Can the assimilation of atmospheric constituents improve the weather forecast?

Transcription

1 Can the assimilation of atmospheric constituents improve the weather forecast? S. Massart Acknowledgement: M. Hamrud Seventh International WMO Symposium on Data Assimilation -5 September 207

2 Very simple schematic of the problem Initial condition (t 0 ) of an atmospheric tracer Forecast (t) Observation (t) S. Massart c ECMWF October 29, 204 / 5

3 Very simple schematic of the problem Initial Initial condition (t 0 ) (t 0 ) of an of an atmospheric tracer tracer Forecast (t) (t) Observation (t) S. Massart c ECMWF October 29, 204 / 5

4 Very simple schematic of the problem Initial condition (t 0 ) of an atmospheric tracer Forecast (t) (t) Observation (t) S. Massart c ECMWF October 29, 204 / 5

5 Formulation Linear analysis equation x a = x b + K [ y o Gx b] where K = BG T [ GBG T + R ] Separation into a physical state ϕ and a chemical state χ ( ) ( ) x x b b = ϕ x, x a a = ϕ, M = x b χ x a χ ( Mϕ,ϕ M ϕ,χ M χ,ϕ and G = HM M χ,χ ), B = ( Bϕ,ϕ B ϕ,χ B χ,ϕ B χ,χ ) S. Massart c ECMWF October 29, / 5

6 Formulation Linear analysis equation x a = x b + K [ y o Gx b] where K = BG T [ GBG T + R ] Separation into a physical state ϕ and a chemical state χ ( ) ( ) x x b b = ϕ x, x a a = ϕ, M = x b χ x a χ ( Mϕ,ϕ M ϕ,χ M χ,ϕ and G = HM M χ,χ ), B = ( Bϕ,ϕ B ϕ,χ B χ,ϕ B χ,χ ) Physical and chemical interaction. M T χ,ϕ : adjoint of the model part interacting between physics and chemistry (for e.g. transport) 2. B ϕ,χ : covariances of the background errors between the physical state and the chemical state S. Massart c ECMWF October 29, / 5

7 Formulation Linear analysis equation x a = x b + K [ y o Gx b] where K = BG T [ GBG T + R ] Separation into a physical state ϕ and a chemical state χ ( ) ( ) x x b b = ϕ x, x a a = ϕ, M = x b χ x a χ ( Mϕ,ϕ M ϕ,χ M χ,ϕ and G = HM M χ,χ ), B = ( Bϕ,ϕ B ϕ,χ B χ,ϕ B χ,χ ) Physical and chemical interaction. M T χ,ϕ : adjoint of the model part interacting between physics and chemistry (for e.g. transport) 2. B ϕ,χ : covariances of the background errors between the physical state and the chemical state Chemical Transport Model (usually state of the art chemistry) No physical part: M ϕ,ϕ 0, x b ϕ 0 No chemistry physic interaction not possible Numerical Weather Prediction (usually simplified chemistry) No feedback of the composition on the physics: M T χ,ϕ 0 and B ϕ,χ 0 S. Massart c ECMWF October 29, / 5

8 Previous studies Daley (995) Extended Kalman filter + D constituent transport equation & prognostic linear wind model Information on wind field can be recovered if the observation are sufficiently frequent and accurate, and data voids are small Riishøjgaard (996) 4D-Var + ozone pseudo observations + barotropic vorticity-equation model Quality of the results depends on the resolution of the model and on the length of the assimilation window Holm et al. (999) 4D-Var + TOVS ozone product + ECMWF s NWP model Details on the wind-ozone coupling and importance of a good enough chemistry parametrization Peuch et al. (2000) 4D-Var + OSSEs for TOVS ozone product + Météo-France s NWP model The accuracy of total-ozone measurements needs to be good enough to get any additional information Semane et al. (2009) 4D-Var + MLS ozone profile + Météo-France s NWP model The ozone assimilation reduces the wind bias in the lower stratosphere S. Massart c ECMWF October 29, / 5

9 Why revisiting this now? Copernicus Atmosphere Monitoring Service (CAMS) Addition of more detailed atmospheric composition in a NWP model Reactive gases: ozone (O 3 ), carbon monoxide (CO),... Aerosols: black carbon, dust,... Greenhouse gases: carbon dioxide (CO 2 ) and methane (CH 4 ) More and more retrievals of satellite data MLS, OMI, SBUV-2, IASI, MOPITT, GOME-2, OMPS, TANSO, PMAp, MODIS,... S. Massart c ECMWF October 29, / 5

