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An overview of ACE-FTS v3.5 validation studies
Patrick E. Sheese1, Kaley A. Walker1,2, Chris D. Boone2
1University of Toronto, Toronto, Canada 2University of Waterloo, Waterloo, Canada
Atmospheric Composition Validation and Evolution, ESA-ESRIN, Frascati, Italy, 20 October 2016
Outline
• ACE-FTS • Recent validation papers and important findings • Error budget analysis • Drift analysis
ACE-FTS
• Canadian satellite SciSat was launched into a circular, high-inclination orbit in August 2003
• ACE-FTS and MAESTRO instruments on board
• ACE-FTS is a solar occultation instrument • High spectral resolution FTS in the 2.2 to 13.3 µm spectral range • 30+ trace species are retrieved, as well as 20+ subsidiary isotopologues • Vertical resolution of 3-4 km
• ACE-FTS level 2 version 3.5 data were used in this study • Complete dataset currently spans 2004-2013 • Data set supplemented with Jan-Apr 2016 data (not yet released)
Atmospheric Chemistry Experiment – Fourier Transform Spectrometer
Validation papers – ACE-FTS/MIPAS/MLS
• ACE-FTS ozone, water vapour, nitrous oxide, nitric acid, and carbon monoxide profile comparisons with MIPAS and MLS
• Sheese et al., JQSRT, 2016, doi:10.1016/j.jqsrt.2016.06.026
• Compared ACE-FTS O3, H2O, N2O, HNO3, CO with MIPAS (ESA and IMK-IAA) and MLS
• O3 is within 2% in lower stratosphere, high bias on order of 10-20% above peak in upper stratosphere
• H2O has dry bias in stratosphere, up to 10% • CO is much improved over v2.2; MLS summer CO likely should not be
used
Validation papers – ACE-FTS/MIPAS/MLS
Species Altitude range (km)
Mean bias (%)
O3 10-45 46-60
+2 0 to +19
H2O 13-16 17-46 47-70
-10 -2 to -10
±8
N2O 20-35 MIPAS: 36-44
-3 -8
HNO3 13-17 18-27 28-38
+7 ±2
+3 to +19
Coincidence criteria of < 3h, < 350 km
Where correlation is greater than 0.8 and standard deviation of relative difference is less than 50%
Validation papers – NOy species • Validation of ACE-FTS version 3.5 NOy species profiles using
correlative satellite measurements • Sheese et al., AMT (accepted) 2016, doi:10.5194/amt-2016-69, 2016
• Compared ACE-FTS NO, NO2, HNO3, N2O5, ClONO2
• Compare with HALOE, GOMOS, MAESTRO, MIPAS, MLS, OSIRIS, POAM III, SAGE III, SCIAMACHY, SMILES, and SMR
• Used photochemical box model to scale ACE-FTS local times to other insts • ACE-FTS NO2 has ~10-15% negative bias above peak in upper
stratosphere • Evening ACE-FTS N2O5 is quite noisy, only morning data recommended for
use
Validation papers – NOy species
GOMOS, HALOE, MAESTRO, MIPAS, OSIRIS, POAM III,
SAGE III, SCIAMACHY
MIPAS, MLS, SMR, SMILES
MIPAS
Species 𝑟𝑟 > 0.8,𝜎𝜎 < 50% Altitude (km) Mean bias (%)
NO HALOE 27-53 -15 to 6
NO MIPAS IMK-
IAA (Summer only)
36-52 -9 to 2
NO2 17-27 28-41
Better than 18 Better than -15
HNO3 9-17
18-26 27-35
Within ±7 Within ±1
1 to 20 N2O5
(Morning only) 22-34 35-38
Better than -7 0 to 7
ClONO2 16-24 21-33
Better than -20 Better than -8
Different coincidence criteria for each species
N2O in mesosphere and lower thermosphere
• Nitrous oxide in the atmosphere: first measurements of a lower thermospheric source
• Sheese et al., GRL, 2016, doi:10.1002/2015GL067353
• First observations of N2O in the lower thermosphere
• Consistently being produced via energetic particle precipitation • Is transported down into upper stratosphere during polar winter
• Not actually a validation paper, but we are now starting a project to compare with WACCM results, with N2O chemistry added to the WACCM (including ion chemistry) runs
N2O in mesosphere and lower thermosphere
January-March Arctic 7-day mean values
M J05 F M J06 F M J07 F M J08 F M J09 F M J10 F M J11 F M J12 F M J13 F M
Month and year
40
50
60
70
80
90
Alti
tude
(km
)
January-February
-80 -60 -40 -20 0 20 40 60 80
Latitude (deg)
40
50
60
70
80
90
Alti
tude
(km
)
July-August
-80 -60 -40 -20 0 20 40 60 80
Latitude (deg)
40
50
60
70
80
90
Error budget
• ACE-FTS currently does not have a comprehensive error budget • For a sample of ACE-FTS occultations, perturb different variables by
their expected uncertainty • Allow errors to propagate through retrieval • Calculate 2σ variation of differences from v3.5 retrievals • Preliminary results for O3, H2O, NO2, and CH4 using 100 sample occultations
• Calculating useful averaging kernels analytically is not possible for individual ACE-FTS occultations
• Calculated numerically by using synthetic spectra from “true” state profiles • Perturb true state and calculate difference in retrieval
Error propagation and numeric averaging kernels • Measurement error
• Inverse instrument signal to noise at each wavenumber
• Spectroscopic error • Line strength and position uncertainty
from HITRAN 2004 • Tangent height error
• Assumed max of ±0.5 km (too large) • A priori error
• Essentially 0% for species examined • Pencil beam error
• Error from using single ray in forward model line of sight. Compare with runs using 7 rays. Typically on order of 5%
• Still need: p/T error, instrument line shape error
%
2σ propagated error for O3 Measurement Spectroscopic Tangent height
-20 -10 0 10 20
10
20
30
40
50
60
70
80
90
Altit
ude
(km
)
-0.2 0 0.2 0.4 0.6 0.8 1 10
20
30
40
50
60
70
80
90
100
Ak
Alti
tude
(km
)
O3 synth avg Ker
Drift analysis
• All coincidences from 2004-2013 and 2016 • Criteria of within ±2 h, ±250 km • Here using global data
• Daily means of relative differences (ACE-FTS – INST) • Take linear fit (iterative reweighted least squares), 95%
confidence in slope as error bounds
• Weighted average of values use weights of INST inverse-squared standard error multiplied by the ACE-FTS to INST correlation coefficient, i.e.,
• 𝑊𝑊𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = 𝑟𝑟𝜎𝜎𝑠𝑠2
Drift for O3
• Comparisons with data from
• MIPAS • ESA v7 • IMK-IAA v5R
• MLS v4.2 • OSIRIS v5.17
• Non-zero negative drift in 17-43 km, on order of 2% dec-1
• Non-zero positive drift near 55 km, on order of 6% dec-1
-10 0 10
Drift (% dec- 1
)
10
20
30
40
50
60
70
-20 0 20
Rel diff (%)
10
20
30
40
50
60
70
0 20 40
1 of rel diff (%)
10
20
30
40
50
60
70
-10 0 10
Drift (% dec- 1
)
10
20
30
40
50
60
70
Alti
tude
(km
)
MIPAS ESA
MIPAS IMK-IAA
MLS
OSIRIS
Weighted avg
Drift for H2O
• Comparisons with data from
• MIPAS • MLS
• Non-zero negative drift in 28-45 km, on order of 5% dec-1
• May be driven by MLS positive drift
• Non-zero positive drift in UTLS
-10 0 10
Drift (% dec- 1
)
10
20
30
40
50
60
70
80
-20 0 20 40 60
Rel diff (%)
10
20
30
40
50
60
70
80
0 50 100
1 of rel diff (%)
10
20
30
40
50
60
70
80
MIPAS ESA
MIPAS IMK-IAA
MLS
Weighted avg
-20 0 20
Drift (% dec- 1
)
10
20
30
40
50
60
70
80
Alti
tude
(km
)
Summary • Validation is fun • 2 papers published this year on v3.5 validation
• Species common to ACE-FTS, MIPAS, and MLS (O3, H2O, N2O, HNO3, CO) • NOy species (NO, NO2, HNO3, N2O5, ClONO2) • Also an N2O paper,
• ACE-FTS N2O will be compared to modified WACCM runs
• More comprehensive error budget calculations are in the works • Started for O3, NO2, H2O, CH4 • Preliminary results for measurement, spectroscopic, tangent height, a priori, and pencil
beam error • Still need to calculate p/T, and ILS errors • Project temporarily on hold due to hardware failures in Waterloo
• Non-zero drift found in: • O3 – negative drift on order of 2-3%/dec near 15-40 km • H2O – negative drift on order of 5%/dec near 30-45 km – May be due to MLS positive drift
• Eventually, version 4 will come out, and we’ll get to do it all over again!
