The Applications of Lightning-Rainfall Prediction on Transmission Lines Disaster Prevention Liao, Shun-An Wang, Kuo-Ying Power Research Institute, Department of Atmospheric Sciences, Taiwan Power Company National Central University

Abstract - The paper applied the lightning information and included the following: that generated by Total Lightning Detection System- -Birth TLDS to investigate the correlations of precipitation, and •Cloud electrification starts with convection tracing the lightning movement route. The results were -Storm onset developed into a software, the “Taiwan Area Lightning- •Early IC reveals the convective areas Rainfall Prediction and Application System-TALPAS, ” •IC can reveal storms dozens of minutes earlier than CG that is an on-line display system, which can real-time -Development display the total lightning and rainfall spatial distribution •IC intensifies with storm development covering the entire Taiwan area. The TALPAS offers •Zones with high IC density pinpoint severe storm areas dispatchers good references, and pre-warns the intensity •IC can amount to 90 - 99 % of total lightning activity, of heavy rainfalls and the density of lightning impact on which makes it essential for storm monitoring and tracking power transmission lines in advance. -Maturity •IC peaks when the storm cell reaches max. height Keywords: Lightning, Cloud Discharge, and Rainfall. •IC peak rate correlates with storm severity •Total lightning activity correlates with precipitation 1. Introduction amount Total Lightning Detection System-TLDS is a complete •Beginning of significant CG activity detection system, which provides data including intra-cloud -Decay / Downburst discharge (IC), cloud to cloud discharge (inter-cloud •CG activity culminates discharge, IC), and cloud to ground discharge (CG), and is •Fast decay of IC precedes and warns of severe downbursts one of most important systems in atmospheric sciences. and hazards at ground level Basically, the integrations of lightning data, radar reflectivity •End of IC indicates storm dissipation data, and infrared brightness temperature data, will provide -Dissipation great help in estimating the duration of lightning-rainfall, •Scarce intra-cloud lightings remain whose work belongs to the Central Weather Bureau (CWB). This study simply pointed out the correlation between The lightning-rainfall seasons in Taiwan area, occur in lightning and heavy surface rainfall analysis. The purpose of March and end in October, while intense lightning-rainfall this research is to aim at lightning and its precipitation occurs in July and August. Basically, the type of lightning- relationship observed, and the results showed that while rainfall storm can be classified into frontal storm (also known depending only on the information of TLDS, we can still as oceanic storm) and thunderstorm (island storm). Frontal reveal the precipitation amount and surface rainfall’s time storm occurs in the spring season and thunderstorm occurs in distribution and spatial distribution in real-time. In addition to the summer season. In addition to the -induced the above functions, tracking the speed and direction of southwesterly flows are commonly observed during the movement of lightning is also absolutely necessary. Real-time passage of over Taiwan area. The southwesterly lightning-rainfall information merged into the transmission flows always induced moisture-rich air mixed with frontal lines geographic information system, dispatchers could make storm and thunderstorm and are quite common, causing the references and optimize the power dispatch, reduce power different varieties of lightning-rainfall. facilities lightning disaster and water source application, thus creating profit for economic efficiency. Due to the demand of 3. Total Lightning Detection System the power dispatchers, the results of study were developed The results of the lightning information acquired were into an on-line observation system for real-time observation applied in the power facilities and the transmission lightning of lightning-rainfall time distribution and spatial distribution. disaster reduction. Taiwan Power Research Institute (TPRI) has set up and operated a total lightning detection system 2. Lightning in Taiwan (TLDS) in Taiwan since November 2002. The TLDS Lightning is an electric discharge phenomenon. The consists of a network of seven microwave tower-based total study adopted intra-cloud, inter-cloud (IC), and cloud to lightning detection stations[1][2]. Each station is equipped ground (CG) discharge to describe lightning discharge with lightning detection antenna sensors based on the phenomena. A well-developed cloud discharge development SAFIR technology. These detection station and the process (thunderstorm cell life-cycle) is formed stage by stage, operating systems adopted Vaisala SAFIR 3000 system.

1 Figure 1 shows the locations of the TLDS ground-based region, moving west towards the , passing the stations, and their spatial coverage over Taiwan area. The Philippines, then turning north towards Taiwan and exerting lightning detection network average efficiency for the its greatest impact over Taiwan in July 1-2. After leaving SAFIR 3000 system is greater than 90% and the localization Taiwan, it moved quickly northward, passing , and accuracy is less than 1 km within 250 km. The diameter of finally disappearing in the Sea of Japan. light detection area, is more than 700 km shown in Figure 2. More information on the SAFIR 3000 system can be found at http://www.dwd.de/EUMETNET. The TLDS system is the first operational SAFIR system in tropical Asia, and the second one in Taiwan.