10 Why revisiting this now? Copernicus Atmosphere Monitoring Service (CAMS) Addition of more detailed atmospheric composition in a NWP model Reactive gases: ozone (O 3 ), carbon monoxide (CO),... Aerosols: black carbon, dust,... Greenhouse gases: carbon dioxide (CO 2 ) and methane (CH 4 ) More and more retrievals of satellite data MLS, OMI, SBUV-2, IASI, MOPITT, GOME-2, OMPS, TANSO, PMAp, MODIS,... How to choose the atmospheric tracer to assimilate? Long-lived tracer is preferred (to be compared to the assimilation window) Low model bias is important Good global coverage of dense and frequent observations S. Massart c ECMWF October 29, / 5

11 Why revisiting this now? Copernicus Atmosphere Monitoring Service (CAMS) Addition of more detailed atmospheric composition in a NWP model Reactive gases: ozone (O 3 ), carbon monoxide (CO),... Aerosols: black carbon, dust,... Greenhouse gases: carbon dioxide (CO 2 ) and methane (CH 4 ) More and more retrievals of satellite data MLS, OMI, SBUV-2, IASI, MOPITT, GOME-2, OMPS, TANSO, PMAp, MODIS,... How to choose the atmospheric tracer to assimilate? Long-lived tracer is preferred (to be compared to the assimilation window) Low model bias is important Good global coverage of dense and frequent observations Choice: CO, CO 2 and CH 4 S. Massart c ECMWF October 29, / 5

12 Why revisiting this now? Copernicus Atmosphere Monitoring Service (CAMS) Addition of more detailed atmospheric composition in a NWP model Reactive gases: ozone (O 3 ), carbon monoxide (CO),... Aerosols: black carbon, dust,... Greenhouse gases: carbon dioxide (CO 2 ) and methane (CH 4 ) More and more retrievals of satellite data MLS, OMI, SBUV-2, IASI, MOPITT, GOME-2, OMPS, TANSO, PMAp, MODIS,... How to choose the atmospheric tracer to assimilate? Long-lived tracer is preferred (to be compared to the assimilation window) Low model bias is important Good global coverage of dense and frequent observations Choice: CO, CO 2 and CH 4 Which method? Semane et al.: M T χ,ϕ 0 but still B ϕ,χ 0 in a 4D-Var environment In this study: M T χ,ϕ 0 but B ϕ,χ 0 S. Massart c ECMWF October 29, / 5

13 Why revisiting this now? Copernicus Atmosphere Monitoring Service (CAMS) Addition of more detailed atmospheric composition in a NWP model Reactive gases: ozone (O 3 ), carbon monoxide (CO),... Aerosols: black carbon, dust,... Greenhouse gases: carbon dioxide (CO 2 ) and methane (CH 4 ) More and more retrievals of satellite data MLS, OMI, SBUV-2, IASI, MOPITT, GOME-2, OMPS, TANSO, PMAp, MODIS,... How to choose the atmospheric tracer to assimilate? Long-lived tracer is preferred (to be compared to the assimilation window) Low model bias is important Good global coverage of dense and frequent observations Choice: CO, CO 2 and CH 4 Which method? Semane et al.: M T χ,ϕ 0 but still B ϕ,χ 0 in a 4D-Var environment In this study: M T χ,ϕ 0 but B ϕ,χ 0 Ensemble Kalman Filter S. Massart c ECMWF October 29, / 5

14 Observations for one day (20-0-0) 8 TANSO CO2 8 TANSO CH4 8 IASI CO2 8 IASI CH4 8 SCIAMACHY CO2 8 SCIAMACHY CH4 8 MOPITT CO S. Massart coctober ECMWF 29, / 5

15 Observations for the period to TANSO CO2 8 TANSO CH4 8 MOPITT CO 8 IASI CO2 8 IASI CH4 8 CO has the better coverage with MOPITT 8 CH4 has the good coverage over land and only IASI over sea 8 SCIAMACHY CO2 8 SCIAMACHY CH4 8 CO2 has the worse coverage but in the tropics with IASI S. Massart coctober ECMWF 29, / 5

16 Vertical information CO 2 CH 4 CO Potential information in the troposphere and lower stratosphere Potential information in the troposphere and lower and middle stratosphere Potential information in the troposphere only, not in the stratosphere S. Massart c ECMWF October 29, / 5

17 Experiments configurations EnKF configuration Horizontal resolution T l 59 ( km 2 ) 37 levels 50 members 6-h assimilation window Hamrud et al. for more details Control experiment (CTR): assimilation of operational data but no constituent data GRG experiment, same as CTR with the additional assimilation of: XCO from MOPITT GHG experiment, same as CTR with the additional assimilation of: XCO 2 and XCH 4 from TANSO XCO 2 and XCH 4 from SCIAMACHY XCO 2 and XCH 4 from IASI Starting date: January 20 JF: January and February 20 MAM: March to May 20 JJA: June to August 20 S. Massart c ECMWF October 29, / 5

