↓ Skip to main content

Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data

Overview of attention for article published in arXiv, January 2020
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
72 Dimensions

Readers on

mendeley
95 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data
Published in
arXiv, January 2020
DOI 10.3389/fdata.2020.00001
Pubmed ID
Authors

Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler Bonfanti, Balázs Kégl, Claire Monteleoni

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 95 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 16%
Researcher 13 14%
Student > Master 8 8%
Student > Doctoral Student 7 7%
Other 5 5%
Other 8 8%
Unknown 39 41%
Readers by discipline Count As %
Earth and Planetary Sciences 15 16%
Computer Science 12 13%
Engineering 10 11%
Environmental Science 5 5%
Economics, Econometrics and Finance 2 2%
Other 9 9%
Unknown 42 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 October 2022.
All research outputs
#7,209,370
of 25,387,668 outputs
Outputs from arXiv
#121,086
of 915,125 outputs
Outputs of similar age
#148,956
of 471,048 outputs
Outputs of similar age from arXiv
#3,728
of 20,667 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 915,125 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 471,048 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 20,667 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.