Figure 3. Evolution and track of Typhoon Mindulle

The northward moving typhoons often induce substantial southwesterly flows from the South Sea toward Taiwan. These induced southwesterly flows bring moisture-rich air Figure 1. Seven lightning detection stations in Taiwan from the South China Sea towards the steep central mountain range of Taiwan, resulting in orographically enhanced heavy precipitation over the windward side of mountain areas. Due moisture-rich air of southwesterly flows, the oceanic storm and island storm bring intense lightning and heavy precipitation, that cause serious disaster for TPC power systems, facilities and transmission lines. These oceanic storm and island storm bring intense lightning, which also challenge the detection ability and data processing ability of TLDS. As Typhoon Mindulle moved northward away from Taiwan, a mesoscale convective system, oceanic storm developed over the South China Sea in the morning of 2 July 2004. During the next 36 hours, this oceanic storm developed as it moved from its oceanic origin towards Taiwan. The storm made landfall and reached its peak intensity in the early morning of 3 July. The storm then gradually decayed as it moved towards central Taiwan and disappeared over the northeastern part of Taiwan in the evening of 3 July 2004. Figure2. Diagram showed the diameter of light detection area Figure 4 shows the 4-hourly movement of lightning is more than 700 km. spatial distribution information from 00:00 UT on 2 July to 00:00 UT on 3 July.( red, orange, violet, green, blue 4. The correlation between lightning and storm and black in a sequence, each color presents 4-hourly A well developed storm, which consisted of both lightning spatial distribution, e.g. read:20:01-24:00, oceanic storm and island storm, is a nice case to analyze orange:16:01-20:00 etc.) While the oceanic storm was the correlation between lightning and storm. Typhoon decaying, a second mesoscale convective system (island Mindulle is such a good case to discuss. storm) gradually developed in the southern part of Taiwan Figure 3 shows the movement of the Typhoon over mountainous areas. This island storm dominated over Mindulle from its birth place in the equatorial west Pacific Taiwan for about 36 hours. Figure 5 shows the 4-hourly 2 movement of lightning spatial distribution information from orders of magnitude increase during the growth stage of the 00:00 UT on 3 July to 00:00 UT on 4 July. Notice that oceanic storm, and peak period at 20:00 UT on 2 July to both the oceanic storm and the island storm developed 08:00 UT on 3 July, the –CG occured rate was 500-800 associated with the passage of Typhoon Mindulle, and and +CG occured rate was 40-60 per hour (caused by developed most intense cloud discharge (IC&CG) and super oceanic storm). Followed by another order of magnitude volume lightning-rainfall. increase during the growth stage of the island storm, and peak period at 02:00 to 08:00 UT on 4 July, the –CG occured rate was 2000 and +CG occured rate was 40-80 per hour. In figure 6, showed a strong positive correlations of rainfall and CG, particually, -CG (black). Beside, the lag- time of rainfall and CG reaching a peak level was also revealed. Figure 7 shows the time evolution of IC flashes within the analysis domain (i.e., the summation of all lightning flashes). The number of CG flashes shows a 5 orders of magnitude increase during the growth stage of the oceanic storm, and 4 orders of magnitude increase during the growth stage of the island storm. The peak period occurred at 05:00 to 08:00 UT on 3 July and 05:00 to 20:00 UT on 4 July. However, during this period, there was no prominent positive correlations between the lag-time of rainfall and IC reaching the peak level.

Figure4. 24-hour lightning spatial distribution in 02 July 2004, by meso-scale convective system (oceanic storm), induced by southwesterly flows after Typhoon Mindulle. ( red, orange, violet, green, blue and black in a sequence, each color presents 4-hourly lightning spatial distribution, e.g. read:20:01-24:00, orange:16:01-20:00 etc.)

