simulation studies on the analysis of radio occultation data andrea k. steiner, ulrich foelsche,...

Simulation StudiesSimulation Studies

on theon the

Analysis of Radio Occultation DataAnalysis of Radio Occultation Data

Andrea K. Steiner, Ulrich Foelsche, Andreas Gobiet, and Gottfried Kirchengast

Institute for Geophysics, Astrophysics, and MeteorologyUniversity of Graz (IGAM/UG), Austria

([email protected])

2nd GRAS SAF User WorkshopHelsingør, Denmark, June 11-13, 2003

2003 by IGAM/UG

Simulation Studies on the Analysis of RO DataSimulation Studies on the Analysis of RO Data

OutlineOutline

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


Properties andProperties and Utility of RO DataUtility 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)


End-to-end Simulations of GNSS RO DataEnd-to-end Simulations of GNSS RO Data

Empirical Error AnalysisEmpirical Error Analysis Study DesignStudy 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 AnalysisEmpirical Error Analysis Simulated ObservablesSimulated Observables

• Interpolation of retrieved (xretr) and ‘true’ co-located (xtrue) 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:

jjii

ijij

ii

Tbiasfreek

biasfreek

kbiasfreek

k

trueretr

SS

SR

S

xxN

xx

kxN

xxx

with

1

1

Events of .No,1,1

R

s

S

b

b

Empirical Error AnalysisEmpirical Error Analysis Error Analysis MethodError 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: Sij = s2 exp(-|zi-zj|/L)

RReell.. SSttddDDeevv ss CCoorrrr.. LLeennggtthh LLzz << zz__ttrrooppoo a1 + a2*x-3 a1 = 0.140

a2 = 483.20.5 km

zz >> zz__ttrrooppoo a3*exp(a4*x) a3 = 0.043a4 = 0.093

0.5 km

Empirical Error Analysis Bending Angle Error - MSIS StatOptBending 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: Sij = s2 exp(-|zi-zj|/L)

RReell.. SSttddDDeevv ss CCoorrrreellaattiioonn LLeennggtthh LLzz << zz__ttrrooppoo a1 + a2*x-1 a1= -0.221

a2 = 4.4612 km

zz >> zz__ttrrooppoo a3*exp(a4*x) a3 = 0.019 a4 = 0.087

linearly decreasing from 2 kmat z_tropo to 1 km at z = 60 km

Empirical Error Analysis Refractivity ErrorRefractivity 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 StudyClimate Monitoring Simulation Study

Study DesignStudy 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+O3

1 control run (natural forcing only)

Change monitoring: In JJA seasonal averageT fields as they evolve from 2001 to 2025Domain: 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


Atmosphere ModelingAtmosphere Modeling

Ionosphere model: NeUoG model (IGAM/UG)Model type: Empirical 3D, time-dependent, sol.activity-dependent modelMode: 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)

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


Ionosphere ModelingIonosphere 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


Observation Simulations - Spatial SamplingObservation Simulations - Spatial Sampling

Typical example of T profile errors (~50 events)

Retrieval of 50-60 Tdry 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)


Temperature Profiles - Temperature TrendsTemperature Profiles - Temperature Trends

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

Bias error in temperature climatology Total observational error

2

12

2

N

TTT

stddevijbias

ijobsij

true

jretrj

ii

biasij TT

NInterpT

1


Performance analysis: Observational errorPerformance analysis: Observational error

Sampling error for the selected events• Difference between the “sampled” JJA

average T field (from the “true” T profilesat 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 occultationevents available in the Bins

• ~750 events per Bin (~13 000 in total)


Performance Analysis: Sampling ErrorPerformance Analysis: Sampling Error

2

122

sam

ijobsij

totalij TTT

Total climatological error (observational plus sampling error)


Performance Analysis: Total Climatological ErrorPerformance 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


Perspectives for the Full Experiment 2001-2025Perspectives for the Full Experiment 2001-2025

Total climatological error of test-bed season

GNSS RO retrieval scheme in the upper stratosphereGNSS RO retrieval scheme in the upper stratosphere

Empirical Background Bias CorrectionEmpirical 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 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.

• Method: Inverse covariance weighting statistical optimization of observed bending angle o with background bending angle b

)()( 1bobopt ααOBBαα

GNSS RO retrieval scheme in the upper stratosphereGNSS RO retrieval scheme in the upper stratosphere

Test-bed Results with Advanced RetrievalTest-bed Results with Advanced Retrieval

Enhanced background bias correction:Inverse covariance weighting optimization with search & fitError reduction in the southern high latitudesand above 30 km.

Basic scheme:Inverse covariance weighting optimization with searchBackground MSISE-90

Mean dry temperature bias of GNSS CLIMATCH test-bed season


Tangent Point TrajectoriesTangent 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°


Temperature Errors as ExampleTemperature Errors as Example

Vertical ReferenceProfile

Retrieved3D TangentPoint Trajectory

“True” 3D Tangent Point Trajectory

Retrievedminus “True”3D TangentPoint Trajectory

All Events All Events

All Events All Events


Summary,Summary, Conclusions and Outlook (1)Conclusions and Outlook (1)

An empirical error analysis of realistically simulated RO data provides errorcharacteristics 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.


Summary,Summary, Conclusions and Outlook (2)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.

simulation studies on the analysis of radio occultation data andrea k. steiner, ulrich foelsche,...

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