The 5TH WMO Symposium on Data Assimilation Melbourne, Australia 5th - 9th October, 2009
mB_CHEM4 (205, Chemical DA)
Ensemble Kalman filter assimilation of atmospheric chemical constituents data with a MRI chemistry-climate model: OSS Experiments Makoto DEUSHI1, Tsuyoshi T. SEKIYAMA1, and Kiyotaka SHIBATA1 Meteorological Research Institute, Tsukuba, Japan1 Contact:
[email protected]
Chemical Data assimilation system: MRICCM2-LETKF
Introduction A data assimilation (DA) system for the analysis of atmospheric trace gases such as carbon monoxide (CO) is developed based on a local ensemble transform Kalman filter (LETKF) method, which is a kind of ensemble Kalman filter (EnKF) technique. In the chemical DA system, a chemistry-climate model (CCM) developed at the Meteorological Research Institutes is used. We conduct a perfect model Observing System Simulation Experiment (OSSE) with a regular observing network to assess sensitivities of LETKF analysis accuracies with localization scale and analysis variable. In addition to the CO analysis accuracies, impacts of assimilation of CO observations on ozone distributions are evaluated.
Assimilation cycle Forecast ensemble members and forecast error covariance
Observation
Ensemble simulation MRI-CCM2 (global CTM)
DA: LETKF
MRI Chemistry-Climate Model, Version 2 (MRI-CCM2) The MRI-CCM2 model (e.g. Shibata et al., 2005) is designed to simulate the distributions and timeevolutions of ozone and related chemical species over the troposphere and the middle atmosphere comprehensively, and used for the operational prediction of surface UV-B and photochemical oxidant near the surface at the Japan Meteorological Agency. In the MRI-CCM2 model, a chemistry module is coupled with a general circulation model (GCM) on-line and treats the following processes: chemical conversions of trace gases, (grid-scale) advective transport, (sub-grid scale) convective transport and boundary-layer diffusion, dry and wet deposition, and emissions. The chemistry module predicts global distributions of 90 chemical species, treating 244 chemical reactions. The employed model horizontal resolution is the triangular truncation at the maximum wave number 42 (T42), corresponding to a grid resolution of 2.8 by 2.8 degrees in longitudelatitude space. In the vertical, the model had 68 layers (L68) extending from the surface to the mesopause (~0.01 hPa).
Analysis and analysis perturbation
The LETKF (e.g. Hunt et al., 2007; Miyoshi and Yamane, 2007) based on the EnKF technique is one of the advanced data assimilation techniques. In the EnKF, the forecast error covariance is advanced by using the model itself, leading to the production of physically and chemically balanced analysis fields. In addition, a localization technique is used to remove sampling errors caused by the limited ensemble size in the LETKF. In this study, the following experimental settings were used. ・ Ensemble member was set to 40. ・ 6-hour assimilation cycle. ・ Covariance inflation parameter was set to 10%. ・ Horizontal and vertical localizations were applied. ・ Analysis variable was CO mixing ratio (or CO and ozone mixing ratios).
MRI-CCM2 Model Overview JMA/MRI GSMUV
Transport
・Grid scale advection … Hybrid Semi-Lagrange ・Sub-grid scale transport and diffusion … Convective transport in the free atmosphere and turbulent mixing in the boundary layer are included. ・Chemical species 90 ・Chemical Reactions 244 （Photolytic 59, Gas phase 169, Heterogeneous 16)
Chemistry
Emissions Dry Deposition Wet Deposition
・Look-Up table with cloud and surface albedo effect ・ Industry, Biomass Burning, Vegetation, Soil, Ocean, Air craft, Lightning, Cosmic Rays ・ NOx, CH4, CO, NMHC, N2O, CFCs, Halons ・Based on dry deposition velocity calculated from resistance series parameterization of Chang et al. (2002, 2003) ・In-cloud and below-cloud scavenging
Stratospheric Ozone
Ozone Hole Cly, Bry
Stratospheric Photochemistry
CFCs Halons
Tropospheric Photochemistry
Description of the perfect model OSSE
Climate impacts Troposphere
Photo dissociation
Input & Target of MRI-CCM2 Stratosphere
GCM
Green house gases
Chemical analysis
Ozone precursor gases (NOx, CO, VOCs )
Tropospheric Ozone
Air pollution by photochemical oxidants
Land surface, Sea surface
On the assumption that the MRI-CCM2 model provides a perfect representation of CO distributions, a nature run was conducted by using the model for generating the true state. The initial time of the run was set to 00 UTC on 1 June 2000 and the initial condition was randomly chosen. Observations of CO were obtained by adding random noise to the true state. Standard deviation in the observational error was set to 5 (ppbv). We assumed that the observations are regularly located at every 4×4×1 model grid points, leading to the 6.25% coverage in the 3-D space. Using the observations, data assimilation cycle experiments were performed.
