A variational data assimilation system for ground‐based GPS slant delays

Tropospheric delay affects the propagation of the microwave signals broadcast by the Global Positioning System (GPS) satellites. Geodetic processing software enable estimation of this effect on the slanted signal paths connecting the satellites with the ground-based receivers. These estimates are called slant delay (SD) observations and they are potentially of benefit in numerical weather prediction. The three-dimensional variational data assimilation system of the High-Resolution Limited-Area Model (HIRLAM 3D-Var) has been modified for data assimilation of the SD observations. This article describes the ground-based GPS observing system, the SD observation operator, the estimation of the observation- and background-error standard deviations, the methodology of accounting for the observation-error correlation, and the tuning of the background quality control for this observation type. The SD data assimilation scheme is evaluated with experiments utilizing hypothetical observations from a single receiver station, as well as a single-case experiment utilizing real observations from a regional GPS receiver network. The ability of the data assimilation system to extract the asymmetric information from the SD observations is confirmed. In terms of analysis increment structure and magnitude, SD observations are found to be comparable with other observation types currently in use, provided that the observation-error correlation is taken into account. Copyright © 2007 Royal Meteorological Society

[1]  Pedro Elosegui.  Accuracy Assessment of GPS Slant-Path Determinations , 2003 .

[2]  J. Rantakokko,et al.  Three-dimensional variational data assimilation for a limited area model : Part II: Observation handling and assimilation experiments , 2001 .

[3]  Nils Gustafsson,et al.  Four-dimensional variational data assimilation for a limited area model , 2001 .

[4]  H. van der Marel,et al.  Comparison of GPS slant delay measurements to a numerical model: case study of a cold front passage , 2002 .

[5]  Heikki Järvinen,et al.  Variational assimilation of time sequences of surface observations with serially correlated errors , 1999 .

[6]  Azimuthal asymmetry in ground-based GPS slant delay observations and their NWP model counterparts , 2007 .

[7]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[8]  Xiaolei Zou,et al.  A Case Study of the Variational Assimilation of GPS Zenith Delay Observations into a Mesoscale Model , 2001 .

[9]  Nils Gustafsson,et al.  Three-dimensional variational data assimilation for a limited area model : Part I: General formulation and the background error constraint , 2001 .

[10]  D. J. Allerton,et al.  Book Review: GPS theory and practice. Second Edition, HOFFMANNWELLENHOFF B., LICHTENEGGER H. and COLLINS J., 1993, 326 pp., Springer, £31.00 pb, ISBN 3-211-82477-4 , 1995 .

[11]  Steven Businger,et al.  GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water , 1994 .

[12]  S. Healy.  Radio occultation bending angle and impact parameter errors caused by horizontal refractive index gradients in the troposphere: A simulation study , 2001 .

[13]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[14]  S. Koch,et al.  The Influence of Mesoscale Humidity and Evapotranspiration Fields on a Model Forecast of a Cold-Frontal Squall Line , 1997 .

[15]  Alexander E. MacDonald,et al.  Diagnosis of Three-Dimensional Water Vapor Using a GPS Network , 2002 .

[16]  Rossella Ferretti,et al.  GPS PW assimilation into MM5 with the nudging technique , 2001 .

[17]  Gunnar Elgered,et al.  COST 716: Exploitation of ground-based GPS for operational numerical weather prediction and climate applications , 2005 .

[18]  Heikki Järvinen,et al.  An observation operator for ground-based GPS slant delays , 2006 .

[19]  Henrik Vedel,et al.  Impact of Ground Based GPS Data on Numerical Weather Prediction , 2004 .

[20]  T. Herring,et al.  GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System , 1992 .

[21]  Rosemary Munro,et al.  Diagnosis of background errors for radiances and other observable quantities in a variational data assimilation scheme, and the explanation of a case of poor convergence , 2000 .

[22]  Heikki Järvinen,et al.  Estimation of Spatial Global Positioning System Zenith Delay observation error covariance , 2005 .

[23]  Gyu-Ho Lim,et al.  Variational assimilation of slant-path wet delay measurements from a hypothetical ground-based GPS network. Part I: Comparison with precipitable water assimilation , 2003 .

[24]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[25]  Heikki Järvinen,et al.  Variational quality control , 1999 .