Can the assimilation of atmospheric constituents improve...

Can the assimilation of atmospheric constituents improve the weatherforecast?

S. MassartAcknowledgement: M. Hamrud

Seventh International WMO Symposium on Data Assimilation

11-15 September 2017

October 29, 2014EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS

Very simple schematic of the problem

Initial condition (t0)of an atmospheric tracer

Forecast (t)

Observation (t)

S. Massart c©ECMWF 1 / 15





Forecast (t)Forecast (t)

Observation (t)



Formulation8 Linear analysis equation

xa = xb +K[yo−Gxb

]where

K = BGT [GBGT+R]−1

and G = HM

8 Separation into a physical state ϕ and a chemical state χ

xb =

(xb

ϕ

xbχ

), xa =

(xa

ϕ

xaχ

), M =

(Mϕ,ϕ Mϕ,χ

Mχ,ϕ Mχ,χ

), B =

(Bϕ,ϕ Bϕ,χ

Bχ,ϕ Bχ,χ

)




xa = xb +K[yo−Gxb

]where


and G = HM


xb =

(xb

ϕ

xbχ

), xa =

(xa

ϕ

xaχ

), M =

(Mϕ,ϕ Mϕ,χ

Mχ,ϕ Mχ,χ

), B =

(Bϕ,ϕ Bϕ,χ

Bχ,ϕ Bχ,χ

)8 Physical and chemical interaction

1. MTχ,ϕ: adjoint of the model part interacting between physics and chemistry (for e.g. transport)

2. Bϕ,χ: covariances of the background errors between the physical state and the chemical state




xa = xb +K[yo−Gxb

]where


and G = HM


xb =

(xb

ϕ

xbχ

), xa =

(xa

ϕ

xaχ

), M =

(Mϕ,ϕ Mϕ,χ

Mχ,ϕ Mχ,χ

), B =

(Bϕ,ϕ Bϕ,χ

Bχ,ϕ Bχ,χ

)8 Physical and chemical interaction

1. MTχ,ϕ: adjoint of the model part interacting between physics and chemistry (for e.g. transport)

2. Bϕ,χ: covariances of the background errors between the physical state and the chemical state

8 Chemical Transport Model (usually state of the art chemistry)ë No physical part: Mϕ,ϕ ≡ 0, xb

ϕ ≡ 0ë No chemistry⇒ physic interaction not possible

8 Numerical Weather Prediction (usually simplified chemistry)ë No feedback of the composition on the physics: MT

χ,ϕ ≡ 0 and Bϕ,χ ≡ 0



Previous studies8 Daley (1995)

ë Extended Kalman filter + 1D constituent transport equation & prognostic linear wind modelë Information on wind field can be recovered if the observation are sufficiently frequent and accurate, and data voids

are small

8 Riishøjgaard (1996)ë 4D-Var + ozone pseudo observations + barotropic vorticity-equation modelë Quality of the results depends on the resolution of the model and on the length of the assimilation window

8 Holm et al. (1999)ë 4D-Var + TOVS ozone product + ECMWF’s NWP modelë Details on the wind-ozone coupling and importance of a good enough chemistry parametrization

8 Peuch et al. (2000)ë 4D-Var + OSSEs for TOVS ozone product + Météo-France’s NWP modelë The accuracy of total-ozone measurements needs to be good enough to get any additional information

8 Semane et al. (2009)ë 4D-Var + MLS ozone profile + Météo-France’s NWP modelë The ozone assimilation reduces the wind bias in the lower stratosphere



Why revisiting this now?Copernicus Atmosphere Monitoring Service (CAMS)

8 Addition of more detailed atmospheric composition in a NWP modelë Reactive gases: ozone (O3) , carbon monoxide (CO), ...ë Aerosols: black carbon, dust, ...ë Greenhouse gases: carbon dioxide (CO2) and methane (CH4)

8 More and more retrievals of satellite dataë MLS, OMI, SBUV-2, IASI, MOPITT, GOME-2, OMPS, TANSO, PMAp, MODIS, ...






How to choose the atmospheric tracer to assimilate?8 Long-lived tracer is preferred (to be compared to the assimilation window)8 Low model bias is important8 Good global coverage of dense and frequent observations






How to choose the atmospheric tracer to assimilate?8 Long-lived tracer is preferred (to be compared to the assimilation window)8 Low model bias is important8 Good global coverage of dense and frequent observations8 Choice: CO, CO2 and CH4







