spectral shape modeling and remote sensing applications

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13/06/2017 Ha Tran 1 Spectral shape modeling and remote sensing applications H. Tran Laboratoire de Météorologie Dynamique, CNRS UMR 8539, Sorbonne Universités, UPMC Univ Paris 06

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Page 1: Spectral shape modeling and remote sensing applications

13/06/2017 Ha Tran 1

Spectral shape modeling and remote sensing applications

H. TranLaboratoire de Météorologie Dynamique, CNRS UMR 8539,

Sorbonne Universités, UPMC Univ Paris 06

Page 2: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 2

Outline

• Introduction: Atmospheric observation and spectral shape

• The Voigt profile

• Line-mixing for closely spaced lines

• Isolated lines: limits of the Voigt profile

• Widely-used non-Voigt approaches

• A new line-shape model for spectroscopic databases and applications

• Conclusions

Page 3: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 3

Measured spectra (satellite, balloon, ground,…)

- P, T profiles- Molecule mole fraction profiles- Cloud altitude, aerosol- etc. …

Input spectroscopic data(Position, Intensity, spectral shape,…)

Spectral shapes Collisional (pressure) effects on the spectral shape

Radiative transferForward model

Introduction: Atmospheric retrievals

Page 4: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 4

The Voigt profile

Page 5: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 5

fi fi fi fiS I

fi

fi

fi fi fiS d d

fi fid I d I 1

The absorption coefficient is given by

Spectral shape of an isolated line

→ Line intensity is distributed around the line position

→ Normalized line profile Ifi()

Page 6: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 6

The Doppler broadening

A molecule having a speed v#0, absorbs or emits at a wavenumber , which is different of 0 of this molecule at v=0.

The corresponding Doppler shift is: = fi (1+vz/c) where vz the radiator velocity component along the wave propagation vector.

The line profile is a Gaussian profile

2

fiD fi

D D

ln2 1I exp ln 2

1 2B

D fi2

2k Twhere ln 2mc

is the HWHM of the line.

Page 7: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 7

The Lorentz broadening and shifting

For an isolated transition, the main effects of intermolecular collisions (pressure) are the (Lorentz) broadening and shifting of the line

3038.4 3038.5 3038.6

3, R(1) line of CH4-N2 0.50 atm 0.24 atm

Abso

rptio

n

wavenumber

0

Abso

rptio

n

wavenumber

and proportional to the # of coll (dens or P)

E ne rg y (H 0+V )E upper

E L ow er

tim e

fi

L fi 2 2fi fi fi

1I ( )

Page 8: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 8

Let’s consider a speed class: [(vz) – (vz+dvz)]the corresponding spectral domain is [(’) – (’+d’)], with ’= 0(1+vz/c)

V fi D fi LI ( ) d ' I ' I '

The Voigt profile is thus a convolution of a Gaussian profile (Doppler effect) and a Lorentzian profile (collisional effect).

The Voigt profile

Due to collisions, the spectral intensity ID(’- fi)d’ is redistributed as a Lorentz profile centered at ’. The final contribution of this speed class at is thus:

ID(’- fi) d’ IL(- ’)

The resulting profile is then obtained by summing over all speed classes (or all ’) :

Page 9: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 9

The Gaussian, Lorentzian and Voigt profiles

Pressure

Doppler

Doppler+Collision

Collisions

Gaussien

Voigt

Lorentzian

Usual behavior of the line width

Page 10: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 10

Comparaison between the Gaussian, the Lorentz and the Voigt profiles.

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08

0

10

20

30

40

50

60

Nor

mal

ized

pro

file

(a.u

.)

