Precise Orbit Determination and Radio Occultation Retrieval Processing at the UCAR CDAAC: Overview and Results

Transcription

1 Precise Orbit Determination and Radio Occultation Retrieval Processing at the UCAR CDAAC: Overview and Results Bill Schreiner B. Kuo, C. Rocken, S. Sokolovskiy, D. Hunt, X. Yue, K. Hudnut, M. Sleziak, T.K. Wee UCAR COSMIC Program Office

2 Challenges for defining the Global Temperature Trend Satellites: changing pla/orms and instruments (diurnal cycle sampling, orbital decay); contribu;on of lower stratospheric to midtropospheric temperature es;mates. Radiosondes: changing instruments and observa;on prac;ces; limited spa;al coverage especially over the oceans. We need measurements with high precision, high accuracy, long term stability, reasonably good temporal and spa;al coverage as climate benchmark observa;ons. Copy right UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

3 Outline COSMIC and CDAAC Overview POD Overview and Results RO Precision Results RO Retrieval Overview and Results Neutral Atmosphere Ionosphere

4 COSMIC at a Glance Constellation Observing System for Meteorology Ionosphere and Climate 6 Satellites launched in 2006 Orbits: alt=800km, Inc=72deg, ecc=0 Weather + Space Weather data Global observations of: - Pressure, Temperature, Humidity - Refractivity - Absolute Total Electron Content (TEC) - Electron Density Profiles (EDPs) - Ionospheric Scintillation Demonstrate quasi-operational GPS limb sounding in near-real time Climate Monitoring

5 BAMS figure

6 COSMIC GPS Radio Occultation GPS Satellite COSMIC LEO Satellite

7 CDAAC processing data for these Radio Occultation missions COSMIC CHAMP GPSMET GRACE SAC-C TerraSAR-X Metop/GRAS, others available soon

8 GPS Antennas on COSMIC Satellites 2 Antennas POD, TEC_pod (1-sec), EDP, 50Hz clock reference Upto 9 GPS COSMIC s/c Upto 4 GPS V leo High-gain occultation antennas for atmospheric profiling (50 Hz) Nadir GPS receiver developed by JPL and built by Broad Reach Eng. Antennas built by Haigh-Farr

9 CDAAC Processing Flow CDAAC File types in RED Atmospheric processing LEO data Level 0--level 1 Excess Phase atmphs Full Spectrum Abel Inversion atmprf (S4) 1-D Var Moisture Correction wetprf bfrprf Fiducial data POD (Orbits and clocks) Real time Task Scheduling Software Profiles podtec (TEC) scnlv1 (S4) tiplv1 (Radiance) Ionospheric processing Excess Phase Processing ionphs (L1,L2 amp/phase) Abel Inversion ionprf (EDPs) Combination with other data

10 Kursinski et al. (1997) ~0.05% error in N at 40km due to 0.05 mm/s velocity error UCAR simulation ~0.1% in N at 40km due to 0.1 mm/s velocity error Impact of Velocity Errors on RO Retrievals

11 Observation Modeling Description System delays Multipath Antenna phase center offsets and variations Carrier phase wind-up

12 LEO POD at CDAAC with Bernese v5.0 - GPS Orbits/EOPs/ Clocks(Final/IGU) - IGS Weekly Station Coordinates - 30-sec Ground GPS Observations - 30-sec LEO GPS Observations - LEO Attitude (quaternian) data - 1-Hz Ground GPS Observations - 50-Hz LEO Occultation GPS Obs. Estimate Ground Station ZTD s and Station Coordinates Estimate 30-sec GPS Clocks OR use CODE/IGS clocks Estimate LEO Orbit And Clocks Single/Double Difference Occultation Processing Excess Phase Data LEO POD Zero-Difference Ionosphere-free carrier phase observables with reduced-dynamic processing (fully automated in CDAAC) Real-Time (~70 ground stations) Dynamic Model: Gravity - EIGEN1S, Tides - (3rd body, solid Earth, ocean) State Parameters: 6 initial conditions (Keplerian elements) 9 solar radiation pressure parameters (bias and 1 cycle per orbital revolution accelerations in radial, transverse, and normal directions) pseudo-stochastic velocity pulses in R-T- N directions every 12 minutes Real ambiguities Quality Control Post-fit residuals Internal overlaps

