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Study on Short-Term Forecast of Localized Heavy Rainfall Based on the Statistical Characteristics of Cumulonimbus Clouds Observed by Ka-band Doppler Radars

吉田, 翔 筑波大学 DOI:10.15068/0002002087

2021.12.02

概要

Recently, it has been focused on the disasters caused by severe weather phenomena in Japan. Typhoons and quasi-stationary band-shaped precipitation systems can be forecasted several days and hours ago, respectively by numerical weather prediction (NWP) models based on the equation of motion. However, meso-γ-scale (spatial scale 2–20 km) cumulonimbus clouds causing localized heavy rainfall and urban floods, can develop so quickly that it is difficult to forecast with NWP models. The lead time for the heavy rainfall is about 20 minutes after being detected by operational centimeter- wavelength (X-, C-, or S-band) weather radars. To detect such clouds with greater lead times, Ka-band radars at a wavelength of 8.6 mm together with operational X-band radars were used in this study.

The beam width of Ka-band radars is very narrow (0.31°). However, since the elevation interval of the plan position indicators (PPIs) is much larger than the beam width, interpolation gaps are severe in the constant altitude PPI (CAPPI). Therefore, to fill these gaps and utilize the CAPPI data at all levels, the vertically averaged reflectivity (VAR) was calculated from the CAPPI of radar reflectivity Z. By adopting VAR, a continuous distribution of Z was obtained.

For statistical analyses of each cumulonimbus cloud, the algorithm for the identification and tracking of convective cells (AITCC) was used to detect and track cloud. The AITCC extracts the regions enclosed by contour lines at a certain threshold of VAR. They were denoted as convective cell groups (CCGs). The characteristic of CCGs, such as area and maximum/averaged VAR in CCGs, are calculated. The process of tracking CCGs is as follows: 1) the average movement vector (MV) of all CCGs is calculated by the cross-correlation method using radar echoes at successive time steps, 2) the MV for individual CCGs is identified by linking the past and current CCGs using the average MV. If there are multiple links to one CCG, the similarity is evaluated based on the area, shape, and reflectivity (maximum and average) between the past and current CCGs. The CCGs for Ka-band and X-band radars were defined as mesoscale cloud echoes (MCEs) and mesoscale precipitation echoes (MPEs), respectively. The thresholds of VAR were –20 dBZ for MCEs, and 5 dBZ for MPEs. The upper limit of the MCE/MPE area (400 km2) was also defined to focus on the cumulonimbus clouds. The MCEs which grew to MPEs were denoted as “developed MCEs” and which dissipated without developing into MPEs were denoted as “non-developed MCEs”.

For case studies, four local heavy rainfall events that occurred in 2016 and 2017 were selected. They are local heavy rainfall events as rainfall with an intensity of 50 mm h-1 observed within 30 minutes of its onset. There were 15 developed MCEs and 39 non-developed MCEs in total. In all cases, the local heavy rainfall occurred in the target domains after the convergence of sea breezes from the east and south. This is the typical conditions when the local heavy rainfall occurs around Tokyo.

The time series of each echo was analyzed by an echo tracking algorithm. On average, developed MCEs were detected 17 minutes earlier than the MPEs and 33 minutes earlier than the peak time of the area-averaged VAR (VARa) for MPEs. There were statistically significant differences between the developed and non-developed MCEs in terms of the maximum VARa (MaxVARa), maximum MCEs areas (MaxAREA), and increase amounts of the VARa (∆VARa) and MCE areas (∆AREA) for the elapsed time ∆t = 6, 9, and 12 minutes, which is the time after the first detection of the MCE. To obtain the optimal indicator and its threshold, threat score (TS) for the prediction of MPEs was calculated. There is a trade-off between TSs and ∆t, so that it is difficult to declare the best indicator. Nevertheless the MaxVARa produces TS of greater than 80 % and thus provides reliable predictions. Therefore, the MaxVARa for ∆t = 9 minutes (threshold: 0 dBZ) would be the best indicator for practical forecasting. This indicator was obtained by limited cumulonimbus clouds triggered by the convergence of sea breeze around Tokyo. However, the thresholds and ∆t are consistent with previous studies and have been shown to be applicable regardless of convective cloud type or region of occurrence.

