing ratio | g/kg |

QSNOW | snow mixing ratio | g/kg |

QGRAUP | graupel mixing ratio | g/kg |

CAPE | convective available potential energy | J/kg |

R_{max} | maximum radar reflectivity | dBZ |

R_{6} | radar reflectivity at 6 km above ground level | dBZ |

R_{9} | radar reflectivity at 9 km above ground level | dBZ |

Experiments | Forecast Time | TS | FAR | POD | Threshold (N) |
---|---|---|---|---|---|

CLSTM-LFN | 1 h | 0.518 | 0.367 | 0.741 | 5.0 |

2 h | 0.342 | 0.569 | 0.625 | ||

3 h | 0.240 | 0.693 | 0.523 | ||

CLSTM-LFN-O | 1 h | 0.472 | 0.337 | 0.621 | 5.0 |

2 h | 0.325 | 0.552 | 0.544 | ||

3 h | 0.218 | 0.666 | 0.387 | ||

CLSTM-LFN-W | 1 h | 0.114 | 0.869 | 0.467 | 2.0 |

2 h | 0.112 | 0.87 | 0.455 | ||

3 h | 0.105 | 0.873 | 0.382 | ||

PR92 | 0–3 h | 0.053 | 0.94 | 0.304 | 0.0 |

dBZ_from_WRF | 0–3 h | 0.007 | 0.869 | 0.007 | 0.0 |

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**MDPI and ACS Style**

Guo, S.; Wang, J.; Gan, R.; Yang, Z.; Yang, Y.
Experimental Study of Cloud-to-Ground Lightning Nowcasting with Multisource Data Based on a Video Prediction Method. *Remote Sens.* **2022**, *14*, 604.
https://doi.org/10.3390/rs14030604

**AMA Style**

Guo S, Wang J, Gan R, Yang Z, Yang Y.
Experimental Study of Cloud-to-Ground Lightning Nowcasting with Multisource Data Based on a Video Prediction Method. *Remote Sensing*. 2022; 14(3):604.
https://doi.org/10.3390/rs14030604

**Chicago/Turabian Style**

Guo, Shuchang, Jinyan Wang, Ruhui Gan, Zhida Yang, and Yi Yang.
2022. "Experimental Study of Cloud-to-Ground Lightning Nowcasting with Multisource Data Based on a Video Prediction Method" *Remote Sensing* 14, no. 3: 604.
https://doi.org/10.3390/rs14030604