Thanks!
The extra bits
-10 0 10 Drift (%/decade)
10
20
30
40
50
60
70
-50 0 50 Rel diff (%)
10
20
30
40
50
60
70
0 50 100 1 of rel diff (%)
10
20
30
40
50
60
70
MIPAS ESA
A l t i
t u d e
( k m
)
-10 0 10 Drift (%/decade)
10
20
30
40
50
60
70
80
90
-50 0 50 Rel diff (%)
10
20
30
40
50
60
70
80
90
0 50 100 1 of rel diff (%)
10
20
30
40
50
60
70
80
90
A l t i
t u d e
( k m
)
MIPAS IMK-IAA
-10 0 10 Drift (%/decade)
10
15
20
25
30
35
40
45
50
55
-50 0 50 Rel diff (%)
10
15
20
25
30
35
40
45
50
55
0 50 100 1 of rel diff (%)
10
15
20
25
30
35
40
45
50
55
A l t i
t u d e
( k m
)
OSIRIS
MLS
A l t i
t u d e
( k m
)
-10 0 10 Drift (%/decade)
10
20
30
40
50
60
70
-50 0 50 Rel diff (%)
10
20
30
40
50
60
70
0 50 100 1 of rel diff (%)
10
20
30
40
50
60
70
Measurement error Inverse instrument signal to noise at each wavenumber • Typically on the
order of 1-5%, greater at upper altitude limits where there is less signal. Typically less than the v3.5 statistical fitting error
Mean diff 2σ
-20 -10 0 10 20
10
20
30
40
50
60
70
%
Alti
tude
(km
)
CH4
-20 -10 0 10 20
10
15
20
25
30
35
40
45
50
%
Alti
tude
(km
)
NO2
-20 -10 0 10 20
10
20
30
40
50
60
70
80
90
%
Alti
tude
(km
)
O3
-30 -20 -10 0 10 20 30
20
40
60
80
100
%
Alti
tude
(km
)
H2O
Error propagation – 100 occultations
ACE Science Team Meeting, 17 May 2016
Error propagation – 100 occultations Spectroscopic error Line strength and position uncertainty from HITRAN 2004
• Typically on the order of 1-5% in stratosphere, 5-20% in upper troposphere. Typically less than the v3.5 statistical fitting error
-20 -10 0 10 20
10
20
30
40
50
60
70
%
Alti
tude
(km
)
CH4
-20 -10 0 10 20
10
15
20
25
30
35
40
45
50
%
Alti
tude
(km
)
NO2
-30 -20 -10 0 10 20 30
20
40
60
80
100
%
Alti
tude
(km
)
H2O
-20 -10 0 10 20
10
20
30
40
50
60
70
80
90
%
Alti
tude
(km
)
O3
Mean diff 2σ
ACE Science Team Meeting, 17 May 2016
-20 -10 0 10 20
10
15
20
25
30
35
40
45
50
%
Alti
tude
(km
)
NO2
-20 -10 0 10 20
10
20
30
40
50
60
70
%
Alti
tude
(km
)
CH4
-30 -20 -10 0 10 20 30
20
40
60
80
100
%
Alti
tude
(km
)
H2O
-20 -10 0 10 20
10
20
30
40
50
60
70
80
90
%
Alti
tude
(km
)
O3
Error propagation – 100 occultations Tangent height error Assumed max of ±0.5 km
• typically result in 2σ variation in VMRs of ~10-20%
• 0.5 km is too large
Mean diff 2σ
ACE Science Team Meeting, 17 May 2016
Pencil beam error
-20 -10 0 10 20
10
20
30
40
50
60
70
80
90
%
Altit
ude
(km
)
o3
-20 -10 0 10 20
10
15
20
25
30
35
40
45
50
%
Altit
ude
(km
)
no2
-20 -10 0 10 20
20
40
60
80
100
%
Altit
ude
(km
) h2o
-20 -10 0 10 20
10
20
30
40
50
60
70
%
Altit
ude
(km
)
ch4
Levenberg-Marquardt least squares fitting
𝐱𝐱𝑖𝑖+1 = 𝐱𝐱𝑖𝑖 + 𝐊𝐊𝑖𝑖𝐼𝐼𝐒𝐒𝑦𝑦−1𝐊𝐊𝑖𝑖 + 𝜆𝜆𝑖𝑖𝐃𝐃𝑖𝑖
−1𝐊𝐊𝑖𝑖𝐼𝐼𝐒𝐒𝑦𝑦−1 𝐲𝐲 − 𝐊𝐊𝑖𝑖𝐱𝐱𝑖𝑖
𝐆𝐆 = 𝜕𝜕𝜕𝜕𝐲𝐲
𝐱𝐱𝑖𝑖+1 =𝜕𝜕𝜕𝜕𝐲𝐲
𝐱𝐱𝑖𝑖 + 𝐊𝐊𝑖𝑖𝐼𝐼𝐒𝐒𝑦𝑦−1𝐊𝐊𝑖𝑖 + 𝜆𝜆𝑖𝑖𝐃𝐃𝑖𝑖
−1𝐊𝐊𝑖𝑖𝐼𝐼𝐒𝐒𝑦𝑦−1 𝐲𝐲 − 𝐊𝐊𝑖𝑖𝐱𝐱𝑖𝑖
𝐂𝐂𝑖𝑖+1 = 𝐂𝐂𝑖𝑖 + 𝐌𝐌𝑖𝑖𝐊𝐊𝑖𝑖
𝐼𝐼𝐒𝐒𝑦𝑦−1(𝐈𝐈 − 𝐊𝐊𝑖𝑖𝐂𝐂𝑖𝑖)
𝐊𝐊𝑖𝑖𝐂𝐂𝑖𝑖+1 = 𝐈𝐈
∴ 𝐆𝐆 =𝜕𝜕𝐱𝐱�𝜕𝜕𝐲𝐲 = 𝐂𝐂𝑖𝑖+1→ 𝐊𝐊𝑖𝑖
−1
= 𝐌𝐌𝑖𝑖 = 𝐂𝐂𝑖𝑖+1
in well behaved retrieval, as 𝑖𝑖 → ∞, 𝐂𝐂𝑖𝑖 → 𝐂𝐂𝑖𝑖+1
∴ 𝐀𝐀 = 𝐆𝐆𝐊𝐊 → 𝐊𝐊𝑖𝑖−1𝐊𝐊𝑖𝑖 = 𝐈𝐈
These are similar to each other, however they require using simulated data and are much more computationally expensive to produce
WTF is an Ak?
-0.2 0 0.2 0.4 0.6 0.8 1 10
20
30
40
50
60
70
80
90
100
Ak
OE synth
-0.2 0 0.2 0.4 0.6 0.8 1 10
20
30
40
50
60
70
80
90
100 OE
Ak -0.2 0 0.2 0.4 0.6 0.8 1
10
20
30
40
50
60
70
80
90
100 LS
Ak
Alti
tude
(km
)
-0.2 0 0.2 0.4 0.6 0.8 1 10
20
30
40
50
60
70
80
90
100
Ak
Alti
tude
(km
)
LS synth
ACE Science Team Meeting, 17 May 2016
Averaging kernels, as typically calculated, do not represent the sensitivity of the retrieval to the true state (as numerically calculated)
WTF is an Ak?
