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Simulation Studies on the Analysis of Radio Occultation Data

2nd GRAS SAF User Workshop Helsing ø r, Denmark, June 11-13, 2003. Simulation Studies on the Analysis of Radio Occultation Data. Andrea K. Steiner, Ulrich Foelsche, Andreas Gobiet, and Gottfried Kirchengast Institute for Geophysics, Astrophysics, and Meteorology

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Simulation Studies on the Analysis of Radio Occultation Data

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  1. 2nd GRAS SAF User Workshop Helsingør, Denmark, June 11-13, 2003 Simulation Studieson theAnalysis of Radio Occultation Data Andrea K. Steiner, Ulrich Foelsche, Andreas Gobiet, and Gottfried Kirchengast Institute for Geophysics, Astrophysics, and Meteorology University of Graz (IGAM/UG), Austria (andi.steiner@uni-graz.at)  2003 by IGAM/UG

  2. Simulation Studies on the Analysis of RO DataOutline • Properties and Utility of RO Data • End-to-end Simulations of GNSS RO Data - Atmosphere and ionosphere modeling - Observation simulations - Retrieval of atmospheric variables • Simulation Studies - Empirical error analysis - Climate monitoring simulation study 2001-2025 - GNSS RO retrieval scheme in the upper stratosphere - Representativity error study (focus on troposphere) • Summary, Conclusions and Outlook

  3. Simulation Studies on the Analysis of RO DataProperties andUtility of RO Data • GNSS Radio occultation observations • are made in an active limb sounding mode • exploiting the atmospheric refraction of GNSS signals • providingmeasurements ofphase path delay for the retrieval of • key atmospheric/climate parameters such as temperatureandhumidity. • The RO method provides a unique combination of • global coverage(equal observation density above oceans as above land) • all-weather capability(virtual insensitivity to clouds & aerosols; wavelengths ~20 cm) • high accuracy and vertical resolution (e.g., T < 1 K at ~1 km resolution) • long-term stabilitydue to intrinsic self-calibration (e.g., T drifts < 0.1 K/decade) • This is the basis for the utility of RO Data for • global climate monitoring • building global climatologies of temperature and humidity • validation and advancement ofclimate modeling • improvement of numerical weather prediction and analysis

  4. Simulation Studies on the Analysis of RO DataEnd-to-end Simulations of GNSS RO Data Realistic modeling of the neutral atmosphere and ionosphere • ECMWF analysis fields T213L50, T511L60; ECHAM5 T42L39 • NeUoG model Realistic simulations of radio occultation observations • Receiver: GNSS Receiver for Atmospheric sounding GRAS • LEO satellite: METOPEuropean Meteorological Operational satellite 6 satellite constellation (COSMIC, ACE+ type) Calculation of excess phase profiles • Forward modeling with a sub-millimetric precision 3D ray tracer • Observation system simulation including instrumental effects and the raw processing system Retrieval of atmospheric profilesin the troposphere and stratosphere • dry air retrieval, optimal estimation retrieval (1DVAR) in the troposphere Simulation tool isthe End-to-end GNSS Occultation Performance SimulatorEGOPS (developed by IGAM/UniGraz and partners)

  5. Empirical Error AnalysisStudy Design • Observation day: September 15, 1999 • METOP as LEO satellite withGRAS • receiver • GPS setting and rising occultation events • Height range: 1 km to 90 km • 574 events total • 300 events globally chosen for study • equally distributed in space and time • 100 events in each of 3 latitude bands • - low latitudes: -30° to +30° • - mid latitudes: ±30° to ±60° • - high latitudes: ±60° to ±90°

  6. Empirical Error AnalysisSimulated Observables ~ 1 mm Mesopause ~ 20 cm Stratopause ~ 20 m Tropopause ~ 1 – 2 km Surface Simulated observables are phase delays and amplitudes – Phase delays for the GPS carrier signals in L band: L1 (~1.6 GHz), L2 (~1.2 GHz) – Atmospheric phase delay (after correction for ionosphere): LC (illustrated above) – LC phase rms error of ~2 mm at 10 Hz sampling rate conservatively reflects METOP/GRAS-type performance

  7. Interpolation of retrieved (xretr) and ‘true’ co-located (xtrue) atmospheric profiles • to a L60 vertical gridwith the uppermost level at ~65 km/0.1 mbar • (inspection at levels 900 mbar < p < 0.75 mbar; 1 km < z < 50 km) • Difference profiles: • Bias: • Bias-free profiles: • Error Covariance Matrix: • Standard Deviation: • Correlation Matrix: Empirical Error AnalysisError Analysis Method

