JDR Vol.12 No.5 pp. 967-979
doi: 10.20965/jdr.2017.p0967


Very Short Time Range Forecasting Using CReSS-3DVAR for a Meso-γ-Scale, Localized, Extremely Heavy Rainfall Event: Comparison with an Extrapolation-Based Nowcast

Ryohei Kato, Shingo Shimizu, Ken-ichi Shimose, and Koyuru Iwanami

Storm, Flood and Landslide Research Division, National Research Institute for Earth Science and Disaster Resilience (NIED)
3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan

Corresponding author

April 3, 2017
September 19, 2017
Online released:
September 27, 2017
October 1, 2017
meso-γ-scale extreme heavy rainfall, numerical weather prediction, extrapolation-based nowcast, blending, predictability

The forecast accuracy of a numerical weather prediction (NWP) model for a very short time range (≤1 h) for a meso-γ-scale (2–20 km) extremely heavy rainfall (MγExHR) event that caused flooding at the Shibuya railway station in Tokyo, Japan on 24 July 2015 was compared with that of an extrapolation-based nowcast (EXT). The NWP model used CReSS with 0.7 km horizontal grid spacing, and storm-scale data from dense observation networks (radars, lidars, and microwave radiometers) were assimilated using CReSS-3DVAR. The forecast accuracy of the heavy rainfall area (≥20 mm h-1), as a function of forecast time (FT), was investigated for the NWP model and EXT predictions using the fractions skill score (FSS) for various spatial scales of displacement error (L). These predictions were started 30 minutes before the onset of extremely heavy rainfall at Shibuya station. The FSS for L=1 km, i.e., grid-scale verification, showed NWP accuracy was lower than that of EXT before FT=40 min; however, NWP accuracy surpassed that of EXT from FT=45 to 60 min. This suggests the possibility of seamless, high-accuracy forecasts of heavy rainfall (≥20 mm h-1) associated with MγExHR events within a very short time range (≤1 h) by blending EXT and NWP outputs. The factors behind the fact that the NWP model predicted heavy rainfall area within the very short time range of ≤1 h more correctly than did EXT are also discussed. To enable this discussion of the factors, additional sensitivity experiments with a different assimilation method of radar reflectivity were performed. It was found that a moisture adjustment above the lifting condensation level using radar reflectivity was critical to the forecasting of heavy rainfall near Shibuya station after 25 min.