W avenum ber (cm -1)

G aussian Voigt Lorentz

D=0.01 cm -1

L=0.01 cm -1

6364.0 6364.5 6365.0 6365.5 6366.0 6366.5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

trans

mis

sion

nombre d'onde (cm-1)

FTS ground-based absorption spectrum (CO2)

The Gaussian, Lorentzian and Voigt profiles

Courtesy of S. Payan

Page 11: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 11

Bernath et al., GRL, 32, L15S01 (2005)

ACE spectra at different tangent altitudesIntroduction: isolated lines and closely spaced lines

Page 12: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 12

Non-isolated transitions: Line-mixing effects

Page 13: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 13

In some cases, for closely spaced lines, the Voigt profile fails when P increases. It predicts shapes that are too broad.

6076.6 6076.8 6077.0 6077.2 6077.4-0.2

0.0

0.2

0.4

0.6

0.8

1.0CH4/N2,R(6) manifold, P=900.5 hPa, T=296 K

A11F11

F22

A21

F21E1

Tran

smis

sion

Wavenumber (cm-1)

Collisions induce transfers of populations between the levels of the two lines that lead to transfers of intensity between the lines.

2E

2E

1E

1E E

E

Line-mixing effects

Page 14: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 14

line kline

10k

LM iPW LΣ kddρσα

Line-mixing effects: Absorption coefficient

rk populationsdk matrix element of radiation-matter coupling tensor, L0 matrix of positionsW relaxation operator. All effects of collisions. Independent of within

the impact approximation (not too far in the wings)

Wlk0 Line coupling between |k> and |l>

Wlk=0 No line coupling (Lorentz)

k kkk

kkk

Lo d

1

222

Page 15: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 15

For moderate line overlapping, a first order perturbation approach is possible. Then we only need to know one coupling parameter (Y, related to the W matrix elements) per line

σσdd2Y

k

k

k kk

W

Line-mixing effects: Relaxation matrix

kkiW k k

llines lSum rule: d l W k 0

k W l l W k : Balance Dkl

rr.et

Relations for W

Page 16: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 16

Nadir looking instruments onboard satellitesGreenhouse gases Observation SATellite (GOSAT, JAXA-NIES, in orbit)Orbiting Carbon Observatory (OCO 2, NASA, in orbit)MicroCarb (CNES, under study), CarbonSat (ESA, under study)

Spectral regions and aims- CO2 from 1.6 mm (weak) and 2.1 mm (strong) bands- Air mass from O2 A band (0.76 mm)- CH4 from 23 band (near 1.7 mm)- aerosols from CO2 and O2 bands

Detection/quantifying sinks and sources (1 ppm for xCO2, 0.3 %)→ Extreme accuracy of spectra modelling. Huge constraints on the spectroscopic data and the prediction of pressure effects (collisions and spectral-shape)

Line-mixing and remote sensing: Monitoring Greenhouse Gases from space

Page 17: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 17

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26376

378

380

382

384

386

388

390

392

394

396

398

400

Tota

l CO

2/to

tal a

ir (p

pm)

Time

4820 4840 4860 4880

0.0

0.2

0.4

0.6

0.8

1.0

Observed Fitted Residuals

Tran

smis

sion

wavenumber (cm-1)

Sza 79.9°, Park Falls, region 2.1 mmWrong air-mass (and time) dependences

6180 6200 6220 6240 6260

0,0

0,2

0,4

0,6

0,8

1,0

wavenumber

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26376

378

380

382

384

386

388

390

392

394

396

398

400

Tota

l CO

2/to

tal a

ir (p

pm)

Time

CO2 retrievedSza 79.9°, Park Falls, region 1.6 mm

CO2: ground-based measurements

Page 18: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 18

7770 7800 7830 7860 7890 7920 7950 7980 8010

0,0

0,2

0,4

0,6

0,8

1,0

trans

mis

sion

wavenumber

Sza 79.9°, Park Falls, 1.27 mm band

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 260,211

0,212

0,213

0,214

0,215

0,216

0,217

0,218

0,219

0,220

0,221

0,222

Tota

l O2/

tota

l air

time

12990 13020 13050 13080 13110 13140 13170

-0.2

0.0

0.2

0.4

0.6

0.8

1.0 Observed Fitted Residuals

Tran

smis

sion

wavenumber (cm-1)