18 Cross-correlation GRG experiment GHG experiment Cross-correlation between CO and T, Q, in the mid and upper stratosphere Cross-correlation between CH 4 and T, Q, D and Vo in the stratosphere Cross-correlation between CO 2 and Q and Vo in the upper stratosphere Expectations Impact of CH 4 on the thermodynamic in the middle stratosphere S. Massart c ECMWF October 29, / 5

19 Change in error in R - Jan. and Feb. 20 Bluish: GRG-CTR t + 2 t + 24 t + 48 t + 72 t + 96 t + 20 GHG-CTR, Reddish: t + 2 t + 24 t + 48 t + 72 t + 96 t + 20 S. Massart c ECMWF October 29, 204 / 5

20 Change in error in R - Jan. and Feb. 20 Bluish: GRG-CTR t + 2 t + 24 t + 48 t + 72 GHG-CTR, Reddish: t + 2 t + 24 t + 48 t + 72 t + 96 t + 20 t + 96 t + 20 S. Massart c ECMWF October 29, 204 / 5

21 Change in error - Jan. and Feb. 20 Bluish: GRG-CTR t + 2 R T VW GHG-CTR t + 2 R T VW, Reddish: GRG-CTR t + 20 R T VW GHG-CTR t + 20 R T VW S. Massart c ECMWF October 29, 204 / 5

22 Change in error at t + 2 other seasons Bluish: GRG-CTR MAM 20 R T VW GHG-CTR MAM 20 R T VW GRG-CTR JJA 20 R T VW, Reddish: GHG-CTR JJA 20 R T VW S. Massart c ECMWF October 29, / 5

23 First guess departure standard deviation JF 20 Pressure [hpa] Pressure [hpa] TEMP-T FG std. dev. [%, normalised] WIND-U FG std. dev. [%, normalised] Altitude [km] Pressure [hpa] GPSRO FG std. dev. [%, normalised] WIND-V FG std. dev. [%, normalised] MAM 20 TEMP-T Pressure [hpa] Pressure [hpa] FG std. dev. [%, normalised] WIND-U FG std. dev. [%, normalised] Altitude [km] Pressure [hpa] GPSRO FG std. dev. [%, normalised] WIND-V FG std. dev. [%, normalised] GHG - CTR GRG - CTR < % > % GPSRO function of altitude (km) Others function of pressure (hpa) 5 hpa 35 km S. Massart c ECMWF October 29, / 5

24 Conclusions Purpose of the study: revisiting the feasibility of inferring physical information from the assimilation of chemical constituent observations (together with operational observations) in the context of Copernicus Atmosphere Monitoring Service (CAMS) exploring other constituents than ozone focusing on covariances between the physical and chemical background errors Results Almost no impact from the assimilation of CO More impact from the assimilation of CO 2 and CH 4 Impact mainly in the stratosphere: troposphere already too well constrained by the operational observations? tracers too well mixed in the troposphere (no gradient)? Cross-correlations not only with dynamical fields but also with temperature and relative humidity the balance operator should account for these cross-correlation of the background errors in a 4D-Var assimilating chemical constituent observations with M T χ,ϕ 0 Strong difference between JJ and other seasons Spin-up effect? Seasonal effect? To be further investigated S. Massart c ECMWF October 29, / 5

25 References R. Daley. Estimating the Wind Field from Chemical Constituent Observations: Experiments with a One-Dimensional Extended Kalman Filter. In: Monthly Weather Review 23. (995), pp M. Hamrud, M. Bonavita, and L. Isaksen. EnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF Implementation. In: Monthly Weather Review 43.2 (205), pp E. V. Holm et al. Multivariate ozone assimilation in four-dimensional data assimilation. In: Proceedings of the SODA Workshop on Chemical Data Assimilation, KNMI, De Bilt, The Netherlands (999), pp A. Peuch, J.-N. Thépaut, and J. Pailleux. Dynamical impact of total-ozone observations in a four-dimensional variational assimilation. In: Quarterly Journal of the Royal Meteorological Society (2000), pp L. P. Riishøjgaard. On four-dimensional variational assimilation of ozone data in weather-prediction models. In: Quarterly Journal of the Royal Meteorological Society (996), pp N. Semane et al. On the extraction of wind information from the assimilation of ozone profiles in Météo-France 4-D-Var operational NWP suite. In: Atmospheric Chemistry and Physics 9.4 (2009), pp S. Massart c ECMWF October 29, / 5