scholarly journals Multi sensor reanalysis of total ozone

2010 ◽  
Vol 10 (4) ◽  
pp. 11401-11448 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Chemical Transport ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Model Driven ◽  
Stratospheric Temperature ◽  
Near Ultraviolet ◽  
Time Period

Abstract. A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measured by polar orbiting satellites in the near-ultraviolet Huggins band in the last thirty years. Fourteen total ozone satellite retrieval datasets from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A) have been used in the MSR. As first step a bias correction scheme is applied to all satellite observations, based on independent ground-based total ozone data from the World Ozone and Ultraviolet Data Center. The correction is a function of solar zenith angle, viewing angle, time (trend), and stratospheric temperature. As second step data assimilation was applied to create a global dataset of total ozone analyses. The data assimilation method is a sub-optimal implementation of the Kalman filter technique, and is based on a chemical transport model driven by ECMWF meteorological fields. The chemical transport model provides a detailed description of (stratospheric) transport and uses parameterisations for gas-phase and ozone hole chemistry. The MSR dataset results from a 30-year data assimilation run with the 14 corrected satellite datasets as input, and is available on a grid of 1×1½ degrees with a sample frequency of 6 h for the complete time period (1978–2008). The Observation-minus-Analysis (OmA) statistics show that the bias of the MSR analyses is less than 1 percent with an RMS standard deviation of about 2 percent as compared to the corrected satellite observations used.

scholarly journals Multi sensor reanalysis of total ozone

2010 ◽  
Vol 10 (22) ◽  
pp. 11277-11294 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Chemical Transport ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Model Driven ◽  
Near Ultraviolet ◽  
Time Period ◽  
Sample Frequency

Abstract. A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measured by polar orbiting satellites in the near-ultraviolet Huggins band in the last thirty years. Fourteen total ozone satellite retrieval datasets from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A) have been used in the MSR. As first step a bias correction scheme is applied to all satellite observations, based on independent ground-based total ozone data from the World Ozone and Ultraviolet Data Center. The correction is a function of solar zenith angle, viewing angle, time (trend), and effective ozone temperature. As second step data assimilation was applied to create a global dataset of total ozone analyses. The data assimilation method is a sub-optimal implementation of the Kalman filter technique, and is based on a chemical transport model driven by ECMWF meteorological fields. The chemical transport model provides a detailed description of (stratospheric) transport and uses parameterisations for gas-phase and ozone hole chemistry. The MSR dataset results from a 30-year data assimilation run with the 14 corrected satellite datasets as input, and is available on a grid of 1× 1 1/2° with a sample frequency of 6 h for the complete time period (1978–2008). The Observation-minus-Analysis (OmA) statistics show that the bias of the MSR analyses is less than 1% with an RMS standard deviation of about 2% as compared to the corrected satellite observations used.

scholarly journals Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012

2015 ◽  
Vol 8 (3) ◽  
pp. 3283-3319 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Forecast Error ◽  
Satellite Observations ◽  
Weather Forecasts ◽  
Stratospheric Temperature ◽  
The Individual ◽  
Data Assimilation Technique ◽  
Ozone Column

Abstract. The ozone multi-sensor reanalysis (MSR) is a multi-decadal ozone column data record constructed using all available ozone column satellite datasets, surface Brewer and Dobson observations and a data assimilation technique with detailed error modelling. The result is a high-resolution time series of 6 hourly global ozone column fields and forecast error fields that may be used for ozone trend analyses as well as detailed case studies. The ozone MSR is produced in two steps. First, the latest reprocessed versions of all available ozone column satellite datasets are collected, and are corrected for biases as function of solar zenith angle, viewing angle, time (trend), and stratospheric temperature using Brewer/Dobson ground measurements from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC; http://www.woudc.org/). Subsequently the debiased satellite observations are assimilated within the ozone chemistry and data assimilation model TMDAM driven by meteorological analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The MSR2 (MSR version 2) reanalysis upgrade described in this paper consists of an ozone record for the 43 year period 1970–2012. The chemistry-transport model and data assimilation system have been adapted to improve the resolution, error modelling and processing speed. BUV satellite observations have been included for the period 1970–1977. The total record is extended with 13 years compared to the first version of the ozone multi sensor reanalysis, the MSR1. The latest total ozone retrievals of 15 satellite instruments are used: BUV-Nimbus4, TOMS-Nimbus7, TOMS-EP, SBUV-7, -9, -11, -14, -16, -17, -18, -19, GOME, SCIAMACHY, OMI and GOME-2. The resolution of the model runs, assimilation and output is increased from 2° x 3° to 1° x 1°. The analysis is driven by three-hourly meteorology from the ERA-interim reanalysis of ECMWF starting from 1979, and ERA-40 before that date. The chemistry parameterization has been updated. The performance of the MSR2 analysis is studied with the help of observation-minus-forecast (OmF) departures from the data assimilation, by comparisons with the individual station observations and with ozone sondes. The OmF statistics show that the mean bias of the MSR2 analyses is less than 1% with respect to debiased satellite observations after 1979.