Sza 79.9°, Park Falls, A bandWrong time (and air-mass) dependences

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 260,211

0,212

0,213

0,214

0,215

0,216

0,217

0,218

0,219

0,220

0,221

0,222

Tota

l O2/

tota

l air

time

O2: Ground-based measurements

Page 19: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 19

Modeling of line-mixing effectW: Scaling laws, from the basic collisional rates. For O2:

Q – Basic collisional rates, modeled by

λ1)L(LTTAQ(L)

N

0

A, N, are adjusted from the mesured line widths using the sum rule

- adiabatic factor

Q(L)1)L(2T)Ω(L,T),Ω(N

JJ

JJ

nL

NJ

NJ

SL

NJ

NJ

SL

0L

0N

0N

0L

0N

0N

JNJNWJNJN

i

f

i'

f'

i

f

f'

f'

f

f

i

i'

i'

i

i

ff'ii'iiffi'i'f'f'

L

Page 20: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 20

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016 DTot

=106 amagatsT = 194K

12900 13000 13100 13200 13300 13400

-0.001

0.000

0.001

0.002

0.003

(cm -1)

O2/N2 A band

___ measurement, ___ Line-mixing ___ Lorentz

4900 4920 4940 4960 4980 5000 5020 50400.0

0.2

0.4

0.6

0.8

(cm -1)

0.00.20.40.60.81.01.2

Abs.

Coe

f (10

-2 c

m-1)

0.0

0.5

1.0

1.5

2.0

CO2/N2

Line-mixing effects: Laboratory studies

Page 21: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 21

12990 13020 13050 13080 13110 13140 13170

-0.05

0.00

0.05

0.10

0.15

observed-(LM+CIA)Res

idua

l

wavenumber (cm-1)

-0.05

0.00

0.05

0.10

0.15

observed-Voigt

0.0

0.2

0.4

0.6

0.8

1.0

Tran

smis

sion

10 12 14 16 18 20 22 24 260.211

0.212

0.213

0.214

0.215

0.216

0.217

0.218

0.219

0.220

0.221

0.222

0.223

0.224

1.27 micron O2 band O2 A band no LM no CIA O2 A band LM and CIA

Tota

l O2/

tota

l air

UT (hour)

Influence on retrievals: O2 ground-based measurements

Spectra: Sza 79.9°, Park Falls O2 A band region O2 A band:

Relative errors on surface pressures retrieved from atmospheric spectra

Page 22: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 22

Fits of a ground based transmission spectra in the region of the 2 1+ 3 band of CO2

Significant errors on the CO2atmospheric amount.Sinks and sources !!!

Influence on retrievals: CO2 ground-based measurements

4820 4840 4860 4880

-0.02

0.00

0.02

observed-LM

Res

idua

ls

wavenumber (cm-1)

-0.02

0.00

0.02

observed-Voigt

0.0

0.2

0.4

0.6

0.8

1.0

Tran

smis

sion

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26376

378

380

382

384

386

388

390

392

394

396

398

400

Tota

l CO

2/to

tal a

ir (p

pm)

UT (hour)

2.1 mm band, no LM 2.1 mm band, with LM 1.6 mm band

Page 23: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 23

Other systems

1000 1100 1200 1300 1400 1500

-5

0

5 Exp-notre m odèleRadi

ance

et R

ésid

us [m

W/(m

2 cm-1sr

)]

(cm -1)

-5

0

5

10

15Exp-Voigt

0

5

10

15

20

25

30

35Expérience

ISO/SWS spectrum of Jupiter, 4 band region of CH4

CH4/H2

920 940 960 980 1000-0.10

-0.05

0.00

0.05

0.10 Exp-notre modèle

Obs

- C

alc

wavenumber (cm-1)

-0.10

-0.05

0.00

0.05

0.10 Exp-Voigt

Obs

- C

alc

0.0

0.2

0.4

0.6

0.8

1.0

ExpérienceTran

smis

sion

500 hPa, 234 K, 6 band

Page 24: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 24

Isolated line

Page 25: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 25

Measured and adjusted spectra using the Voigt profile.