Figure6. Hourly (UT) evolution of the oceanic storm (2-3 July) and the island storm (3-4 July) calculated in the area (112°E-128°E and 14°N-30°N). -CG (black), and +CG (green) lightning (flashes hr-1), total rainfall flux (m3 hr-1, blue), per km2 over Taiwan. (Using logarithm coordinate system)

5. Predicting Rainfall From Lightning One of the most important applications concerning lightning detection is the use of lightning information in Figure5. 24-hour lightning spatial distribution in 03 July severe rainfall prediction[3][4]. Though several studies have 2004,by island storm after oceanic storm of Typhoon correlated lightning to surface rainfall, more studies of their Mindulle. (color definition as figure 4) temporal and spatial correlations following the lifetime of storms are needed. This is likely due to the lack of total Figure 6 shows the time evolution of -CG, and +CG lightning data available to previous investigators. In figure flashes within the analysis domain (i.e., the summation of 8 and figure 9 show that by using total lightning all lightning flashes). The number of CG flashes shows a 3 information data merged with rainfall data, to display spatial

3 distribution, could reveal a well positive correlation in Right side displayed IC, CG, and IC+CG three type spatial central Taiwan (12:00 of 03 July 2004 and 22:00 of 03 July distributions, and left side displayed correlations of 2004). precipitation/rainfall spatial distribution. The time of lightning-rainfall distribution can set 10 minutes display or

Figure7. Hourly (UT) evolution of the oceanic storm (2-3 July) and the island storm (3-4 July) calculated in the area (112°E-128°E, and 14°N-30°N). IC(black), lightning (flashes hr-1) total rainfall flux (m3 hr-1, blue), per km2 over Taiwan. Figure8. Total lightning information data IC (green), (Using logarithm coordinate system) +CG(red), and -CG (blue) merged with rainfall data, displaying spatial distribution at 03 July 02:00. Using total lightning data and rainfall data to study the evolution of deep convection, lightning, and surface rainfall is shown as follows. The indirect methods of lightning-rainfall forecast technology are informations of atmosphere assimilation, which include convection intensity, mist, water of cloud, and lightning[5][6][7]. Through this assimilation, lightning is proven to be one of most important key point references. Due to the demand of power dispatching, disaster prevention of transmission lines and towers, this study transferred the real-time lightning data of TLDS to convert surface rain information directly, and also developed an on- line application system, named Taiwan Area Lightning- Rainfall Prediction and Application System (TALPAS) for observation of lightning-rainfall. The framework of TALPAS has three segments that include lightning data receiver unit (hardware), lightning-rainfall calculation unit, and lightning- rainfall display interfaces unit (software). Because TLDS is sending data continously second by second, therefore, TALPAS data receiver has to filter blank data and acquires valid lightning data. The acquired lightning data (IC and CG) through lightning-rainfall calculation unit, was then calculated, and displayed the lightning-rainfall distribution in Figure9. Total lightning information data IC (green), Taiwan area. After installation of the software of TALPAS in +CG(red), and -CG (blue) merged with rainfall data, a personal computer and receiver, through router, moden and displaying spatial distribution at 03 July 22:00. lease line to receive lightning data, the real-time lightning- rainfall spatial distribution could be displayed. TALPAS real- time operation and screen display were shown in Figure 10. accumulation display.

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6. The forecast function of TALPAS Lightning and rainfall correlations forecast is an experience converted that followed the formula (1) Ri= Ki Li (1) Where Ki= mm/flash; I:1,2,3 1: IC, 2: -CG, 3: +CG Ri is lightning to surface rainfall convert in 1km area range, and Li is number of lightning, L1 represents IC 、L2 represents -CG 、L3 represents +CG；Ki is rainfall rate of lightning ( mm/flash)。Due formula (1) is nonlinear equation, and season lightning-rainfall is variety; therefore Ki value turning is necessary under different season. In other words, Ki value has to tune under user’s experience. To use history lightning data and experience, the TALPAS set range for K1: 03-1.5 mm/flash, K2: 3-5 mm/flash K3: 4-6 mm/flash. Figure Figure11. Precipitation/rainfall forcast of TALPAS at 21 11 shows postive relation factor of lightning and rainfall, July 2004. under the setting of Ri, where R1: 4.5 mm/flash, R2: 45 7. The tracking of lightning mm/flash R3: 45 mm/flash. The relation factor of real rainfall From the power system facilities and transmission lines and forecast rainfall, R(IC) = 0.236, R(+CG)=0.502 R(-CG)=0.662. disaster prevention viewpoint, lightning rainfall spatial and Through more case study, that reveals lightning and rainfall time distribution are very important. In addition to this, correlation conversion ability of TALPAS is good during lightning pre-warning and tracking is more useful for intense thunderstorm in summer season, but conversion prevention work. To track lightning, this study adopted ability is not good during other season. TLDS cell displaying function based on SAFIR technology. Actually, lightning data format such as number of sample The real-time lightning data make a mathematical rate of IC, rise time, duration, amplitude of discharge current calculation which can display a lightning cell, containing of CG etc. will affect lightning and rainfall correlations, information about cell movement, speed, and direction. After which can serve as topics for future study. merging this information with the TPC transmission lines geographic information, then lightning pre-warning and tracking function will meet the power dispatching demand. The function of lightning tracking is shown in Figure 12. The figure displayed two main cells and small cell mapped on transmission lines (green: 345kV, violet: 161kV), and the movement speed of the three cells were all 35 km per hour, with the direction forward to northeast. Violet cell and green cell were kernel cells, which contained a lot of density lightning.

Figure10. TALPAS rea-time operation and screen display

Figure12. Lightning pre-warning and tracking function of TLDS. Green color of transmission lines presented a 345kV system, and violet color of transmission lines presented a

5 161 kV system. lightning on power system facilities. Co-author, Wang Kuo- Ying, is a professor of Department of Atmospheric Sciences, 8. Conclusions and Suggestions National Central University. He joined me in this research on Although integrations of lightning data, radar reflectivity “The Applications of Lightning-Rainfall Prediction on data, and infrared brightness temperature data, can greatly Transmission Lines Disaster Prevention ”. During this period, contribute in the estimation of the duration time of lightning- Professor Wang provided me with a lot of information on rainfall. However, the above functions could already be meteorological sciences, and I wanted to thank his guidance achieved by using the lightning informations gathered from and kindness. the TLDS alone. Because TLDS is sending data continuously second by second, so TALPAS can display real-time lightning-rainfall at 10 minutes resolution distribution, and 1km spatial 10. References distribution. It is an on-line real-time operation system, [1] Liao, S.-A., The study on application and calibration of the therefore, the forecasting information gathered is almost a lightning detection systems in Taiwan, Rep. 195, 73 pp., real- time response. Taiwan Power Co., Taipei,2005. The lightning and rainfall correlation conversion ability [2] Liao, S.-A., and J.-S. Yang, Construction and of TALPAS is good during intense thunderstorm in summer applicationsof lightning detection system in Taiwan, season, but conversion ability is not good during other paper presented at 2004 International Conference on seasons. Electromagnetic Application and Compatibility, Natl. Sci. Counc. ROC, Taipei. 2004. Using lightning and rainfall conversion data of summer [3] K.-Y., and S.-A. Liao, “The Taiwan total lightning season to compare with the rain information of CWB, could detection system-First results: 2002-2003 ”, paper obtain a satisfied result, as shown in Figure 13. The lightning presented at 18th International Lightning Detection data format, such as number of sample rate of IC, rise time, Conference, Vaisala, Helsinki, 2004. duration, amplitude of discharge current of CG etc. affecting [4] Wang, Kuo-Ying and Liao, Shun-An, “Lightning, radar lightning and rainfall correlations, are subjects for future reflectivity, infrared brightness temperature, and surface studies. rainfall during the 2-4 July 2004 severe convective TLDS lightning cell mode, which consisted of cell system over Taiwan area ”, Journal of Geophys. Res, movement, speed, direction information, merges into TPC VOL. 111,11 March 2006. transmission lines geographic information, then lightning [5] Carte, A. E., and R. E. Kidder, Lightning in relation to pre-warning and tracking function will meet the power precipitation, J. Atmos. Terr. Phys., 39, 139-148. 1977. dispatching demand. [6] Carey, L. D., and S. A. Rutledge, “ The relationship between precipitation and lightning in tropical island 9. Acknowledgments convection: A C-band polarimetric radar study”, Mon. Both the researchers of atmospheric and meteorological Weather Rev., 128, 2687-2710. 2000. fields are interested in the correlation between lightning and [7] Petersen, W. A., L. D. Carey, S. A. Rutledge, J. C. heavy surface rainfall study, but the researchers of high Knievel, N. J. Doesken, “cloud-to-ground lightning voltage field are interested in lightning phenomena study, and convective rainfall ”, Journal of Geophys. particularly, in the prevention of the disastrous effect of Res.,103(D12), 14,025-14,040.

Figure13. To use lightning and rainfall conversion data of summer season to compare the rain information of CWB. 6