This study proposed a new blending approach for forecasting “senjo-kousuitai,” combining extrapolation-based nowcasting (EXT) and numerical weather prediction (NWP), to support early decision-making by municipalities regarding evacuation. A major deficiency in the short-term (1–2 h) operational forecasts of the Japan Meteorological Agency is underestimation of the precipitation area associated with senjo-kousuitai formation, which is mainly attributed to the EXT component. To address this problem, our blending approach emphasizes NWP using a cloud-resolving model for the 2-h forecast. A notable aspect of our approach involves incorporating a spatial maximum filter (MF) to account for spatial displacements between the EXT and the NWP outputs, replacing forecasted rainfall with the maximum value in the surrounding area. Compared with conventional blending methods, statistical verification of the results obtained using our proposed approach revealed marked improvements in both underestimation bias and probability of detection during the senjo-kousuitai formation stage. These findings highlight the potential of the MF-based approach for reducing forecast misses and facilitating timely municipal decision-making. The simplicity of the method also underscores its value as an urgently required disaster mitigation strategy against the increasing occurrence of senjo-kousuitai. However, the rise in false alarms, as a trade-off for fewer misses, implies an increase in the cost associated with protective actions. Although the proposed method entails increased costs, adopting this approach can be a cost-effective strategy for preserving lives by mitigating misses.

1. [1] J. M. Fritsch and R. E. Carbone, “Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy,” Bull. Am. Meteorol. Soc., Vol.85, No.7, pp. 955-966, 2004. https://doi.org/10.1175/BAMS-85-7-955
2. [2] K. Nagata, “Quantitative precipitation estimation and quantitative precipitation forecasting by the Japan Meteorological Agency,” RSMC Tokyo - Typhoon Center Technical Review, No.13, pp. 37-50, 2011.
3. [3] J. Sun et al., “Use of NWP for nowcasting convective precipitation: Recent progress and challenges,” Bull. Am. Meteorol. Soc., Vol.95, No.3, pp. 409-426, 2014. https://doi.org/10.1175/BAMS-D-11-00263.1
4. [4] A. Atencia et al., “Improving QPF by blending techniques at the Meteorological Service of Catalonia,” Nat. Hazards Earth Syst. Sci., Vol.10, No.7, pp. 1443-1455, 2010. https://doi.org/10.5194/nhess-10-1443-2010
5. [5] C. Radhakrishnan and V. Chandrasekar, “CASA prediction system over Dallas–Fort Worth urban network: Blending of nowcasting and high-resolution numerical weather prediction model.” J. Atmos. Ocean. Technol., Vol.37, No.2, pp. 211-228, 2020. https://doi.org/10.1175/JTECH-D-18-0192.1
6. [6] K. Saito, M. K. Hung, and D. D. Tien, “Development of a prototype system of the very short-range forecast of precipitation in Vietnam.” J. Hydro-Meteorol., Vol.15, pp. 59-79, 2023. https://doi.org/10.36335/VNJHM.2023(15).59-79
7. [7] Y. Hwang, A. J. Clark, V. Lakshmanan, and S. E. Koch, “Improved nowcasts by blending extrapolation and model forecasts,” Weather Forecast., Vol.30, No.5, pp. 1201-1217, 2015. https://doi.org/10.1175/WAF-D-15-0057.1
8. [8] R. S. Schumacher and R. H. Johnson, “Organization and environmental properties of extreme-rain-producing mesoscale convective systems,” Mon. Weather Rev., Vol.133, No.4, pp. 961-976, 2005. https://doi.org/10.1175/MWR2899.1