0.0 0.2 0.4 0.6 0.8 1.0 1.20.6

0.7

0.8

0.9

1.0

Pression (atm)

Coe

ffici

ent d

'éla

rgis

sem

ent (

cm-1/a

tm)

H2O/N2, 3 band, 111,11101,10 110,11 100,10

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Abs

orpt

ion

300 Torr 250 Torr 200 Torr 150 Torr 100 Torr 50 Torr

-0.3 -0.2 -0.1 0.0 0.1 0.2

-0.4

0.0

0.4

Relative wave number (cm-1)

100

x(ob

serv

ed-a

djus

ted)

Limits of the Voigt profile

Page 26: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 26

1. The velocity changes induced by collisions.

The detailed balance:

change from v to v’ < v is more probable than that from v to v’ > v. reduction of the Doppler broadening Dicke narrowing effect

2. The speed-dependences of the collisional width (v) and shift (v) of the line.This also (in general) leads to a narrowing of the line

Limits of the Voigt profile

The Voigt profile neglects:

0 200 400 600 800 1000 1200 1400 16000.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

H2O, 296 K

Rel

ativ

e de

nsity

Molecule speed (m/s)

Page 27: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 27

Widely used non-Voigt approaches

Page 28: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 28

The Galatry (soft collisions, SC) profile:- Assumes that radiators undergo only a very small changes per collision- Introduces a velocity changing rate b or Diff related to the diffusion coefficient

The Nelkin-Ghatak (hard collisions, HC) profile:- Assumes that velocity memory is lost after each collision- Introduces a velocity changing rate VC also related to the diffusion coefficient

Simple non-Voigt approaches: the velocity changes effect

We can show that the Galatry profile is more appropriate for systems for which the radiator molecule is much heavier than the perturber.

However, experiences show that the Galatry profile and the Nelkin-Ghatak profile lead to similar quality in term of residual fit of measured spectra

Page 29: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 29

Simple non-Voigt approaches: the velocity changes effect

Page 30: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 30

Limits of the Voigt profile: atmospheric retrieval In situ absorption spectrum of tropospheric H2O recorded by balloonborne diode laser and its fit using the Voigt profile

Durry et al, JQSRT 94, 387,2005

Page 31: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 31

Limits of the Voigt profile: Influence on atmospheric retrievals

Barret et al, JQSRT 95, 499, 2005

HF profile retrieved from ground-based absorption FTIR measurements (Jungfraujoch station) in the (1-0) R(1) micro-window by using the Voigt profile and the Soft Collision model, compared with the HALOE (Halogen Occultation Experiment) profiles smoothed.

Page 32: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 32

Velocity changes effects: Remaining problems

Pressure dependence of the frequency optical collisions obtained for the R(24) line of CO2 in air from the NGP, GP and single-spectrum fits. The green line is the frequency of optical collisions calculatedfrom the mass diffusion coefficient

The collisional narrowing parameter is non linear with pressure

Page 33: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 33

Velocity changes effects: Remaining problems

Only Dicke narrowing is insufficient

404-303 3 band of H2O in N2: multi-spectrum fitting technique99, 197, 294, 398, 499, 600, 797, 1000, 1200 hPa

Lisak et al, JQSRT, 2015

Page 34: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 34

Simple non-Voigt approaches: the speed dependences

- The quadratic dependence of Rohart

a

2a 0 2 a 0 a v

(v ) [(v / v) 3 / 2] with (v )

pr r(v ) v

- The polynomial dependence of Berman-Pickett

va < < vr # vb

G(va) # Ctea

b

mactive > > mbuffer

Weak speed dependence

va # vr > > vb

G(va) # G(vr)b

a

mactive << mbuffer

Strong speed dependence

a r r a r(v ) (v ). f (v v )dv

Courtesy of F. Rohart

Page 35: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 35

Simple non-Voigt approaches: the speed dependences707<-606 and 717<-616 lines (at 0,83nm) of pure H2O (left) and H2O/air (right)

Ngo et al, JQSRT, 113, 870, 2012

Page 36: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 36

Speed dependent Voigt profile: Remaining problems

Larcher et al, JQSRT, 2015

2/0 non constant vs pressure!