• As typically calculated in a retrieval • It is NOT the sensitivity to the true state, where a value of 1 means only
sensitive to the true state and 0 means not at all
• It is the sensitivity to the retrieval at the previous iteration, where a value of 1 means no sensitivity to the constraint and 0 means you’re only getting back the constraint
Highlights: ACE/MIPAS/MLS CO
ACE Science Team Meeting, 17 May 2016
10 -8
10 -7
10 -6
10 -5
20
30
40
50
60
70
VMR (ppv)
MIPAS__IMK
Alti
tude
(km
)
ACE NH winter IMK NH winter ACE SH winter IMK SH winter ACE NH summer IMK NH summer ACE SH summer IMK SH summer
Alti
tude
(km
)
10 -8
10 -7
10 -6
10 -5
20
30
40
50
60
70
VMR (ppv)
MLS
ACE NH winter MLS NH winter ACE SH winter MLS SH winter ACE NH summer MLS NH summer ACE SH summer MLS SH summer
0 500 1000
20
30
40
50
60
70
Coincidences
Alti
tude
(km
)
0 0.5 1
20
30
40
50
60
70
Correlation coeff -50 0 50
20
30
40
50
60
70
Rel diff (%) 0 50 100
20
30
40
50
60
70
1 σ of rel diff (%)
NH winter SH winter NH summer SH summer
0 200 400
20
30
40
50
60
70
Coincidences
Alti
tude
(km
)
0 0.5 1
20
30
40
50
60
70
Correlation coeff -50 0 50
20
30
40
50
60
70
Rel diff (%) 0 50 100
20
30
40
50
60
70
1 σ of rel diff (%)
NH winter SH winter NH summer SH summer
0 200 400
20
30
40
50
60
70
Coincidences
Altit
ude
(km
)
0 0.5 1
20
30
40
50
60
70
Correlation coeff -50 0 50
20
30
40
50
60
70
Rel diff (%) 0 50 100
20
30
40
50
60
70
1 σ of rel diff (%)
NH winter SH winter NH summer SH summer
10 -8
10 -7
10 -6
10 -5
20
30
40
50
60
70
VMR (ppv)
MIPAS__IMK
Alti
tude
(km
)
10 -8
10 -7
10 -6
10 -5
20
30
40
50
60
70
VMR (ppv)
MLS
ACE NH winter MLS NH winter ACE SH winter MLS SH winter ACE NH summer MLS NH summer ACE SH summer MLS SH summer
ACE NH winter IMK NH winter ACE SH winter IMK SH winter ACE NH summer IMK NH summer ACE SH summer IMK SH summer
Global N2O
• Agree within ±3% below 26 km; ACE-FTS is ~10% smaller near 28-35 km • Positive drift of ~5 ppbv/decade near 23 km
0 10 20
VMR (ppv) 10-8
20
25
30
35
40
45
50
55
Alti
tude
(km
)
MLS
5881
17759
17759
17759
17759
17759
13172
1487
Linear trend correlation coeff -20 0 20
Drift (%/decade) -50 0 50
Rel diff (%)
20
25
30
35
40
45
50
55
0 50 100 1 of rel diff (%)
20
25
30
35
40
45
50
55
0 0.1
20
25
30
35
40
45
50
55
A l t i
t u d e
( k m
)
N2O
0.2
20
25
30
35
40
45
50
55
ACE-FTS – MLS
Global N2O
• No significant drift is found when comparing ACE-FTS and MLS v3 N2O • v3 N2O uses the 640 GHz channel, v4 uses 190 GHz
ACE-FTS – MLS v3
0 0.02 0.04 0.06
Linear trend correlation coeff
20
30
40
50A
ltitu
de (k
m)
N2O
-20 0 20
Drift (%/decade)
20
30
40
50
-50 0 50
Rel diff (%)
20
30
40
50
0 50 100
1 of rel diff (%)
20
30
40
50
NOTE: Lower altitude limits are different between v3 and v4!
