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Neural networks algorithms for the estimation of atmospheric ozone from Envisat-SCIAMACHY and Aura-OMI measurements
Full text linkClimate changes and atmospheric pollution are currently topical issues given their possible
dramatic effects from the health, social and economical points of view. Assessing the
causes and possible adaptation/mitigation strategies is a challenge in modern science.
To understand and quantify the anthropic role in such changes is of a particular interest
to depict future scenarios and to warn politicians about local and global intervention in
emissions control.
Ozone is one of the most important trace gases in the Earth's atmosphere. It is mainly
present in the stratosphere, with only 10% in the troposphere. Despite its small amount,
(2-7) 10ô 3 % in molar fraction, the solar radiation at wavelengths below 310 nm does
not reach the Earth surface because of the large absorption cross sections characterizing
ozone molecules at those wavelengths. Variations in the stratospheric ozone content may
play a dramatic role in a possible increase of the surface UV radiation. The discovery of
the Antarctic ozone hole, i.e. a considerable reduction of ozone in the polar stratosphere,
was a dramatic evidence of the effects of anthropogenic emissions on the ozone layer.
Human activity is likely responsible also for tropospheric ozone enhancements caused
by the photochemistry associated to industrial emissions involving ozone precursors as
the nitrogen dioxide. The effect of these variations at lower altitudes, with respect to
background values, have been estimated to be the third largest source of the greenhouse
effect.
To support interpretation of the atmospheric phenomena, as well as interactions with
the oceans and the ground, a constant and systematic monitoring of several atmospheric
parameters, and with a good spatial coverage, is crucial. In this framework, global and
systematic space-based observations of the atmospheric composition and its variations
in time and space play a major role. Satellite measurements of atmospheric parameters
has a proven and recognized effectiveness for such tasks. The advantage of atmospheric
sounding performed from space, with respect to ground based techniques, lies in the
very high number of available measurements per day and in the global coverage of the
Earth, allowing for a detailed and continuous investigation of the atmospheric state. A number of different techniques are available, using different instruments, bands and
viewing geometries. For all of them, a major problem is related to the intrinsically
indirect nature of the measurements, as they result from the interaction between the
electromagnetic radiation and the atmospheric constituents. The retrieval phase requires
the solution of an inverse problem, which is never trivial and can be computationally
very intensive, especially for this kind of nonlinear problems. A significant concurrent
requirement is an adequate spatial resolution. Horizontal resolution is very hard to
achieve by limb measurements, while it can be attained by nadir observations. Nadir
measurements, however, can have poor vertical resolutions, and the inversion problem
can be particularly computationally expensive.
In this thesis we present novel approaches to the inversion of the nadir UV/VIS satellite
Earth's radiance spectra for the retrieval of height resolved ozone information. The
considered platforms are ESA EnviSat-SCIAMACHY and NASA-Aura OMI, which are
particularly suited for these tasks owing to their combined high spectral and spatial resolutions.
