A digital lightning prototype system: DLPS/HUST

Figures & data

Table 1. Schematic attribute tables for lightning thematic data.

(a) Lightning raw data
Frame Tag	Frame Size ID	Detector ID	Year	Month	Day	Hour	Minute	Second	Data Type	…
Frame code	Frame code	Detector code	yyyy	MM	dd	hh	mm	ss	0 or 1
(b) Lightning ripe data
Object ID	Date	Datetime	Polarity	Lightning type	Longitude	Latitude	Amplitude	Slope	…
ID code		hh:mm:ss	±	CC or CG
(c) Detector status
Detector ID	Year	Month	Day	Hour	Minute	Second	Self test result	Threshold	GPS state	AD error	…
Detector code	Yyyy	MM	dd	hh	mm	ss	0 or 1
(d) Feature class of the spatial distribution of the detector
Detector ID	Name	Longitude	Latitude	Latest state
Detector code				0 or 1

Notes: The raw data of lightning and detector status data are automatically received frame by frame. The property fields of raw data are different from those of ripe data. Lightning can be mainly classified into two types: cloud-to-cloud (CC) and cloud-to-ground (CG). CG can be further classified into positive (+) and negative (−).

Figure 1. Multi-source geospatial data relevant to the DLPS.

Figure 2. Architecture of the DLPS.

Figure 3. Data processing flow in the DLPS.

Figure 4. Three-tier architecture of lightning spatial database in the DLPS.

Figure 5. 2D lightning real-time monitoring of the current day. The CG of the whole day is divided into four groups, rendered with different colours. The diamond symbol represents the state of the detector, the green and the red represent normal and abnormal, respectively.

Figure 6. Lightning thematic grid construction. The Hainan province is partitioned into 10×10 grids, 100 parts altogether. In this figure, the polygon is a regular rectangle, which can also be irregular. According to the mean thunderstorm days of different years stored in each grid, statistical analysis is carried out.

Figure 7. Information flow of the LIRS.