scholarly journals Validation of OCO-2 error analysis using simulated retrievals

2019 ◽  
Vol 12 (10) ◽  
pp. 5317-5334 ◽  
Author(s):  
Susan S. Kulawik ◽  
Chris O'Dell ◽  
Robert R. Nelson ◽  
Thomas E. Taylor
Keyword(s):  
Measurement Error ◽  
Negative Correlation ◽  
Bias Correction ◽  
Retrieval System ◽  
Regional Scale ◽  
Optimal Estimation ◽  
Strongly Correlated ◽  
Linear Relationships ◽  
Imperfect Sensitivity

Abstract. Characterization of errors and sensitivity in remotely sensed observations of greenhouse gases is necessary for their use in estimating regional-scale fluxes. We analyze 15 orbits of the simulated Orbiting Carbon Observatory-2 (OCO-2) with the Atmospheric Carbon Observations from Space (ACOS) retrieval, which utilizes an optimal estimation approach, to compare predicted versus actual errors in the retrieved CO2 state. We find that the nonlinearity in the retrieval system results in XCO2 errors of ∼0.9 ppm. The predicted measurement error (resulting from radiance measurement error), about 0.2 ppm, is accurate, and an upper bound on the smoothing error (resulting from imperfect sensitivity) is not more than 0.3 ppm greater than predicted. However, the predicted XCO2 interferent error (resulting from jointly retrieved parameters) is a factor of 4 larger than predicted. This results from some interferent parameter errors that are larger than predicted, as well as some interferent parameter errors that are more strongly correlated with XCO2 error than predicted by linear error estimation. Variations in the magnitude of CO2 Jacobians at different retrieved states, which vary similarly for the upper and lower partial columns, could explain the higher interferent errors. A related finding is that the error correlation within the CO2 profiles is less negative than predicted and that reducing the magnitude of the negative correlation between the upper and lower partial columns from −0.9 to −0.5 results in agreement between the predicted and actual XCO2 error. We additionally study how the postprocessing bias correction affects errors. The bias-corrected results found in the operational OCO-2 Lite product consist of linear modification of XCO2 based on specific retrieved values, such as the CO2 grad del (δ∇CO2), (“grad del” is a measure of the change in the profile shape versus the prior) and dP (the retrieved surface pressure minus the prior). We find similar linear relationships between XCO2 error and dP or δ∇CO2 but see a very complex pattern of errors throughout the entire state vector. Possibilities for mitigating biases are proposed, though additional study is needed.

scholarly journals Validation of OCO-2 error analysis using simulated retrievals

2018 ◽  
Author(s):  
Susan S. Kulawik ◽  
Chris O'Dell ◽  
Robert R. Nelson ◽  
Thomas E. Taylor
Keyword(s):  
Measurement Error ◽  
Bias Correction ◽  
Retrieval System ◽  
Regional Scale ◽  
Optimal Estimation ◽  
Strongly Correlated ◽  
Linear Relationships ◽  
Processing Bias ◽  
Imperfect Sensitivity

Abstract. Characterization of errors and sensitivity in remotely sensed observations of greenhouse gases is necessary for their use in estimating regional-scale fluxes. We analyze 15 orbits of simulated OCO-2 with the Atmospheric Carbon Observations from Space (ACOS) retrieval, which utilizes an optimal estimation approach, to compare predicted versus actual errors in the retrieved CO2 state. We find that the non-linearity in the retrieval system results in XCO2 errors of ~0.9 ppm. The predicted measurement error (resulting from radiance measurement error), about 0.2 ppm, is accurate, and an upper bound on the smoothing error (resulting from imperfect sensitivity) is not more than 0.3 ppm greater than predicted. However, the predicted XCO2 interferent error (resulting from jointly retrieved parameters) is a factor of 4 larger than predicted. This results from some interferent parameter errors larger than predicted, as well as some interferent parameter errors more strongly correlated with XCO2 error than predicted. Variations in the magnitude of CO2 Jacobians at different retrieved states, which vary similarly for the upper and lower partial columns, could explain the higher interferent errors. A related finding is that the error correlation within the CO2 profiles is less negative than predicted, and that reducing the magnitude of the negative correlation between the upper and lower partial columns from −0.9 to −0.5 results in agreement between the predicted and actual XCO2 error. We additionally study the post-processing bias correction affects errors. The bias corrected results found in the operational OCO-2 Lite product consists of linear modification of XCO2 based on specific retrieved values, such as the CO2_grad_delta (a measure of the change in the profile shape versus the prior) and dP (the retrieved surface pressure minus the prior). We find similar linear relationships between XCO2 error and dP or CO2_grad_delta, but see a very complex pattern of errors throughout the entire state vector. Possibilities for mitigating biases are proposed, though additional study is needed.