R(20) line of pure CO2 at 1,6 mm

Page 37: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 37

Speed dependent Voigt profile: Remaining problems

→ Speed dependence only is not sufficient

R(9) line of the fundamental band of HCl perturbed by Ar

Page 38: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 38

Dicke narrowing+Speed dependences effect: Which line-shape?

An isolated line of CO2 in air An isolated line of pure H2O

De Vizia et al, PRA, 83, 052506, 2011Long et al, J Chem Phys, 135, 064308, 2011

Page 39: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 39

“Today” situation for line-shape study

1- Thanks to the development of high resolution and S/N laboratory techniques (egCEAS, CRDS) and spectra analysis techniques (multispectrum fits) the vast majority of experimental studies now clearly evidence the limits of the Voigt. Various fitting line shapes are used, chosen according to ad hoc criteria, so that the available data are inconsistent with no consensus.

2- Very ambitious and precision-demanding remote sensing experiments are operational, under study or being developed (GOSAT, OCO-2, TCCON, MERLIN, Micro Carb…) which require an accuracy of spectra simulations (<0.3%) that prohibits the use of the Voigt profile

Page 40: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 40

A functional form for non-Voigt line shapesfor spectroscopic databases and applications

Page 41: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 41

Urgent need for a better isolated line shape model and associated data to fit experimental/calculated spectra and feed databases used for remote sensing. This line shape should fulfill various constraints:

1- Take into account the various processes that affect the line profile (Doppler, molecule velocity changes, speed dependences of broadening and shifting) and be sufficiently physically-based to describe the (experimental) line profiles of various transitions of various gases with an accuracy (<0.1%) fulfilling the remote sensing accuracy needs

2- Be based on well identified line-by-line parameters with known and physically based pressure dependences for storage in databases

3- Contain simpler models as limiting cases in order to maximize the possibility to use previously published results obtained using simpler profiles

4- Require CPU time compatible with a use in atmospheric spectra calculations (many lines and layers).

5- Be compatible with a treatment of line-mixing

Requirements for the proposed line-shape

Page 42: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 42

The (complex) area normalized spectral profile for an isolated line centered at w0 isgiven by:

t,vdvivv.ki'vdt,'vd'v,vFt,vd'vdv,'vFt,vdt

Velocity changes Speed dependent width and shift

w

ww

0

ti )t(dedtRe1I 0

vd)t,v(d)t(d

Theoretical background

where d(t) is the time autocorrelation function of the tensor (dipole, polarizability) for the considered optical transition. The latter results from the integration over all molecular velocities , i.e.:

If we assume no correlation (in time) between collisional changes of the moleculevelocity and rotational state, one can write:

Page 43: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 43

Limit cases: - No velocity changes nor speed dependence ↔ Voigt

- No velocity changes but speed dependence ↔ SD Voigt

- Velocity changes but no speed dependence ↔ Dicke narrowed profiles

Various combinaisons, e.g.: speed dependent Nelkin-Ghatak profile, speed dependent Galatry profile, etc etc, with or without the inclusion of a time-correlation between velocity and rotational state changing collisions

Theoretical background

t,vdvivv.ki'vdt,'vd'v,vFt,vd'vdv,'vFt,vdt

Velocity changes Speed dependent width and shift

Page 44: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 44

1- Speed dependence: quadratic dependences of (v) and (v) (Rohart et al) on absolute speed v