9. [9] T. Unuma and T. Takemi, “Characteristics and environmental conditions of quasi-stationary convective clusters during the warm season in Japan.” Q. J. R. Meteorol. Soc., Vol.142, No.696, pp. 1232-1249, 2016. https://doi.org/10.1002/qj.2726
10. [10] T. Kato, “Quasi-stationary band-shaped precipitation systems, named ‘senjo-kousuitai’, causing localized heavy rainfall in Japan,” J. Meteorol. Soc. Jpn, Ser. II, Vol.98, No.3, pp. 485-509, 2020. https://doi.org/10.2151/jmsj.2020-029
11. [11] Y. Hirockawa, T. Kato, H. Tsuguti, and N. Seino, “Identification and classification of heavy rainfall areas and their characteristic features in Japan,” J. Meteorol. Soc. Jpn., Ser. II, Vol.98, No.4, pp. 835-857, 2020. https://doi.org/10.2151/jmsj.2020-043
12. [12] Japan Meteorological Agency, “What is the weather information on significant heavy rainfall?” (in Japanese). https://www.jma.go.jp/jma/kishou/know/bosai/kishojoho_senjoukousuitai.html#b [Accessed November 27, 2023]
13. [13] Japan Meteorological Agency, “Improvement of very short-range forecasting of precipitation,” Technical Information, 2019 (in Japanese). https://www.jma.go.jp/jma/kishou/books/yohkens/24/chapter7.pdf [Accessed November 27, 2023]
14. [14] D. Hatsuzuka, R. Kato, S. Shimizu, and K. Shimose, “Verification of forecasted three-hour accumulated precipitation associated with ‘senjo-kousuitai’ from very-short-range forecasting operated by the JMA,” J. Meteorol. Soc. Jpn., Ser. II, Vol.100, No.6, pp. 995-1005, 2022 (in Japanese). https://doi.org/10.2151/jmsj.2022-052
15. [15] Cabinet Office, Government of Japan, “About the heavy rainfall disaster in northern Kyushu,” White Paper on Disaster Management, pp. 9-29, 2018 (in Japanese). https://www.bousai.go.jp/kaigirep/hakusho/pdf/H30_tokushu2.pdf [Accessed March 29, 2024]
16. [16] T. Fukuhara, K. Takami, and Y. Kamata, “Methods to evaluate predicted rainfall to prevent underestimation of inundation prediction in small-scaled rivers,” RTRI Rep., Vol.32, No.7, pp. 5-10, 2018 (in Japanese with English abstract).
17. [17] R. Kato et al., “Improvement of two-hour-ahead QPF using blending technique with spatial maximum filter for tolerating forecast displacement errors and water vapor lidar assimilation,” J. Meteorol. Soc. Jpn., Ser. II, Vol.102, No.4, pp. 445-464, 2024. https://doi.org/10.2151/jmsj.2024-024
18. [18] H. Godo, M. Naito, and S. Tsuchiya, “Improvement of the observation accuracy of X-band dual polarimetric radar by expansion of the condition to use KDP-R relationship,” J. Jpn. Soc. Civ. Eng., Ser. B1 (Hydraul. Eng.), Vol.70, No.4, pp. I_505-I_510, 2014 (in Japanese). https://doi.org/10.2208/jscejhe.70.I_505
19. [19] S. Kigawa, “Techniques of precipitation analysis and prediction for high-resolution precipitation nowcasts,” 2015. https://www.jma.go.jp/jma/en/Activities/Techniques_of_Precipitation_Analysis_and_Prediction_developed_for_HRPNs.pdf [Accessed November 27, 2023]
20. [20] K. Tsuboki and A. Sakakibara, “Large-scale parallel computing of cloud resolving storm simulator,” Proc. of the 4th Int. Symp. on High Performance Computing 2002 (ISHPC 2002), pp. 243-259, 2002. https://doi.org/10.1007/3-540-47847-7_21
21. [21] R. Kato, K. Shimose, and S. Shimizu, “Predictability of precipitation caused by linear precipitation systems during the July 2017 Northern Kyushu heavy rainfall event using a cloud-resolving numerical weather prediction model,” J. Disaster Res., Vol.13, No.5, pp. 846-859, 2018. https://doi.org/10.20965/jdr.2018.p0846
