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Analyses of Extreme Precipitation Associated with the Kinugawa River Flood in September 2015 Using a Large Ensemble Downscaling Experiment

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Analyses of Extreme Precipitation Associated with the Kinugawa River Flood in September 2015 Using a Large Ensemble Downscaling Experiment JournalApril 2019 of the Meteorological Society of Japan, 97(2),M. 387−401, FUJITA et2019. al. doi:10.2151/jmsj.2019-022 387 Special Edition on Tropical Cyclones in 2015 − 2016 Analyses of Extreme Precipitation Associated with the Kinugawa River Flood in September 2015 Using a Large Ensemble Downscaling Experiment Mikiko FUJITA Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan Tomonori SATO Faculty of Environmental Earth Science, Hokkaido University, Hokkaido, Japan Tomohito J. YAMADA Faculty of Engineering, Hokkaido University, Hokkaido, Japan Sho KAWAZOE, Masuo NAKANO Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan and Kosuke ITO University of the Ryukyus, Okinawa, Japan (Manuscript received 1 April 2018, in final form 29 November 2018) Abstract We investigated extremely heavy precipitation that occurred around the Kinugawa River, Japan, in September 2015, and the probability of extreme precipitation occurrence, using data from a large ensemble forecast of more than 1,000 members that were dynamically downscaled to 1.6 km horizontal grid spacing. The observed event was statistically rare among simulated cases and the 3-day accumulated precipitation around the target area was equivalent to the 95th percentile among all simulated ensemble members. Our results show that this extreme precipitation event occurred under specific conditions: two coexisting typhoons at close proximity that produced a high atmospheric instability, and water vapor transported from the Pacific Ocean. We also assessed the prob- ability of extreme precipitation in mountainous areas other than the Kinugawa River case. Heavy precipitation also occurred southwest of the Kinugawa River region due to two typhoons, similar to the Kinugawa River case. The tracks of these typhoons shifted marginally; however, there was a difference in the water vapor supplied to the area, causing heavy precipitation. The large-ensemble downscaled data used in this study hence enabled us to evaluate the occurrence probability of a torrential rainfall event that was rarely observed, which may contribute to updating a disaster-mitigating plan for possible similar disasters in future. Corresponding author: Mikiko Fujita, JAMSTEC, 3173-25 Showa-machi Kanazawaku, Yokohama, Kanagawa 236-0001, Japan E-mail: [email protected] J-stage Advance Published Date: 17 December 2018 ©The Author(s) 2019. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0). 388 Journal of the Meteorological Society of Japan Vol. 97, No. 2 Keywords extreme precipitation; ensemble forecast; downscaling; disaster prevention Citation Fujita, M., T. Sato, T. J. Yamada, S. Kawazoe, M. Nakano, and K. Ito, 2019: Analyses of extreme precipi- tation associated with the Kinugawa River flood in September 2015 using a large ensemble downscaling experiment. J. Meteor. Soc. Japan, 97, 387–401, doi:10.2151/jmsj.2019-022. uncertainty in large ensemble datasets explains the 1. Introduction probability of even more extreme events and helps From September 7 to 11, 2015, very heavy continu- improve our understanding of the 2015 Kinugawa ous precipitation occurred in central Japan. The Kinu- heavy rainfall event. gawa River, a tributary of the Tone River, which is a 2. Design of the large-ensemble numerical major river in Japan, experienced extensive flooding, experiment resulting in the worst disaster-related damage in this area over the past century (Japan Society of Civil The use of a large amount of data derived from Engineers Committee 2015; Kitabatake et al. 2017; operational ensemble forecast output, which subsumes Ushiyama et al. 2017). This extreme rainfall event uncertainty caused by chaotic atmospheric behavior, was indirectly affected by two typhoons. The remote can be helpful in the investigation of the mechanisms influence of tropical cyclones (TCs) on synoptic-scale causing record-breaking heavy precipitation and its phenomena is a key condition for producing an ex- probability of occurrence under similar conditions treme precipitation event (e.g., Galarneau et al. 2010; elsewhere. In this study, we used the output of a Tsuguti and Kato 2014). Nguyen-Le and Yamada global ensemble forecast to generate members of a (2017) investigated Typhoon Lionrock (TY1610) and large ensemble and performed dynamical downscaling its remote effect on heavy rainfall in Hokkaido, north- using the large ensemble forecast to investigate the ern Japan. Their numerical simulation, which removed 2015 flooding in the Kinugawa region. the vortex associated with the typhoon, showed that The Global Ensemble Forecast System of the remote rainfall in Hokkaido was largely suppressed, National Center for Environmental Prediction (GEFS– thereby highlighting the importance of moisture NCEP, Table 1) consists of 21 ensemble members at a transport toward Hokkaido by the synoptic-scale cir- horizontal resolution of 1° × 1° (Whitaker et al. 2008). culation related to the TC location. The forecast is performed with 21 different initial To better prepare for and mitigate similar hazards conditions at 6 h intervals, and 16 days of forecast of this type, the probability of extreme events and data are available. These data were adopted in view their contributing preconditions must be investigated. of their compatibility with the physics of the regional Although the flooding event in September 2015 im- model (see Section 3) and data accessibility. The target pacted only a limited region, similar events including period during which extreme precipitation occurred heavy precipitation may have occurred in adjacent in the real world (from 0000 UTC on September 8 to areas due to chaotic atmospheric behavior. The fre- 0000 UTC on September 11, 2015) was set for dy- quency of extreme precipitation events has increased namical downscaling by the regional model using the worldwide over the past century (e.g., Frich et al. GEFS–NCEP ensemble forecast as forcing data (see 2002), including in Japan (Fujibe et al. 2005); there- Section 3 for details of the regional model). Note that fore, the risk of disasters due to such events has also the dates and duration of the downscaling experiment increased. In addition, changes in the frequency of by the regional model are the same among all ensem- heavy precipitation events have been projected based ble members. However, the input data for the regional on future climate scenarios (e.g., Endo et al. 2017). model are different in each case due to variation in the In this pilot study, we applied statistical methods initial conditions of the GEFS–NCEP forecast (Fig. to investigate an extreme precipitation event and the 1a). We used lagged initial conditions for the forecast probabilities of such an event in the surrounding area to create the ensemble field, as performed in previous using ensemble forecast data with more than 1,000 studies (e.g., Hoffman and Kalnay 1983; Nakano et al. members. We used the extremely heavy rainfall that 2017). As shown in Figure 1a, 1,029 ensemble mem- occurred around the Kinugawa River as an example. bers were adopted from the GEFS–NCEP forecasts: Our results show that the consideration of chaotic 21 ensemble members × 49 initial conditions corre- April 2019 M. FUJITA et al. 389 Table 1. Data used for the dynamical downscaling experiment. Data name Horizontal resolution Type GEFS-NCEP 16-days forecast, four-times a day (Global Ensemble Forecast System of the National 1° × 1° with forecasts (00, 06, 12, 18 UTC), Center for Environmental Prediction) 21 ensemble members FNL 1° × 1° Operational Analysis (NCEP Final Operational Global Analysis data) Fig. 1. (a) Schematic diagram of the ensemble forecast data for dynamical downscaling. (b) Regional model domains. The color shading in Domain 3 (enlargement) indicates the topography [m] and black lines show the rivers. sponding to 6 h interval data (four times per day). event may depend on the model predictability. We The 49 sets of initial conditions were 12 days from assumed that both the ensemble spread (representing August 27 to September 7, plus 0000 UTC September uncertainty in the initial conditions) and lagged initial 8, resulting in 49 sets of initial conditions in total conditions (representing uncertainty due to prediction (i.e., 4 × 12 + 1). All the data of the 1,029 ensemble error) adequately represented uncertainty in the initial members from GEFS–NCEP were used as input data and boundary conditions for the dynamical downscal- for the regional model. ing experiment. It is likely that the calculated prob- Because the prediction ability of each forecast model ability of heavy rainfall would be different if other generally has limitations in terms of the synoptic fields parent forecast models or initial dates were selected. of the target event, these forecast data may contain However, because this was a pilot study, we used only model bias derived from errors in the particular model forecast data obtained from GEFS–NCEP to evaluate and its physical scheme. Therefore, the uncertainty (or the ability of large ensemble dynamical downscaling ensemble spread) of the synoptic fields at the target to assess a particular event. 390 Journal of the Meteorological Society of Japan Vol. 97, No. 2 Table 2. Names of the dynamical downscaling experiment. 3. Regional model settings and used data Input (initial condition and Experiment Number of To reproduce heavy rainfall events, we performed boundary data) for name experiments dynamical downscaling using the Weather Research the dynamical downscaling and Forecasting (WRF) model V3.5.1 (Skamarock DS-GEFS GEFS-NCEP 1029 et al. 2008). Each ensemble forecast output of GEFS– DS-FNL FNL 1 NCEP was used as initial conditions and boundary data for each of the 1,029 ensemble members during the target period, from 0000 UTC September 8 to of the typhoon center was determined as the grid 0000 UTC September 11, 2015 (hereafter, these cell with the lowest SLP below 1,000 hPa in the sub- ensemble experiments are referred to as DS–GEFS; sequent area.
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