English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  The size distribution of spatiotemporal extreme rainfall clusters around the globe

Traxl, D., Boers, N., Rheinwalt, A., Goswami, B., Kurths, J. (2016): The size distribution of spatiotemporal extreme rainfall clusters around the globe. - Geophysical Research Letters, 43, 18, 9939-9947.
https://doi.org/10.1002/2016GL070692

Item is

Files

show Files
hide Files
:
7351oa.pdf (Any fulltext), 2MB
Name:
7351oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Traxl, Dominik1, Author              
Boers, Niklas1, Author              
Rheinwalt, Aljoscha1, Author              
Goswami, Bedartha1, Author              
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: The scaling behavior of rainfall has been extensively studied both in terms of event magnitudes and in terms of spatial extents of the events. Different heavy‐tailed distributions have been proposed as candidates for both instances, but statistically rigorous treatments are rare. Here we combine the domains of event magnitudes and event area sizes by a spatiotemporal integration of 3‐hourly rain rates corresponding to extreme events derived from the quasi‐global high‐resolution rainfall product Tropical Rainfall Measuring Mission 3B42. A maximum likelihood evaluation reveals that the distribution of spatiotemporally integrated extreme rainfall cluster sizes over the oceans is best described by a truncated power law, calling into question previous statements about scale‐free distributions. The observed subpower law behavior of the distribution's tail is evaluated with a simple generative model, which indicates that the exponential truncation of an otherwise scale‐free spatiotemporal cluster size distribution over the oceans could be explained by the existence of land masses on the globe.

Details

show
hide
Language(s):
 Dates: 2016
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/2016GL070692
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7351
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Extremes
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Geophysical Research Letters
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 43 (18) Sequence Number: - Start / End Page: 9939 - 9947 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals182