The Complete Data Fusion for a Full Exploitation of Copernicus Atmospheric Sentinel Level 2 Products

Abstract. The new platforms for Earth observation from space are characterized by measurements made with great spatial and temporal resolution. While this abundance of information makes it possible to detect and study localized phenomena, on the other hand it may be difficult to manage this large amount of data in the study of global and large scale phenomena. A particularly significant example is the use by assimilation systems of level 2 products that represent gas profiles in the atmosphere. The models on which assimilation systems are based are discretized on spatial grids with horizontal dimensions of the order of tens of kilometres in which tens or hundreds of measurements may fall. A simple procedure to overcome this problem is to extract a subset of the original measurements. However, this procedure involves a loss of information and is therefore justifiable only as a temporary solution. A more refined solution is to resort to the so-called fusion algorithms, capable of compressing the size of the dataset limiting the information loss. A novel data fusion method, the Complete Data Fusion, was recently developed to merge a-posteriori a set of retrieved products in a single one. In the present paper, the Complete Data Fusion method is applied to ozone profile measurements simulated in the thermal infrared and ultraviolet bands, in a realistic scenario, according to the specifications of the Sentinel 4 and 5 missions of the Copernicus programme. Then the fused products are compared with the input profiles; comparisons show that the output products of data fusion have in general smaller errors and higher information contents. The most significant improvement is an increased vertical resolution together with a reduction of the errors. The comparisons of the fused with the fusing products are presented both at single fusion grid-box scale and with a statistical analysis. The grid box size impact was also evaluated, showing that the Complete Data Fusion method can be used with a wide range of grid-box size, the quality of the products improving with larger grid boxes.

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Application of the Complete Data Fusion algorithm to the ozone profiles measured by geostationary and low-Earth-orbit satellites: a feasibility study

Atmospheric Measurement Techniques ◽

10.5194/amt-14-2041-2021 ◽

2021 ◽

Vol 14 (3) ◽

pp. 2041-2053

Author(s):

Nicola Zoppetti ◽

Simone Ceccherini ◽

Bruno Carli ◽

Samuele Del Bianco ◽

Marco Gai ◽

...

Keyword(s):

Data Fusion ◽

Large Scale ◽

Simple Procedure ◽

Low Earth Orbit ◽

Complete Data ◽

Fusion Method ◽

Fusion Algorithm ◽

Large Sets ◽

Ozone Profile ◽

Low Earth Orbit Satellites

Abstract. The new platforms for Earth observation from space are characterized by measurements made at great spatial and temporal resolutions. While this abundance of information makes it possible to detect and study localized phenomena, it may be difficult to manage this large amount of data for the study of global and large-scale phenomena. A particularly significant example is the use by assimilation systems of Level 2 products that represent gas profiles in the atmosphere. The models on which assimilation systems are based are discretized on spatial grids with horizontal dimensions of the order of tens of kilometres in which tens or hundreds of measurements may fall in the future. A simple procedure to overcome this problem is to extract a subset of the original measurements, but this involves a loss of information. Another option is the use of simple averages of the profiles, but this approach also has some limitations that we will discuss in the paper. A more advanced solution is to resort to the so-called fusion algorithms, capable of compressing the size of the dataset while limiting the information loss. A novel data fusion method, the Complete Data Fusion algorithm, was recently developed to merge a set of retrieved products in a single product a posteriori. In the present paper, we apply the Complete Data Fusion method to ozone profile measurements simulated in the thermal infrared and ultraviolet bands in a realistic scenario. Following this, the fused products are compared with the input profiles; comparisons show that the output products of data fusion have smaller total errors and higher information contents in general. The comparisons of the fused products with the fusing products are presented both at single fusion grid box scale and with a statistical analysis of the results obtained on large sets of fusion grid boxes of the same size. We also evaluate the grid box size impact, showing that the Complete Data Fusion method can be used with different grid box sizes even if this possibility is connected to the natural variability of the considered atmospheric molecule.

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Importance of interpolation and coincidence errors in data fusion

10.5194/amt-2017-347 ◽

2017 ◽

Author(s):

Simone Ceccherini ◽

Bruno Carli ◽

Cecilia Tirelli ◽

Nicola Zoppetti ◽

Samuele Del Bianco ◽

...

Keyword(s):

Data Fusion ◽

Degrees Of Freedom ◽

Thermal Infrared ◽

Complete Data ◽

Forward Model ◽

Fusion Method ◽

Model Errors ◽

The Impact ◽

Vertical Grids

Abstract. The Complete Data Fusion method is applied to ozone profiles obtained from simulated measurements in the ultraviolet and in the thermal infrared in the framework of the Sentinel 4 mission of the Copernicus programme. We observe that the quality of the fused products is degraded when the fusing profiles are either retrieved on different vertical grids or referred to different true profiles. To address this shortcoming, a generalization of the complete data fusion method, which takes into account interpolation and coincidence errors, is presented. This upgrade overcomes the encountered problems and provides products of good quality when the fusing profiles are both retrieved on different vertical grids and referred to different true profiles. The impact of the interpolation and coincidence errors on number of degrees of freedom and errors of the fused profile is also analyzed. The approach developed here to account for the interpolation and coincidence errors can also be followed to include other error components, such as forward model errors.

