scholarly journals Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model

2014 ◽  
Vol 7 (1) ◽  
pp. 283-302 ◽  
Author(s):  
B. Gaubert ◽  
A. Coman ◽  
G. Foret ◽  
F. Meleux ◽  
A. Ung ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Regional Scale ◽  
Chemical Transport ◽  
Quality Analysis ◽  
Observation Error ◽  
Chemical Transport Model ◽  
Sensitivity Tests

Abstract. An ensemble Kalman filter (EnKF) has been coupled to the CHIMERE chemical transport model in order to assimilate ozone ground-based measurements on a regional scale. The number of ensembles is reduced to 20, which allows for future operational use of the system for air quality analysis and forecast. Observation sites of the European ozone monitoring network have been classified using criteria on ozone temporal variability, based on previous work by Flemming et al. (2005). This leads to the choice of specific subsets of suburban, rural and remote sites for data assimilation and for evaluation of the reference run and the assimilation system. For a 10-day experiment during an ozone pollution event over Western Europe, data assimilation allows for a significant improvement in ozone fields: the RMSE is reduced by about a third with respect to the reference run, and the hourly correlation coefficient is increased from 0.75 to 0.87. Several sensitivity tests focus on an a posteriori diagnostic estimation of errors associated with the background estimate and with the spatial representativeness of observations. A strong diurnal cycle of both these errors with an amplitude up to a factor of 2 is made evident. Therefore, the hourly ozone background error and the observation error variances are corrected online in separate assimilation experiments. These adjusted background and observational error variances provide a better uncertainty estimate, as verified by using statistics based on the reduced centered random variable. Over the studied 10-day period the overall EnKF performance over evaluation stations is found relatively unaffected by different formulations of observation and simulation errors, probably due to the large density of observation sites. From these sensitivity tests, an optimal configuration was chosen for an assimilation experiment extended over a three-month summer period. It shows a similarly good performance as the 10-day experiment.

scholarly journals Regional scale ozone data assimilation using an Ensemble Kalman Filter and the CHIMERE Chemical-Transport Model

2013 ◽  
Vol 6 (2) ◽  
pp. 3033-3083
Author(s):  
B. Gaubert ◽  
A. Coman ◽  
G. Foret ◽  
F. Meleux ◽  
A. Ung ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Regional Scale ◽  
Random Variable ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Background Error ◽  
Filter Performance

Abstract. The Ensemble Kalman Filter is an efficient algorithm for data assimilation; it allows for an estimation of forecast and analysis error by updating the model error covariance matrices at the analysis step. This algorithm has been coupled to the CHIMERE chemical transport model in order to assimilate ozone ground measurements at the regional scale. The analyzed ozone field is evaluated using a consistent set of observations and shows a reduction of the quadratic error by about a third and an improvement of the hourly correlation coefficient despite of a low ensemble size designed for operational purposes. A classification of the European observation network is derived from the ozone temporal variability in order to qualitatively determine the observation spatial representativeness. Then, an estimation of the temporal behavior of both model and observations error variances of the assimilated stations is checked using a posteriori Desroziers diagnostics. The amplitude of the additive noise applied to the ozone fields can be diagnosed and tuned online. The evaluation of the obtained background error variance distribution through the Reduced Centered Random Variable standard deviation shows improved statistics. The use of the diagnostics indicates a strong diurnal cycle of both the model and the representativeness errors. Another design of the ensemble is constructed by perturbing model parameter, but does not allow creating enough variability if used solely. Finally, the overall filter performance over evaluation stations is found to be relatively unaffected by different formulations of observation and simulation errors.

scholarly journals EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06)

2016 ◽  
Vol 9 (8) ◽  
pp. 2893-2908 ◽  
Author(s):  
Sergey Skachko ◽  
Richard Ménard ◽  
Quentin Errera ◽  
Yves Christophe ◽  
Simon Chabrillat
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Chemical Transport ◽  
Error Variance ◽  
Model Error ◽  
Chemical Processes ◽  
Observation Error ◽  
Chemical Transport Model ◽  
Error Covariance ◽  
Chemical Tracer