Nowcasting model for predicting MCE development was proposed using the above indicator. This model is expected to forecast rainfall earlier than conventional X-band radars, although there are following restrictions on its use; 1) this indicator is only applicable to convective echoes formed near Ka-band radar sites because the minimum detectable reflectivity decreases with the distance from radar sites due to attenuation, 2) the target of this model is limited to isolated cumulonimbus clouds, which are relatively unaffected by precipitation attenuation, 3) it is difficult to quantitatively forecast the rainfall amount caused by MCE whose development is predicted by this indicator. Forecasted MCEs have the potential for rainfall, but they do not always cause localized heavy rainfall. In order to forecast local heavy rainfall based on this indicator, it is necessary to examine the MCEs that occur around such precipitation systems.

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参考文献

Borque, P., P. Kollias, and S. Giangrande, 2014: First observations of tracking clouds using scanning ARM cloud radars, J. Appl. Meteor. Climatol., 53, 2732-2746, doi: 10.1175/JAMC-D-13-0182.1.

Ceperuelo, M, M. C. Llasat, and T. Rigo, 2006: Rainfall events and hailstorms analysis program (RHAP), Adv. Geosci., 7, 205-213, doi: 10.5194/adgeo-7-205-2006.

Chandrasekar, V., H. Fukatsu, and K. Mubarak, 2003: Global mapping of atten uation at Ku- and Ka-band, IEEE Trans. Geosci. Remote Sens. 41, 2166-2176, doi: 10.1109/TGRS.2003.815973.

Cressman P.G., 1959: An operational objective analysis system, Mon. Wea. Rev., 87, 367- 374.

Fujibe, F., K. Sakagami, K. Chubachi, and K. Yamashita, 2002: Surface wind patterns preceding short-time heavy rainfall in Tokyo in the afternoon of midsummer days, Tenki, 49, 31-41 (in Japanese with English abstract).

Fukao, S., and K. Hamazu, 2009: Radar remote sensing of weather and atmosphere, Kyoto University Press, 502p (in Japanese).

Hamazu, K., H. Hashiguchi, T. Wakayama, T. Matsuda, R. J. Doviak, and S. Fukao, 2003: A 35-GHz scanning Doppler radar for fog observations, J. Atmos. Oceanic Technol., 20, 972-986, doi: 10.1175/1520-0426(2003)20<972:AGSDRF>2.0.CO;2.

Hirano, K., and M. Maki, 2010: Method of VIL calculation for X-band polarimetric radar and potential of VIL for nowcasting of localized severe rainfall -Case study of the Zoshigaya downpour, 5 August 2008-, SOLA, 6, 89–92, doi: 10.2151/sola.2010-023.

Hirano, K., and M. Maki, 2018: Imminent nowcasting for severe rainfall using vertically integrated liquid water content derived from X-band polarimetric radar, J. Meteor. Soc. Japan, 96, 201-220, doi: 10.2151/jmsj.2018-028.

Ishihara, M., 2012: Radar echo population of thunderstorms generated on the 2008 Zoshigaya-rainstorm day and nowcasting of thunderstorm-induced local heavy rainfalls. Part I: Three-dimensional radar echo population of the thunderstorms, Tenki, 59, 27-39 (in Japanese with English abstract).

Kato, A., and M. Maki, 2009: Localized heavy rainfall near Zoshigaya, Tokyo, Japan on 5 August 2008 observed by X-band polarimetirc radar: Preliminary analysis, SOLA, 5, 89-92, doi:10.2151/sola.2009‒023.