Both ozone concentration profiles and tropospheric ozone column are retrieved
by means of NNs algorithms. NNs are made of interconnected elementary processing
units, called neurons, and can learn from a training dataset; they were proven to be
robust on systematic errors and calibration uncertainties on the input measurements
vector, and they are likely to work better than OE with respect to cloudy scenarios or in
presence of significant aerosols burdens. Once a net is trained it can perform retrievals
in real-time
An Overview of Methodologies to Retrieve Aerosol Optical Depth by Means of Brewer Spectrophotometry
No full textTropospheric ozone column retrieval from the Ozone Monitoring Instrument by means of a neural network algorithm
No full textOzone concentration levels in the troposphere have several impacts on both climate and air quality. Monitoring tropospheric ozone concentrations and trends, especially in highly polluted locations, is a relevant topic in recent geosciences research. To observe height-resolved ozone concentrations from satellite platform is an exciting task, owing to the global and continuous nature of the obtained information. The Ozone Monitoring Instrument represents a good chance to contribute at the understanding of ozone related phenomena, also within the troposphere, owing to the relatively small pixel size; it is also a useful means for an effective monitoring of such phenomena, owing to its daily global coverage. Different techniques have been recently proposed to monitor tropospheric ozone content from satellite using the OMI. Mainly two techniques exist within this field: the Tropospheric Ozone Residual (TOR) methodology and the Optimal Estimation (OE). Neural networks (NN) algorithms have demonstrated encouraging capabilities as an alternative tool to retrieve height-resolved ozone concentrations from satellite data. Recently, NN algorithms for both ozone concentration profiles retrieval and tropospheric ozone columns (TOC) retrievals from SCIAMACHY and GOME have been presented. NNs have some advantages over the TOR and the OE techniques. When used for the mentioned applications, NNs are intended to learn the inverse relationship between the satellite reflectance spectra and the TOC from a set of sample data, and a direct model is not necessary. Theyre also expected to be less sensitive to systematic errors on the input spectra with respect to OE. Once trained, a NN is able to operate in real time. Here we present a NN algorithm to retrieve TOCs from OMI Level 1b data, which we called the OMI-TOC NN. First results and a validation exercise will be also discussed. Our NN retrievals are also compared to co-located TOC retrievals obtained with the OE
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Neural Network Algorithms for Height Resolved Ozone Retrievals from OMI Level 1b Data
No full textOzone is one of the most important trace gases in the Earth's atmosphere. It is mainly present in the stratosphere, with only about 10% of the total column amount in the troposphere. Despite the relatively small ozone abundance, the solar radiation at wavelengths below 310 nm does not reach the Earth surface because of the large ozone absorption and Rayleigh scattering. Anthropogenic releases of ozone depleting substances play a dramatic role in a possible increase of the surface UV radiation. Human activity is very likely to be responsible also for an increase in tropospheric ozone caused by the photochemical production from industrial emission of ozone precursors such as carbon monoxide, volatile organic compounds, and nitrogen oxides. The effect of these variations at lower altitudes, with respect to background values, have been estimated to be the third largest source of the greenhouse effect. Monitoring ozone concentrations and trends, especially in highly polluted locations, is a relevant topic in recent geosciences research. Obtaining height resolved information of ozone from satellite platform is an exciting task. Difficulties stem e.g. from the weak sensitivity of the Earth’s radiances to variations of ozone at lower altitudes and from the relatively high horizontal resolution needed to resolve small scale features of regional air pollution, as well as for climatological issues. A daily global coverage may be required to continuously observe the air masses and check the air quality.
The Dutch-Finnish Ozone Monitoring Instrument (OMI) on board the NASA-Aura spacecraft matches these requirements by combining a satisfactory spatial resolution and a daily global coverage. From this point of view, the OMI can more satisfactorily provide useful data with respect to the Envisat SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) or the ERS-2 Global Ozone Monitoring Experiment (GOME). The OMI is an ESA third party mission.
Usually height resolved information about ozone is inferred from the satellite signal by means of Optimal Estimation (OE) inversion techniques. Recently, we proposed Neural Network (NN) based schemes to retrieve both ozone profiles and Tropospheric Ozone Column (TOC) from Earth’s radiance UV/VIS nadir measurements. NN algorithms are particularly suited to solve complex non-linear problems like the inversion of satellite radiance measurements for atmospheric profiles retrieval. NNs are expected to be less sensitive to calibration uncertainties than the OE, and to work more reliably in aerosols/cloudy scenarios. The networks were trained using both simulated and experimental data. The input wavelengths were selected following an RTM-NN extended pruning method to objectively exploit the information budget of the satellite measurements and the role of ultraviolet and visible radiation was investigated.
In this work we report on the potential of NNs in the retrieval of atmospheric ozone concentration from NASA-Aura OMI Level 1b data. The results obtained with two NNs, one for ozone pProfile retrievals and the second for a direct TOC retrievals, respectively, are shown and the different design issues addressed. Both data from ozone sondes and complementary satellite data, e.g. Aura Microwave Limb Sounder (MLS) measurements, are considered as the “true†outputs during the training phase. A comparison with operational OMI Level 2 tropospheric ozone and Level 2 ozone profiles products is also presented and the results are discussed. OMI, MLS and some correlative data were provided by the Aura Validation Data Center (AVDC)
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