Chemical Data Assimilation using CAM and DART:Chemical Data Assimilation using CAM and DART:Tests with CO Remote-Sensed MeasurementsTests with CO Remote-Sensed Measurements

Avelino Arellano, Jr. and Peter HessAvelino Arellano, Jr. and Peter HessAtmospheric Chemistry Division, NCARAtmospheric Chemistry Division, NCAR

Kevin Raeder and Jeffrey AndersonKevin Raeder and Jeffrey AndersonData Assimilation Research Section, NCARData Assimilation Research Section, NCAR

• Provide a consistent and likely representation of CO distributionProvide a consistent and likely representation of CO distribution

• Develop a spatially and temporally robust estimates of CO emissions. Develop a spatially and temporally robust estimates of CO emissions.

ProblemProblem: Current estimation procedure (or inversion) rely on GCTMs to map the emission (surface fluxes) to observable CO state variables. As such, errors need to be reasonably accounted for.

A promising technique is a joint data assimilation and parameter estimation using Ensemble Kalman Filter

(EnKF).

(In the context of model improvement through parameter estimation)(In the context of model improvement through parameter estimation)

Goal:

MODELSMODELS

DART DART • http://www.image.ucar.edu/DAReS

CAM3.1 with CO as a tracerCAM3.1 with CO as a tracer

• Used standard CAM3.1 with FV dycore (4ox5o) and a simplified CO chemistry, implified CO chemistry, mainly based on CAM’s carbon aerosol package. Emissions and sinks of COmainly based on CAM’s carbon aerosol package. Emissions and sinks of CO prescribed.prescribed.

OBSERVATIONSOBSERVATIONSFor T,U,VFor T,U,V : NCEP BUFR (includes radiosonde T,U,V; ACARS data T,U,V; SATwind U,V, etc): NCEP BUFR (includes radiosonde T,U,V; ACARS data T,U,V; SATwind U,V, etc)

For CO For CO : NASA Terra MOPITT CO Retrievals (used 500 hPa subset, dynamic avg kernels): NASA Terra MOPITT CO Retrievals (used 500 hPa subset, dynamic avg kernels)

e.g.e.g.

Conduct Observing System Simulation Experiments Conduct Observing System Simulation Experiments (OSSEs)(OSSEs)

To check the validity and performance of current DART-CAM-CO To check the validity and performance of current DART-CAM-CO implementation:implementation:

Given that we know the ‘true’ state and we have a Given that we know the ‘true’ state and we have a best guess of the probability distribution of the best guess of the probability distribution of the

initial state, can we reproduce the truth by initial state, can we reproduce the truth by assimilating various synthetic observations?assimilating various synthetic observations?

A. Generate Initial Ensembles A. Generate Initial Ensembles (80 members)(80 members)

• For CAM variables taken from previous CAM climatological runs (Kevin Raeder)

• For CO generated by running CAM (CO) with FV dycore for 1 week using MOZARTv2 initial field and the ensemble of CAM/CLM2 initial conditions. (i.e. variability of CO generated from the ensemble alone).

B. Generate Synthetic ObservationsB. Generate Synthetic ObservationsGenerating synthetic obs is easily facilitated in DART (as one of the DART tools):Generating synthetic obs is easily facilitated in DART (as one of the DART tools):

1) Took Took initial ens #40initial ens #40 and run CAM-CO with and run CAM-CO with prescribed sources and sinkprescribed sources and sink.. Assumed that generated model states are the “true” statesAssumed that generated model states are the “true” states..

2)2) We We sample the true statessample the true states using the NCAR BUFR and MOPITT CO obs location using the NCAR BUFR and MOPITT CO obs location and time and time ((truthtruth)). .

3)3) Perturbed the sample by adding a Perturbed the sample by adding a Gaussian noiseGaussian noise with variance represented with variance represented by the by the obs instrument error varianceobs instrument error variance ( (synthetic obssynthetic obs))..

