Quasi-Real Time Monitoring of Lightning and Weather in the and Western North Pacific for the Severe Weather Intensity Prediction Mitsuteru Sato(1), Yukihiro Takahashi(1), Shunsuke Niwa(1), Hisayuki Kubota(1), Akira Noda(1), Junichi Hamada(2), and Glenn Vincent C. Lopez(3) EGU21-13950 (1) Hokkaido University, (2) Metropolitan University, (3) ASTI, DOST 1. Introduction 4. Typhoon (2013,2014) vs. Lightning Parameters 5. Typhoon (2018) vs. Lightning Frequency u Many countries in the western north Pacific region suffer from the attack of Typhoon UTOR (Aug. 8-15, 2013) : Category-5 Typhoon YAGI (Aug. 6-16, 2018) : Category-1 (TC) (typhoon) and have a strong demand to predict the intensity development of typhoons by means of a cost-effective way. 08/10, 06UT 8/11, 06UT 08/12, 06UT 08/13, 06UT 08/07 12UT 08/09 12UT 08/11 12UT u However, it is difficult to predict the typhoon intensity development even though the very recent numerical meteorological model is used. (Fig.1) u It is reported that there is clear relation between TC intensity development and lightning activity. (Fig.2)

Fig.7 IR cloud images with lightning locations (red points) for the typhoon YAGI. Fig.4 IR cloud images with lightning locations (red points) for the typhoon UTOR. [Niwa, 2021] (a) (c) Inner Core (R ≦ 100 km)

Ip Qdl Q

Fig.1 Prediction error of the typhoon track and intensities (pressure & wind speed) Fig.2 Relation between TC intensity development and using meteorological model. [Ito, 2016] lightning activity. [Price, et al., 2009] (b) (d) 2. Purpose of This Study u Establishment of a dense network of lightning and weather detectors in Metro Fig.5 Relation between typhoon wind speed development and the development of the peak current (left), charge moment change (middle), and charge and western north Pacific in order to provide thunderstorm “now-casting” and supplement amount (right) for the lightning in the typhoon inner core area (R ≦ 100 km). [Niwa, 2021] weather-related research and disaster response studies and strategies. Rain Band (100 km < R ≦ 500 km) u Clarification of typhoon intensity development and lightning activities, and establishment Fig.8 (a) Time variation of the lightning detection number (black) and the typhoon wind speed (red). (b) Same as (a) except for the typhoon minimum of a methodology for short-term forecasts. pressure. (c) Cross-correlation analysis between the lightning detection number and the typhoon wind speed. (d) Same as (c) except for the typhoon minimum pressure. u Development of software for sharing information on short term forecast weather to Ip Qdl Q concerned agencies. 6. Conclusion u The electrical parameters of -CG during the development stage in the 3. Data & Method super typhoon peaked 1-2 days before the typhoon intensity max.. ( I ) Typhoon (2013, 2014) vs. Lightning Parameters Same as Fig. 5 except for the lightning in the typhoon rain band area (100 km < R ≦ 500 km). [Niwa, 2021] Fig.6 u The electrical parameters of -CG as l Typhoon Data : JTWC best track data for the typhoons in 2013 and 2014 well as lightning detection number l Lightning Data :WWLLN, GEON (global ELF observation network) can be a good predictor of the typhoon intensity development. Ø lightning frequency (Ncg) using WWLLN data

Ø peak current (Ip), charge moment change (Qdl) using Syowa u We found clear relation between the ELF data typhoon intensity development and the lightning detection number even Ø charge amount (Q) using Kuju ELF data and empirical in the typhoon with the weakest equation [Shimizu et al., 2016] category. ( II ) Typhoon (2018) vs. Lightning Frequency Acknowledgement This work was supported by Japan Science and Technology Agency (JST) and Japan International Cooperation Agency (JICA) under SATREPS. l Typhoon Data : JMA typhoon data for the typhoons in 2018 l References Lightning Data :V-POTEKA (deployed by the ULAT project) l Ito, K., Errors in tropical cyclone intensity forecast by RSMC Tokyo and statistical correction using environmental parameters, Sola, 12, 247-252, 2016. (Fig.3) l Price, C., et al., Maximum hurricane intensity preceded by increase in lightning frequency, Nature Geosci., doi: 10.1038/NGEO477, 2009. l Shimizu, C., et al., Relation between charge amounts of lightning discharges derived from ELF waveform data and severe weather, IEEJ Transactions Fig.3 Picture of the lightning and weather on Fundamentals and Materials, 136(5), 252-258, doi:10.1541/ieejfms.136.252, 2016. Ø lightning frequency (Ncg) using V-POTEKA data observation system (V-POTEKA). l Niwa, S., Estimation of the relationship between electrical properties of lightning and intensity of tropical cyclones in western north Pacific, Master [Niwa, 2021] thesis, Hokkaido Univ., 2021