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Comment on “Synergetic use of IASI and TROPOMI space borne sensors for generating a tropospheric methane profile product”

10.5194/amt-2021-98 ◽

2021 ◽

Author(s):

Simone Ceccherini

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Covariance Matrices ◽

Complete Data ◽

Fusion Method ◽

Error Covariance ◽

Noise Error

Abstract. A great interest is growing about methods that combine measurements from two or more instruments that observe the same species either in different spectral regions or with different geometries. Recently, a method based on the Kalman filter has been proposed to combine IASI and TROPOMI methane products. We show that this method is equivalent to the Complete Data Fusion method. Therefore, the choice between these two methods is driven only by the advantages of the different implementations. From the comparison of the two methods a generalization of the Complete Data Fusion formula, which is valid also in the case that the noise error covariance matrices of the fused products are singular, is derived.

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Importance of interpolation and coincidence errors in data fusion

Atmospheric Measurement Techniques ◽

10.5194/amt-11-1009-2018 ◽

2018 ◽

Vol 11 (2) ◽

pp. 1009-1017 ◽

Cited By ~ 7

Author(s):

Simone Ceccherini ◽

Bruno Carli ◽

Cecilia Tirelli ◽

Nicola Zoppetti ◽

Samuele Del Bianco ◽

...

Keyword(s):

Data Fusion ◽

Degrees Of Freedom ◽

Thermal Infrared ◽

Complete Data ◽

Forward Model ◽

Fusion Method ◽

Model Errors ◽

The Impact ◽

Vertical Grids

Abstract. The complete data fusion (CDF) method is applied to ozone profiles obtained from simulated measurements in the ultraviolet and in the thermal infrared in the framework of the Sentinel 4 mission of the Copernicus programme. We observe that the quality of the fused products is degraded when the fusing profiles are either retrieved on different vertical grids or referred to different true profiles. To address this shortcoming, a generalization of the complete data fusion method, which takes into account interpolation and coincidence errors, is presented. This upgrade overcomes the encountered problems and provides products of good quality when the fusing profiles are both retrieved on different vertical grids and referred to different true profiles. The impact of the interpolation and coincidence errors on number of degrees of freedom and errors of the fused profile is also analysed. The approach developed here to account for the interpolation and coincidence errors can also be followed to include other error components, such as forward model errors.

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Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Remote Sensing ◽

10.3390/rs13010104 ◽

2020 ◽

Vol 13 (1) ◽

pp. 104

Author(s):

Maximilian Rodger ◽

Raffaella Guida

Keyword(s):

Data Fusion ◽

Large Scale ◽

Data Association ◽

Classification Model ◽

Automatic Identification ◽

Identification System ◽

Maritime Surveillance ◽

Research Activities ◽

Wide Range ◽

Sar Imagery

A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.

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Supplementary material to "The Complete Data Fusion for a Full Exploitation of Copernicus Atmospheric Sentinel Level 2 Products"

10.5194/amt-2019-436-supplement ◽

2019 ◽

Author(s):

Nicola Zoppetti ◽

Simone Ceccherini ◽

Bruno Carli ◽

Samuele Del Bianco ◽

Marco Gai ◽

...

Keyword(s):

Data Fusion ◽

Complete Data ◽

Supplementary Material ◽

Level 2

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Design of Indoor Large-Scale Multi-Target Precise Positioning and Tracking System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1049-1050.1233 ◽

2014 ◽

Vol 1049-1050 ◽

pp. 1233-1236

Author(s):

Hui Qin Sun

Keyword(s):

Data Fusion ◽

Large Scale ◽

Tracking System ◽

Optical Tracking ◽

Sensor Data ◽

Optical Tracking System ◽

Precise Positioning ◽

Multi Sensor Data Fusion ◽

Wide Range ◽

Tracking Technology

This paper is to build a interior Large-scale multi-target precise positioning and tracking system.In-depth resear of a visual-based optical tracking technology, inertial tracking technology and multi-sensor data fusion technology.Breaking the graphics, images, data fusion, tracking and other related key technologies.Develop high versatility, real-time, robustness of a wide range of high-precision optical tracking system for interior Large-scale multi-target precise positioning and tracking system.