Results
Figure 3. Time series of the global RMS errors of O3 mixing ratios (in ppbv) from the surface to the height of 100 hPa. The mean RMS error of the free-running ensemble simulations (left) and LETKF with analysis variable of CO mixing ratio (middle) and CO and O3 mixing ratios (right) are shown.
Figure 1. Ensemble mean (upper panels) and spread (lower panels) of CO (left panels) and O3 (right panels) mixing ratios (in ppbv) at initial time (0000UTC on June 1, 2000 at 700 hPa).
Free-run, 40 ensemble mean 40ens, local 500km and ln(p)=0.2, CO 40ens, local 500km and ln(p)=0.6, CO 40ens, local 500km and ln(p)=1.0, CO 40ens, local 1000km and ln(p)=0.2, CO 40ens, local 500km and ln(p)=0.6, CO & O3
Figure 2. Temporal series of global root mean square (RMS) errors of CO mixing ratios at 700 hPa. The mean RMS error of the free-running ensemble simulations (without any assimilation, black line) and the RMS errors of each LETKF analysis (color lines) are shown.
Table 1. Global mean RMS errors of CO mixing ratios at 700 hPa averaged from June 7, 2003 to June 9, 2000. The RMS errors of each LETKF with different localization parameters and analysis variables are shown. Analysis Variable
Horizontal localization [km]
Vertical CO localization RMSE (ppbv) ln(P) [Pa]
CO
500
0.2
CO
500
0.6
0.964 0.907
CO
500
1.0
0.920
CO
1000
0.2
0.855
CO and O3
500
0.6
0.911
-
7.619
Free ensemble run
Figure 4. Northern hemispheric distributions of O3 mixing ratios (contour, in ppbv) at 0000UTC on June 7, 2000 at 200 hPa which are obtained from the nature run (left), the ensemble mean of the free-running simulations (middle), and the LETKF analysis with analysis variables of CO and O3 mixing ratios (right) . The errors (minus the nature run) are also shown by using color shades (middle and right)
Summary ☯The LETKF DA system for the analysis of atmospheric trace gases such as CO was developed by using the MRI-CCM2 model. To evaluate the performance of the DA scheme, the perfect model OSSE with the regular observing network was conducted.
References Hunt, B. R., E. J. Kostelich and I. Szunyogh (2007), Efficient Data Assimilation for Spatiotemporal Chaos: A Local Ensemble Transform Kalman Filter. Physica D, 230, 871 112.126. Miyoshi, T. and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 38413861. Shibata, K., M. Deushi, T. Sekiyama, and K. Orito (2005), Development of a MRI chemical transport model for the study of stratospheric chemistry, Pap. Meteorol. Geophys., 55, 75. 119.
☯ The OSSE results showed that the chemical DA system successfully analyzed distributions of CO with the greatly reduced RMSE compared to the ensemble mean of the free-running simulations. Some sensitivity tests for localization scale and analysis variables were conducted, although not thoroughly. To optimize the chemical DA system, further sensitivity tests are needed for other parameters such as covariance inflation parameter and ensemble size.
Acknowledgement
☯ Errors of O3 distributions were significantly affected by choice of analysis variables in the DA system, when CO observations are only assimilated,. When CO and O3 mixing ratios were chosen as analysis variables, the O3 errors are significantly reduced compared to the free-running simulations.
This study was partly supported by the Global Environment Research Fund (B-93) by the Ministry of the Environment, Japan.
