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Multi Sensor Reanalysis of Total Ozone : Volume 10, Issue 4 (28/04/2010)

By Van Der A, R. J.

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Book Id: WPLBN0003995330
Format Type: PDF Article :
File Size: Pages 48
Reproduction Date: 2015

Title: Multi Sensor Reanalysis of Total Ozone : Volume 10, Issue 4 (28/04/2010)  
Author: Van Der A, R. J.
Volume: Vol. 10, Issue 4
Language: English
Subject: Science, Atmospheric, Chemistry
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany


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F. Allaar, M. A., Eskes, H. J., & Van Der A, R. J. (2010). Multi Sensor Reanalysis of Total Ozone : Volume 10, Issue 4 (28/04/2010). Retrieved from

Description: KNMI, P.O. Box 201, 3730 AE De Bilt, The Netherlands. 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.

Multi sensor reanalysis of total ozone

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