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 University of Graz (IGAM/UG), Austria 2nd GRAS SAF User Workshop Helsingør, Denmark, June 11-13, 2003  2003 by IGAM/UG

Simulation Studies on the Analysis of RO Data Outline  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 GNSS RO retrieval scheme in the upper stratosphere - Representativity error study (focus on troposphere)  Summary, Conclusions and Outlook

Simulation Studies on the Analysis of RO Data Properties and Utility of RO Data 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 stability due to intrinsic self-calibration (e.g., T drifts < 0.1 K/decade) GNSS Radio occultation observations are made in an active limb sounding mode exploiting the atmospheric refraction of GNSS signals providing measurements of phase path delay for the retrieval of key atmospheric/climate parameters such as temperature and humidity. This is the basis for the utility of RO Data for global climate monitoring building global climatologies of temperature and humidity validation and advancement of climate modeling improvement of numerical weather prediction and analysis

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: METOP European 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 profiles in the troposphere and stratosphere  dry air retrieval, optimal estimation retrieval (1DVAR) in the troposphere Simulation tool is the End-to-end GNSS Occultation Performance Simulator EGOPS (developed by IGAM/UniGraz and partners) Simulation Studies on the Analysis of RO Data End-to-end Simulations of GNSS RO Data

Empirical Error Analysis Study Design Observation day: September 15, 1999 METOP as LEO satellite with GRAS 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°

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 ~ 1 mm Mesopause ~ 20 cm Stratopause ~ 20 m Tropopause ~ 1 – 2 km Surface Empirical Error Analysis Simulated Observables

Interpolation of retrieved (x retr ) and ‘true’ co-located (x true ) atmospheric profiles to a L60 vertical grid with 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 Analysis Error Analysis Method

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: S ij = s 2 exp(-|z i -z j |/L) Bending Angle Error - MSIS StatOpt Empirical Error Analysis Bending Angle Error - MSIS StatOpt

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: S ij = s 2 exp(-|z i -z j |/L) Refractivity Error Empirical Error Analysis Refractivity Error

 Summer seasons (JJA) during 2001 to 2025  ECHAM5-MA with resolution T42L39 (64x128 grid points, 2.8°resolution)  6 LEO satellites, 5x5yrs  Dry air temperature 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 Objective is to test the capability of a small GNSS occultation observing system for detecting temperature trends within the coming two decades Climate Monitoring Simulation Study Study Design

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+O 3 1 control run (natural forcing only) Change monitoring: 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 Date: July 15, 1997; UT: 1200 [hhmm]; SliceFixDim=Lon: 0.0 [deg]Mean T field in selected domain: “True” JJA 1997 average temperature Climate Monitoring Simulation Study Atmosphere Modeling

Ionosphere model: 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 activity prescription: Representative day-to-day F107 values (weekly history averages) Future F107 data ( ): from past data of solar cycles 21, 22, and 23 ( ) Month: July; UT: 1200 [hhmm]; SAc/F107: 120; SliceFixDim=Lon: 0.0 [deg]Solar activity : day-to-day F107 values and monthly mean values Climate Monitoring Simulation Study Ionosphere Modeling

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 Climate Monitoring Simulation Study Observation Simulations - Spatial Sampling

Typical example of T profile errors (~50 events) Retrieval of T dry air profiles per latitude Bin Temperature errors < 0.5 K within upper troposphere and lower stratosphere for individual T profiles Errors in T Av for ~50 events < 0.2 K (8 km < z < 30 km) Climate Monitoring Simulation Study Temperature Profiles - Temperature Trends Temperature trends estimation using T JJA 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

Bias error in temperature climatology Total observational error Climate Monitoring Simulation Study Performance analysis: Observational 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 (~ in total) Climate Monitoring Simulation Study Performance Analysis: Sampling Error

Total climatological error (observational plus sampling error) Climate Monitoring Simulation Study Performance Analysis: Total Climatological Error Total climatological error for all eventsTotal climatological error for selected events

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) Exemplary simulated temperature trends 2001–2025 Climate Monitoring Simulation Study Perspectives for the Full Experiment Total climatological error of test-bed season

GNSS RO retrieval scheme in the upper stratosphere Empirical Background Bias Correction 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 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. Method: Inverse covariance weighting statistical optimization of observed bending angle  o with background bending angle  b

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

Representativity Error Study Study Design 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) Reference Profiles - vertical vs tangent point trajectories

Representativity Error Study Tangent 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°

Representativity Error Study Temperature Errors as Example Vertical Reference Profile Retrieved 3D Tangent Point Trajectory “True” 3D Tangent Point Trajectory Retrieved minus “True” 3D Tangent Point Trajectory All Events

Simulation Studies on the Analysis of RO Data Summary, 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.

Simulation Studies on the Analysis of RO Data Summary, Conclusions and Outlook (2)  A climate monitoring simulation study for the years 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 like COSMIC and ACE+ need to be implemented with high priority.