  8. Empirical Error AnalysisBending Angle Error - MSIS StatOpt Relative StdDev: 8 < h < 35 km: 0.3% – 1% 3 < h < 8 km: < 8% h > 35 km: < 5% Relative Bias: 5 < h < 38 km: < 0.1% 5 > h > 38 km: < 0.5% Covariance Matrix Model:Sij = s2 exp(-|zi-zj|/L)

  9. Empirical Error AnalysisRefractivity Error Relative StdDev: 5 < h < 40 km: 0.1% – 0.75% 5 > h > 40 km: < 2% Relative Bias: 2.5 < h < 40 km: < 0.1% h > 40 km: < 0.3% Covariance Matrix Model:Sij = s2 exp(-|zi-zj|/L)

  10. Climate Monitoring Simulation StudyStudy Design Objective is to test the capability of a small GNSS occultation observing system for detecting temperature trends within the coming two decades • Summer seasons (JJA)during2001 to 2025 • ECHAM5-MA with resolution T42L39 (64x128 grid points, 2.8°resolution) • 6 LEO satellites, 5x5yrs • Dry airtemperature profiles retrieval in the troposphere and stratosphere to establish a set of realistic simulated temperature measurements. • An statistical analysis of temporal trends in the “measured” states from the simulated temperature measurements (and the “true” states from the modeling, for reference). • An assessment of how well a GNSS occultation observing system is able to detect climatic trends in the atmosphere over the coming two decades. • Testbed for setup of tools and performance analysis: JJA 1997

  11. Climate Monitoring Simulation StudyAtmosphere Modeling Date: July 15, 1997; UT: 1200 [hhmm]; SliceFixDim=Lon: 0.0 [deg] Mean T field in selected domain: “True” JJA 1997 average temperature Atmosphere model:ECHAM5-MA (MPIM Hamburg) Model resolution: T42L39 (up to 0.01hPa/~80km) Model mode: Atmosphere-only (monthly mean SSTs) Model runs: 1 run with transient GHGs+Aerosols+O3 1 control run (natural forcing only) Changemonitoring:In JJA seasonal average T fields as they evolve from 2001 to 2025 Domain: 17 latitude bins of 10 deg width 34 height levels from 2 km to 50 km vertical resolution 1 – 2 km core region 8 km to 40 km

  12. Climate Monitoring Simulation StudyIonosphere Modeling Month: July; UT: 1200 [hhmm]; SAc/F107: 120; SliceFixDim=Lon: 0.0 [deg] Solar activity 1996-2025: day-to-day F107 values and monthly mean values Ionospheremodel:NeUoG model (IGAM/UG) Model type: Empirical 3D, time-dependent, sol.activity-dependent model Mode: Driven by day-to-day sol.act. variability (incl. 11-yrs solar cycle, etc.) Solar activityprescription:Representative day-to-day F107 values (weekly history averages) Future F107 data (2001-2025): from past data of solar cycles 21, 22, and 23 (1979-1999)

  13. Climate Monitoring Simulation StudyObservation Simulations - Spatial Sampling Sampling into 17 equal area latitude Bins – 85°S to 85°N (10°lat x 15°lon at equator) – No. of occultation events > 50 per Bin for each JJA season (max. 60/Bin) No. of occultation events per Bin and month – light gray: June events only – light&medium gray: June+July events – light&medium&dark gray: June+July+August

  14. Climate Monitoring Simulation StudyTemperature Profiles - Temperature Trends Typical example of T profile errors (~50 events) Temperature trends estimation • using TJJA Av • Time period 2001 to 2025 • Latitude x height slices (17 x 34 matrix) Detection tests on temperature trends • in the model run with transient forcings • in the control run for comparison • relative to estimated natural variability • Retrieval of 50-60Tdry air profiles per latitude Bin • Temperature errors < 0.5 K within upper troposphere • and lower stratosphere for individual T profiles • Errors in TAv for ~50 events < 0.2 K (8 km < z < 30 km)

  15. Climate Monitoring Simulation StudyPerformance analysis: Observational error Bias error in temperature climatology Total observational error

  16. Climate Monitoring Simulation StudyPerformance Analysis: Sampling Error • Sampling error for the selected events • Difference between the “sampled” JJA • average T field (from the “true” T profiles • at the event locations) and the “true” one • ~55 selected events per Bin (total ~1000) • Sampling error if all events used • Difference “sampled”-minus-“true” JJA • average T field using all occultation • events available in the Bins • ~750 events per Bin (~13000 in total)

