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

Doppler Effect - II01:05

Doppler Effect - II

4.2K
The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
4.2K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

5.1K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
5.1K
Precipitation Gravimetry01:03

Precipitation Gravimetry

12.8K
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
12.8K
Precipitation Processes01:12

Precipitation Processes

5.0K
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...
5.0K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

4.8K
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...
4.8K
Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

870
Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
870

You might also read

Related Articles

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

Sort by
Same author

Post-colonoscopic splenic rupture and hemorrhage: diagnostic challenges and successful transcatheter embolization-a case report.

Journal of medical case reports·2026
Same author

Magneto alignment of Ni<sub>0.5</sub>Co<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub>@graphene flakes for highly efficient low-frequency microwave absorption.

Nanotechnology·2026
Same author

A Phase 1 Microtracer Study Evaluating the Mass Balance, Excretion, and Pharmacokinetics of Osivelotor in Healthy Participants.

Clinical and translational science·2026
Same author

EdgeGeoDiff: A Novel Two-Stage Diffusion Approach for Precipitation Downscaling with Edge Details and Geographical Priors.

Sensors (Basel, Switzerland)·2026
Same author

Anti-skin aging effects of black ginseng extracts through regulating inflammatory factors and the CLOCK-ERK pathway in mice.

Journal of ginseng research·2026
Same author

Discovery and Characterization of Divarasib (GDC-6036), a Potent Covalent Inhibitor of KRAS G12C.

Journal of medicinal chemistry·2026

Related Experiment Video

Updated: May 1, 2026

Test Samples for Optimizing STORM Super-Resolution Microscopy
16:52

Test Samples for Optimizing STORM Super-Resolution Microscopy

Published on: September 6, 2013

31.1K

Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function.

Wulin Yang1, Hao Yang1, Hang Zhou1

  • 1School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
Summary

This study introduces MS-DD3D-RSTN, a novel model for short-term precipitation forecasting. It significantly improves radar echo prediction accuracy by better capturing spatiotemporal dependencies, outperforming existing methods.

Keywords:
deep learningloss functionradar echo extrapolationshort-term precipitation forecastingspatiotemporal convolution

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.8K
Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy
14:23

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

Published on: March 6, 2018

10.8K

Related Experiment Videos

Last Updated: May 1, 2026

Test Samples for Optimizing STORM Super-Resolution Microscopy
16:52

Test Samples for Optimizing STORM Super-Resolution Microscopy

Published on: September 6, 2013

31.1K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.8K
Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy
14:23

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

Published on: March 6, 2018

10.8K

Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Data Science

Background:

  • Short-term precipitation forecasting is crucial for various sectors.
  • Radar echo extrapolation is a common forecasting method.
  • Existing methods struggle with forecast degradation and capturing spatiotemporal data dependencies.

Purpose of the Study:

  • To propose a new model, MS-DD3D-RSTN, to enhance radar echo extrapolation.
  • To improve the accuracy and reliability of short-term precipitation forecasts.
  • To address limitations in capturing spatiotemporal dependencies in radar echo signals.

Main Methods:

  • Developed the MS-DD3D-RSTN model incorporating spatiotemporal convolutional blocks (STCBs).
  • Utilized a spatial-temporal loss (STLoss) function for end-to-end training.
  • Evaluated the model on the Sichuan and HKO-7 datasets.

Main Results:

  • MS-DD3D-RSTN demonstrated superior performance compared to advanced models.
  • Achieved high CSI metrics (e.g., 0.538 for 20 dBZ, 2h forecast) surpassing existing methods.
  • Showcased improved capture of spatiotemporal dependencies and reduced forecast degradation.

Conclusions:

  • The MS-DD3D-RSTN model significantly enhances radar echo prediction.
  • The model effectively mitigates forecast degradation and improves accuracy.
  • This advancement offers better tools for essential short-term precipitation forecasting.