scholarly journals Characterization of Noise Level Inside a Vehicle under Different Conditions

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2471 ◽  
Author(s):  
Daniel Flor ◽  
Danilo Pena ◽  
Luan Pena ◽  
Vicente A. de Sousa ◽  
Allan Martins
Keyword(s):  
Regression Analysis ◽  
Noise Level ◽  
Acoustic Noise ◽  
Experimental Setup ◽  
Strongly Correlated ◽  
Noise Levels ◽  
Noise Sources ◽  
Statistical Tools ◽  
Window Position

Vehicular acoustic noise evaluations are a concern of researchers due to health and comfort effects on humans and are fundamental for anyone interested in mitigating audio noise. This paper focuses on the evaluation of the noise level inside a vehicle by using statistical tools. First, an experimental setup was developed with microphones and a microcomputer located strategically on the car’s panel, and measurements were carried out with different conditions such as car window position, rain, traffic, and car speed. Regression analysis was performed to evaluate the similarity of the noise level from those conditions. Thus, we were able to discuss the relevance of the variables that contribute to the noise level inside a car. Finally, our results revealed that the car speed is strongly correlated to interior noise levels, suggesting the most relevant noise sources are in the vehicle itself.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals Generational Gaps in Media Trust and its Antecedents in Europe

2021 ◽  
pp. 194016122110394
Author(s):  
Anna Brosius ◽  
Jakob Ohme ◽  
Claes H. de Vreese
Keyword(s):  
Negative Correlation ◽  
Generational Differences ◽  
Political Trust ◽  
Media Bias ◽  
Political Interest ◽  
Strongly Correlated ◽  
Strong Negative Correlation ◽  
Media Trust ◽  
Original Survey

We test generational differences in media trust and its antecedents, including political trust, interest, and orientation, as well as perceptions of media inaccuracy and media bias. We rely on original survey data from ten European countries, collected in 2019. We find no differences in the levels of media trust between generations, but we find that key correlates of media trust relate differently to it in different generations. For example, political interest is more strongly correlated with media trust for Millennials than for other generations. Perceptions of bias and inaccuracy have a strong negative correlation with media trust overall, but it is stronger for older generations. These results suggest, that in the long term, societal developments, and in particular debates about media bias and misinformation may impact media trust of young generations differently as they grow older—however, our data give no indication of that creating generational gaps in media trust.

scholarly journals Relationship Between the Application of Foliar Chemicals to Reduce Common Scab Disease of Potato and Correlation with Thaxtomin A Toxicity

Plant Disease ◽  
2012 ◽  
Vol 96 (1) ◽  
pp. 97-103 ◽  
Author(s):  
Robert S. Tegg ◽  
Ross Corkrey ◽  
Calum R. Wilson
Keyword(s):  
Measurement Error ◽  
Common Scab ◽  
Russet Burbank Potato ◽  
Disease Suppression ◽  
Tuber Initiation ◽  
Strongly Correlated ◽  
Symptom Development ◽  
Thaxtomin A ◽  
Foliar Sprays ◽  
Streptomyces Spp

Production of the phytotoxin thaxtomin A by pathogenic Streptomyces spp. is essential for induction of common scab disease in potato. The disease can be significantly reduced by a range of chemicals applied as foliar sprays before tuber initiation. We tested a range of chemicals that had previously demonstrated varying capacities to reduce common scab for both disease suppression and their ability to inhibit thaxtomin A toxicity in both ‘Desiree’ and ‘Russet Burbank’ potato. Our results for disease suppression generally supported previous studies. Our tuber slice assays with thaxtomin A showed a strong correlation between the ability of the chemical to suppress common scab symptom development and the ability of the chemical to inhibit thaxtomin A toxicity. A Bayesian measurement error linear regression model was derived for each cultivar and trial and demonstrated a clear positive relationship between disease and thaxtomin-A-induced necrosis. The relationships obtained were much stronger than would have been obtained without adjustment for measurement error. This demonstrates that disease mitigation using chemical foliar sprays is strongly correlated with the ability of the chemical to inhibit thaxtomin A toxicity, suggesting this mechanism as a key mode of action for understanding this novel disease control strategy.