  17. Climate Monitoring Simulation StudyPerformance Analysis: Total Climatological Error Total climatological error for selected events Total climatological error for all events Total climatological error (observational plus sampling error)

  18. Climate Monitoring Simulation StudyPerspectives for the Full Experiment 2001-2025 Total climatological error of test-bed season Exemplary simulated temperature trends 2001–2025 • GNSS occultation based JJA T errors are • expected to be < 0.5 K in most of the core • region (8–40 km) northward of 50°S. • 2001–2025 JJA T trends are expected to be • > 0.5 K per 25 yrs in most of the core region • northward of 50°S. (ECHAM4 T42L19 GSDIO experiment) Significant trends (95% level) expected to be detectable within 20 yrs in most of the core region Aspects to be more clearly seen in the long-term: ionospheric residual errors, sampling errors, performance southward of 50°S (high-latitude winter region)

  19. GNSS RO retrieval scheme in the upper stratosphereEmpirical Background Bias Correction • Method: Inverse covariance weighting statistical optimization of observed bending angle o with background bending angle b • Background data: bending angle derived from MSISE-90 model • Error covariance matrices: • Background B: 20% error, exponential drop off with correlation length L = 6 km • Observation O: rms deviation of o from b between 70-80 km, L = 1 km • Basic scheme: Search the best fit bending angle profile in the climatology • Advanced scheme: Linearly fitting of the background to the observation in addition to the basic scheme (background B: 15% error) • Result: In general the effect of fitting is small - background bending angles are modified by < 1%, negligible effect on temperature profiles. In extreme cases background bending angles are modified up to ~15%, seen in temperature profiles (1 K level) down to 20 km.

  20. GNSS RO retrieval scheme in the upper stratosphereTest-bed Results with Advanced Retrieval Mean dry temperature bias of GNSS CLIMATCH test-bed season Basic scheme: Inverse covariance weighting optimization withsearch Background MSISE-90 Enhanced background bias correction: Inverse covariance weighting optimization withsearch & fit Error reduction in the southern high latitudes and above 30 km.

  21. Representativity Error StudyStudy Design Reference Profiles - vertical vs tangent point trajectories Azimuth Sectors – Sector 1: 0° < |Azimuth| < 10° – Sector 2: 10° < |Azimuth| < 20° – Sector 3: 20° < |Azimuth| < 30° – Sector 4: 30° < |Azimuth| < 40° – Sector 5: 40° < |Azimuth| < 50° 581 occ. events in total (1 day MetOp/GRAS), ~100 in each sector, during 24 hour period ECMWF analysis field T511L60 (512x1024)

  22. Representativity Error StudyTangent Point Trajectories Occultation events are never vertical Average elevation angle in the height interval 2-3 km: Sector 1: 6.6°, Sector 3: 4.9°, Sector 5: 3.2°

  23. Retrievedminus “True”3D TangentPointTrajectory VerticalReferenceProfile All Events All Events Retrieved3D TangentPointTrajectory “True” 3D Tangent PointTrajectory All Events All Events Representativity Error StudyTemperature Errors as Example

  24. Simulation Studies on the Analysis of RO DataSummary,Conclusions and Outlook (1) • An empirical error analysis of realistically simulated RO data provides error characteristics for key atmospheric variables. Simple analytical functions for covariance matrices were deduced for bending angle and refractivity, which can be used as total observational error covariance matrices for data assimilation systems. • A representativity error study shows that the comparison of RO profiles with vertical reference profiles introduces large representativity errors, especially in the lower troposphere. The average zenith angle of the tangent point trajectory near the Earth’s surface is about 85°. Errors decrease significantly if the retrieved profiles are compared to reference profiles along a tangent point trajectory deduced purely from observed data. • An advanced GNSS RO retrieval scheme in the upper stratosphere was developed including background profile search and empirical background bias correction. It was successfully tested with simulation data and is currently under evaluation with CHAMP data.

  25. Simulation Studies on the Analysis of RO DataSummary,Conclusions and Outlook (2) • A climate monitoring simulation study for the years 2001-2025 is ongoing. The preliminary results for the test-bed season suggest that the expected temperature trends over the coming two decades could be detected in most parts of the upper troposphere and stratosphere. • Based on our simulation studies we aim to built first real RO based global climatologies from the CHAMP and SAC-C missions. • Current multi-year single RO sensors such as on CHAMP, SAC-C, GRACE, and METOP are important initial components for starting continuous RO based climate monitoring. As a next step, constellations likeCOSMIC and ACE+ need to be implemented with high priority.

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