Synthetic Obs – Truth (example: 07-Jan-2003 500 hPa)

C. Carry out 3 OSSEsC. Carry out 3 OSSEs

Using the Using the same initial ensembles (1same initial ensembles (1stst 20 members) 20 members) for T,U,V,CO and the for T,U,V,CO and the same CO same CO sources and sinkssources and sinks prescribed in generating synthetic observations, we carry out prescribed in generating synthetic observations, we carry out

the following experiments for a the following experiments for a 7-day7-day period with period with 20-member20-member ensemble: ensemble:

1. Forecast –>Run DART-CAM-CO w/ no assimilation.

2. Analysis T,U,V –>Run DART-CAM-CO w/ assimilation of T,U,V only.

3. Analysis T,U,V,CO –>Run DART-CAM-CO w/ assimilation of T,U,V and CO.

RMSEs for both assimilation approaches to the prescribed observation RMSEs.

Initial ResultsInitial Results

• Similar improvements in RMSE and RMS.

• RMSEs for both assimilation approaches to the prescribed observation RMSEs.

Assimilation of T,U,V also provides better match of modeled atmospheric pressure with observations.

Bias for both assimilation approaches to the mean bias of the observations relative to the truth.

RMSEs for both assimilation approaches to the prescribed observation RMSEs.

Assimilating T,U,V alone provides large constraints to CO.

Relative Comparison in Model SpaceRelative Comparison in Model Space

Assimilation able to Assimilation able to reasonably reasonably reproduce the true reproduce the true state even for state even for surface CO (which surface CO (which is not currently is not currently observed)observed)

Note: Variability of Note: Variability of CO attributed to CO attributed to T,U,V. Emissions T,U,V. Emissions are fixed.are fixed.

SummarySummary

• The setup for DART-CAM (CO) has been implemented using synthetic The setup for DART-CAM (CO) has been implemented using synthetic CO observations based on MOPITT retrievals.CO observations based on MOPITT retrievals.

• Initial results show the potential of current setup for model evaluation and Initial results show the potential of current setup for model evaluation and longer assimilation studies.longer assimilation studies.

• Simultaneously constraining T,U,V and CO in a GCTM offers an opportunity Simultaneously constraining T,U,V and CO in a GCTM offers an opportunity for model improvements (i.e. source parameter estimation).for model improvements (i.e. source parameter estimation).

• ChallengesChallenges

a) limitation in run time ( increasing overhead with additional observation)a) limitation in run time ( increasing overhead with additional observation) b) what is the optimal number of ensembles to use?b) what is the optimal number of ensembles to use? c) explore sensitivity of assimilation parametersc) explore sensitivity of assimilation parameters

• Future researchFuture research a) Assimilation of real obs (MOPITT CO retrievals and/or radiances)a) Assimilation of real obs (MOPITT CO retrievals and/or radiances) b) Joint CO data assimilation and CO source estimationb) Joint CO data assimilation and CO source estimation

AcknowledgementsAcknowledgements

NSF ITR Grant 115912 NSF ITR Grant 115912 NCAR MOZART group NCAR MOZART group

NCAR MOPITTNCAR MOPITTTim Hoar (IMAGe)Tim Hoar (IMAGe)Francis Vitt (ACD)Francis Vitt (ACD)

Louisa Emmons (ACD)Louisa Emmons (ACD)

OSSEsOSSEs

06 12 18 24

06 12 18 24

06 12 18 24

model advancemodel advance

Case 2: Assimilation (T,U,V)

Case 3: Assimilation (T,U,V, CO)Case 3: Assimilation (T,U,V, CO)

Case 1: ForecastCase 1: Forecast

assimilationassimilation Prior Posterior

ensemble state variables

obsobs

Overarching Goal:

Provide a consistent and likely representation of CO distributionProvide a consistent and likely representation of CO distribution

Develop spatially and temporally robust emissions (surface fluxes) of Develop spatially and temporally robust emissions (surface fluxes) of tropospheric CO. Has important implication to biogeochemistry. tropospheric CO. Has important implication to biogeochemistry.

Overarching Goal:

Provide a consistent and likely representation of CO distributionProvide a consistent and likely representation of CO distribution

Develop spatially and temporally robust emissions (surface fluxes) of Develop spatially and temporally robust emissions (surface fluxes) of tropospheric CO. Has important implication to biogeochemistry. tropospheric CO. Has important implication to biogeochemistry.

(model improvement through parameter estimation)(model improvement through parameter estimation)Current estimation procedure (or inversion) rely on GCTMs to map the source parameters to observable CO state variables. As such, errors need to be reasonably accounted for.