Which method?8 Semane et al.: MT

χ,ϕ 6= 0 but still Bϕ,χ ≡ 0 in a 4D-Var environment

8 In this study: MTχ,ϕ ≡ 0 but Bϕ,χ 6= 0







Which method?8 Semane et al.: MT

χ,ϕ 6= 0 but still Bϕ,χ ≡ 0 in a 4D-Var environment

8 In this study: MTχ,ϕ ≡ 0 but Bϕ,χ 6= 0⇒ Ensemble Kalman Filter



Observations for one day (2010-01-01)8 TANSO CO2

8 IASI CO2

8 SCIAMACHY CO2

8 TANSO CH4

8 IASI CH4

8 SCIAMACHY CH4

8 MOPITT CO



Observations for the period 2010-01-01 to 2010-03-018 TANSO CO2

8 IASI CO2

8 SCIAMACHY CO2

8 TANSO CH4

8 IASI CH4

8 SCIAMACHY CH4

8 MOPITT CO

8 CO has the bettercoverage with MOPITT

8 CH4 has the goodcoverage over land andonly IASI over sea

8 CO2 has the worsecoverage but in the tropicswith IASI



Vertical information

CO2

8 Potential information in thetroposphere and lowerstratosphere

CH4

8 Potential information in thetroposphere and lower andmiddle stratosphere

CO

8 Potential information in thetroposphere only, not in thestratosphere



Experiments configurations8 EnKF configuration

ë Horizontal resolution Tl159 (≈ 125×125 km2)ë 137 levelsë 50 membersë 6-h assimilation windowë Hamrud et al. for more details

8 Control experiment (CTR):ë assimilation of operational data but no constituent data

8 GRG experiment, same as CTR with the additional assimilation of:ë XCO from MOPITT

8 GHG experiment, same as CTR with the additional assimilation of:ë XCO2 and XCH4 from TANSOë XCO2 and XCH4 from SCIAMACHYë XCO2 and XCH4 from IASI

8 Starting date: 1 January 2010ë JF: January and February 2010ë MAM: March to May 2010ë JJA: June to August 2010



Cross-correlationGRG experiment GHG experiment 8 Cross-correlation between CO and T,

Q, in the mid and upper stratosphere8 Cross-correlation between CH4 and T,

Q, D and Vo in the stratosphere8 Cross-correlation between CO2 and Q

and Vo in the upper stratosphere

ExpectationsImpact of CH4 on the thermodynamic inthe middle stratosphere



Change in error in R - Jan. and Feb. 2010 – Bluish: , Reddish:GRG-CTR

t +12 t +24

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Change in error in R - Jan. and Feb. 2010 – Bluish: , Reddish:GRG-CTR

t +12 t +24

−0.2

−0.1

0.0

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t +96 t +120

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a

GHG-CTRt +12 t +24

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MS

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Change in error - Jan. and Feb. 2010 – Bluish: , Reddish:GRG-CTR t +12

R

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−0.1

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Change in error at t +12 – other seasons – Bluish: , Reddish:GRG-CTR MAM 2010

R

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GHG-CTR MAM 2010R

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GRG-CTR JJA 2010R

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GHG-CTR JJA 2010R

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First guess departure standard deviationJF 2010

TEMP-T GPSRO

97 98 99 100 101 102 103FG std. dev. [%, normalised]

1000

850

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Pre

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hP

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94 96 98 100 102FG std. dev. [%, normalised]

3579

1113151719212325272931333537394143454749

Altitu

de

[km

]

WIND-U WIND-V


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MAM 2010TEMP-T GPSRO

97 98 99 100 101 102FG std. dev. [%, normalised]

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97 98 99 100 101 102FG std. dev. [%, normalised]

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WIND-U WIND-V

99.0 99.5 100.0 100.5 101.0 101.5 102.0FG std. dev. [%, normalised]

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99.0 99.5 100.0 100.5 101.0 101.5FG std. dev. [%, normalised]

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8 GHG - CTR8 GRG - CTR8 < 100%8 > 100%8 GPSRO function

of altitude (km)8 Others function

of pressure(hPa)

8 5 hPa ≈ 35 km



Conclusions

8 Purpose of the study: revisiting the feasibility of inferring physical information from the assimilation ofchemical constituent observations (together with operational observations)

ë in the context of Copernicus Atmosphere Monitoring Service (CAMS)ë exploring other constituents than ozoneë focusing on covariances between the physical and chemical background errors

8 Resultsë Almost no impact from the assimilation of COë More impact from the assimilation of CO2 and CH4ë Impact mainly in the stratosphere:

troposphere already too well constrained by the operational observations?tracers too well mixed in the troposphere (no gradient)?

ë Cross-correlations not only with dynamical fields but also with temperature and relative humiditythe balance operator should account for these cross-correlation of the background errors in a 4D-Varassimilating chemical constituent observations with MT

χ,ϕ 6= 0ë Strong difference between JJ and other seasons

Spin-up effect?Seasonal effect?To be further investigated



References

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M. Hamrud, M. Bonavita, and L. Isaksen. “EnKF and Hybrid Gain Ensemble Data Assimilation. Part I:EnKF Implementation”. In: Monthly Weather Review 143.12 (2015), pp. 4847–4864.

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Can the assimilation of atmospheric constituents improve...

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