2- Velocity changes: hard collision model (HC)

3- Correlation between velocity and rotational state changes:

Replace VC by:

Four P and T dependent parameters: 0 and 2 for broadening, 0 and 2for spectral shift

One P and T dependent parameters parameter: VC

Four T dependent parameter : h

( ' ) ( ')VC MBf v v f v

( ) ( )VC VC v i v h

20 0 2 2(v) i (v) ( i ) ( i )[(v / v) 3/ 2]

Proposed model

Page 45: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 45

For each transition of a given absorbing species colliding with a given “perturber” (eg H2O-H2O, H2O-O2, etc) we have

Hence: pressure dependence known → compatible with databases if Temperature dependence parameterized.

1- For Speed Dependence (0 , 2, 0 and 2 ) : X(P,T)=PX0(T)

For absorbing molecule in mixture: combination of values for absorber-pertuber pairs

2- For Velocity changes : VC(P,T)=PVC0(T)

3- For Correlation : h (P,T)=h (T)

iVC i VC

i

i ii

i

i i ii

i

x ,

(v) i (v) x (v) i (v) ,

(v) i (v) x (v) i (v) ,

h h

Fulfilling the needs: 1- Database

Page 46: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 46

Some simpler models are directly obtained from the pCqSDHC

For Soft collisions (Galatry) and hypergeometric SD relations have been derived (see Ngo et al, JQSRT 129, 89, 2013)

Profile parameters Limit of the pCqSDHC profile

VP 0 0, VC 2 2 0 h

RP VC 0 0, , 2 2 0h

qSDVP 0 0 2 2, , , VC 0 h

qSDRP VC 0 0 2 2, , , , 0h

Hence: The simpler models can be cast onto the pCqSDHC so that published data can be used

Fulfilling the needs: 2- Simpler models

Page 47: Spectral shape modeling and remote sensing applications

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Fulfilling the needs: 3- Quality, H2O/N2

Lisak et al, JQSRT, 2015

Page 48: Spectral shape modeling and remote sensing applications

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With the proposed SD and VC models, the line shape becomes analytical and can be expressed in terms of two (complex) Voigt (or complex probability) functionsw(z)

for which a plethora of efficient Fortran routines have been proposed

Using the CPF routine by Humlicek [JQSRT 1979;21:309-313. ], a Fortran routine was built [Tran et al, JQSRT 129, 199-203 (2013)] and numerical tests showed that

- The relative accuracy is always better than 10-5

- The CPU time is, at most 5 times larger than for the computation of a Voigt profile

22

tzi e dtw(z) e erfc( iz)

z t

Fulfilling the needs: 4- CPU

Page 49: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 49

Line Mixing can be easily included, provided that two approximations can be made:

1- One can use the so-called (Rosenkranz) first order approximation, one can simply use the expression

2- One can neglect the speed dependence of line-mixing

In this case one has:

( ) ( ) 1pCqSDHC LM pCqSDHC LMI I iY w w

Fulfilling the needs: 5- line-mixing

Page 50: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 50

Spectroscopy for MERLIN: 0,2% accuracy required!

Voigt Profile

Accuracy: About 1-2%

Page 51: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 51

0,1-0,2% around λon

Spectroscopy for MERLIN: 0,2% accuracy required!

Delahaye et al, 2015

Page 52: Spectral shape modeling and remote sensing applications

Date 13/06/2017 Ha Tran 52

Summary

• Spectral shape has become a key issue for high precision soundings (OCO, ACE, …)

• When line is isolated, the Voigt profile fails to model isolated line-shape, velocity effects should be taken into account

• When lines are closely spaced, line-mixing should be taken into account

• Increasing evidences of influence of refined spectral shape effects for remote sensing

• Need to take into account both line-mixing and velocity effects; through the HT profile -> profile recommended by IUPAC and adopted by the HITRAN spectroscopic database