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Search Results: 1 - 10 of 297473 matches for " J. Tamminen "
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Aerosol model selection and uncertainty modelling by adaptive MCMC technique
M. Laine,J. Tamminen
Atmospheric Chemistry and Physics Discussions , 2008,
Abstract: We apply Bayesian model selection techniques on the statistical inversion problem of the GOMOS instrument. The motif is to study which type of aerosol model best fits the data and to show how the uncertainty of the aerosol model can be included in the error estimates. The competing models consist of various formulations, each having different unknown parameter vectors. We have developed an Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ) for sampling values from the posterior distributions of the unknowns of the models. The algorithm is easy to implement and can readily be employed for model selection as well as for model averaging, to properly take into account the uncertainty of the modelling.
Aerosol model selection and uncertainty modelling by adaptive MCMC technique
M. Laine,J. Tamminen
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2008,
Abstract: We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way. The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ). It uses Markov chain Monte Carlo (MCMC) technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use. We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.
Direct comparisons of GOMOS and SAGE III NO3 vertical profiles
J. Hakkarainen, J. Tamminen, J. R. Moore,E. Kyr l
Atmospheric Measurement Techniques (AMT) & Discussions (AMTD) , 2012,
Abstract: In this paper, we present the first global comparisons between the two unique satellite-borne data sets of nitrogen trioxide (NO3) vertical profiles retrieved from the GOMOS (Global Ozone Monitoring by the Occultation of Stars) stellar occultations and the SAGE III (Stratospheric Aerosol and Gas Experiment) lunar occultations. The comparison results indicate that between the altitudes 25 km and 45 km the median difference between these two data sets is within ± 25%. The study of zonal median profiles shows that the climatologies calculated from GOMOS and SAGE III profiles are comparable and represent the same features in all latitude bands. No clear systematic differences are observed. The median profiles are closest in the tropics and slightly deviating at high latitudes.
Direct comparisons of GOMOS and SAGE III NO3 vertical profiles
J. Hakkarainen,J. Tamminen,J. R. Moore,E. Kyr?l?
Atmospheric Measurement Techniques Discussions , 2012, DOI: 10.5194/amtd-5-1497-2012
Abstract: In this paper we present first global comparisons between the two unique satellite-borne data sets of NO3 vertical profiles retrieved from the GOMOS (Global Ozone Monitoring by the Occultation of Stars) stellar occultations and the SAGE III (Stratospheric Aerosols and Gas Experiment) lunar occultations. The comparison results indicate that between the altitudes 25 km and 45 km the median difference between these two data sets is within ±25%. The study of zonal median profiles shows agreement between these data sets. The agreement is at its best in tropics and slightly deviating towards the poles.
Use of satellite erythemal UV products in analysing the global UV changes
I. Ialongo, A. Arola, J. Kujanp ,J. Tamminen
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2011,
Abstract: Long term changes in solar UV radiation affect global bio-geochemistry and climate. The satellite-based dataset of TOMS (Total Ozone Monitoring System) and OMI (Ozone Monitoring Instrument) of erythemal UV product was applied for the first time to estimate the long-term ultraviolet (UV) changes at the global scale. The analysis of the uncertainty related to the different input information is presented. OMI and GOME-2 (Global Ozone Monitoring Experiment-2) products were compared in order to analyse the differences in the global UV distribution and their effect on the linear trend estimation. The results showed that the differences in the inputs (mainly surface albedo and aerosol information) used in the retrieval, affect significantly the UV change calculation, pointing out the importance of using a consistent dataset when calculating long term UV changes. The areas where these differences played a major role were identified using global maps of monthly UV changes. Despite the uncertainties, significant positive UV changes (ranging from 0 to about 5 %/decade) were observed, with higher values in the Southern Hemisphere at mid-latitudes during spring-summer, where the largest ozone decrease was observed.
Development of a neural network model for cloud fraction detection using NASA-Aura OMI VIS radiance measurements
G. Saponaro,P. Kolmonen,J. Karhunen,J. Tamminen
Atmospheric Measurement Techniques Discussions , 2013, DOI: 10.5194/amtd-6-1649-2013
Abstract: The discrimination of cloudy pixels is required in almost any estimate of a parameter retrieved from a satellite image in the ultraviolet (UV), visual (VIS) or infra-red (IR) parts of the electromagnetic spectrum. Also, the distincion of clouds within satellite imagery and the distribution of their micro-physical properties is essential to the understanding of radiative transfer through the atmosphere. This paper reports the development of neural network algorithms for cloud detection for the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained by training mathematical neural networks with simultaneous application to OMI and Aqua-MODerate Resolution Imaging Spectrometer (MODIS) data. The neural network delivers cloud fraction estimates in a fast and automated way. The developed neural network approach performs generally well in the training. Highly reflective surfaces, such as ice, snow, sun glint and desert, or atmospheric dust mislead the neural network to a wrong predicted cloud fraction.
