Assimilation Impact of Different GPS Analysis Methods on Precipitation Forecast: A Heavy Rainfall Case Study of Kani City, Gifu Prefecture on July 15, 2010
Shingo Shimizu*,†, Seiichi Shimada**, and Kazuhisa Tsuboki***
*National Research Institute for Earth Science and Disaster Resilience (NIED)
3-1 Tennodai, Tsukuba City, Ibaraki, Japan
**Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
***Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
In this study, we examined variations in predicted precipitable water produced from different Global Positioning System (GPS) zenith delay methods, and assessed the corresponding difference in predicted rainfall after assimilating the obtained precipitable water data. Precipitable water data estimated from the GPS and three-dimensional horizontal wind velocity field derived from the X-band dual polarimetric radar were assimilated in CReSS and rainfall forecast experiments were conducted for the heavy rainfall system in Kani City, Gifu Prefecture on July 15, 2010. In the GPS analysis, a method to simultaneously estimate coordinates and zenith delay, i.e., the simultaneous estimation method, and a method to successively estimate coordinates and zenith delay, i.e., the successive estimation method, were used to estimate precipitable water. The differences generated from using predicted orbit data provided in pseudo-real time from the International GNSS (Global Navigation Satellite System) Service for geodynamics (IGS) versus precise orbit data released after a 10-day delay were examined. The change in precipitable water due to varying the analysis methods was larger than that due to the type of satellite orbit information. In the rainfall forecast experiments, those using the successive estimation method results had a better precision than those using the simultaneous estimation method results. Both methods that included data assimilation had higher rainfall forecast precisions than the forecast precision without precipitable water assimilation. Water vapor obtained from GPS analysis is accepted as important in rainfall forecasting, but the present study showed additional improvements can be attained from incorporating a zenith delay analysis method.
-  Y. H. Kuo, X. Zou, and Y.R. Guo, “Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model,” Part I: Moisture retrieval and sensitivity experiments. Mon. Wea. Rev., Vol.124, pp. 122-147, 1996. https://doi.org/10.1175/1520-0493(1996)124 <0122:VAOPWU=2.0.CO;2
-  H. Seko, T. Kawabata, H. Nakamura, K. Koizumi, and T. Iwabuchi,“Impact of GPS-derived water vapor and radial wind measured by Doppler radar on numerical prediction of precipitation,” J. Meteor. Soc. Japan, Vol.82, pp. 473-489, 2004. http://doi.org/10.2151/jmsj.2004.473
-  H. Nakamura, K. Koizumi, and N. Mannoji, “Data assimilation of GPS precipitable water into the JMA mesoscale numerical prediction model and its impact on rain fall forecast,” J. Meteor. Soc. Japan, Vol.82, pp. 441-452, 2004, http://doi.org/10.2151/jmsj.2004.441
-  K. Koizumi and Y. Sato, “Impact of GPS and TMI precipitable water data on mesoscale numerical weather prediction model forecasts,” J. Meteor. Soc. Japan, Vol.82, pp. 453-457, 2004, http://doi.org/10.2151/jmsj.2004.453
-  Y. Shoji, “A study of near real-time water vapor analysis using a nationalwide dense GPS network of Japan,” J. Meteor. Soc. Japan, Vol.87, pp. 1-18, 2009, http://doi.org/10.2151/jmsj.87.1
-  Y. Shoji, M. Kunii, and K. Saito, “Mesoscale data assimilation of Myanmar Cyclone Nargis. Part II: Assimilation of GPS-derived precipitable water vapor,” J. Meteor. Soc., Japan, Vol.89, pp. 67-88, 2011, http://doi.org/10.2151/jmsj.2011-105
-  Y. Shoji, H. Yamauchi, W. Mashiko, and E. Sato, “Estimation of local-scale precipitable water vapor distribution around each GNSS station using slant path delay,” SOLA, Vol.10, pp. 29-33, 2014, http://doi.org/10.2151/sola.2014-007
-  Y. Shoji, W. Mashiko, H. Yamauchi, and E. Sato, “Estimation of local-scale precipitable water vapor distribution around each GNSS station using slant path delay: Evaluation of a severe tornado case using high-resolution NHM,” SOLA, Vol.11, pp. 31-35, 2015, http://doi.org/10.2151/sola.2015-008
-  T. Kawabata, Y. Shoji, H. Seko, and K. Saito, “A numerical study on a mesoscale convective system over a subtropical island with 4D-Var assimilation of GPS slant total delays,” J. Meteor. Soc., Japan, Vol.91, pp. 705-721, 2013, http://doi.org/10.2151/jmsj.2013-510
-  H. Hatanaka, “Problems in near real-time GPS analysis,” Kisho-Kenkyu note, Vol.192, pp. 145-158, 1998 (in Japanese).
-  H. Tsuji, “Principles of GPS, Kisho-kenkyu note,” Vol.192, pp. 1-14, 1998 (in Japanese).
-  S. Shimada, S. Shimizu, and K. Tsuboki, “Impact of advanced ZTD estimation method – Separation from site coordinates estimation –,” Japan Geoscience Union Meeting, May 24th-28th, Makuhari, Japan: Available from, 2015, http://www2.jpgu.org/meeting/2015/session/PDF/M-TT05/MTT05-02_E.pdf [accessed Sep. 18, 2017]
-  T. Washimi, “Report of heavy rainfall around Kani city on July 2010,” Natural Disaster Science, Vol.29, No.2, pp. 259-267, 2010 (in Japanese).
