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Fast Kalman Processing of Carrier-Phase Signals from GNSS for Water Vapor Tomography 1st Colloquium Scientific and Fundamental Aspects of the Galileo Programme CNES, Toulouse 1-4 Oct. 2007.
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Fast Kalman Processing of Carrier-Phase Signals from GNSS for Water Vapor Tomography1st Colloquium Scientific and Fundamental Aspects of the Galileo ProgrammeCNES, Toulouse 1-4 Oct. 2007 Antti Lange PhDFinnish Meteorological Institute (FMI)Observation ServicesP. O. Box 503, 00101 Helsinki 10, FinlandAntti.Lange@fmi.fi
Outline:- SuomiNet (USA) and tomography- Helmert-Wolf blocking (HWb) method - Fast Kalman processing- Quality Control (QC) by estimating errors- Concluding remarks
Professor V. E. Suomi, University of Wisconsin, Madison”Father of Satellite Meteorology”
Location of Network Location of L1 network around ARM SGP Central Facility NOAA GPS network with ARM SGP Region
Volume Display of Satellite Ray Paths 5 minutes of observations at 30 second epochs
Simulated Tomography Layers Tomography Estimate Input Field
The national Virtual Reference Station (VRS) network of dual-frequency GPS receivers in Finland
1-band GPS-receiver from Vaisala radiosonde: • GPS Receiver iTrax03 is the size of a stamp 26x26x4.7mm • Ultra-low power consumption • Low cost • Carrier phase detection feasible for GPS positioning of high-precision applications
Error Covariances of the Helmert-Wolf blocking (HWB), in 1982, were given by:
Accuracy estimation based on internal consistency of raw observations, 1970: = unbiased standard errors
The mass pile-up of 17 March 2005 (air temperature -60C, 300 cars wrecked, 70 injuries and 3 deaths):
Concluding remarks: - GNSS tomography detects water vaporunlike weather radar - Galileo signals will improve vertical resolution of tomography - The FKF processing is based on the HWb method for real-time applications i.e. VRS2MOBILE - The HWb method was extended to making reliable estimates (C.R.Rao’s MINQUE) of operational accuracy for nowcasting, prediction, navigation and tsunami-buoy applications
Optimal Kalman Filtering: Measurement Equation: yt = Htst + Fytct + et for t = 1, 2,... System Equation: st = At st-1 + Btut-1 + Fstct + at for t = 1, 2,... where ct = the vector representing calibration and model errors that are constant over several time points t.
Diagonalization of the observation and forecast error covariances, 1989:
Stability of Kalman Filtering: st andctmust be observable • These correlations can factored with the help of matrices Fyt and Fst by using Singular Value Decomposition (SVD) and Canonical Correlation techniques. utmust be controllable etand atmust neitherauto- norcross-correlate!
Meteorological R&D on the use of ground-based GPS signals in Europe: n EU COST Action 716: project ended March 2004 n FP5 TOUGH: Towards Optimal Use of GPS data for Humidity measurements, continues n E-GVAP (EUCOS GPS water VAPour): e.g.Forward modeling of the GPS signal delays for NWP by FMI n GPS /GALILEO water vapourtomography: raw GPSdatafrom denseVirtual Reference Station (VRS) landsurvey networks etc. 5.2.02