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

Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.5K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.4K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.4K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.6K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.6K
Poisson Probability Distribution01:09

Poisson Probability Distribution

12.3K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
12.3K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.4K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.4K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

691
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
691

You might also read

Related Articles

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

Sort by
Same author

The Impact of a Stochastic Parameterization Scheme on Climate Sensitivity in EC-Earth.

Journal of geophysical research. Atmospheres : JGR·2020
Same author

The Sensitivity of Euro-Atlantic Regimes to Model Horizontal Resolution.

Geophysical research letters·2019
Same author

Forcing Single-Column Models Using High-Resolution Model Simulations.

Journal of advances in modeling earth systems·2018
Same journal

The three causal pathways of ENSO teleconnections to High Mountain Asia winter precipitation.

Climate dynamics·2026
Same journal

Mesoscale Convective Systems in Northeastern North America: identification and evaluation with the convection-permitting version of the Canadian Regional Climate Model.

Climate dynamics·2026
Same journal

Anatomy of moist heatwaves in India during the summer monsoon season.

Climate dynamics·2026
Same journal

Influence of global climate modes on wildfire occurrence in the contiguous United States under recent and future climates.

Climate dynamics·2025
Same journal

Systematic biases over the equatorial Indian Ocean and their influence on seasonal forecasts of the IOD.

Climate dynamics·2025
Same journal

Quantifying the state-dependent causal effect of Barents-Kara Sea ice loss on the stratospheric polar vortex in a large ensemble simulation.

Climate dynamics·2025
See all related articles
  1. Home
  3. Seasonal Forecasting Using The Gencast Probabilistic Machine Learning Model.
  1. Home
  3. Seasonal Forecasting Using The Gencast Probabilistic Machine Learning Model.

Related Experiment Video

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Seasonal forecasting using the GenCast probabilistic machine learning model.

Bobby Antonio1, Kristian Strommen1,2, Hannah M Christensen1

  • 1Atmospheric, Oceanic and Planetary Physics, University of Oxford, Sherrington Road, Oxford, OX1 3PU UK.

Climate Dynamics
|March 19, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine-learnt weather prediction models show promise for seasonal forecasting, accurately predicting precipitation patterns for El Niño and La Niña events. GenCast-Forced, using observed sea surface temperatures, demonstrated reliability comparable to established systems.

Keywords:
Machine learningNumerical weather predictionSeasonal forecasting

Related Experiment Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Earth and Climate Science
  • Artificial Intelligence in Meteorology
  • Atmospheric Physics

Background:

  • Machine-learnt weather prediction (MLWP) models rival conventional numerical weather prediction (NWP) in medium-range forecasts.
  • Extending MLWP performance to longer timescales, crucial for seasonal forecasting, remains an area of active research.
  • Interactions with slower Earth system components are vital for seasonal climate prediction.

Purpose of the Study:

  • To evaluate the efficacy of the GenCast MLWP model for seasonal forecasting using prescribed and observed sea surface temperatures (SST).
  • To compare GenCast's seasonal forecasting performance against the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system, SEAS5.
  • To assess the potential of MLWP models for comprehensive seasonal forecasting, including coupled Earth system components.

Main Methods:

  • GenCast, a probabilistic MLWP model, was adapted for seasonal forecasting in two configurations: GenCast-Persisted (climatological SST anomalies) and GenCast-Forced (observed SSTs).
  • Forecasts were generated and compared with SEAS5, focusing on precipitation, 2-meter temperature, mean sea level pressure (MSLP), and the North Atlantic Oscillation (NAO) index.
  • Reliability diagrams were used to assess forecast skill relative to climatology and SEAS5.

Main Results:

  • GenCast-Persisted accurately captured precipitation patterns associated with El Niño and La Niña events, with GenCast-Forced correcting some erroneous patterns.
  • Ensemble-based uncertainty in precipitation forecasts from GenCast compared favorably with SEAS5.
  • While SEAS5 excelled in tropical 2-meter temperature and MSLP, GenCast-Persisted showed higher skill in higher latitudes and mountainous regions for MSLP, correlating better with the NAO index.

Conclusions:

  • GenCast-Forced demonstrated forecast reliability comparable to SEAS5, indicating the potential of MLWP for seasonal forecasting.
  • GenCast-Persisted showed limited skill relative to climatology, highlighting the importance of accurate sea surface temperature forcing for seasonal predictions.
  • These findings suggest that MLWP models, when coupled with other Earth system components, could significantly advance seasonal forecasting capabilities.