Cite this article as:
R. Kato, S. Shimizu, K. Shimose, and K. Iwanami, “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., Vol.12 No.5, pp. 967-979, 2017.
Data files:
  1. [1] A. Kato and M. Maki, “Localized heavy rainfall near Zoshigaya, Tokyo, Japan on 5 August 2008 observed by X-band polarimetric radar – Preliminary analysis –,” Sola, Vol.5, pp. 89-92, 2009.
  2. [2] D. S. Kim, M. Maki, S. Shimizu, and D. I. Lee, “X-Band dual-polarization radar observations of precipitation core development and structure in a multi-cellular storm over Zoshigaya, Japan, on August 5, 2008,” J. Meteor. Soc. Japan, Vol.90, pp. 701-719, 2012.
  3. [3] C. Pierce, A. Seed, S. Ballard, D. Simonin, and Z. Li, “Nowcasting. Doppler radar observations – weather radar, wind profiler,” in ionospheric radar, and other advanced applications, Bech J and Chau JL (Eds.), Rijeka: InTech, pp. 97-142, 2012.
  4. [4] S. P. C., R. Misumi, T. Nakatani, K. Iwanami, M. Maki, A. W. Seed, and K. Hirano, “Comparison of rainfall nowcasting derived from the STEPS model and JMA precipitation nowcasts,” Hydrol. Res. Lett., Vol.9, pp. 54-60, 2015.
  5. [5] J. Sun, M. Xue, J. W. Wilson, I. Zawadzki, S. P. Ballard, J. Onvlee-Hooimeyer, P. Joe, D. M. Barker, P. W. Li, B. Golding, M. Xu, and J. Pinto, “Use of NWP for nowcasting convective precipitation: Recent progress and challenges,” Bull. Am. Meteorol. Soc., Vol.95, pp. 409-426, 2014.
  6. [6] Y. Hwang, A. J. Clark, V. Lakshmanan, and S. E. Koch, “Improved nowcasts by blending extrapolation and model forecasts,” Wea. Forecasting, Vol.30, pp. 1201-1217, 2015.
  7. [7] R. Kato, S. Shimizu, K. Shimose, T. Maesaka, K. Iwanami, and H. Nakagaki, “Predictability of meso-γ-scale, localized, extreme, heavy rainfall during the warm season in Japan using high-resolution precipitation nowcasts,” Q. J. R. Meteorol. Soc., Vol.143, pp. 1406-1420, 2017.
  8. [8] S. Kigawa, “Techniques of precipitation analysis and prediction for high-resolution precipitation nowcasts,” p. 15, 2014, available online at [accessed March 29, 2017]
  9. [9] S. Kigawa, “Techniques of precipitation analysis and prediction for high-resolution precipitation nowcasts,” Weather service bulletin, Vol.81, p. 22, 2014 (in Japanese), available online at [accessed March 29, 2017]
  10. [10] N. M. Roberts and H. W. Lean, “Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events,” Mon. Wea. Rev., Vol.136, pp. 78-97, 2008.
  11. [11] K. Nagata, “Quantitative precipitation estimation and quantitative precipitation forecasting by the Japan meteorological agency,” RSMC Tokyo Typhoon Center Technical Review, Vol.13, pp. 37-50, 2011, available online at [accessed March 29, 2017]
  12. [12] T. Kawabata, H. Iwai, H. Seko, Y. Shoji, K. Saito, S. Ishii, and K. Mizutani, “Cloud-resolving 4D-var assimilation of Doppler wind lidar data on a meso-gamma-scale convective system,” Mon. Wea. Rev., Vol.142, pp. 4484-4498, 2014.
  13. [13] T. Miyoshi, M. Kunii, J. Ruiz, G. Lien, S. Satoh, T. Ushio, K. Bessho, H. Seko, H. Tomita, and Y. Ishikawa, ““Big data assimilation” revolutionizing severe weather prediction,” Bull. Amer. Meteor. Soc., Vol.97, pp. 1347-1354, 2016.
  14. [14] K. Tsuboki and A. Sakakibara, “Large-scale parallel computing of cloud resolving storm simulator,” in Lecture Notes in Computer Science, High Performance Computing, ISHPC 2002, Vol.2327, H. P. Zima, K. Joe, M. Sato, Y. Seo, and M. Shimasaki (Eds.), Berlin: Springer, pp. 243-259, 2002.
  15. [15] K. Tsuboki and A. Sakakibara, “Numerical prediction of high-impact weather systems,” The text book for Seventeenth IHP training course in 2007, HyARC, Nagoya University, Japan, and UNESCO, p. 281, 2007.
  16. [16] T. Maesaka, M. Maki, K. Iwanami, S. Tsuchiya, K. Kieda, and A. Hoshi, “Operational rainfall estimation by X-band MP radar network in MLIT, Japan,” Preprints, 35th Conf. on Radar Meteor., Pittsburgh, PA, Amer. Meteor. Soc., 29 Sep. 2011.
  17. [17] F. Solheim, J. R. Godwin, E. R. Westwater, Y. Han, S. J. Keihm, K. Marsh, and R. Ware, “Radiometric profiling of temperature, water vapor and cloud liquid water using various inversion methods,” Radio Sci., Vol.33, pp. 393-404, 1998.
  18. [18] M. Murakami, T. L. Clark, and W. D. Hall, “Numerical simulation of convective snow clouds over the Sea of Japan; two-dimensional simulations of mixed layer development and convective snow cloud formation,” J. Meteor. Soc. Japan, Vol.72, pp. 43-62, 1994.
  19. [19] J. W. Deardorff, “Three-dimensional numerical study of the height and mean structure of a heated planetary boundary layer,” Boundary-Layer Meteor., Vol.7, pp. 81-106, 1974.
  20. [20] J. W. Deardorff, “Stratocumulus-capped mixed layers derived from a three-dimensional model,” Boundary-Layer Meteor., Vol.18, pp. 495-527, 1980.
  21. [21] J. F. Louis, M. Tiedtke, and J. F. Geleyn, “A short history of the operational PBL parameterization at ECMWF,” Preprints, Workshop on Planetary Boundary Layer Parameterization, Shinfield Park, Reading, ECMWF, 25-27 Nov. 1981.
  22. [22] J. Kondo, “Air-sea bulk transfer coefficients in diabatic conditions,” Boundary-Layer Meteor., Vol.9, pp. 91-112, 1975.