Abstract. We compare two optimized chemical data assimilation systems, one based on the ensemble Kalman filter (EnKF) and the other based on four-dimensional variational (4D-Var) data assimilation, using a comprehensive stratospheric chemistry transport model (CTM). This work is an extension of the Belgian Assimilation System for Chemical ObsErvations (BASCOE), initially designed to work with a 4D-Var data assimilation. A strict comparison of both methods in the case of chemical tracer transport was done in a previous study and indicated that both methods provide essentially similar results. In the present work, we assimilate observations of ozone, HCl, HNO3, H2O and N2O from EOS Aura-MLS data into the BASCOE CTM with a full description of stratospheric chemistry. Two new issues related to the use of the full chemistry model with EnKF are taken into account. One issue is a large number of error variance parameters that need to be optimized. We estimate an observation error variance parameter as a function of pressure level for each observed species using the Desroziers method. For comparison purposes, we apply the same estimate procedure in the 4D-Var data assimilation, where both scale factors of the background and observation error covariance matrices are estimated using the Desroziers method. However, in EnKF the background error covariance is modelled using the full chemistry model and a model error term which is tuned using an adjustable parameter. We found that it is adequate to have the same value of this parameter based on the chemical tracer formulation that is applied for all observed species. This is an indication that the main source of model error in chemical transport model is due to the transport. The second issue in EnKF with comprehensive atmospheric chemistry models is the noise in the cross-covariance between species that occurs when species are weakly chemically related at the same location. These errors need to be filtered out in addition to a localization based on distance. The performance of two data assimilation methods was assessed through an 8-month long assimilation of limb sounding observations from EOS Aura MLS. This paper discusses the differences in results and their relation to stratospheric chemical processes. Generally speaking, EnKF and 4D-Var provide results of comparable quality but differ substantially in the presence of model error or observation biases. If the erroneous chemical modelling is associated with moderately fast chemical processes, but whose lifetimes are longer than the model time step, then EnKF performs better, while 4D-Var develops spurious increments in the chemically related species. If, however, the observation biases are significant, then 4D-Var is more robust and is able to reject erroneous observations while EnKF does not.

scholarly journals Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM<sub>2.5</sub>

2021 ◽  
Author(s):  
Soon-Young Park ◽  
Uzzal Kumar Dash ◽  
Jinhyeok Yu ◽  
Keiya Yumimoto ◽  
Itsushi Uno ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
South Korea ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Ground Level ◽  
Relative Reduction ◽  
Air Quality Model ◽  
Chemical Transport Model ◽  
Quality Model

Abstract. In this study, we developed a data assimilation (DA) system for chemical transport model (CTM) simulations using an ensemble Kalman filter (EnKF) technique. This DA technique is easy to implement to an existing system without seriously modifying the original CTM, and can provide flow-dependent corrections based on error covariance by short-term ensemble propagations. First, the PM2.5 observations at ground stations were assimilated in this DA system every 6 hours over South Korea for the period of the KORUS–AQ campaign, from 1 May to 12 June, 2016. The DA performances with the EnKF were then compared to a control run (CTR) without DA, as well as a run with three-dimensional variational (3DVAR) DA. Consistent improvements due to the ICs assimilated with the EnKF were found in the DA experiments with 6 h interval, compared to the CTR run, and to the run with 3DVAR. In addition, we attempted to assimilate the ground observations from China to examine the impacts of improved boundary concentrations (BCs) on the PM2.5 predictability over South Korea. The contributions of the ICs and BCs to improvements in the PM2.5 predictability were also quantified. For example, the relative reductions in terms of the normalized mean bias (NMB) were found to be about 27.2 % for the 6 h reanalysis run. A series of 24 hour PM2.5 predictions were additionally conducted each day at 00 UTC with the optimized initial concentrations (ICs). The relative reduction of the NMB was 17.3 % for the 24 h prediction run, when the updated ICs were applied at 00 UTC. This means that after the application of the updated BCs, an additional 9.0 % reduction in the NMB was achieved for 24 h PM2.5 predictions in South Korea.

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

2011 ◽  
Vol 139 (6) ◽  
pp. 2008-2024 ◽  
Author(s):  
Brian C. Ancell ◽  
Clifford F. Mass ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Complex Terrain ◽  
Error Variance ◽  
Small Scale ◽  
Observation Error ◽  
Grid Spacing ◽  
Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

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

2011 ◽  
Vol 139 (11) ◽  
pp. 3389-3404 ◽  
Author(s):  
Thomas Milewski ◽  
Michel S. Bourqui
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Covariance Matrix ◽  
Ensemble Kalman Filter ◽  
Climate Model ◽  
Observation Error ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Error Covariance ◽  
Analysis Error

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 chemical–dynamical 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.

Estimation of Arctic O3 loss during winter 2006/2007 using data assimilation and comparison with a chemical transport model

2011 ◽  
Vol 137 (654) ◽  
pp. 118-128 ◽  
Author(s):  
O. A. Søvde ◽  
Y. J. Orsolini ◽  
D. R. Jackson ◽  
F. Stordal ◽  
I. S. A. Isaksen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Using Data

scholarly journals Assimilation of OMI NO<sub>2</sub> retrievals into the limited-area chemical transport model DEHM (V2007.0) with a 2-D OI algorithm

2012 ◽  
Vol 5 (1) ◽  
pp. 309-346
Author(s):  
J. D. Silver ◽  
J. Brandt ◽  
M. Hvidberg ◽  
J. Frydendall
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Initial Conditions ◽  
Chemical Transport ◽  
Optimal Interpolation ◽  
Limited Area ◽  
Two Dimensional ◽  
Chemical Transport Model ◽  
Background Error ◽  
Geophysical Field

