DOI: 10.1002/2013JD020763
论文题名: A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility
作者: Li Y. ; Kirchengast G. ; Scherllin-Pirscher B. ; Wu S. ; Schwaerz M. ; Fritzer J. ; Zhang S. ; Carter B.A. ; Zhang K.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 23 起始页码: 13022
结束页码: 13040
语种: 英语
英文关键词: Dynamic Algorithm
; GNSS Radio Occultation
; Statistical Optimization
Scopus关键词: Algorithms
; Covariance matrix
; Global positioning system
; Radio
; Sensors
; Statistics
; Weather forecasting
; Dynamic algorithm
; European centre for medium-range weather forecasts
; Global Navigation Satellite Systems
; Radio occultations
; Refractivity profiles
; Satellite remote sensing
; Statistical optimization
; Statistical optimization algorithms
; Optimization
; algorithm
; CHAMP
; climate modeling
; COSMIC
; data set
; environmental monitoring
; error analysis
; GNSS
; MetOp
; optimization
; satellite data
; satellite imagery
; uncertainty analysis
; weather forecasting
英文摘要: Global Navigation Satellite System (GNSS)-based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium-range Weather Forecasts (ECMWF) short-range forecast and analysis fields, together with averaged-observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records. Key Points New dynamical statistical optimization algorithm for GNSS radio occultation Allows for geographically variable and time-dependent error characteristics Improves the quality of atmospheric profiles compared to existing algorithms ©2013. American Geophysical Union. All Rights Reserved. 资助项目: LP0883288
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63111
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: Satellite Positioning for Atmosphere, Climate, and Environment (SPACE) Research Centre, RMIT University, Melbourne VIC, Australia; Wegener Center for Climate and Global Change (WEGC), Institute for Geophysics, Astrophysics, and Meteorology/Institute of Physics (IGAM/IP), University of Graz, Graz, Austria
Recommended Citation:
Li Y.,Kirchengast G.,Scherllin-Pirscher B.,et al. A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(23)