Modelled ozone concentrations often differ from measured concentrations quite substantially, partly due to measurement errors, but mainly due to uncertainties in the model. Modelling studies would therefore benefit highly from more reliable model simulations. One way to achieve this is the application of data assimilation, a technique that uses measurement information within the model simulation in a way that is consistent with the model itself. This aim of this paper is to show that this is indeed one way to go with atmospheric transport chemistry models (ATCMs) by presenting results of data assimilation simulations of ozone with the model LOTOS. The assimilation technique used in this study is the Ensemble Kalman Filter. A simulation for a period of 4 weeks has been performed in which ground-level ozone measurements have been assimilated. The necessary noise input consisted of uncertainties in the emissions of NOx, SOx, VOC and CO in 17 groups of countries. The main conclusion is that it is possible to improve ATCM simulations of ozone by data assimilation, but that noise inputs other than emissions only are essential for the reduction of the differences between measured and modelled concentrations to acceptable margins.
[1]
Stefan Tilmes,et al.
Investigation on the spatial scales of the variability in measured near‐ground ozone mixing ratios
,
1998
.
[2]
Arnold W. Heemink,et al.
Large scale data assimilation based on RRSQRT-filters; application on atmospheric chemistry models
,
1970
.
[3]
Hendrik Elbern,et al.
Variational data assimilation for tropospheric chemistry modeling
,
1997
.
[4]
Geir Evensen,et al.
Advanced Data Assimilation for Strongly Nonlinear Dynamics
,
1997
.
[5]
M. Verlaan,et al.
Tidal flow forecasting using reduced rank square root filters
,
1997
.
[6]
Hauke Schmidt,et al.
A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling
,
1999
.
[7]
M. C. Dodge,et al.
A photochemical kinetics mechanism for urban and regional scale computer modeling
,
1989
.
[8]
A. W. Heemink,et al.
Kalman filtering for nonlinear atmospheric chemistry models : first experiences
,
1997
.