Abstract. Data assimilation is the process of combining real-world observations with a modelled geophysical field. The increasing abundance of satellite retrievals of atmospheric trace gases makes chemical data assimilation a powerful tool for improving air quality forecasts. We implemented a two-dimensional optimal interpolation (OI) algorithm to assimilate satellite-derived estimates of tropospheric NO2 column concentrations into the Danish Eulerian Hemispheric Model (DEHM, version V2007.0), a three-dimensional, European-scale, chemical transport model. In particular, we describe how we used observational data to estimate the background error covariance matrix, B. In the assimilation, the tropospheric column NO2 field was adjusted and the modelled NO2 profile was scaled accordingly; other species were only adjusted indirectly via changes to NO2 concentrations. We ran a number of experiments to compare different parameterisations of B; this involved varying the length scale used in B, the relative weighting of the background and observation errors, the errors assigned to observations and the influence of clustered observations. We assessed model performance by comparing the analysed fields to an independent set of observations: ground-based measurements of NO2 concentrations. Ozonosonde profiles were also used for verification. The analysed NO2 and O3 concentrations were more accurate than those from a reference simulation without assimilation, with lower bias for both species and improved correlation for NO2. The experiments showed that appropriately chosen parameters for the B matrix, estimated using innovation statistics, yielded more accurate surface NO2 concentrations. There was good agreement between the seasonally-averaged observed and modelled O3 profiles. The simple OI scheme was effective and computationally feasible in this context, where only a single species was assimilated and only a two-dimensional field was adjusted. However there are certain limitations to using this assimilation scheme for more highly multi-dimensional problems. Although forecast accuracy was not examined here, we discuss the potential for improving NO2 forecasts by using assimilation to generate initial conditions.

scholarly journals An Evapotranspiration Assimilation Method Based on Ensemble Kalman Filter and À Trous Wavelet

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Shaohui Chen ◽  
Jianwei Qi ◽  
Xiaomin Sun ◽  
Xiangzheng Deng ◽  
Jing Tian
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
High Frequency ◽  
Spatial Information ◽  
Regional Scale ◽  
Observation Error ◽  
Integrated Method ◽  
Surface Models ◽  
Better Than

It is challenging to assimilate the evapotranspiration product (EP) retrieved from satellite data into land surface models (LSMs). In this paper, a perturbed ensemble Kalman filter (PEKF) and à trous wavelet transform (AWT) integrated method are proposed to implement the evapotranspiration assimilation. In this method, the AWT is used to decompose the EPs into multiple channels since it is very powerful in fusing high frequency spatial information of multisource data, and then the Kalman filter is performed in the AWT domain. The proposed method combines the advantages of the PEKF that is capable of accommodating model error and observation error, and the AWT can effectively perform multiresolution fusion. Assimilation experiment conducted with the Noah model and the EP retrieved from the MODIS data shows that the proposed method performs better than the traditional ensemble Kalman filter (EnKF) and PEKF methods. The analysis results fit well with the evapotranspiration observation at two field sites with different land surface conditions. These indicate that the proposed method is promising for assimilating regional scale satellite retrieved EP into LSMs.

scholarly journals Street-in-Grid modeling of gas-phase pollutants in Paris city

2019 ◽  
Author(s):  
Lya Lugon ◽  
Karine Sartelet ◽  
Youngseob Kim ◽  
Jérémy Vigneron ◽  
Olivier Chrétien
Keyword(s):  
Transport Model ◽  
Regional Scale ◽  
Chemical Transport ◽  
Scale Model ◽  
Background Concentrations ◽  
Chemical Transport Model ◽  
Time Step ◽  
Multi Scale ◽  
Pollutant Concentrations ◽  
Stationary Approach

Abstract. Regional-scale chemistry-transport models have coarse spatial resolution, and thus can only simulate background concentrations. They fail to simulate the high concentrations observed close to roads and in streets, i.e. where a large part of the urban population lives. Local-scale models may be used to simulate concentrations in streets. They often assume that background concentrations are constant and/or use simplified chemistry. Recently developed, the multi-scale model Street-in-Grid (SinG) estimates gaseous pollutant concentrations simultaneously at local and regional scales, coupling them dynamically. This coupling combines the regional-scale chemistry-transport model Polair3D and the street network model MUNICH (Model of Urban Network of Intersecting Canyons and Highway). MUNICH models explicitly street canyons and intersections, and it is coupled to the first vertical level of the chemical-transport model, enabling the transfer of pollutant mass between the street canyon roof and the atmosphere. The original versions of SinG and MUNICH adopt a stationary hypothesis to estimate pollutant concentrations in streets. Although the computation of NOx concentration is numerically stable with the stationary approach, the partitioning between NO and NO2 is highly dependent on the time step of coupling between transport and chemistry processes. In this study, a new non-stationary approach is presented with a fine coupling between transport and chemistry, leading to numerically stable partitioning between NO and NO2. Simulations of NO, NO2 and NOx concentrations over Paris city with SinG, MUNICH and Polair3D are compared to observations at traffic and urban stations to estimate the added value of multi-scale modeling with a dynamical coupling between the regional and local scales. As expected, the regional chemical-transport model underestimates NO and NO2 concentrations in the streets. However, there is a good agreement between the measurements and the concentrations simulated with MUNICH and SinG. The dynamic coupling between the local and regional scales tends to be important for streets with an intermediate aspect ratio and with high traffic emissions.

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