Assimilation of Stratospheric Temperature and Ozone with an Ensemble Kalman Filter in a Chemistry–Climate Model

Abstract

A new stratospheric chemical-dynamical data assimilation system was developed, based upon an ensemble Kalman filter coupled with a Chemistry-Climate Model [i.e., the intermediate-complexity general circulation model Fast Stratospheric Ozone Chemistry (IGCM-FASTOC)], with the aim to explore the potential of chemical-dynamical coupling in stratospheric data assimilation. The system is introduced here in a context of a perfect-model, Observing System Simulation Experiment. The system is found to be sensitive to localization parameters, and in the case of temperature (ozone), assimilation yields its best performance with horizontal and vertical decorrelation lengths of 14 000 km (5600 km) and 70 km (14 km). With these localization parameters, the observation space background-error covariance matrix is underinflated by only 5.9% (overinflated by 2.1%) and the observation-error covariance matrix by only 1.6% (0.5%), which makes artificial inflation unnecessary. Using optimal localization parameters, the skills of the system in constraining the ensemble-average analysis error with respect to the true state is tested when assimilating synthetic Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) retrievals of temperature alone and ozone alone. It is found that in most cases background-error covariances produced from ensemble statistics are able to usefully propagate information from the observed variable to other ones. Chemical-dynamical covariances, and in particular ozone-wind covariances, are essential in constraining the dynamical fields when assimilating ozone only, as the radiation in the stratosphere is too slow to transfer ozone analysis increments to the temperature field over the 24-h forecast window. Conversely, when assimilating temperature, the chemicaldynamical covariances are also found to help constrain the ozone field, though to a much lower extent. The uncertainty in forecast/analysis, as defined by the variability in the ensemble, is large compared to the analysis error, which likely indicates some amount of noise in the covariance terms, while also reducing the risk of filter divergence.

Limitations of wind extraction from 4D-Var assimilation of ozone

“…This is intuitively correct since, in the TLM, wind increments can only influence the tracer through the background gradients, not the increment gradients. Larger gradient regions will allow greater influence on the wind field, as illustrated in the full 4D-Var context by Peuch et al (2000). Third, all of the increments have similar weighting or "gain" factors.…”

“…A study using realistic sampling of total column ozone and a full numerical weather prediction (NWP) forecast model was performed by Peuch et al (2000). They examined windtracer coupling by assimilating simulated TOVS total column ozone measurements with a 4D-Var assimilation system for a 12 h period.…”

“…Given these results it is not surprising that when Peuch et al (2000) attempted to assimilate real TOVS total column ozone data, the wind analyses were degraded. Similarly, Han and McNally (2010) reported that solar-backscatter ultra violet (SBUV) ozone assimilation could degrade the operational European Centre for Medium-Range Weather Forecasts (ECMWF) 4D-Var wind analyses.…”

“…2). Theoretical discussions of cost function minimization provided in Riishøjgaard (1996) and Peuch et al (2000) using continuous functions are very helpful to understand how the wind field can be influenced by tracer observations, but these discussions do not provide practical understanding of the discrete problem. By examining the analytic solution to the discrete problem we gain insight into the relative roles of various factors in facilitating/inhibiting wind extraction.…”

“…A bias correction scheme may help alleviate this problem, but this may be complicated by observation biases and/or inadequate photochemical parameterizations in the forecast model. The results from these test cases, although highly idealized, are useful for demonstrating some of the limitations of tracer-wind extraction that have been identified in previous studies (Daley, 1995;Riishøjgaard, 1996;Peuch et al, 2000;Semane et al, 2009). First, geophysical distributions (i.e., tracer gradients and orientation with respect to wind) must be sufficient for wind extraction.…”