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Machine Learning of Weather Forecasting Rules from Large Meteorological Data Bases

Overview of attention for article published in Advances in Atmospheric Sciences, February 2014
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Mentioned by

twitter
1 X user
video
1 YouTube creator

Citations

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6 Dimensions

Readers on

mendeley
13 Mendeley
Title
Machine Learning of Weather Forecasting Rules from Large Meteorological Data Bases
Published in
Advances in Atmospheric Sciences, February 2014
DOI 10.1007/bf03342038
Authors

Honghua Dai

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 15%
Student > Master 2 15%
Student > Bachelor 2 15%
Librarian 1 8%
Lecturer 1 8%
Other 3 23%
Unknown 2 15%
Readers by discipline Count As %
Computer Science 2 15%
Earth and Planetary Sciences 2 15%
Physics and Astronomy 2 15%
Environmental Science 1 8%
Business, Management and Accounting 1 8%
Other 3 23%
Unknown 2 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 January 2018.
All research outputs
#18,572,036
of 23,002,898 outputs
Outputs from Advances in Atmospheric Sciences
#760
of 871 outputs
Outputs of similar age
#231,737
of 309,938 outputs
Outputs of similar age from Advances in Atmospheric Sciences
#5
of 8 outputs
Altmetric has tracked 23,002,898 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 871 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 32.4. This one is in the 2nd percentile – i.e., 2% of its peers scored the same or lower than it.
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 309,938 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.