Assimilation Impact of Different GPS Analysis Methods on Precipitation Forecast: A Heavy Rainfall Case Study of Kani City, Gifu Prefecture on July 15, 2010

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.

[1]  David R. Stauffer,et al.  Multiscale four-dimensional data assimilation , 1994 .

[2]  K. Tsuboki.  Westward movement and splitting of an equatorial cloud cluster associated with tropical large-scale vortices , 1997 .

[3]  Atsushi Sakakibara,et al.  Large-Scale Parallel Computing of Cloud Resolving Storm Simulator , 2002, ISHPC.

[4]  K. Tsuboki,et al.  Mesoscale Cyclogenesis in Winter Monsoon Air Streams : Quasi-geostrophic Baroclinic Instability as a , 1992 .

[5]  Hajime Nakamura,et al.  Data assimilation of GPS precipitable water vapor into the JMA mesoscale numerical weather prediction model and its impact on rainfall forecasts , 2004 .

[6]  Wei Huang,et al.  A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results , 2004 .

[7]  Kazuo Saito,et al.  The Operational JMA Nonhydrostatic Mesoscale Model , 2006 .

[8]  Yoshimasa Takaya,et al.  Structure and Formation Mechanism on the 24 May 2000 Supercell-Like Storm Developing in a Moist Environment over the Kanto Plain, Japan , 2008 .

[9]  S. Shimada,et al.  A small persistent locked area associated with the 2011 Mw9.0 Tohoku‐Oki earthquake, deduced from GPS data , 2012 .

[10]  T. Herring,et al.  Introduction to GAMIT/GLOBK , 2006 .

[11]  Kazuo Saito,et al.  A Numerical Study on a Mesoscale Convective System over a Subtropical Island with 4D-Var Assimilation of GPS Slant Total Delays , 2013 .

[12]  H. Uyeda,et al.  Algorithm for the Identification and Tracking of Convective Cells Based on Constant and Adaptive Threshold Methods Using a New Cell-Merging and -Splitting Scheme , 2012 .

[13]  Lawrence L. Takacs,et al.  Data Assimilation Using Incremental Analysis Updates , 1996 .

[14]  Hajime Nakamura,et al.  The impact of atmospheric mountain lee waves on systematic geodetic errors observed using the Global Positioning System , 2002 .

[15]  Ying-Hwa Kuo,et al.  Variational Assimilation of Precipitable Water Using a Nonhydrostatic Mesoscale Adjoint Model. Part I: Moisture Retrieval and Sensitivity Experiments , 1996 .

[16]  Yoshiaki Sato,et al.  Impact of GPS and TMI Precipitable Water Data on Mesoscale Numerical Weather Prediction Model Forecasts , 2004 .

[17]  Ken-ichi Shimose,et al.  Impact of Observation Operators on Low-Level Wind Speed Retrieved by Variational Multiple-Doppler Analysis , 2016 .

[18]  Mariko Oue,et al.  Polarimetric Doppler Radar Analysis of Organization of a Stationary Rainband with Changing Orientations in July 2010 , 2014 .

[19]  Eiichi Sato,et al.  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 , 2015 .

[20]  Kazuo Saito,et al.  Mesoscale Data Assimilation of Myanmar Cyclone Nargis Part II: Assimilation of GPS-Derived Precipita , 2011 .

[21]  Hajime Nakamura,et al.  Impacts of GPS-derived Water Vapor and Radial Wind Measured by Doppler Radar on Numerical Prediction of Precipitation , 2004 .

[22]  気象庁.  Outline of the operational numerical weather prediction at the Japan Meteorological Agency , 1977 .

[23]  S. Shimada.  Comparison of the Coordinates Solutions Between the Absolute and the Relative Phase Center Variation Models in the Dense Regional GPS Network in Japan , 2012 .

[24]  Yoshinori Shoji,et al.  A Study of Near Real-time Water Vapor Analysis Using a Nationwide Dense GPS Network of Japan , 2009 .

[25]  Eiichi Sato,et al.  Estimation of Local-scale Precipitable Water Vapor Distribution Around Each GNSS Station Using Slant Path Delay , 2014 .