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A data fusion method to improve winter PM10 concentration predictions in Budapest based on the CAMS air quality models

10.5194/egusphere-egu2020-16133 ◽

2020 ◽

Author(s):

Adrienn Varga-Balogh ◽

Ádám Leelőssy ◽

István Lagzi ◽

Róbert Mészáros

Keyword(s):

Air Quality ◽

Data Fusion ◽

Real Time ◽

Large Scale ◽

Urban Traffic ◽

Pm10 Concentration ◽

Fusion Method ◽

Concentration Data ◽

Model Predictions ◽

Urban Monitoring

<div> Winter air pollution in Budapest is a major environmental issue, caused by an interaction of residential heating, urban traffic and large-scale transport.&#160;Increasing public and political&#160;demand&#160;are&#160;present to achieve&#160;more accurate air quality predictions to support both real-time public health measures and long-term mitigation policies.&#160;&#160;Atmospheric chemistry and transport models of the Copernicus Atmospheric Monitoring Service (CAMS) provide&#160;near-real-time&#160;air quality forecasts for Europe. The validation of these model predictions for Budapest showed that although large-scale processes are well captured,&#160;the complex interaction of large-scale plumes with significant and highly variable local residential emissions leads to the underestimation of winter PM10 concentrations. Furthermore, CAMS models are not expected to fully predict the non-representative concentrations at specific urban monitoring locations, which, on the other hand, serve as the legal basis of all public policies and measures. Therefore, obtaining a relationship between monitoring site observations and CAMS model predictions is of primary importance.&#160; </div><div> In this study, we used observed PM10 concentration data from 12 air quality monitoring sites within Budapest, as well as 24-hour predictions from 7 of the 9 CAMS models to produce an optimal linear combination of models that best matched, in terms of RMSE, the observed time series. A zero-degree term to correct the model bias was also applied. The applied data fusion method was cross-validated on urban monitoring sites not used in fitting the model, and&#160;found to improve PM10 forecast validation statistics compared to the pointwise model median (CAMS ensemble) as well as each of the 7 single models. The presented fusion of CAMS models can therefore provide an improved prediction of PM10 concentrations at urban monitoring sites in Budapest.&#160;&#160; </div>

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An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM

Remote Sensing ◽

10.3390/rs12213673 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3673

Author(s):

Mengxue Liu ◽

Xiangnan Liu ◽

Xiaobin Dong ◽

Bingyu Zhao ◽

Xinyu Zou ◽

...

Keyword(s):

Data Fusion ◽

Large Scale ◽

Spatial Information ◽

Interpolation Method ◽

Spatiotemporal Data ◽

Absolute Difference ◽

Fusion Method ◽

Fusion Algorithm ◽

Central Pixel ◽

Modified Algorithm

The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as it is widely used to generate synthetic data. However, the ESTARFM algorithm uses moving windows with a fixed size to get the information around the central pixel, which hampers the efficiency and precision of spatiotemporal data fusion. In this paper, a modified ESTARFM data fusion algorithm that integrated the surface spatial information via a statistical method was developed. In the modified algorithm, the local variance of pixels around the central one was used as an index to adaptively determine the window size. Satellite images from two regions were acquired by employing the ESTARFM and modified algorithm. Results showed that the images predicted using the modified algorithm obtained more details than ESTARFM, as the frequency of pixels with the absolute difference of mean value of six bands’ reflectance between true observed image and predicted between 0 and 0.04 were 78% by ESTARFM and 85% by modified algorithm, respectively. In addition, the efficiency of the modified algorithm improved and the verification test showed the robustness of the modified algorithm. These promising results demonstrated the superiority of the modified algorithm to provide synthetic images compared with ESTARFM. Our research enriches the spatiotemporal data fusion method, and the automatic selection of moving window strategy lays the foundation of automatic processing of spatiotemporal data fusion on a large scale.

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Electron microscopy study of reactively sputter-deposited Ti/N films on silicon

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100131449 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1362-1363

Author(s):

V. C. Kannan ◽

A. K. Singh ◽

R. B. Irwin ◽

S. Chittipeddi ◽

F. D. Nkansah ◽

...

Keyword(s):

Integrated Circuits ◽

Large Scale ◽

Hexagonal Phase ◽

Microscopy Study ◽

Electron Microscopy Study ◽

Process Conditions ◽

Tin Films ◽

Wide Range ◽

Fcc Phase ◽

Circuits Vlsi

Titanium nitride (TiN) films have historically been used as diffusion barrier between silicon and aluminum, as an adhesion layer for tungsten deposition and as an interconnect material etc. Recently, the role of TiN films as contact barriers in very large scale silicon integrated circuits (VLSI) has been extensively studied. TiN films have resistivities on the order of 20μ Ω-cm which is much lower than that of titanium (nearly 66μ Ω-cm). Deposited TiN films show resistivities which vary from 20 to 100μ Ω-cm depending upon the type of deposition and process conditions. TiNx is known to have a NaCl type crystal structure for a wide range of compositions. Change in color from metallic luster to gold reflects the stabilization of the TiNx (FCC) phase over the close packed Ti(N) hexagonal phase. It was found that TiN (1:1) ideal composition with the FCC (NaCl-type) structure gives the best electrical property.

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