22nd october d4l2kerridge1 data comparison methods lecture by b.kerridge rutherford appleton...
TRANSCRIPT
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22nd October D4L2 Kerridge 1
Data Comparison Methods
Lecture by B.Kerridge
Rutherford Appleton Laboratory, UK
ESA-MOST DRAGON 2 PROGRAMMEAdvanced Training Course in Atmospheric Remote Sensing
19-24th October 2009, Nanjing University
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Contents
1. Introduction
2. Radiances
3. Ozone
4. Aerosol
5. Surface networks
6. Summary
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1. Introduction– Why are data comparisons needed?
1. Comparison of observation with theory is fundamental.2. Validation of satellite data is also essential
→ inform users of data attributes and quality
– Which approaches are used?
– Quantitative comparison of new data set with existing data sets of established attributes and quality (ie resolution, precision & accuracy)
– Spatial & temporal distributions and variances– Biases and standard deviations between new and established data– Sensor-to-sensor, sensor-to-model or -analysis
– What level of stringency is necessary (ie precision and accuracy)?
– Atmospheric variability ← lifetime– Quality of established observing techniques
• What (scientific or other) value are the new observations required to add?• First detection ↔ well-established measurement techniques / sensors
→ Determine level of sophistication
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2. Radiances / reflectances– The measured quantities (L1 data)
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Wavelength / nm
R
efle
ctan
ce
GOME coverage
v55 v87v67 v16AATSR channels
Comparison of GOME-1/ATSR-2 & SCIA/AATSR reflectances
SCIA spectra
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Inter-comparison requires:– Accurate co-location of imager/spectrometer– Spectral averaging of SCIA/GOME-1– Spatial averaging of AATSR/ATSR-2
AATSR / SCIA / GOME spatial coverage
SCIAMACHY
GOME
80 km
40 km
Comparison of GOME-1/ATSR-2 & SCIA/AATSR reflectances
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Comparison of AATSR, ATSR-2, GOME-1 and SCIAMACHY 670nm Reflectances
Two different orbits on 15th December 2002
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3. Ozone– A retrieved quantity
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Direct comparison of MIPAS O3 with HALOE IR solar occultation
• Solar occultation: – High vertical resolution, precision & accuracy– Sparse geographical coverage
• HALOE: – 79 co-locations between 22/07/02-14/12/02– Accuracy: 30-60km 6%; 15-30km 20%– Co-location: <250km on same day
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High latitude Subtropics
Individual HALOE Profiles
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0.5 – 50 hPa: +5 – +15% (+/- 20%) >50hPa: +ve biases and increases in RMS
HALOE sampling weighted to N. mid. lats; only 10% in tropics
Ensemble of HALOE Profiles
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Factors Limiting Direct Comparisons• Precision:
– Direct satellite comparisons underestimate MIPAS precision, due to:
• Imperfect co-location cf atmospheric variability
• Differences in representation of O3 vertical profile
• Precision of the correlative sensor factored in
• Accuracy:– Vertical structure of bias partly reflects different vertical
resolutions of respective sensors
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Direct Assimilation of MIPAS Data
Ozone: July 10th 2003, 850K level
• Direct assimilation equivalent to
Kalman Smoother, but far lower
computational cost.
• Uses isentropic advection as
modelling constraint.
→ No chemistry or vertical advection
• Field allowed to adjust continually
towards the observations.
→ Analysis unbiased relative to I/P data.
• Applied to MIPAS ozone, methane
and water vapour products (Feb. to
July 2003 v4.61/2)
M.Juckes, RAL
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MIPAS Ozone Comparisons with Other SensorsMean [ppmv] Standard error [ppmv]
Assimilation allows comparison against other observations which are not coincident.
Biases (left) found to be small in the lower and mid-stratosphere.
Standard error (right) is <10% in most of the stratosphere.
Using a range of independent instruments helps establish confidence.
Hei
ght [
km]
In both graphs, the outer limit of shading is 10% of sample mean profile, inner boundary of shading 1%, transition 5%.