-  Japan Meteorological Agency, “Heavy rainfall associated with Baiu front from 10 to 15 July, 2010,” 2010, available from http://www.data.jma.go.jp/obd/stats/data/bosai/report/2010/20100710/jyun_sokuji20100710-16.pdf [accessed Sep. 18, 2017]
-  M. Oue, K. Inagaki, T. Shinoda, T. Ohigashi, T. Kouketsu, M. Kato, K. Tsuboki, and H. Uyeda, “Polarimetric Doppler radar analysis of organization of a stationary rainband with changing orientations in July 2010.,” J. Meteor. Soc. Japan, Vol.92, pp. 457-481, 2014, http://doi.org/10.2151/jmsj.2014-503
-  T. Herring, R. King, M.A. Floyd, and S.C. McClusky, “Introduction to GAMIT/GLOBK release 10.6,” Massachusetts Institute of Technology, Cambridge, MA, 2015, available from http://www-gpsg.mit.edu/˜simon/gtgk/Intro_GG.pdf [accessed Sep. 18, 2017]
-  S. Shimada and K. Hioki, “Outline of GPS analysis,” Kisho-Kenkyu note, Vol.192, pp. 61-72, 1998 (in Japanese).
-  K. Hioki, S. Shimada, and R. Ohtani, “GPS software,” Kisho-Kenkyu note, Vol.192, pp. 73-92, 1998 (in Japanese).
-  R. Ohtani and I. Naito, “Physics in GPS precipitable water and its evaluation,” Kisho-Kenkyu note, Vol.192, pp. 15-34, 1998 (in Japanese).
-  H. Nakagawa, T. Toyofuku, K. Kotani, B. Miyahara, C. Iwashita, S. Kawamoto, Y. Hatanaka, H. Munekane, M. Ishimoto, and T. Yutsudo, “GEONET new analysis strategy (version 4),” J. Geogr. Surv. Inst., Vol.118, pp. 1-8, 2009 (in Japanese).
-  S. Shimizu and T. Maesaka, “Multiple Doppler radar analysis using variational technique to retrieve three-dimensional wind field,” Report of the National Research Institute for Earth Science and Disaster Prevention, Vol.70, pp. 1-8, 2007 (in Japanese), http://dil-opac.bosai.go.jp/publication/nied_report/PDF/70/70shimizu.pdf [accessed Sep. 18, 2017]
-  K. Shimose, S. Shimizu, T. Maesaka, R. Kato, K. Kieda, and K. Iwanami, “Impact of observation operators on low-level wind speed retrieved by variational multiple-Doppler analysis,” SOLA, Vol.12, pp. 215-219, 2016, http://doi.org/10.2151/sola.2016-043
-  K. Tsuboki and A. Sakakibara, “Large-scale parallel computing of cloud resolving storm simulator,” High Performance computing, Springer, H. P. Zima eds., pp. 243-259, 2002, http://www.rain.hyarc.nagoya-u.ac.jp/˜tsuboki/src/pdf_papers/ishpc2002_tsuboki.pdf [accessed Sep. 18, 2017]
-  S. Shimizu, H. Uyeda, Q. Moteki, T. Maesaka, M. Takaya, K. Akaeda, T. Kato, and M. Yoshizaki, “Structure and formation mechanism on 24 May 2000 supercell-like storm developing in a moist environment over the Kanto Plain,” Mon. Wea. Rev., Vol.136, pp. 2389-2407, 2008. https://doi.org/10.1175/2007MWR2155.1
-  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, https://doi.org/10.1175/MWR3120.1
-  Japan Meteorological Agency, “Outline of the operational numerical weather prediction at the Japan Meteorological Agency,” WMO Technical progress report on the global data-processing and forecasting system and numerical weather prediction, 2007, available from http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline-nwp/index.htm [accessed Sep. 18, 2017]
-  Japan Meteorological Agency, “Outline of the operational numerical weather prediction at the Japan Meteorological Agency,” WMO Technical progress report on the global data-processing and forecasting system and numerical weather prediction, 2013, available from http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp/index.htm [accessed Sep. 18, 2017]
-  Y. Kurihara, T. Sakurai, and T. Kuragano, “Global daily sea surface temperature analysis using data from satellite microwave radiometer,” satellite infrared radiometer and in-situ observations. Weather Bulletin, JMA, Vol.73, pp. s1-s18, 2006 (in Japanese).
-  D. M. Barker, W. Huang, Y.-R. Guo, 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, https://doi.org/10.1175/1520-0493(2004)132 <0897:ATVDAS= 2.0.CO;2
-  S. C. Bloom, L. L. Takacs, A. M. da Silva, and D. Ledvina, “Data assimilation using Incremental analysis updates.,” Mon. Wea. Rev., Vol.124, pp. 1256-1271, 1996, https://doi.org/10.1175/1520-0493(1996)124 <1256:DAUIAU= 2.0.CO;2
-  S. Shimizu, T. Kawabata, S. Yokota, H. Seko, M. Kunii, H. Yamauchi, K. Saito, Y. Shoji, S. Origuchi, D. Le, and K. Araki, “Development of data assimilation method,” Kisho-Kenkyu note, chapter 5, pp. 1-5, 2017 (in print, in Japanese).
-  J. T. Stauffer and N. L. Seaman, “Multiscale four-dimensional data assimilation,” J. Appl. Meteor., Vol.33, pp. 416-434, 1994, https://doi.org/10.1175/1520-0450(1994)033 <0416:MFDDA= 2.0.CO;2
-  Y. Ishikawa , “Use of ground-base GPS data for mesoscale analysis, Report of numerical,” Annual report of the Numerical Prediction Division of JMA, Vol.56, pp. 56-60, 2011 (in Japanese).