Latitudinal body-mass trends in Oligo-Miocene mammals

Paleobiology ◽  
2016 ◽  
Vol 42 (4) ◽  
pp. 643-658
Author(s):  
John D. Orcutt ◽  
Samantha S. B. Hopkins
Keyword(s):  
Body Size ◽  
Body Mass ◽  
Regional Scale ◽  
Latitudinal Gradients ◽  
Bergmann’S Rule ◽  
Strongly Correlated ◽  
Bergmann's Rule ◽  
Deep Time ◽  
Causal Factors ◽  
Primary Driver

AbstractPaleecological data allow not only the study of trends along deep-time chronological transects but can also be used to reconstruct ecological gradients through time, which can help identify causal factors that may be strongly correlated in modern ecosystems. We have applied such an analysis to Bergmann’s rule, which posits a causal relationship between temperature and body size in mammals. Bergmann’s rule predicts that latitudinal gradients should exist during any interval of time, with larger taxa toward the poles and smaller taxa toward the equator. It also predicts that the strength of these gradients should vary with time, becoming weaker during warmer periods and stronger during colder conditions. We tested these predictions by reconstructing body-mass trends within canid and equid genera at different intervals of the Oligo-Miocene along the West Coast of North America. To allow for comparisons with modern taxa, body mass was reconstructed along the same transect for modernCanisandOdocoileus. Of the 17 fossil genera analyzed, only two showed the expected positive relationship with latitude, nor was there consistent evidence for a relationship between paleotemperature and body mass. Likewise, the strength of body-size gradients does not change predictably with climate through time. The evidence for clear gradients is ambiguous even in the modern genera analyzed. These results suggest that, counter to Bergmann’s rule, temperature alone is not a primary driver of body size and underscore the importance of regional-scale paleoecological analyses in identifying such drivers.

Measurement error-filtered machine learning in digital soil mapping

2021 ◽  
Author(s):  
Stephan van der Westhuizen ◽  
Gerard Heuvelink ◽  
David Hofmeyr
Keyword(s):  
Machine Learning ◽  
Measurement Error ◽  
Soil Property ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Simulation Studies ◽  
True Value ◽  
Linear Relationships ◽  
The Difference ◽  
The Relationship

<p>Digital soil mapping (DSM) may be defined as the use of a statistical model to quantify the relationship between a certain observed soil property at various geographic locations, and a collection of environmental covariates, and then using this relationship to predict the soil property at locations where the property was not measured. It is also important to quantify the uncertainty with regards to prediction of these soil maps. An important source of uncertainty in DSM is measurement error which is considered as the difference between a measured and true value of a soil property.</p><p>The use of machine learning (ML) models such as random forests (RF) has become a popular trend in DSM. This is because ML models tend to be capable of accommodating highly non-linear relationships between the soil property and covariates. However, it is not clear how to incorporate measurement error into ML models. In this presentation we will discuss how to incorporate measurement error into some popular ML models, starting with incorporating weights into the objective function of ML models that implicitly assume a Gaussian error. We will discuss the effect that these modifications have on prediction accuracy, with reference to simulation studies.</p>

Landscape Biodiversity Characterization in Ecoregion 29 Using MODIS

2013 ◽  
pp. 815-831
Author(s):  
Nitin Kumar Tripathi ◽  
Aung Phey Khant
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Global Warming ◽  
Forest Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Modis Data ◽  
Moderate Resolution ◽  
Landscape Biodiversity

Biodiversity conservation is a challenging task due to ever growing impact of global warming and climate change. The chapter discusses various aspects of biodiversity parameters that can be estimated using remote sensing data. Moderate resolution satellite (MODIS) data was used to demonstrate the biodiversity characterization of Ecoregion 29. Forest type map linked to density of the study area was also developed by MODIS data. The outcome states that remote sensing and geographic information systems can be used in combination to derive various parameters related to biodiversity surveillance at a regional scale.

scholarly journals Pseudo-Continuous Flow FTIR System for Glucose, Fructose and Sucrose Identification in Mid-IR Range

Micromachines ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 517 ◽  
Author(s):  
Hamza Landari ◽  
Mourad Roudjane ◽  
Younès Messaddeq ◽  
Amine Miled
Keyword(s):  
Absorption Spectrum ◽  
Measurement Error ◽  
Wavelength Range ◽  
Continuous Flow ◽  
Flow System ◽  
Microfluidic System ◽  
Sample Distribution ◽  
Peak Intensity ◽  
Conventional Systems

In this paper, we present a new FTIR-based microfluidic system for Glucose, Fructose and Sucrose detection. The proposed microfluidic system is based on a pseudo-continuous flow coupled to a microscope-FTIR instrument. The detection and characterization of sugar samples were performed by recording their absorption spectrum in the wavelength range 700–1000 cm − 1 of the Mid-IR region. The proposed pseudo-continuous flow system is designed to improve the uniformity of the sample distribution in the analyzed area versus conventional systems. The obtained results for different sugars concentrations, show a very low measurement error of 4.35% in the absorption peak intensity, which is ten times lower than the error obtained using the conventional measurements.

Sign in / Sign up

Export Citation Format

Share Document