10%
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Assimilation of MLS ozone profiles: comparison of analyses with ozone sondes
0 5 10 15 20 25 30
Ozone in mPa
1000800600500400300
200
1008060504030
20
1086543
2
Pre
ssure
hPa
Month = 200708 ( 10 sondes)Neymayer (Lat = -70.7, Lon = -8.3)
Ozone profiles from sondes and analyses
Sonde CTRL MLS
0 5 10 15 20 25 30
Ozone in mPa
1000800600500400300
200
1008060504030
20
1086543
2
Pre
ssure
hPa
Month = 200709 ( 8 sondes)HOHENPEISSENBERG (Lat = 47.8, Lon = 11.0)
Ozone profiles from sondes and analyses
Sonde CTRL MLS
0 5 10 15 20 25 30
Ozone in mPa
1000800600500400300
200
1008060504030
20
1086543
2
Pre
ssure
hPa
Month = 200709 ( 3 sondes)NAHA (Lat = 26.2, Lon = 127.7)
Ozone profiles from sondes and analyses
Sonde CTRL MLS
• 1st July – 30th Sept 2007• Ozone data actively assimilated into ECMWF operational system:
• CTRL: SCIAMACHY total column (KNMI)• MLS: SCIAMACHY total column + MLS O3 profile
→ Agreement with ozonesonde profiles <200hPa improved by assimilating MLS
R.Dragani, ECMWF
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)( DIAL aafinal xxAxx
TES a priori profile
DIAL or sonde profile
TES averaging kernel
Comparison of TES Retrieved O3 Profileswith Ozonesondes & Airborne DIAL
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TES O3 – Ozonesondes
Worden et al.
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TES O3 – Ozonesondes (contd.)
TES V1 TES V2
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Airborne DIAL Measurements • DIAL profiles ozone simultaneously
above & below DC-8 aircraft
• DIAL profiles <0.15o (lat/long) from TES profile averaged & interpolated to TES p grid.
• Missing data in DIAL profile → TES a priori
•DIAL: accuracy <10% (2 ppbv)•Vertical resolution of 300 m.
Richards et al
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TES O3 – DIAL
TES V2 TES V3
– Care needed in comparisons near tropopause, where O3 vertical gradient strong.– Using AKs, TES bias in upper troposphere seen to be reduced in V3
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Comparison of OMI integrated ozone column to ECMWF analyses
• 70-day period from 12 Aug 2005
• Assimilation of operational obs:– ozonesondes – aircraft – satellite IR rads– SBUV/2 partial columns– SCIAMACHY columns – MLS ozone profiles
– OMI data:– No sunglint – SZA< 84 deg– Coincidence criterion: within ± 1 hour of analysis time
Analysis by S.Migliorini, U.Reading
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Total column ozone comparisons
• Total column should be compared to
ˆ ( )Ta a a x x
ana anaˆ ( )T
a a a x x
ECMWF analyses firstinterpolated to OMI pixel locations
ˆ ( )a a x x x A x x ε
T g x T Ta g A Tx g εIntegrated column:
Retrieved profile:
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OMI ozone layer averaging kernels
a < 1
15 Oct 2005 12 UTC
nadir pixel
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OMI total column ozone
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Simulation with ECMWF 3-D analysis
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Integrated Column Differences (%)
For 70 day’s data: [OMI-ECMWF] bias -3.773 % and RMS 5.2 %
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4. Aerosol
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Height-Integrated Aerosol
• Aerosol optical thickness (AOT), (), at = 550 and 870 nm
Cloud screening & handling of surface BRDF critical for aerosol retrieval from satellite sensors
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Comparisons of AATSR & SEVIRI with MODIS and MISR
MODIS & MISR on EOS-Terra Day-time equator crossing time
similar to Envisat (~10am) Terra ascending node in daytime
whereas Envisat descending, → Observing times at high latitude
quite different to AATSR.
SEVIRI on MSG samples hourly, though viewing geometry a fixed function of geographical location
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Impact of coincident sampling on AATSR – MODIS Comparison in Sept’04
Cor=.64 Cor=.48 Cor=.56SD=.16
Cor=.71SD=.08
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C=0.86 C=0.53 C=0.80 C=0.45
SEVIRI
MODISMISR
SEVIRI aerosol optical thickness comparisons with MODIS & MISRJuly 2005
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5. Ground-based networks
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Aeronet
AErosol RObotic NETwork. http://aeronet.gsfc.nasa.gov
Standardized instruments, calibration & processing.