Kato, R., S. Shimizu, K. Shimone, and K. Iwanami, 2017: Very short time range forecasting using CReSS-3DVAR for a meso- γ -scale, localized extremely heavy rainfall event: Comparison with an extrapolation-based nowcast, J. Disaster Res., 12, 967-979, doi: 10.20965/jdr.2017.p0967.

Kato, T, 2020: Quasi-stationary band-shaped precipitation systems, named “Senjo- Kousuitai”, causing localized heavy rainfall in Japan, J. Meteor. Soc. Japan, 98, 485- 509, doi:10.2151/jmsj.2020-029.

Khain, A., M. Pinsky, L. Magaritz, O. Krasnov, and H. W. J. Russchenberg, 20 08: Combined observational and model investigations of the Z-LWC relationsh ip in stratocumulus clouds, J. Appl. Meteor. Climatol., 47, 591-606, doi: 10.1175/ 2007JAMC1701.1.

Kigawa, S., 2014a: Techniques of precipitation analysis and prediction nowcasts, available online at https://www.jma.go.jp/jma/en/Activities/Techniques_of_Preci pitation_Analysis_and_Prediction_developed_for_HRPNs.pdf [accessed 27 Janua ry 2021].

Kigawa, S., 2014b: Techniques of precipitation analysis and prediction nowcasts, Weather service bulletin, 81, 55-76 (in Japanese).

Kim, D.-S., M. Maki, S. Shimizu, and D.-I. Lee, 2012: X-band dual-polarization radar observations of precipitation core development and structure in a multicellular storm over Zoshigaya, Japan, on August 5, 2008. J. Meteor. Soc. Japan, 90, 701–719, doi: 10.2151/jmsj.2012-509.

Kollias, P., N. Bharadwaj, K. Widener, I. Jo, and K. Johnson, 2014: Scanning ARM cloud radars. Part I: operational sampling strategies, J. Atmos. Oceanic Technol., 31, 569-582, doi: 10.1175/JTECH-D-13-00044.1.

Liebe, H. J., 1985: An updated model for millimeter wave propagation in moist air, Radio Sci., 20, 1069-1089, doi: 10.1029/RS020i005p01069.

Maki, M., S.-G. Park, and V. N. Bringi, 2005: Effect of natural variations in rain drop size distributions on rain rate estimators of 3 cm wavelength polarimetric radar, J. Meteor. Soc. Japan, 83, 871−893, doi: 10.2151/jmsj.83.871.

Maesaka, T., M. Maki, K. Iwanami, S. Tsuchiya, K. Kieda, and A. Hoshi, 2011: Operational rainfall estimation by X-band MP radar network in MLIT, Japan, Proceedings of 35th Conference on Radar Meteorology, Pittsburgh, P11.142.

Maesaka, T., K. Iwanami, S. Suzuki, Y. Shusse, and N. Sakurai, 2015: Cloud radar network in Tokyo metropolitan area for early detection of cumulonimbus generation, Proceedings of 37th Conference on Radar Meteorology, Norman, 174.

Maesaka, T., 2018: Cloud radars, Remote Sensing of Clouds and Precipitation, C. Andronache, Ed., Springer-Verlag, 137-152.

Marrocu, M., and L. Massidda, 2020: Performance comparison between deep learning and optical flow-based techniques for nowcast precipitation from radar images, MDPI, 2, 194-210, doi: 10.3390/forecast2020011.

Matrosov, S. Y., 2005: Attenuation-based estimates of rainfall rates aloft with vertically pointing Ka-band radars, J. Atmos. Oceanic Technol, 22, 43-54, doi: 10.1175/JTECH-1677.1.

Ministry of Land, Infrastructure, Transport and Tourism, 2011: Disaster report of Typhoon 1112(TALAS) (in Japanese), available online at http://www.mlit.go.j p/saigai/saigai_110901.html [accessed 27 January 2021].

Ministry of Land, Infrastructure, Transport and Tourism, 2020: Disaster report of heavy rainfall in July 2020 (in Japanese), available online at http://www.m lit.go.jp/saigai/saigai_200704.html [accessed 27 January 2021].

Misumi, R., N. Sakurai, T. Maesaka, S. Suzuki, S. Shimizu, and K. Iwanami, 2018a: Transition process from non-precipitating cumuli to precipitating convective clouds over mountains: obsevation by Ka-band Doppler radar and stereo photogrammetry, J. Meteor. Soc. Japan, 96A, 51-66, doi: 10.2151/jmsj.2017-021.

Misumi, R., Y. Uji, Y. Tobo, K. Miura, J. Uetake, Y. Iwamoto, T. Maesaka, and K. Iwanami, 2018b: Characteristics of droplet size distribution in low-level stratiform clouds observed from Tokyo Skytree, J. Meteor. Soc. Japan, 96, 405-413, doi: 10.2151/jmsj.2018-040.

Nakakita, E., R. Nishiwaki, H. Yamabe, and K. Yamaguchi, 2013: Research on the prognostic risk of baby cell for guerilla-heavy rainfall considering by vorticity with Doppler velocity, J. JSCE, Ser. B1, 69, I_325-I_330 (in Japanese with English abstract).

Nakakita, E., H. Sato, and K. Yamaguchi, 2016: Fundamental analysis of vortex tubes inside the baby cells of guerilla-heavy rainfall, J. JSCE, Ser. B1, 72, I_199-I_204 (in Japanese with English abstract).

Nakakita, E., T. Niibo, H. Sato, K. Yamaguchi, T. Ohigashi, T. Shinoda, and K. Tsuboki, 2017: Preliminary analysis of cumulonimbus cloud structure using multi parameter information of Ka-band polarimetric Dopper radar, DPRI Annuals, 60B, 559-575 (in Japanese with English abstract).

Nishikawa, M., K. Nakamura, Y. Fujiyoshi, K. Nakagawa, H. Hanado, H. Minda, S. Nakai, T. Kumakura, and R. Oki, 2016: Radar attenuation and reflectivity measurements of snow with dual Ka-band radar, IEEE Trans. Geosci. Remote Sens. 54, 714-722, doi: 10.1109/TGRS.2015.2464099.

Nishiwaki, N., R. Misumi, S. Shimizu, T. Maesaka, K. Iwanami, N. Sakurai, M. Maki, S. Suzuki, A. Kato, and A. Yamaji, 2013: Behavior and structure of convective clouds developing around a mountainous area observed by stereo photogrammetry and Ka-band and X-band radars: Case study of norther Kanto, Japan, J. Meteor. Soc. Japan, 91, 609-626, doi: 10.2151/jmsj.2013-504.

Oh, S.-B., Y. H. Lee, J.-H. Jeong, Y.-H. Kim, and S. Joo, 2018: Estimation of the liquid water content and Z-LWC relationship using Ka-band cloud radar and a microwave radiometer, Meteorol. Appl., 25, 423-434, doi: 10.1002/met.1710.

Otsuka, S., G. Tuerhong, R. Kikuchi, Y. Kitano, Y. Taniguchi, J. J. Ruiz, S. Satoh, T. Ushio, and T. Miyoshi, 2016: Precipitation nowcasting with three-dimensional space-time extrapolation of dense and frequent phased-array weather radar observations, Wea. Forecasting, 31, 329-340, doi: 10.1175/WAF-D-15-0063.1.

Park, S.-G., V. N. Bringi, V. Chandrasekar, M. Maki and K. Iwanami, 2005: Correction of radar reflectivity and differential reflectivity for rain attenuation at X band. Part1: Theoretical and empirical basis, J. Atmos. Oceanic Technol, 22, 1621-1632, doi: 10.1175/JTECH1803.1.

