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Related Concept Videos

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

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Precipitation Gravimetry

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

Minwei Zhang, Amir Ibrahim, Bryan A Franz

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    This study introduces a new method to calculate uncertainty in ocean color data, improving accuracy for chlorophyll-a and light attenuation estimates. Accounting for spectral error covariance reduces uncertainty in key oceanographic products.

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    Area of Science:

    • Oceanography and Remote Sensing
    • Bio-optical and Biogeochemical Ocean Properties

    Background:

    • Spectral remote sensing reflectance (Rrs) is crucial for deriving water column properties from satellite ocean color measurements.
    • Accurate estimation of uncertainty in derived geophysical products necessitates understanding the uncertainty in Rrs and its spectral error covariance.

    Purpose of the Study:

    • To establish a derivative-based approach for propagating uncertainties into Rrs, generating pixel-level error covariance.
    • To apply this approach to Moderate Resolution Imaging Spectroradiometer (MODIS) data and assess its impact on chlorophyll-a and diffuse attenuation coefficient (Kd(490)) uncertainty.

    Main Methods:

    • Developed a derivative-based method to propagate instrument noise, systematic uncertainty, and forward model uncertainty into Rrs.
    • Applied the method to MODIS Aqua data using the Multiple-Scattering Epsilon (MSEPS) atmospheric correction algorithm.
    • Verified the approach using Monte Carlo (MC) analysis and compared propagated uncertainty with observed differences using in situ Rrs.

    Main Results:

    • The derivative-based approach successfully generated pixel-level error covariance in Rrs.
    • Accounting for Rrs error covariance generally reduced estimated relative uncertainty in chlorophyll-a (chl_a) by 1-10% and in Kd(490) by approximately 2%, with higher reductions in specific regions.
    • An 8-day global composite demonstrated that the goal of 35% uncertainty in chl_a can be achieved over deep ocean waters.

    Conclusions:

    • The derivative-based approach provides a reasonable estimation of Rrs error covariance, enhancing the accuracy of derived oceanographic products.
    • The method effectively reduces uncertainty in chlorophyll-a and Kd(490) estimations, contributing to more reliable ocean color data.
    • Future improvements should focus on refining assumptions regarding inter-band error correlation and uncertainties in calibration and ancillary data.