→Geographically distributed observations of aerosol spectral optical depths & retrieved products
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Aeronet • Each ground station has CIMEL sun-photometer
’s: 340, 380, 440, 500, 675, 870, 940 and 1020 nm– Widths: 2nm at 340nm, 4 at 380nm, others 10nm.– Narrow FOV ~1o
• Direct sun measurement every 15 mins
• AOT from direct sun extinction → Beer’s law – Corrected for Rayleigh scat. + trace-gas abs.– Rel. acc. cf other photometers <0.004– Abs. acc. <0.01-0.02
• [Angstrom coefficient fitted to AOT at 440, 500, 675, 870nm]
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• Satellite grid-box containing AERONET station identified.
• For each day, AOT (and Angstrom coeff.) extracted within ±20km from this satellite pixel.
• Aeronet measurements for each station extracted within ±30 minutes of satellite measurements.
• For both Aeronet and satellite sensor, no. of valid retrievals, means and standard deviation stored
• Minimum of 4 matches required.
• If SD of either satellite or Aeronet exceeds 0.15, comparison discounted.
Satellite – Aeronet Comparison
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SEVIRI – Aeronet individual time series comparison
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SEVIRI
AeronetMODIS
MISR
SEVIRI – Aeronet time series comparison for North Atlantic Ocean
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SEVIRI – Aeronet: statistical comparison
C=.2SD=.07
C=.84SD=.04
C=.38SD=.12
C=.81SD=.07
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SEVIRI – Aeronet: Europe & Africa
C=.64SD=.06
C=.36SD=.15
– SEVIRI performance reasonable over sea and coasts– High-reflectance land sites (Africa) are problematic
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– Representativeness – point location vs satellite field of view
– Only five aerosol types in retrievals– aerosol composition varies continuously
– Cloud flagging may be too stringent– upper limit on allowed AOT set too low
– High land surface reflectance not modelled well
Some Factors Limiting Aeronet Comparisons
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Network for the Detection of Atmospheric Composition Change (NDACC)
>70 high-quality stations observing stratosphere and upper troposphere
→ impact of stratospheric changes on troposphere and on global climate LIDAR profiles:Raman lidar - water vapor Differential Absorption Lidar (DIAL) - O3 Backscatter lidars - aerosol Raman and Rayleigh lidars - temperature
Microwave radiometers: ozone, water vapor, and ClO profiles
UV/VISIBLE SPECTROMETERS: column ozone, NO2 (OClO and BrO) FTIR SPECTROMETERS: column ozone, HCl, NO, NO2, ClONO2, and HNO3
DOBSON/BREWER: column ozone
SONDES: ozone and aerosol profiles
UV SPECTRORADIOMETERS: UV radiation at the ground
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• Network of ground-based FTS• Near-IR solar absorption spectrometry• 4,000–14,000 cm−1 at 0.02 cm−1 resolution • Analysis with standard algorithm (GFIT)
– Non-linear least squares → scale profile
→ CO2, CH4, O2 & other columns
• O2 to convert column densities to pressure- • weighted column average mixing ratios
• Complementary surface in-situ at each site
• To usefully constrain global carbon budget – directly, and through validation of satellite column measurements – precision of 0.1% required!
• SCIAMACHY & GOSAT validation
→ Stringent accuracy reqs. eg:- solar tracking- correction for source fluctuations- FTS instrument line shape
-permanently monitored with HCl cell in solar beam.
- spectroscopic parameters- retrieval algorithms
Total Column Carbon Observing Network - TCCON
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– Variability reflects atmospheric lifetimes: CH4 ~10yrs, N2O >100yrs
– Correction needed for stratospheric column variability
Sherlock et al
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22nd October D4L2 Kerridge 45
6. Summary– Comparison of observation with theory is fundamental– Validation of satellite data also essential– Important to compare like-with-like as far as possible
→ Apply observation operators to model fields and in assimilation→ Account for sensor vertical smoothing & a priori also in
comparisons with profiles observed at higher resolution
– Techniques of increasing sophistication are being used→ to compare with models and other observations→ to quantify value added in assimilation
– Production of long-term satellite data-sets on the essential climate variables will depend on surface networks for ground truth.