Retrieval of atmospheric parameters from GOMOS data
E. Kyr?l?,J. Tamminen,V. Sofieva,J. L. Bertaux
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2010, DOI: 10.5194/acp-10-11881-2010
Abstract: The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument on board the European Space Agency's ENVISAT satellite measures attenuation of stellar light in occultation geometry. Daytime measurements also record scattered solar light from the atmosphere. The wavelength regions are the ultraviolet-visible band 248–690 nm and two infrared bands at 755–774 nm and at 926–954 nm. From UV-Visible and IR spectra the vertical profiles of O3, NO2, NO3, H2O, O2 and aerosols can be retrieved. In addition there are two 1 kHz photometers at blue 473–527 nm and red 646–698 nm. Photometer data are used to correct spectrometer measurements for scintillations and to retrieve high resolution temperature profiles as well as gravity wave and turbulence parameters. Measurements cover altitude region 5–150 km. Atmospherically valid data are obtained in 15–100 km. In this paper we present an overview of the GOMOS retrieval algorithms for stellar occultation measurements. The low signal-to-noise ratio and the refractive effects due to the point source nature of stars have been important drivers in the development of GOMOS retrieval algorithms. We present first the Level 1b algorithms that are used to correct instrument related disturbances in the spectrometer and photometer measurements The Level 2 algorithms deal with the retrieval of vertical profiles of atmospheric gaseous constituents, aerosols and high resolution temperature. We divide the presentation into correction for refractive effects, high resolution temperature retrieval and spectral/vertical inversion. The paper also includes discussion about the GOMOS algorithm development, expected improvements, access to GOMOS data and alternative retrieval approaches.
Retrieval of atmospheric parameters from GOMOS data
E. Kyr?l?,J. Tamminen,V. Sofieva,J. L. Bertaux
Atmospheric Chemistry and Physics Discussions , 2010, DOI: 10.5194/acpd-10-10145-2010
Abstract: The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument on board the European Space Agency's ENVISAT satellite measures attenuation of stellar light in occultation geometry. Daytime measurements also record scattered solar light from the atmosphere. The wavelength regions are the ultraviolet-visible band 248–690 nm and two infrared bands at 755–774 nm and at 926–954 nm. From UV-Visible and IR spectra the vertical profiles of O3, NO2, NO3, H2O, O2 and aerosols can be retrieved. In addition there are two 1 kHz photometers at blue 473–527 nm and red 646–698 nm. Photometer data are used to correct spectrometer measurements for scintillations and to retrieve high resolution temperature profiles as well as gravity wave and turbulence parameters. Measurements cover altitude region 5–150 km. Atmospherically valid data are obtained in 15–100 km. In this paper we present an overview of the GOMOS retrieval algorithms for stellar occultation measurements. The low signal-to-noise ratio and the refractive effects due to the point source nature of stars have been important drivers in the development of GOMOS retrieval algorithms. We present first the Level 1b algorithms that are used to correct instrument related disturbances in the spectrometer and photometer measurements The Level 2 algorithms deal with the retrieval of vertical profiles of atmospheric gaseous constituents, aerosols and high resolution temperature. We divide the presentation into correction for refractive effects, high resolution temperature retrieval and spectral/vertical inversion. The paper also includes discussion about the GOMOS algorithm development, expected improvements, access to GOMOS data and alternative retrieval approaches.
Retrievals from GOMOS stellar occultation measurements using characterization of modeling errors
V. F. Sofieva,J. Vira,E. Kyr?l?,J. Tamminen
Atmospheric Measurement Techniques Discussions , 2010,
Abstract: In this paper, we discuss the development of the inversion algorithm for the GOMOS (Global Ozone Monitoring by Occultation of Star) instrument on board the Envisat satellite. The proposed algorithm takes accurately into account the wavelength-dependent modeling errors, which are mainly due to the incomplete scintillation correction in the stratosphere. The special attention is paid to numerical efficiency of the algorithm. The developed method is tested on a large data set and its advantages are demonstrated. Its main advantage is a proper characterization of the uncertainties of the retrieved profiles of atmospheric constituents, which is of high importance for data assimilation, trend analyses and validation.
GOMOS O3, NO2, and NO3 observations in 2002–2008
E. Kyr?l?,J. Tamminen,V. Sofieva,J. L. Bertaux
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2010, DOI: 10.5194/acp-10-7723-2010
Abstract: The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument onboard the European Space Agency's ENVISAT satellite measures ozone, NO2, NO3, H2O, O2, and aerosols using the stellar occultation method. Global coverage, good vertical resolution and the self-calibrating measurement method make GOMOS observations a promising data set for building various climatologies and time series. In this paper we present GOMOS nighttime measurements of ozone, NO2, and NO3 during six years 2002–2008. Using zonal averages we show the time evolution of the vertical profiles as a function of latitude. In order to get continuous coverage in time we restrict the latitudinal region to 50° S–50° N. Time development is analysed by fitting constant, annual and semi-annual terms as well as solar and QBO proxies to the daily time series. Ozone data cover the stratosphere, mesosphere and lower thermosphere (MLT). NO2 and NO3 data cover the stratosphere. In addition to detailed analysis of profiles we derive total column distributions using the fitted time series. The time-independent constant term is determined with a good accuracy (better than 1%) for all the three gases. The median retrieval accuracy for the annual and semi-annual term varies in the range 5–20%. For ozone the annual terms dominate in the stratosphere giving early winter ozone maxima at mid-latitudes. Above the ozone layer the annual terms change the phase which results in ozone summer maximum up to 80 km. In the MLT the annual terms dominate up to 80 km where the semiannual terms start to grow. In the equatorial MLT the semi-annual terms dominate the temporal evolution whereas in the mid-latitude MLT annual and semi-annual terms compete evenly. In the equatorial stratosphere the QBO dominates the time development but the solar term is too weak to be determined. In the MLT above 85 km the solar term grows significantly and ozone has 15–20% dependence on the solar cycle. For NO2 below 32 km the annual summer maxima dominates at mid-latitudes whereas in the equatorial region a strong QBO prevails. In northern mid-latitudes a strong solar term appears in the upper stratosphere. For NO3 the annual variation dominates giving rise to summer maxima. The NO3 distribution is controlled by temperature and ozone.
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