scholarly journals Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012

2015 ◽  
Vol 8 (7) ◽  
pp. 3021-3035 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Zenith Angle ◽  
Total Ozone ◽  
Transport Model ◽  
Forecast Error ◽  
Satellite Observations ◽  
Data Sets ◽  
Stratospheric Temperature ◽  
Ozone Column

Abstract. The ozone multi-sensor reanalysis (MSR) is a multi-decadal ozone column data record constructed using all available ozone column satellite data sets, surface Brewer and Dobson observations and a data assimilation technique with detailed error modelling. The result is a high-resolution time series of 6-hourly global ozone column fields and forecast error fields that may be used for ozone trend analyses as well as detailed case studies. The ozone MSR is produced in two steps. First, the latest reprocessed versions of all available ozone column satellite data sets are collected and then are corrected for biases as a function of solar zenith angle (SZA), viewing zenith angle (VZA), time (trend), and stratospheric temperature using surface observations of the ozone column from Brewer and Dobson spectrophotometers from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Subsequently the de-biased satellite observations are assimilated within the ozone chemistry and data assimilation model TMDAM. The MSR2 (MSR version 2) reanalysis upgrade described in this paper consists of an ozone record for the 43-year period 1970–2012. The chemistry transport model and data assimilation system have been adapted to improve the resolution, error modelling and processing speed. Backscatter ultraviolet (BUV) satellite observations have been included for the period 1970–1977. The total record is extended by 13 years compared to the first version of the ozone multi sensor reanalysis, the MSR1. The latest total ozone retrievals of 15 satellite instruments are used: BUV-Nimbus4, TOMS-Nimbus7, TOMS-EP, SBUV-7, -9, -11, -14, -16, -17, -18, -19, GOME, SCIAMACHY, OMI and GOME-2. The resolution of the model runs, assimilation and output is increased from 2° × 3° to 1° × 1°. The analysis is driven by 3-hourly meteorology from the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) starting from 1979, and ERA-40 before that date. The chemistry parameterization has been updated. The performance of the MSR2 analysis is studied with the help of observation-minus-forecast (OmF) departures from the data assimilation, by comparisons with the individual station observations and with ozone sondes. The OmF statistics show that the mean bias of the MSR2 analyses is less than 1 % with respect to de-biased satellite observations after 1979.

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.

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

Predictability of Total Ozone Using a Global Three-Dimensional Chemical Transport Model Coupled with the MRI/JMA98 GCM

2005 ◽  
Vol 133 (8) ◽  
pp. 2262-2274 ◽  
Author(s):  
T. T. Sekiyama ◽  
K. Shibata
Keyword(s):  
Total Ozone ◽  
General Circulation ◽  
Transport Model ◽  
Three Dimensional ◽  
Circulation Model ◽  
Chemical Transport ◽  
Japan Meteorological Agency ◽  
High Latitudes ◽  
Chemical Transport Model ◽  
Mean Square Errors

Abstract A global three-dimensional chemical transport model is being developed for forecasting total ozone. The model includes detailed stratospheric chemistry and transport and couples with a dynamical module of the Meteorological Research Institute/Japan Meteorological Agency 1998 (MRI/JMA98) general circulation model, which can yield realistic atmospheric fields through a meteorological assimilation system. Its predictability on total ozone is investigated for up to 4 weeks from 1997 to 2000. Global root-mean-square errors (rmses) of a control run are approximately 10 DU (3% of total ozone) throughout a year; the control run results are used as initial values for hindcast experiments. Rmses of the hindcast experiments globally range from 10 to 30 DU. The anomaly correlation between the 5-day forecasts and satellite measurements is approximately 0.6 throughout a year in the mid- and high latitudes of both the Northern and Southern Hemispheres. Thus, the model has potential for utilization on total ozone forecasts up to 5 days. In the northern mid- and high latitudes, the model produces better total ozone forecasts than the persistence up to 2 weeks, indicating that the deterministic limit of the total ozone forecasts is durationally comparable to that of weather forecasts. Good correlations between changes in total ozone and 100-hPa geopotential height reveal that the predictability of the dynamical field in the lower stratosphere critically affects the predictability of total ozone.

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