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Related Concept Videos

Mean Absolute Deviation01:13

Mean Absolute Deviation

3.0K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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Calculating Standard Deviation01:08

Calculating Standard Deviation

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The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
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Distance Corrections01:15

Distance Corrections

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Random Error01:04

Random Error

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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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Root Mean Square00:57

Root Mean Square

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If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero.
For example, consider the velocity of gas molecules in a container. The gas molecules are moving in different directions, which might impart positive and negative...
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Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

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Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
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Related Experiment Video

Updated: Oct 14, 2025

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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A median absolute deviation-neural network (MAD-NN) method for atmospheric temperature data cleaning.

Oluwafisayo Owolabi1, Daniel Okoh1, Babatunde Rabiu1

  • 1Centre for Atmospheric Research, National Space Research and Development Agency, Anyigba, Nigeria.

Methodsx
|November 10, 2021
PubMed
Summary

We developed a novel Median Absolute Deviation-Neural Network (MAD-NN) method to clean coarse atmospheric datasets and fill data gaps. This approach enhances data consistency and accuracy for climate change research.

Keywords:
Atmospheric datasetClimate changeCoarse datasetData cleaningMAD-NNNeural networkObservational data gapsOutliersSurface air temperatureWeather observatories

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Area of Science:

  • Atmospheric science
  • Data science
  • Climate science

Background:

  • Inaccurate atmospheric datasets pose significant challenges in climate change research.
  • Existing methods for cleaning coarse atmospheric data are often insufficient.

Purpose of the Study:

  • To introduce a novel method for cleaning coarse atmospheric datasets.
  • To improve the accuracy and consistency of atmospheric data streams.
  • To address data gaps and remove erroneous spikes in observational data.

Main Methods:

  • Developed the Median Absolute Deviation-Neural Network (MAD-NN) method.
  • Combined Median Absolute Deviation (MAD) technique with neural network training.
  • Applied the method to atmospheric temperature data from 17 Nigerian weather stations.

Main Results:

  • Successfully generated a consistent data stream from coarse atmospheric data.
  • Demonstrated the MAD-NN method's capability to fill observational data gaps.
  • Showcased the method's effectiveness in removing data spikes.

Conclusions:

  • The MAD-NN method offers a robust solution for improving atmospheric data quality.
  • This technique is particularly valuable for weather observatories with coarse datasets.
  • Enhanced data credibility supports more reliable scientific findings in climate studies.