22. [22] T. Kawano and R. Kawamura, “Genesis and maintenance processes of a quasi-stationary convective band that produced record-breaking precipitation in Northern Kyushu, Japan on 5 July 2017,” J. Meteorol. Soc. Jpn., Ser. II, Vol.98, No.4, pp. 673-690, 2020. https://doi.org/10.2151/jmsj.2020-033
23. [23] K. Shimose, S. Shimizu, R. Kato, and K. Iwanami, “Analysis of the 6 September 2015 tornadic storm around the Tokyo Metropolitan area using coupled 3DVAR and incremental analysis updates,” J. Disaster Res., Vol.12, No.5, pp. 956-966, 2017. https://doi.org/10.20965/jdr.2017.p0956
24. [24] Japan Meteorological Agency, “Outline of the operational numerical weather prediction at the Japan Meteorological Agency,” 2019. https://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2019-nwp/pdf/outline2019_all.pdf [Accessed November 28, 2023]
25. [25] S. Shimizu, R. Kato, and T. Maesaka, “Predictability of quasi-stationary line-shaped precipitation system causing heavy rainfall around Saga Pref. on 28th August 2019,” Nat. Disaster Res. Rep. Natl. Res. Inst. Earth Sci. Disaster Resil., No.56, pp. 11-23, 2022 (in Japanese with English abstract). https://doi.org/10.24732/NIED.00002406
26. [26] Y. Hirockawa, T. Kato, K. Araki, and W. Mashiko, “Characteristics of an extreme rainfall event in Kyushu District, southwestern Japan in early July 2020,” SOLA, Vol.16, pp. 265-270, 2020. https://doi.org/10.2151/sola.2020-044
27. [27] D. S. Wilks, “Statistical Methods in the Atmospheric Sciences,” 3rd Edition, Elsevier, 2011.
28. [28] P. J. Roebber, “Visualizing multiple measures of forecast quality,” Weather Forecast., Vol.24, No.2, pp. 601-608, 2009. https://doi.org/10.1175/2008WAF2222159.1
29. [29] R. de Elía, “The false alarm/surprise trade-off in weather warnings systems: An expected utility theory perspective,” Environ. Syst. Decis., Vol.42, No.3, pp. 450-461, 2022. https://doi.org/10.1007/s10669-022-09863-1
30. [30] A. Lopez et al., “Bridging forecast verification and humanitarian decisions: A valuation approach for setting up action-oriented early warnings,” Weather Clim. Extrem., Vol.27, Article No.100167, 2020. https://doi.org/10.1016/j.wace.2018.03.006
31. [31] R. de Elía et al., “Early warning systems and end-user decision-making: A risk formalism tool to aid communication and understanding,” Risk Analysis, Vol.44, No.5, pp. 1128-1142, 2024. https://doi.org/10.1111/risa.14221
32. [32] Y. Maejima, T. Kawabata, H. Seko, and T. Miyoshi, “Observing system simulation experiments of a rich phased array weather radar network covering Kyushu for the July 2020 heavy rainfall event,” SOLA, Vol.18, pp. 25-32, 2022. https://doi.org/10.2151/sola.2022-005
33. [33] S. Yoshida et al., “Lidar observations and data assimilation of low-level moist inflows causing severe local rainfall associated with a mesoscale convective system,” Mon. Weather Rev., Vol.150, No.7, pp. 1781-1798, 2022. https://doi.org/10.1175/MWR-D-21-0213.1
34. [34] S. Yoshida et al., “Water vapor lidar observation and data assimilation for a moist low-level jet triggering a mesoscale convective system,” Mon. Weather Rev., Vol.152, No.4, pp. 1119-1137, 2024. https://doi.org/10.1175/MWR-D-23-0094.1
35. [35] L. Duc, T. Kawabata, K. Saito, and T. Oizumi, “Forecasts of the July 2020 Kyushu heavy rain using a 1000-member ensemble Kalman filter,” SOLA, Vol.17, pp. 41-47, 2021. https://doi.org/10.2151/sola.2021-007