Climate Prediction of Extreme Events

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 3933

Special Issue Editors


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Guest Editor
Regional Atmospheric Modeling (MAR) Group, Department of Physics, University of Murcia, Murcia, Spain
Interests: climate change; climate predictions; natural hazards

E-Mail Website
Guest Editor
Department of Physics, University of Murcia, Murcia, Spain
Interests: climate change; climate variability; regional climate models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Geosciences aims to gather high-quality, original research articles, reviews, and technical notes on climate prediction, with a particular emphasis on extreme events.

Monthly to decadal predictions continue to improve due to enhancements in climate science and in computing power. On the other hand, there is a growing awareness of the importance of using climate predictions, including forecasting extreme climate events, for a more effective and dynamic adaptation to climate variability and change.

With this in mind, we would like to invite you to submit articles about your recent work, experimental research, or case studies, with respect to the above and/or the following topics:

  • Progress in extreme event understanding and, thereby, predictions
  • Dynamical, statistical, and hybrid prediction systems for climate forecasts
  • Subseasonal-to-Seasonal (S2S) prediction
  • Decadal forecasts
  • Impact-based, long-term forecasts
  • Processing and calibration methods applied to climate predictions, and climate services providing and using climate predictions
  • Early warning systems and climate-driven risk management
  • Climate forecast verification
  • User engagement, including communication strategies for decision-making and transfer of knowledge on forecasted long-term impacts to end-users

We also encourage you to send us a short abstract delineating the goal of the study and the principal results obtained, in order to verify at an early stage if the contribution you intend to submit is in line with the purposes of the Special Issue.

Dr. Marco Turco
Dr. Sonia Jerez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate predictions
  • subseasonal forecasts
  • seasonal forecasts
  • decadal predictions
  • climate services
  • extreme events

Published Papers (1 paper)

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Research

22 pages, 1280 KiB  
Article
Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate
by Huiling Hu and Bilal M. Ayyub
Geosciences 2019, 9(5), 209; https://doi.org/10.3390/geosciences9050209 - 9 May 2019
Cited by 12 | Viewed by 3580
Abstract
Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against [...] Read more.
Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed. Full article
(This article belongs to the Special Issue Climate Prediction of Extreme Events)
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