  23. [23] A. Segami, K. Kurihara, H. Nakamura, M. Ueno, I. Takano, and Y. Tatsumi, “Operational mesoscale weather prediction with Japan Spectral Model,” J. Meteor. Soc. Japan, Vol.67, pp. 907-924, 1989.
  24. [24] D. M. Barker, W. Huang, Y.-R. Guo, and A. Bourgeois, “A three-dimensional variational (3DVAR) data assimilations system for use with MM5,” NCAR Tech. Note. NCAR/TN-453 + STR, NCAR, p. 68, 2003.
  25. [25] D. M. Barker, W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao, “A three-dimensional variational data assimilation system for MM5: implementation and initial results,” Mon. Wea. Rev., Vol.132, pp. 897-914, 2004.
  26. [26] D. P. Dee and A. M. Da Silva, “The Choice of Variable for Atmospheric Moisture Analysis,” Mon. Wea. Rev., Vol.131, pp. 155-171, 2003.
  27. [27] T. Kawabata, H. Seko, K. Saito, T. Kuroda, K. Tamiya, T. Tsuyuki, Y. Honda, and Y. Wakazuki, “An assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensional variational data assimilation system,” J. Meteor. Soc. Japan, Vol.85, pp. 255-276, 2007.
  28. [28] J. Gao et al., “A realtime weather-adaptive 3DVAR analysis system for severe weather detections and warnings with automatic storm positioning capability,” Wea. Forecasting, Vol.28, pp. 727-745.
  29. [29] P. Courtier and J. N. Thépaut, and A. Hollingsworth, “A strategy for operational implementation of 4D-Var, using an incremental approach,” Quart. J. Roy. Meteor. Soc., Vol.120, pp. 1367-1388, 1994.
  30. [30] P. Courtier, “Dual formulation of four-dimensional variational assimilation,” Quart. J. Roy. Meteor. Soc., Vol.123, pp. 2449-2461, 1997.
  31. [31] X. Zou, I. M. Navon, M. Berger, K. H. Phua, T. Schlick, and F. X. Le Dimet, “Numerical experience with limited-memory quasi-Newton and truncated Newton methods,” SIAM J. on Optimization, Vol.3, pp. 582-608, 1993.
  32. [32] A. J. Koscielny, R. J. Doviak, and R. Rabin, “Statistical considerations in the estimation of divergence from single-Doppler radar and application of prestorm boundary-layer observations,” J. Appl. Meteor., Vol.21, pp. 197-210, 1982.
  33. [33] R. R. Rogers, “An extension of the ZR relation for Doppler radar,” Preprints, 11th Conf. on Radar Meteor., Boulder, CO, Amer. Meteor. Soc., pp. 158-161, 1964.
  34. [34] P. W. Baker and M. C. Hodson, “Effect of deviations from the Marshall-Palmer drop size distribution on the calculation of vertical air velocity by Rogers’s method,” Vol.24, pp. 495-498, 1985.
  35. [35] A. Lorenc, “Iterative analysis using covariance functions and filters,” Quart. J. Roy. Meteor. Soc., Vol.118, pp. 569-591, 1992.
  36. [36] C. M. Hayden and R. J. Purser, “Recursive filter objective analysis of meteorological fields: application to NESDIS operational processing,” J. Appl. Meteor., Vol.34, pp. 3-15, 1995.
  37. [37] R. J. Purser, W.-S. Wu, D. F. Parrish, and N. M. Roberts, “Numerical aspects of the application of recursive filter to variational statistical analysis. Part I: spatially homogeneous and isotropic Gaussian covariance,” Mon. Wea. Rev., Vol.131, pp. 1524-1535, 2003.
  38. [38] D. F. Parrish and J. C. Derber, “The National Meteorological Center’s spectral statistical-interpolation analysis system,” Mon. Wea. Rev., Vol.120, pp. 1747-1763, 1992.
  39. [39] Y. Lin, P. S. Ray, and K. W. Johnson, “Initialization of a modeled convective storm using Doppler radar-derived fields,” Mon. Wea. Rev., Vol.121, pp. 2757-2775, 1993.
  40. [40] S. S. Weygandt, A. Shapiro, and K. K. Droegemeier, “Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part II: Thermodynamic retrieval and numerical prediction,” Mon. Wea. Rev., Vol.130, pp. 454-476, 2002.
  41. [41] Y. Liou, J. Chiou, W. Chen, and H. Yu, “Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations,” Mon. Wea. Rev., Vol.142, pp. 4017-4035, 2014.
  42. [42] M. Hu, M. Xue, and K. Brewster, “3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact,” Mon. Wea. Rev., Vol.134, pp. 675-698, 2006.
  43. [43] O. Caumont, V. Ducrocq, É. Wattrelot, G. Jaubert, and S. Pradier-Vabre, “1D+3DVar assimilation of radar reflectivity data: A proof of concept,” Tellus, Vol.62A, pp. 173-187, 2010.
  44. [44] E. Wattrelot, O. Caumont, J. F. Mahfouf, “Operational implementation of the 1D+3D-Var assimilation method of radar reflectivity data in the AROME model,” Mon. Weather Rev., Vol 142, pp. 1852-1873, 2014.
  45. [45] J. Gao and D. J. Stensrud, “Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification,” J. Atmos. Sci., Vol.69, pp. 1054-1065, 2012.
  46. [46] P. L. Jr. Smith, C. G. Myers, and H. D. Orville, “Radar reflectivity factor calculations in numerical cloud models using bulk parameterization of precipitation processes,” J. Appl. Meteor., Vol.14, pp. 1156-1165, 1975.
  47. [47] D. Hauser and P. Ameyanc, “Retrieval of cloud water and water vapor contents from Doppler radar data in a tropical squall line: Application to the case of a tropical squall line,” J. Atmos. Sci., Vol.43, pp. 823-838, 1986.
  48. [48] K. Saito, T. Fujita, Y. Yamada, J. Ishida, Y. Kumagai, K. Aranami, S. Ohmori, R. Nagasawa, S. Kumagai, C. Muroi, T. Kato, H. Eito, and Y. Yamazaki, “The operational JMA nonhydrostatic mesoscale model,” Mon. Wea. Rev., Vol.134, pp. 1266-1298, 2006.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 19, 2024