Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Precipitation Processes

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

Prediction Intervals

Prediction Intervals

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.

Rapidly Varying Flow

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

Wind Turbine Machine Models

Wind Turbine Machine Models

In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...

What is Weather?

What is Weather?

You might also read

Related Articles

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

Sort by

Same author

AI for atmosphere-ocean sciences: advancements, challenges and ways forward.

National science review·2026

Same author

Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.

National science review·2022

Same author

Deep-learning-based information mining from ocean remote-sensing imagery.

National science review·2021

Same author

Purely satellite data-driven deep learning forecast of complicated tropical instability waves.

Science advances·2020

Same author

Human and mouse studies establish TBX6 in Mendelian CAKUT and as a potential driver of kidney defects associated with the 16p11.2 microdeletion syndrome.

Kidney international·2020

Same author

A<sub>2A</sub> R inhibition in alleviating spatial recognition memory impairment after TBI is associated with improvement in autophagic flux in RSC.

Journal of cellular and molecular medicine·2020

Search research articles

Related Experiment Video

Updated: Jun 1, 2025

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

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

Published on: July 3, 2020

Advancing forecasting capabilities: A contrastive learning model for forecasting tropical cyclone rapid

Chong Wang^1,2,3, Nan Yang^1,2,3, Xiaofeng Li^1,2,3

¹Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China.

Proceedings of the National Academy of Sciences of the United States of America

|January 21, 2025

Summary

Forecasting rapidly intensifying tropical cyclones (TCs) is improved with a new contrastive learning model. This model enhances prediction accuracy and reduces false alarms for these dangerous weather events.

Keywords:

deep learning rapid intensification tropical cyclone

More Related Videos

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

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

Published on: December 9, 2015

Simulating Impacts of Ice Storms on Forest Ecosystems

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

Related Experiment Videos

Last Updated: Jun 1, 2025

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

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

Published on: July 3, 2020

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

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

Published on: December 9, 2015

Simulating Impacts of Ice Storms on Forest Ecosystems

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

Area of Science:

Meteorology and Atmospheric Sciences
Geospatial Data Science
Artificial Intelligence in Earth Science

Background:

Tropical cyclones (TCs) pose significant threats, with rapid intensification (RI) periods being particularly challenging to forecast accurately.
Existing models for predicting RI TCs (intensification of at least 13 m/s within 24 h) have limitations in probability of detection (POD) and false alarm rate (FARate).

Purpose of the Study:

To develop and evaluate a novel contrastive-based model for forecasting rapid intensification in tropical cyclones.
To improve the accuracy and reduce false alarms in RI TC prediction using integrated data sources.

Main Methods:

Development of a contrastive-based RI TC forecasting (RITCF-contrastive) model.
Integration of satellite infrared imagery with atmospheric and oceanic data.
Addressing sample imbalance and incorporating TC structural features within the model.

Main Results:

The RITCF-contrastive model achieved a POD of 92.3% and a FARate of 8.9% on 1,149 TC periods in the Northwest Pacific (2020-2021).
Demonstrated an 11.7% improvement in POD and a threefold reduction in FARate compared to existing deep learning methods.
Successfully addressed sample imbalance and incorporated crucial TC structural features.

Conclusions:

The RITCF-contrastive model significantly enhances the forecasting of rapidly intensifying tropical cyclones.
This approach offers a unique and effective method for predicting dangerous weather events, improving upon current deep learning techniques.