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

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

461
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
461
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

289
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
289
Deconvolution01:20

Deconvolution

220
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
220
Computed Tomography01:10

Computed Tomography

4.7K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.7K

You might also read

Related Articles

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

Sort by
Same author

High rate of chimeric gene origination by retroposition in plant genomes.

The Plant cell·2006
Same author

Enzyme catalysis: tool to make and break amygdalin hydrogelators from renewable resources: a delivery model for hydrophobic drugs.

Journal of the American Chemical Society·2006
Same author

Theoretical probing of deltahedral closo-auroboranes B(x)Au(x)2- (x = 5-12).

Inorganic chemistry·2006
Same author

Density functional theory/time-dependent DFT studies on the structures, trend in DNA-binding affinities, and spectral properties of complexes [Ru(bpy)2(p-R-pip)]2+ (R = -OH, -CH3, -H, -NO2).

The journal of physical chemistry. A·2006
Same author

Sn12(2-): stannaspherene.

Journal of the American Chemical Society·2006
Same author

High efficient mammalian expression and secretion of a functional humanized single-chain Fv/human interleukin-2 molecules.

World journal of gastroenterology·2006

Related Experiment Video

Updated: Aug 15, 2025

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.0K

Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning.

Ling Yang1,2, Qian Zhao1,2, Yunheng Xue1,2,3

  • 1College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study uses deep learning with satellite data to estimate weather radar reflectivity, improving storm tracking in areas lacking radar coverage. An Attention U-Net model enhanced accuracy and detail reconstruction for convective storm monitoring.

Keywords:
FY-4A geostationary meteorological satellitedeep learningradar composite reflectivity

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

7.9K
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.4K

Related Experiment Videos

Last Updated: Aug 15, 2025

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.0K
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

7.9K
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.4K

Area of Science:

  • Meteorology
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Weather radars offer high-resolution storm tracking but have limited coverage, particularly in challenging terrains and over oceans.
  • Geostationary satellites provide extensive near real-time observations, crucial for complementing sparse radar data.

Purpose of the Study:

  • To estimate radar composite reflectivity using data from China's FY-4A geostationary satellite and topographic information.
  • To develop a deep learning model capable of generating reliable radar reflectivity products for areas without ground-based radar coverage.

Main Methods:

  • A deep learning approach was employed, utilizing observations from the FY-4A satellite and associated topographic data.
  • The study compared a standard U-Net model with a modified Attention U-Net model to assess performance improvements.

Main Results:

  • The deep learning model successfully reproduced the general position, shape, and intensity of radar echoes.
  • The Attention U-Net model demonstrated superior performance over the traditional U-Net, showing improvements in Probability of Detection (POD), Critical Success Index (CSI), and Root-Mean-Square Error (RMSE).
  • The modified model exhibited enhanced capabilities in reconstructing fine details and intense echo features.

Conclusions:

  • Deep learning methods, particularly the Attention U-Net, can effectively estimate radar reflectivity from geostationary satellite data.
  • This approach significantly enhances storm monitoring capabilities in regions with limited or no radar coverage, improving meteorological forecasting accuracy.