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Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Absorption of Radiation01:05

Absorption of Radiation

The rate of heat transfer by emitted radiation is described by the Stefan-Boltzmann law of radiation:
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

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Related Experiment Video

Updated: Jul 6, 2026

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings
09:01

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

Published on: April 16, 2017

Systematic Errors that are Due to the Monochromatic-Equivalent Radiative Transfer Approximation in Thermal Emission

D S Turner

    Applied Optics
    |March 21, 2008
    PubMed
    Summary
    This summary is machine-generated.

    Fast radiative transfer models introduce height-dependent bias in data assimilation. Using Planck-weighted mean transmittances in these models can effectively reduce or eliminate this bias at its source.

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    Published on: June 1, 2016

    Area of Science:

    • Atmospheric science
    • Radiative transfer modeling
    • Satellite remote sensing

    Background:

    • Data assimilation models rely on accurate radiative transfer models (RTMs) for simulating satellite radiances.
    • Fast, parameterized RTMs are used for efficiency but often simplify spectral integration, introducing potential errors.
    • The monochromatic-equivalent approach in fast RTMs replaces spectral integration with spectrally averaged values, an approximation assumed to be negligible.

    Purpose of the Study:

    • To quantify the error introduced by the monochromatic-equivalent approximation in fast RTMs.
    • To demonstrate an improved fast RTM using Planck-weighted mean transmittances to mitigate these errors.
    • To investigate the impact on satellite data assimilation, focusing on a specific channel of the High-Resolution Infrared Radiation Sounder (HIRS).

    Main Methods:

    • Analyzed the error magnitude arising from the monochromatic-equivalent approach in parameterized RTMs.
    • Developed and tested a fast RTM incorporating Planck-weighted mean transmittances.
    • Focused on Channel 12 of the NOAA-14 HIRS instrument, known for exhibiting significant errors.

    Main Results:

    • The monochromatic-equivalent approach introduces a systematic, height-dependent bias in data assimilation.
    • The proposed fast RTM with Planck-weighted mean transmittances significantly reduces or eliminates this bias at the source.
    • Channel 12 of the NOAA-14 HIRS instrument shows the largest error due to this approximation.

    Conclusions:

    • The assumption of negligible error in fast RTMs is invalid and leads to systematic bias in data assimilation.
    • Correcting the source of the error within the RTM is more effective than post-assimilation bias correction.
    • Implementing Planck-weighted mean transmittances offers a viable solution for improving the accuracy of fast RTMs in satellite data assimilation.