Radhakrishnan, C., and V. Chandrasekar, 2020: CASA prediction system over Dallas- Fort Worth urban network: Blending of nowcasting and high-resolution numerical weather prediction model, J. Atmos. Oceanic Technol, 37, 211-228, doi: 10.1175/JTECH- D-18-0192.1.

Ruzanski, E., V. Chandrasekar, and Y. Wang, 2010: The CASA nowcasting system, J. Atmos. Oceanic Technol, 28, 640-655, doi: 10.1175/2011JTECHA1496.1.

Saito, K., M. Kunii, and K. Araki, 2018: Cloud resolving simulation of a local heavy rainfall event on 26 August 2011 observed in TOMACS, J. Meteor. Soc. Japan, 96, 175– 199, doi: 10.2151/jmsj.2018-027.

Sakurai, N., K. Iwanami, T. Maesaka, S. Suzuki, S. Shimizu, R. Misumi, D.–S. Kim, and M. Maki, 2012: Case study of misoscale convective echo behavior associated with cumulonimbus development observed by Ka-band Doppler radar in the Kanto Region, Japan, SOLA, 8, 107-110, doi: 10.2151/sola.2012-027.

Sauvageot, H., and J. Omar, 1986: Radar reflectivity of cumulus clouds, J. Atmos. Oceanic Technol, 4, 264-272, doi: 10.1175/1520-0426(1987)004<0264:RROCC>2.0.CO;2.

Shiino, J, 1986: A study of the precipitation and dynamical behavior of maritime convective clouds, J. Meteorol, Res., 37, 115-190 (in Japanese with English abstract).

Shimizu, S., and H. Uyeda, 2012: Algorithm for the identification and tracking of convective cells based on constant and adaptive threshold methods using a new cell-merging and-splitting scheme, J. Meteor. Soc. Japan, 90, 869–889, doi: 10.2151/jmsj.2012-602.

Takahashi, N., T. Ushio, K. Nakagawa, F. Mizutani, K. Iwanami, A. Yamaji, T. Kawagoe, M. Osada, T. Ohta, and M. Kawasaki, 2019: Development of multi- parameter phased array radar (MP-PAWR) and early detection of torrential rainfall and tornado risk, J. Disaster Res., 14, 235-247, doi: 10.20965/jdr.2019.p0235.

Uematsu, A., H. Hashiguchi, M. Teshiba, H. Tanaka, K. Hirashima, and S. Fukao, 2005: Moving cellular structure of fog echoes obtained with a millimeter-wave scanning Doppler radar at Kushiro, Japan, J. Appl. Meteor., 44, 1260-1273, doi: 10.1175/JAM2274.1.

Ushio, T., T. Wu, and S. Yoshida, 2015: Review of recent progress in lightning and thunderstorm detection techniques in Asia, Atmos. Res., 154, 89–102, doi: 10.1016/j.atmosres.2014.10.001.

Yoshikawa, E., T. Ushio, Z. Kawasaki, S. Yoshida, T. Morimoto, F. Mizutani, and M. Wada, 2013: MMSE beam forming on fast-scanning phased array weather radar, IEEE Trans. Geosci. Remote Sens., 51, 3077–3088, doi: 10.1109/TGRS.2012.2211607.

Yoshida, S., R. Misumi, S. Shimizu, T. Maesaka, K. Iwanami, and M. Maki, 2012: Validation of short-term forecasting of meso-γ-scale convective systems based on a cell-tracking system, SOLA, 8, 141-144, doi: 10.2151/sola.2012-035.

Yoshida, S., T. Ushio, S. Yoshida, S. Shimamura, K. Maruo, and N. Takada, 2013: The structure of convective systems observed by Phased array radar in the Kinki region, Japan, Proceedings of 36th Conference on Radar Meteorology, Breckenridge, 139.

Wilson, J. W., N. A. Crook, K. Mueller, J. Sun, and M. Dixon, 1998: Nowcasting thunderstorms: A status report, Bull. Am. Meteorol. Soc. (BAMS), 79, 2079-2100, doi: 10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2.

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