scholarly journals Retrievals from GOMOS stellar occultation measurements using characterization of modeling errors

2010 ◽  
Vol 3 (1) ◽  
pp. 579-597 ◽  
Author(s):  
V. F. Sofieva ◽  
J. Vira ◽  
E. Kyrölä ◽  
J. Tamminen ◽  
V. Kan ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Data ◽  
Inversion Algorithm ◽  
Data Set ◽  
High Importance ◽  
Numerical Efficiency ◽  
Modeling Errors ◽  
Stellar Occultation ◽  
Ozone Monitoring

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.

scholarly journals Retrievals from GOMOS stellar occultation measurements using characterization of modeling errors

2010 ◽  
Vol 3 (4) ◽  
pp. 1019-1027 ◽  
Author(s):  
V. F. Sofieva ◽  
J. Vira ◽  
E. Kyrölä ◽  
J. Tamminen ◽  
V. Kan ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Data ◽  
Inversion Algorithm ◽  
Data Set ◽  
High Importance ◽  
Numerical Efficiency ◽  
Modeling Errors ◽  
Stellar Occultation ◽  
Ozone Monitoring

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.

scholarly journals GOMOS bright limb ozone data set

2015 ◽  
Vol 8 (8) ◽  
pp. 3107-3115 ◽  
Author(s):  
S. Tukiainen ◽  
E. Kyrölä ◽  
J. Tamminen ◽  
J. Kujanpää ◽  
L. Blanot
Keyword(s):  
Stray Light ◽  
Atmospheric Composition ◽  
Profile Data ◽  
Satellite Measurements ◽  
Data Set ◽  
Infrared Imager ◽  
Ozone Profile ◽  
Stellar Occultation ◽  
Ozone Monitoring ◽  
Better Than

Abstract. We have created a daytime ozone profile data set from the measurements of the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument on board the Envisat satellite. This so-called GOMOS bright limb (GBL) data set contains ∼ 358 000 stratospheric daytime ozone profiles measured by GOMOS in 2002–2012. The GBL data set complements the widely used GOMOS nighttime data based on stellar occultation measurements. The GBL data set is based on the GOMOS daytime occultations but instead of the transmitted star light we use limb-scattered solar light. The ozone profiles retrieved from these radiance spectra cover the 18–60 km altitude range and have approximately 2–3 km vertical resolution. We show that these profiles are generally in better than 10 % agreement with the NDACC (Network for the Detection of Atmospheric Composition Change) ozonesonde profiles and with the GOMOS nighttime, MLS (Microwave Limb Sounder), and OSIRIS (Optical Spectrograph and InfraRed Imager System) satellite measurements. However, there is a 10–13 % negative bias at 40 km altitude and a 10–50 % positive bias at 50 km for solar zenith angles > 75°. These biases are most likely caused by stray light which is difficult to characterize and to remove entirely from the measured spectra. Nevertheless, the GBL data set approximately doubles the amount of useful GOMOS ozone profiles and improves coverage of the summer pole.

Morphological and Anatomical Characterization of Solidago × niederederi and Other Sympatric Solidago Species

Weed Science ◽  
2016 ◽  
Vol 64 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Birutė Karpavičienė ◽  
Jolita Radušienė
Keyword(s):  
Parental Species ◽  
Pollen Viability ◽  
Large Data ◽  
Hybrid Origin ◽  
Putative Hybrid ◽  
Data Set ◽  
Anatomical Characteristics ◽  
Solidago Virgaurea ◽  
Somatic Chromosome Numbers

Two species of invasive goldenrods, Solidago canadensis and Solidago gigantea, are spread over all territories of Lithuania. Solidago × niederederi, a putative hybrid between S. canadensis and native Solidago virgaurea, was found in 27 populations mixed with the parental species. This research represents the characterization of S. × niederederi in comparison to the other mentioned Solidago species, with the use of one-way analysis of variance (ANOVA), principal-components analysis (PCA), and discriminant analysis of a large data set. Twenty quantitative, four qualitative, and five ratio morphological and anatomical characteristics, pollen viability, and somatic chromosome numbers of the four Solidago species were studied with the aim to ascertain inter- and intraspecific variation and reliable features identifying S. × niederederi, and to test the hypothesized hybrid origin. The PCA of morphological and anatomical characteristics showed the clear intermediate position of S. × niederederi compared to S. canadensis and S. virgaurea. The results showed that the most informative characteristics for the distinction of hybrids from parental species are floral traits such as the lengths of the disc, ray florets, and involucre. The intermediate stomatal characteristics and sharply decreased pollen viability discovered herein could potentially be used as an additional discriminating character in Solidago hybrid identification and support the hybrid origin of S. × niederederi.

Fast Data-Obtaining Algorithm for Data Assimilation with Large Data Set

2019 ◽  
Vol 48 (4) ◽  
pp. 750-770
Author(s):  
Junmin Xiao ◽  
Guizhao Zhang ◽  
Yanan Gao ◽  
Xuehai Hong ◽  
Guangming Tan
Keyword(s):  
Data Assimilation ◽  
Large Data ◽  
Data Set ◽  
Large Data Set

High-resolution geophysical characterization of shallow-water wetlands

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. B101-B109 ◽  
Author(s):  
Nasser Mansoor ◽  
Lee Slater ◽  
Francisco Artigas ◽  
Esben Auken
Keyword(s):  
Surface Water ◽  
Shallow Water ◽  
A Priori ◽  
Aerial Photographs ◽  
Inversion Algorithm ◽  
Data Set ◽  
Surface Water Chemistry ◽  
Sources Of Pollution ◽  
Magnetic Gradiometry

We describe a procedure for rapid characterization of shallow-water, contaminated wetlands. Terrain-conductivity (TC), vertical-magnetic-gradiometry, and surface-water-chemistry data were obtained from a shallow-draft paddleboat operable in as little as [Formula: see text] of water. Measurements were taken every [Formula: see text], with data-acquisition rates exceeding [Formula: see text] of line ([Formula: see text] data points) per 8-hr field day. We applied this procedure to an urban wetland that is affected by point and nonpoint sources of pollution. We used a one-dimensional, laterally constrained inversion algorithm to invert the apparent-conductivity data set obtained from the TC survey and to create a pseudo-2D image of sediment conductivity. The continuously recorded surface-water depth and conductivity values were input as a priori information in the inversion. We used soil chemistry determined for 28 sediment samples collected from the site, as well as lithologic logs from across the wetland, to constrain interpretation of the geophysical data. The inverted sediment conductivity describes a pattern of contamination probably attributable to leachates from adjacent landfills and/or to saltwater ingress from a partial tidal connection that is not obvious in the surface-water data. Magnetic-gradiometry values and the in-phase component of an EM31 response both reflect primarily the distribution of junk metal associated with a legacy of illegal dumping. Historic aerial photographs suggest that this distribution reflects land-use history and defines the maximum previous extent of an adjacent landfill and a pattern of dumping correlated with historic roadways.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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