13 High-Rate (30 s) GPS Clock Estimation The Bernese CLKEST program is used to generate ground station and GPS satellite clock corrections as described in [Bock et al, 2000]. The ground network carrier phase observation equation for a given receiver i and satellite j and epoch l are modeled as φ il j If ionosphere-free observations are considered, and previously solved for GPS orbits, station coordinates, and ZTDs are used to subtract known terms, then the modified observations are only a function of clock terms LC il j = ρ il j c Δt j j l + c Δt il + Δρ il, ion = c Δt l j + c Δt il + ε φ j + Δρ il, trop + λ N i j +ε φ Estimate precise phase-derived clock offset differences from epoch to epoch Align precise epoch to epoch GPS clock offsets to IGS clocks

14 CDAAC Post-Processed Internal Orbit Overlaps Orbit differences at day boundaries (3-hour overlap) CHAMP (2007 May, ) Radial Along-Track Cross-Track 3-D RSS (0.04) (0.03) (0.04) (0.07) COSMIC (2006 Aug 4-6, ) No data gaps, Good attitude control Radial Along-Track Cross-Track 3-D RSS (0.04) (0.04) (0.04) (0.07)

15 COSMIC Post-Processed External Overlaps Inter-Agency (UCAR,NCTU,GFZ,JPL) orbit differences FM s 1-6, no data gaps, good attitude control UCAR - NCTU ( ) Position Radial Along-track Cross-track Velocity Radial Along-Track Cross-Track 3-D RSS Mean (0.01) (-0.03) (0.00) STD (0.13) (0.14) (0.18) (0.26)

16 COSMIC Post-Processed External Overlaps UCAR - GFZ (G. Michalak) ( ) Radial Along-Track Cross-Track 3-D RSS Mean (0.00) (0.05) (0.00) STD (0.15) (0.12) (0.12) (0.22) UCAR - JPL (Da Kuang) ( ) Radial Along-Track Cross-Track 3-D RSS Mean (0.04) (0.03) (0.00) STD (0.10) (0.13) (0.13) (0.21)

17 GRACE Post-Processed External Overlaps UCAR - GFZ(RSO) ( ) Radial Along-Track Cross-Track 3-D RSS Mean (-0.01) (-0.04) (0.00) STD (0.05) (0.08) (0.04) (0.10)

18 COSMIC s Bad Attitude

19 METOP/GRAS POD

20 POD Summary Current COSMIC POD quality ~ cm ( mm/s) 3D RMS, GRACE < 10 cm 3D RMS Significant error sources Attitude knowledge errors Phase center offsets and variations Local spacecraft multipath Changing center of mass location Dynamic modeling Observation geometry - Use both POD antennas

21 Computation of excess atmospheric phase Double Difference Advantage: Station clock errors removed, satellite clock errors mostly removed (differential light time creates different transmit times), general and special relativistic effects removed Problem: Fid. site MP, atmos. noise, thermal noise Single Difference LEO clock errors removed use solved-for GPS clocks Main advantage: Minimizes ground station errors Zero(un)-Difference - Use solved-for GPS/LEO clocks - Minimize noise

22 High-Rate GPS Clock Comparison GPS Cesium clocks CDAAC 1-s GPS clocks CDAAC 30-s GPS clocks IGS 15-min GPS clocks

23 Impact of GPS Clock Sampling Interval For Single-Differencing Statistical comparison of RO to ECMWF for 5,596 profiles from May 2-8, 2008 for the different excess phase processing strategies. Solid lines show mean differences and dotted lines shows the standard deviation of the differences. Schreiner et al., GPS Sol., 2009

24 Determining Bending from observed Doppler Bending angle α Φ Transmitted wave fronts From orbit determination we know the location of source and We know the receiver orbit. Thus we know Thus we know ψ Earth We measure Doppler frequency shift: f v Δx 1 Δt. And compute the bending angle α = Φ ψ Φ ψ v v Δx k Wave vector of received wave fronts v cosψ λ d = = = = f T v c cosψ

25 Ionospheric calibration Is performed by linear combination of L1 and L2 bending angles at the same impact parameter (by accounting for the separation of ray tangent points). α( a) = α a f α1( a) f2 α 2( a) 2 2 f1 f2 bending angle impact parameter Effect of the small-scale ionospheric irregularities with scales comparable to ray separation is not eliminated by the linear combination, thus resulting in the residual noise on the ionospheric-free bending angle.

26 RO Retrieval Error Estimates Previous Results First estimates: Yunck et al. [1988] and Hardy et al. [1994] Detailed analysis: Kursinski et al. [1997] ~0.2 % error in N at 20 km (horizontal along track variations) ~1 % at surface and ~1 % at 40 km ROSE inter-agency (GFZ, JPL, UCAR) comparison [Ao et al., 2003; Wickert et al., 2004] and GFZ-UCAR [von Engeln, 2006] Experimental validation: Kuo et al. [2004] Errors slightly larger than Kursinski et al. [1997] Experimental precision estimates: Hajj et al. [2004] ~0.4 % fractional error (0.86K) between 5 and 15 km

27 COSMIC Collocated Occultations Occultation map of atmphs.c g nc Occultation map of atmphs.c g nc

28 Collocated Retrievals Inversions of pairs of collocated COSMIC occultations with horizontal separation of ray TP < 10 km. Upper panel: tropical soundings, 2006, DOY 154, 15:23 UTC, 22.7S, 102.9W. Lower panel: polar soundings: 2006, DOY 157, 13:14 UTC, 72.6S, 83.5W.

29 Statistics of Collocated Soundings Setting Occultations with Firmware > v4.2 Tangent Point separations < 10km Same QC for all retrievals One outlier removed Near real-time products used ( ) FM3-FM4 ( ) ALL Collocated pairs Pairs with similar straightline tracking depths Schreiner, W.S., C. Rocken, S. Sokolovskiy, S. Syndergaard, and D. Hunt, Estimates of the precision of GPS radio occultations from the COSMIC/FORMOSAT-3 mission, GRL, 2007

30 Systematic Bending Angle Differences COSMIC COSMIC (FM3 FM4) COSMIC collocations within 2 hours/300 km, Jan-Dec 2009 Legend: Mean = Black STD = Green STD of mean = Red Count = Blue STD of mean ~7e-8 rad No systematic difference between FM3 and FM4 Schreiner et al., AMS, 2010

31 Systematic Bending Angle Differences COSMIC VS Metop/GRAS (~10,000 Collocations within 2 hours/300 km, Jun-Dec 2009) COSMIC (Single Difference) Metop/GRAS Beta (Zero Difference) Bending angle bias between 40 and 60km ~ -3e-8 +/- 3e-8 rad May be due to COM-PC offset of Metop/GRAS 3e-8 rad translates into ~0.04 K (0.02%) at 20 km altitude Bias of ~ -3e-8 rad STD of mean ~ 3e-8 Schreiner et al., AMS, 2010

32 RO Trends Study of Structural Uncertainty Inter-Agency (UCAR,JPL,GFZ,WegC) N Comparison for CHAMP %/5 yrs can be considered an upper bound in the processingscheme induced uncertainty for global refractivity trend monitoring Ho et al., JGR-Atmos, 2009

33 COSMIC Occultation Count > 2.1 Million Profiles in Real Time Launch to present

34 Penetration of setting/rising soundings (From Anthes et al., 2007)

35 Impact on NWP At NCEP, remarkable improvement in anomaly correlation scores (i.e. forecast skill) of up to eight hours at day four and up to 15 hours at day seven (Cucurull et al., 2008).

36 Impact on NWP Impact on ECMWF short-range (24-h) forecasts (Cardinali, 2009)

37 Acknowledgments NSF Taiwan s NSPO NASA/JPL, NOAA, USAF, ONR, NRL Broad Reach Engineering