Simulation Studies on the Analysis of Radio Occultation Data

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

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Simulation Studies on the Analysis of RO
DataProperties and Utility of RO Data

• 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.

• 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 lt
  1 K at 1 km resolution)
• long-term stability due to intrinsic
  self-calibration (e.g., T drifts lt 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 of climate modeling
• improvement of numerical weather prediction and
  analysis

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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 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)

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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

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Empirical Error Analysis Simulated 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
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Empirical Error Analysis Error Analysis Method
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Empirical Error Analysis Bending Angle Error -
MSIS StatOpt
Relative StdDev 8 lt h lt 35 km 0.3 1 3 lt h
lt 8 km lt 8 h gt 35 km lt 5 Relative
Bias 5 lt h lt 38 km lt 0.1 5 gt h gt 38 km lt
0.5
Covariance Matrix Model Sij s2
exp(-zi-zj/L)
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Empirical Error Analysis Refractivity Error
Relative StdDev 5 lt h lt 40 km 0.1 0.75 5
gt h gt 40 km lt 2 Relative Bias 2.5 lt h lt 40
km lt 0.1 h gt 40 km lt 0.3
Covariance Matrix Model Sij s2
exp(-zi-zj/L)
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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) during 2001 to 2025
• ECHAM5-MA with resolution T42L39 (64x128 grid
  points, 2.8resolution)
• 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

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Climate Monitoring Simulation StudyAtmosphere
Modeling
Date July 15, 1997 UT 1200 hhmm
SliceFixDimLon 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 GHGsAerosolsO3
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
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Climate Monitoring Simulation StudyIonosphere
Modeling
Month July UT 1200 hhmm SAc/F107 120
SliceFixDimLon 0.0 deg
Solar activity 1996-2025 day-to-day F107 values
and monthly mean values
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 (2001-2025) from past
data of solar cycles 21, 22, and 23
(1979-1999)
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Climate Monitoring Simulation StudyObservation
Simulations - Spatial Sampling
Sampling into 17 equal area latitude Bins
85S to 85N (10lat x 15lon at equator) No.
of occultation events gt 50 per Bin for each
JJA season (max. 60/Bin)
No. of occultation events per Bin and month
light gray June events only lightmedium
gray JuneJuly events lightmediumdark
gray JuneJulyAugust
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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 50-60 Tdry air profiles per latitude
  Bin
• Temperature errors lt 0.5 K within upper
  troposphere
• and lower stratosphere for individual T
  profiles
• Errors in TAv for 50 events lt 0.2 K (8 km lt z lt
  30 km)

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Climate Monitoring Simulation StudyPerformance
analysis Observational error
Bias error in temperature climatology
Total observational error
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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 (13 000 in total)

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Climate Monitoring Simulation StudyPerformance
Analysis Total Climatological Error
Total climatological error for all events
Total climatological error for selected events
Total climatological error (observational plus
sampling error)
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Climate Monitoring Simulation StudyPerspectives
for the Full Experiment 2001-2025
Exemplary simulated temperature trends 20012025
Total climatological error of test-bed season

• GNSS occultation based JJA T errors are
• expected to be lt 0.5 K in most of the core
• region (840 km) northward of 50S.

• 20012025 JJA T trends are expected to be
• gt 0.5 K per 25 yrs in most of the core region
• northward of 50S. (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 50S
(high-latitude winter region)
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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 lt 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.

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GNSS RO retrieval scheme in the upper
stratosphereTest-bed Results with Advanced
Retrieval
Mean dry temperature bias of GNSS CLIMATCH
test-bed season
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
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Representativity Error Study Study Design
Reference Profiles - vertical vs tangent point
trajectories
Azimuth Sectors Sector 1 0 lt Azimuth
lt 10 Sector 2 10 lt Azimuth lt 20
Sector 3 20 lt Azimuth lt 30 Sector 4
30 lt Azimuth lt 40 Sector 5 40 lt
Azimuth lt 50
581 occ. events in total (1 day MetOp/GRAS), 100
in each sector, during 24 hour period ECMWF
analysis field T511L60 (512x1024)
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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
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Representativity Error Study Temperature Errors
as Example
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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 Earths 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.

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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 like COSMIC and ACE need to be
  implemented with high priority.