Weather Radar Spatiotemporal Saliency: A First Look at an Information Theory–Based Human Attention Model Adapted to Reflectivity Images

Schvartzman, David; Torres, Sebastián; Yu, Tian-You

doi:10.1175/JTECH-D-16-0092.1

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Weather Radar Spatiotemporal Saliency: A First Look at an Information Theory–Based Human Attention Model Adapted to Reflectivity Images

2017
By Schvartzman, David ; Torres, Sebastián ; Yu, Tian-You
Source: Journal of Atmospheric and Oceanic Technology, 34(1), 137-152

[PDF-2.34 MB]

Details You May Also Like

Details:

Journal Title:

Journal of Atmospheric and Oceanic Technology
Personal Author:

Schvartzman, David ; Torres, Sebastián ; Yu, Tian-You

Schvartzman, David ; Torres, Sebastián ; Yu, Tian-You Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) Less -
Description:

Forecasters often monitor and analyze large amounts of data, especially during severe weather events, which can be overwhelming. Thus, it is important to effectively allocate their finite perceptual and cognitive resources for the most relevant information. This paper introduces a novel analysis tool that quantifies the amount of spatial and temporal information in time series of constant-elevation weather radar reflectivity images. The proposed Weather Radar Spatiotemporal Saliency (WR–STS) is based on the mathematical model of the human attention system (referred to as saliency) adapted to radar reflectivity images and makes use of information theory concepts. It is shown that WR-STS highlights spatially and temporally salient (attention attracting) regions in weather radar reflectivity images, which can be associated with meteorologically important regions. Its skill in highlighting current regions of interest is assessed by analyzing the WR-STS values within regions in which severe weather is likely to strike in the near future as defined by National Weather Service forecasters. The performance of WR-STS is demonstrated for a severe weather case and analyzed for a set of 10 diverse cases. Results support the hypothesis that WR-STS can identify regions with meteorologically important echoes and could assist in discerning fast-changing, highly structured weather echoes during complex severe weather scenarios, ultimately allowing forecasters to focus their attention and spend more time analyzing those regions.
Keywords:

[+]

Algorithms Radars/Radar Observations
Source:

Journal of Atmospheric and Oceanic Technology, 34(1), 137-152
DOI:

https://doi.org/10.1175/JTECH-D-16-0092.1
Document Type:

Journal Article
Funding:

Grant no. NA11OAR4320072
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:5e794323c13e126d443aa9d0a4249e439c93b224c659869f2a736362b0600a0b
Download URL:

https://repository.library.noaa.gov/view/noaa/32323/noaa_32323_DS1.pdf
File Type:

No Additional Files

More +

Weather Radar Spatiotemporal Saliency: A First Look at an Information Theory–Based Human Attention Model Adapted to Reflectivity Images

Details:

Supporting Files

You May Also Like