<title>Retrieval and molecule sensitivity studies for the global ozone monitoring experiment and the scanning imaging absorption spectrometer for atmospheric chartography</title>

Abstract. A recently developed cloud retrieval algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer data. The results of the cloud top height retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively.

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FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2

Atmospheric Measurement Techniques ◽

10.5194/amt-12-2485-2019 ◽

2019 ◽

Vol 12 (4) ◽

pp. 2485-2498 ◽

Cited By ~ 3

Author(s):

Marine Desmons ◽

Ping Wang ◽

Piet Stammes ◽

L. Gijsbert Tilstra

Keyword(s):

Satellite Data ◽

Surface Albedo ◽

Cloud Fraction ◽

Mean Difference ◽

Retrieval Algorithm ◽

Retrieval Scheme ◽

Ozone Monitoring ◽

The Difference ◽

Scanning Imaging ◽

Absorption Spectrometer

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) algorithm is a simple, fast and robust algorithm used to retrieve cloud information in operational satellite data processing. It has been applied to GOME-1 (Global Ozone Monitoring Experiment), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography), GOME-2 and more recently to TROPOMI (Tropospheric Monitoring Instrument). FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B band around 687 nm. Such a method is interesting for vegetated surfaces where the surface albedo is much lower in the B band than in the A band, which limits the ground contribution to the top-of-atmosphere reflectances. In this study we first perform retrieval simulations. These show that the retrieved cloud pressures from FRESCO-B and FRESCO differ only between −10 and +10 hPa, except for high, thin clouds over vegetation where the difference is larger (about +15 to +30 hPa), with FRESCO-B yielding higher pressure. Next, inter-comparison between FRESCO-B and FRESCO retrievals over 1 month of GOME-2B data reveals that the effective cloud fractions retrieved in the O2 A and B bands are very similar (mean difference of 0.003), while the cloud pressures show a mean difference of 11.5 hPa, with FRESCO-B retrieving higher pressures than FRESCO. This agrees with the simulations and is partly due to deeper photon penetrations of the O2 B band in clouds compared to the O2 A-band photons and partly due to the surface albedo bias in FRESCO. Finally, validation with ground-based measurements shows that the FRESCO-B cloud pressure represents an altitude within the cloud boundaries for clouds that are not too far from the Lambertian reflector model, which occurs in about 50 % of the cases.

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The semianalytical cloud retrieval algorithm for SCIAMACHY I. The validation

Atmospheric Chemistry and Physics ◽

10.5194/acp-6-1905-2006 ◽

2006 ◽

Vol 6 (7) ◽

pp. 1905-1911 ◽

Cited By ~ 23

Author(s):

A. A. Kokhanovsky ◽

V. V. Rozanov ◽

T. Nauss ◽

C. Reudenbach ◽

J. S. Daniel ◽

...

Keyword(s):

Retrieval Algorithm ◽

Modis Data ◽

Look Up Table ◽

Ozone Monitoring ◽

Scanning Imaging ◽

Using Data ◽

The Look ◽

Along Track ◽

Resolution Spectrometer ◽

Absorption Spectrometer

Abstract. A recently developed cloud retrieval algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer (MODIS) data. The results of the cloud top height retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively.

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Scanning imaging absorption spectrometer for atmospheric chartography

10.1117/12.46619 ◽

1991 ◽

Cited By ~ 17

Author(s):

John P. Burrows ◽

Kelly V. Chance

Keyword(s):

Scanning Imaging ◽

Absorption Spectrometer

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SCIAMACHY Absorbing Aerosol Index – calibration issues and global results from 2002–2004

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-5-3367-2005 ◽

2005 ◽

Vol 5 (3) ◽

pp. 3367-3389 ◽

Cited By ~ 3

Author(s):

M. de Graaf ◽

P. Stammes

Keyword(s):

Good Representation ◽

Correction Factors ◽

Lookup Tables ◽

Scanning Imaging ◽

Aerosol Index ◽

Reflectance Data ◽

Absorption Spectrometer

Abstract. The validity of the Absorbing Aerosol Index (AAI) product from the SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) is discussed. The operational SCIAMACHY AAI product suffers from calibration errors in the reflectance as measured by SCIAMACHY and design errors. Therefore, the AAI product was recalculated, compensating for the errors, with reflectance data from the start of measurements of SCIAMACHY until December 2004. Appropriate correction factors were determined for the UV to correct for the radiometric error in the SCIAMACHY reflectances. The algorithm was provided with LookUp Tables in which a good representation of polarisation effects was incorporated, as opposed to the LookUp Tables of the operational product, in which polarisation effects were not accounted for. The results are presented, their validity discussed, and compared to the operational product. The AAI is very sensitive to calibration errors and can be used to monitor calibration errors and changes. From 2004 onwards, the new SCIAMACHY AAI is suitable to add to the continuation of the long-term AAI record. Recommendations are given for improvement of the operational AAI product.

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Multi-year comparison of stratospheric BrO vertical profiles retrieved from SCIAMACHY limb and ground-based UV-visible measurements

Atmospheric Measurement Techniques ◽

10.5194/amt-2-273-2009 ◽

2009 ◽

Vol 2 (1) ◽

pp. 273-285 ◽

Cited By ~ 11

Author(s):

F. Hendrick ◽

A. Rozanov ◽

P. V. Johnston ◽

H. Bovensmann ◽

M. De Mazière ◽

...

Keyword(s):

Cross Sections ◽

Relative Difference ◽

Early Spring ◽

Vertical Profiles ◽

Altitude Range ◽

Comparison Results ◽

Uv Visible ◽

Scanning Imaging ◽

Relative Differences ◽

Absorption Spectrometer

Abstract. Vertical profiles of stratospheric bromine monoxide (BrO) retrieved daily from ENVISAT/SCIAMACHY (ENVIronmental SATellite/SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) limb scatter data and from ground-based UV-visible observations performed at Harestua (60° N, 11° E), Observatoire de Haute-Provence (44° N, 5.5° E), and Lauder (45° S, 170° E) are compared in the 15–27 km altitude range for the 2002–2006, 2005–2006, and 2002–2005 periods, respectively. At the three stations, the SCIAMACHY and ground-based UV-visible mean profiles agree reasonably well, with relative difference smaller than 23%. When comparing the BrO partial columns, the agreement obtained is good, with mean relative differences smaller than 11% and corresponding standard deviations in the 13–19% range. These comparison results are obtained, however, using different BrO cross sections in SCIAMACHY limb and ground-based UV-visible retrievals. The seasonal variation of the BrO columns at the three stations is consistently captured by both retrievals as well as large BrO column events occurring during the winter and early spring at Harestua which are associated with bromine activation.

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Geophysical validation and long-term consistency between GOME-2/MetOp-A total ozone column and measurements from the sensors GOME/ERS-2, SCIAMACHY/ENVISAT and OMI/Aura

Atmospheric Measurement Techniques ◽

10.5194/amt-5-2169-2012 ◽

2012 ◽

Vol 5 (9) ◽

pp. 2169-2181 ◽

Cited By ~ 33

Author(s):

M. E. Koukouli ◽

D. S. Balis ◽

D. Loyola ◽

P. Valks ◽

W. Zimmer ◽

...

Keyword(s):

Total Ozone ◽

Satellite Monitoring ◽

Total Ozone Column ◽

Satellite Sensors ◽

Data Processor ◽

Ozone Monitoring ◽

Scanning Imaging ◽

Ozone Column ◽

Seasonal Dependence

Abstract. The main aim of the paper is to assess the consistency of five years of Global Ozone Monitoring Experiment-2/Metop-A [GOME-2] total ozone columns and the long-term total ozone satellite monitoring database already in existence through an extensive inter-comparison and validation exercise using as reference Brewer and Dobson ground-based measurements. The behaviour of the GOME-2 measurements is being weighed against that of GOME (1995–2011), Ozone Monitoring Experiment [OMI] (since 2004) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY [SCIAMACHY] (since 2002) total ozone column products. Over the background truth of the ground-based measurements, the total ozone columns are inter-evaluated using a suite of established validation techniques; the GOME-2 time series follow the same patterns as those observed by the other satellite sensors. In particular, on average, GOME-2 data underestimate GOME data by about 0.80%, and underestimate SCIAMACHY data by 0.37% with no seasonal dependence of the differences between GOME-2, GOME and SCIAMACHY. The latter is expected since the three datasets are based on similar DOAS algorithms. This underestimation of GOME-2 is within the uncertainty of the reference data used in the comparisons. Compared to the OMI sensor, on average GOME-2 data underestimate OMI_DOAS (collection 3) data by 1.28%, without any significant seasonal dependence of the differences between them. The lack of seasonality might be expected since both the GOME data processor [GDP] 4.4 and OMI_DOAS are DOAS-type algorithms and both consider the variability of the stratospheric temperatures in their retrievals. Compared to the OMI_TOMS (collection 3) data, no bias was found. We hence conclude that the GOME-2 total ozone columns are well suitable to continue the long-term global total ozone record with the accuracy needed for climate monitoring studies.

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A method for colocating satellite XCO2 data to ground-based data and its application to ACOS-GOSAT and TCCON

Atmospheric Measurement Techniques ◽

10.5194/amt-7-2631-2014 ◽

2014 ◽

Vol 7 (8) ◽

pp. 2631-2644 ◽

Cited By ~ 22

Author(s):

H. Nguyen ◽

G. Osterman ◽

D. Wunch ◽

C. O'Dell ◽

L. Mandrake ◽

...

Keyword(s):

Mean Squared Error ◽

Weighted Average ◽

Satellite Observations ◽

Total Carbon ◽

Satellite Measurements ◽

Temporal Window ◽

Squared Error ◽

Scanning Imaging ◽

Total Column ◽

Absorption Spectrometer

Abstract. Satellite measurements are often compared with higher-precision ground-based measurements as part of validation efforts. The satellite soundings are rarely perfectly coincident in space and time with the ground-based measurements, so a colocation methodology is needed to aggregate "nearby" soundings into what the instrument would have seen at the location and time of interest. We are particularly interested in validation efforts for satellite-retrieved total column carbon dioxide (XCO2), where XCO2 data from Greenhouse Gas Observing Satellite (GOSAT) retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) are often colocated and compared to ground-based column XCO2 measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO2 (XCO2) with ground-based measurements typically involve locating and averaging the satellite measurements within a latitudinal, longitudinal, and temporal window. We examine a geostatistical colocation methodology that takes a weighted average of satellite observations depending on the "distance" of each observation from a ground-based location of interest. The "distance" function that we use is a modified Euclidian distance with respect to latitude, longitude, time, and midtropospheric temperature at 700 hPa. We apply this methodology to XCO2 retrieved from GOSAT spectra by the ACOS team, cross-validate the results to TCCON XCO2 ground-based data, and present some comparisons between our methodology and standard existing colocation methods showing that, in general, geostatistical colocation produces smaller mean-squared error.

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