BRAM release 2 (BRAM-2) is available for download since September 18, 2018. This new release benefited on several updates in the BASCOE system: improved PSC scheme, improved solar spectral irradiance datasets, improved EnKF, no more missing outputs (hopefully)...

The significant differences between BRAM-1 and 2 are:

  • BRAM-2 improves HCl and ClO in polar winter, above 30 hPa due to updated setting for aerosols and PSC parameterization.
  • BRAM-2 provides the std dev of the ensemble as an estimation of the analysis uncertainties (not provided in BRAM-1 because not properly saved during BRAM-1 production).
  • BRAM-2 extended for 2017

More details on the differences between BRAM-1 and 2 are reported here.

BRAM-2 dataset is available to download on the protected FTP server of BIRA-IASB. Please, contact This email address is being protected from spambots. You need JavaScript enabled to view it. to get login and password. Note that BRAM-1 is no more available on the FTP server.

Several presentations and posters have discussed BRAM-1 and most of their conclusions remain valid for BRAM-2:



Q. Errera, G. Braathen, S. Chabrillat, Y. Christophe, J. Debosscher, M. Santee, and S. SkachkoBASCOE Reanalysis of Aura MLS, 2nd Release (BRAM-2), SPARC General Assembly, Kyoto, Japan, 1-5 October 2018. Download

Q. Errera, G. Braathen, S. Chabrillat, Y. Christophe, J. Debosscher, M. Santee, and S. Skachko, BASCOE Reanalysis of Aura MLS (BRAM) with a focus on stratospheric polar winter conditions, EGU2018-15484 | Posters | AS3.23. Download

Q. Errera, S. Chabrillat, Y. Christophe, J. Debosscher, M. Santee, and S. Skachko,, BASCOE Reanalysis of Aura MLS (BRAM) with a focus on water vapour analyses, EGU2018-15176 | Posters | AS1.32. Download

J. Debosscher, C. Lerot, M. Van Roozendael, and Q. Errera, Sampling error estimation in monthly mean SCIAMACHY datasets using a data assimilation system, EGU2018-16571 | Posters | AS3.24/CL2.07. Download

BRAM R1 dataset is available to download on the protected FTP server of BIRA-IASB. Please, contact This email address is being protected from spambots. You need JavaScript enabled to view it. to get login and password to access to the FTP server.

Currently, the only available documentation on BRAM is provided by Errera et al., PICO presentation given at EGU, 2017, that can be downloaded here.

Also provided on the FTP site, validation plots based on the Forecast-minus-Observations (F-O) given for different seasons and years, for all BRAM assimilated species using observations from MLS, ACEFTS, MIPAS (IMK retrieval) and O3_CCI L2 HARMOZ profiles. An example of these plots is given here. It presents the (F-O) statistics for water vapor (H2O) for the season June-July-August (JJA) and for each year of BRAM (2005-2016) (one line per year). Each column presents statistics for a different latitude band going from south (left) to north(right). Rows 1 and 3 displays the mean(BRAM-MLS) in % and in ppmv, respectively. Rows 2 and 4 displays the StdDev(BRAM-MLS) in % and in ppmv, respectively. Row 5 shows the mean MLS profiles. Also shown, in black, the instrument error budget (accuracy and precision) in their native units (only available for BRAM-MLS). The MLS accuracy is compared to the mean(BRAM-MLS) while the MLS precision is compared to the StdDev(BRAM-MLS). Since, for H2O, MLS error budget is given in %, it is only displayed for statistical plots given in %.


Validation plots for O3

Validation plots for H2O

Validation plots for N2O

Validation plots for HNO3

Validation plots for HCl

Validation plots for ClO

Validation plots for CH3Cl

Validation plots for CO


The main tool used by the stratospheric modelling group is the Belgian Assimilation System for Chemical ObsErvations (BASCOE). Developed at BIRA-IASB, it is used to study and to monitor the chemical composition of the Earth stratosphere. Assimilation methods aim at optimizing a model state in order to reproduce a set of observations available for a given time window, as illustrated below. These methods have been developed in the eighties by meteorologists in order to improve the weather forecast. Combining real observations of the day and a numerical model, the assimilation methods allow numerical weather prediction systems to produce the best possible weather forecast. Since 2000, thanks to the increase of the number of satellite instruments dedicated to measurements of the atmospheric composition, assimilation methods have been applied to chemical observations.

The above animation illustrates the data assimilation principle in the case of ozone around 20 km during the development of the ozone hole above the South Pole. The animation period is between September 1 (image 0/120) and October 1 (image 120/120), 2008. The left animation shows ozone observed by the MLS satellite instruments every 6 hours during September 2008. The right animation shows the BASCOE model state (no assimilation) every 6 hours. The centre animation shows the results of the assimilation of MLS ozone observations by BASCOE every 6 hours.Note how data assimilation combine the better accuracy of the observations and the better coverage of the model: assimilated fields are not biased against observations and are filling unobserved regions by MLS.

The BASCOE system is based on a three-dimensional Chemical Transport Model (CTM) and can use two data assimilation methods: 4D-Var and EnKF. The CTM calculates the evolution of around 60 stratospheric chemical constituents taking into account the advection, the chemistry and the microphysics of the Polar Stratospheric Clouds (PSC). The CTM is driven by the wind and temperature analyses produced by meteorological centres like the European Centre for Medium range Weather Forecast (ECMWF). The chemical scheme includes around 200 reactions (gas phase, photodissociation, and heterogeneous reactions). Further information on BASCOE is available in Errera and Fonteyn (2001), Errera et al. (2008), Viscardy et al. (2010), Errera and Ménard (2012), Skachko et al. (2014, 2016).

Errera, Q. and Fonteyn, D., Four-dimensional variational chemical assimilation of CRISTA stratospheric measurements, J. Geophys. Res., 106, 12,253-12, 265 (2001).

Errera, Q., Daerden, F., Chabrillat, S., Lambert, J. C., Lahoz, W. A., Viscardy, S., Bonjean, S., and Fonteyn, D., 4D-Var Assimilation of MIPAS chemical observations: ozone and nitrogen dioxide analyses, Atmos. Chem. Phys., 8, 6169-6187 (2008).

Errera, Q. and Ménard, R.: Technical Note: Spectral representation of spatial correlations in variational assimilation with grid point models and application to the Belgian Assimilation System for Chemical Observations (BASCOE), Atmos. Chem. Phys., 12, 10015-10031, doi:10.5194/acp-12-10015-2012, 2012.

Skachko, S., Errera, Q., Ménard, R., Christophe, Y., and Chabrillat, S.: Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model, Geosci. Model Dev., 7, 1451-1465, doi:10.5194/gmd-7-1451-2014, 2014.

Skachko, S., Ménard, R., Errera, Q., Christophe, Y., and Chabrillat, S.: EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06), Geosci. Model Dev., 9, 2893-2908, doi:10.5194/gmd-9-2893-2016, 2016.

Viscardy, S., Errera, Q., Christophe, Y., Chabrillat, S., and Lambert, J.-C., Evaluation of ozone analyses from UARS MLS assimilation by BASCOE between 1992 and 1997, JSTARS3, 190-202 (2010).