Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.6K
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...
1.6K
Random and Systematic Errors01:20

Random and Systematic Errors

16.2K
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...
16.2K
Random and Systematic Errors01:20

Random and Systematic Errors

946
946
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

395
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
395
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.2K
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...
2.2K
Random Error01:04

Random Error

10.1K
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...
10.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Solvent Extraction of Metals in the Circular Economy: Enhancing Resource Efficiency and Sustainability.

TheScientificWorldJournal·2026
Same journal

Agronomic Performance and Nutritive Value Evaluation of Desho Grass Varieties Under Supplementary Irrigation in Western Oromia, Ethiopia.

TheScientificWorldJournal·2026
Same journal

Physicians' and Hospital Administrators' Perspectives of Diagnosis-Related Groups (DRGs) in High-Income Countries: A Systematic Review.

TheScientificWorldJournal·2026
Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026
Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026
Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026
See all related articles

Related Experiment Video

Updated: Apr 4, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

689

Time Evolution of Initial Errors in Lorenz's 05 Chaotic Model.

Hynek Bednář1, Aleš Raidl1, Jiří Mikšovský1

  • 1Department of Meteorology and Environment Protection, Faculty of Mathematics and Physics, Charles University in Prague, V Holešovičkách 2, 180 00 Prague, Czech Republic.

Thescientificworldjournal
|September 9, 2015
PubMed
Summary
This summary is machine-generated.

Weather prediction errors stabilize over time, defining the limit of predictability. Modified hypotheses improve error approximation, with the quadratic hypothesis best matching model asymptotic values and time limits.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

Related Experiment Videos

Last Updated: Apr 4, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

689
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

Area of Science:

  • Atmospheric Science
  • Meteorology
  • Dynamical Systems

Background:

  • Initial errors in weather prediction grow and saturate over time.
  • The time to reach this saturation point defines the limit of predictability.
  • Understanding error growth is crucial for improving weather forecast accuracy.

Purpose of the Study:

  • To study asymptotic error values and predictability limits in a chaotic atmospheric model.
  • To evaluate error approximation hypotheses (quadratic, logarithmic) against model data.
  • To improve these hypotheses for better prediction limit estimation.

Main Methods:

  • Utilized an ensemble prediction method with a chaotic atmospheric model.
  • Applied quadratic and logarithmic hypotheses, including modifications, for error approximation.
  • Compared hypothesis-derived limits with model-derived limits for five initial errors.

Main Results:

  • Modified hypotheses better approximate model time limits but have disadvantages.
  • Further hypothesis improvements enhance the match with model time limits.
  • The quadratic hypothesis best approximates the model's asymptotic error value.

Conclusions:

  • Improved hypotheses, particularly the quadratic one, offer better approximations of weather model predictability limits.
  • The quadratic hypothesis shows strong performance for asymptotic values and time limits across various initial errors.
  • Refined error approximation methods are key to advancing weather forecasting capabilities.