Ozone profile retrieval from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY))

Müller, Martin; Kaifel, Anton; Weber, Mark; Tellmann, S.; Burrows, J. P.; Loyola, Diego

doi:10.1029/2002jd002784

Cited by 60 publications

(41 citation statements)

References 43 publications

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“…Liu (xliu@cfa.harvard.edu) and surface in the wavelength range 240-790 nm (ESA, 1995). Observations with spectral resolution of 0.2 nm and high signal to noise ratio in the Hartley and Huggins bands enable retrieval of the vertical distribution of ozone from ∼50 km down into the troposphere (Chance et al, 1997;Munro et al, 1998;Hoogen et al, 1999;Hasekamp and Landgraf, 2001;van der A et al, 2002;Müller et al, 2003;Liu et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Improved ozone profile retrievals from GOME data with degradation correction in reflectance

2007

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Abstract. We present a simple method to perform degradation correction to Global Ozone Monitoring Experiment (GOME) reflectance spectra by comparing the average reflectance for 60 • N-60 • S with that at the beginning of GOME observations (July-December 1995) after removing the dependences on solar zenith angle and seasonal variation. The results indicate positive biases of up to ∼15-25% in the wavelength range 289-370 nm during 2000-2002; the degradation also exhibits significant dependence on wavelength and viewing zenith angle. These results are consistent with previous studies using radiative transfer models and ozone observations. The degradation causes retrieval biases of up to ∼3% (10 DU, 1 DU=2.69×10 16 molecules cm −2 ), 30% (10 DU), 10%, and 40% in total column ozone, tropospheric column ozone, stratospheric ozone and tropospheric ozone, respectively, from our GOME ozone profile retrieval algorithm. In addition, retrieval biases due to degradation vary significantly with latitude. The application of this degradation correction improves the retrievals relative to Dobson and ozonesonde measurements at Hohenpeißenberg station during 2000-2003 and improves the spatiotemporal consistency of retrieval quality during 1996-2003. However, because this method assumes that the deseasonalized globally-averaged reflectance does not change much with time, retrievals with this correction may be inadequate for trend analysis. In addition, it does not correct for instrument biases that have occurred since launch.

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Section: Introductionmentioning

confidence: 99%

Improved ozone profile retrievals from GOME data with degradation correction in reflectance

2007

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“…Del Frate et al, 2002Müller et al, 2003;Iapaolo et al, 2005;Sellitto et al, 2011). Recently, NN algorithms for both ozone profile retrieval (e.g.…”

Section: Introductionmentioning

confidence: 99%

Tropospheric ozone column retrieval at northern mid-latitudes from the Ozone Monitoring Instrument by means of a neural network algorithm

Sellitto

Bojkov²,

Liu

et al. 2011

Atmos. Meas. Tech.

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Abstract. Monitoring tropospheric ozone from space is of critical importance in order to gain more thorough knowledge on phenomena affecting air quality and the greenhouse effect. Deriving information on tropospheric ozone from UV/VIS nadir satellite spectrometers is difficult owing to the weak sensitivity of the measured radiance spectra to variations of ozone in the troposphere. Here we propose an alternative method of analysis to retrieve tropospheric ozone columns from Ozone Monitoring Instrument radiances by means of a neural network algorithm. An extended set of ozone sonde measurements at northern mid-latitudes for the years 2004-2008 has been considered as the training and test data set. The design of the algorithm is extensively discussed. Our retrievals are compared to both tropospheric ozone residuals and optimal estimation retrievals over a similar independent test data set. Results show that our algorithm has comparable accuracy with respect to both correlative methods and its performance is slightly better over a subset containing only European ozone sonde stations. Possible sources of errors are analyzed. Finally, the capabilities of our algorithm to derive information on boundary layer ozone are studied and the results critically discussed.

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“…12 also indicate an improvement for assimilated ozone data, including NNORSY (Neural Network Ozone Retrieval System; Müller et al, 2003) retrieved ozone profiles from GOME (Global Ozone Monitoring Experiment), which have been assimilated together with IMK MIPAS ozone profile data and MOZAIC aircraft in situ ozone data. (Note that efforts are underway to make SCIAMACHY neural network based retrievals available in the future.)…”

Section: Distribution Of Tropospheric Pollutantsmentioning

confidence: 99%

The Assimilation of Envisat data (ASSET) project

et al. 2007

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Abstract. This paper discusses the highlights of the EUfunded "Assimilation of Envisat data" (ASSET) project, which has involved assimilation of Envisat atmospheric constituent and temperature data into systems based on Numerical Weather Prediction (NWP) models and chemical transport models (CTMs). Envisat was launched in 2002 and is one of the largest Earth Observation (EO) satellites ever built. It carries several sophisticated EO instruments providing insights into chemistry and dynamics of the atmosphere. In this paper we focus on the assimilation of temperature and constituents from Envisat.The overarching theme of the ASSET project has been to bring together experts from all aspects of the data assimilation problem. This has allowed ASSET to address several themes comprehensively: enhancement of NWP analyses by assimilation of research satellite data; studies of the distribution of stratospheric chemical species by assimilation of research satellite data into CTM systems; objective assessment of the quality of ozone analyses; studies of the spatial and temporal evolution of tropospheric pollutants; enhanced retrievals of Envisat data; and data archival and dissemination.Correspondence to: W. A. Lahoz (swslahoz@rdg.ac.uk) Among the results from the ASSET project, many of which are firsts in their field, we can mention: a positive impact on NWP analyses from assimilation of height-resolved stratospheric humidity and temperature data, and assimilation of limb radiances; the extraction of temperature information from the assimilation of chemical species into CTMs; a first intercomparison between ozone assimilation systems; the extraction of information on tropospheric pollution from assimilation of Envisat data; and the large potential of the Envisat MIPAS dataset. This paper discusses these, often novel, developments and results. Finally, achievements of, and recommendations from, the ASSET project are presented.

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Ozone profile retrieval from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY))

Cited by 60 publications

References 43 publications

Improved ozone profile retrievals from GOME data with degradation correction in reflectance

Improved ozone profile retrievals from GOME data with degradation correction in reflectance

Tropospheric ozone column retrieval at northern mid-latitudes from the Ozone Monitoring Instrument by means of a neural network algorithm

The Assimilation of Envisat data (ASSET) project

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