Author:
Shimizu Shingo,Shimada Seiichi,Tsuboki Kazuhisa, , ,
Abstract
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.
Publisher
Fuji Technology Press Ltd.
Subject
Engineering (miscellaneous),Safety, Risk, Reliability and Quality
Reference33 articles.
1. 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
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
3. 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
4. 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
5. 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
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