predicting-extreme-weather-events-through-spatio-temporal-bayesian-models.
This repository contains the code developeb for my master thesis.
Science Score: 44.0%
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Low similarity (2.1%) to scientific vocabulary
Repository
This repository contains the code developeb for my master thesis.
Basic Info
- Host: GitHub
- Owner: luisvidalj
- Default Branch: main
- Size: 75.2 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Predicting-extreme-weather-events-through-spatio-temporal-Bayesian-models.
This repository contains the code developeb for my master thesis.
The ERA5 Extreme Climate Index (E3CI) is a measure used to identify and quantify extreme weather events such as heat waves, cold snaps, droughts and intense precipitation. This index is crucial for better understanding climate variability and its impact on various sectors, including agriculture, public health and natural resource management. The ability to accurately predict the E3CI is critical to mitigating the adverse effects of these extreme events, allowing decision-makers to implement effective prevention and response strategies.
It is from the code presented in this repository that the results of the last chapter of the Master's thesis have been extracted.
Owner
- Login: luisvidalj
- Kind: user
- Repositories: 1
- Profile: https://github.com/luisvidalj
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: R
message: >-
Predicting extreme weather events through spatio-temporal
Bayesian models
type: software
authors:
- given-names: Luis Jaime
family-names: Vidal Jordi
email: luisjvidalj@gmail.ocm
- name: University Carlos III of Madrid
city: Madrid
country: ES
location: 'Ronda de Toledo, 1, Centro, Madrid'
post-code: '28005'
region: Madrid
website: 'https://uc3m.es'
repository-code: >-
https://github.com/luisvidalj/Predicting-extreme-weather-events-through-spatio-temporal-Bayesian-models.
abstract: >-
This repository contains the code developeb for my master
thesis.
The ERA5 Extreme Climate Index (E3CI) is a measure used to
identify and quantify extreme weather events such as heat
waves, cold snaps, droughts and intense precipitation.
This index is crucial for better understanding climate
variability and its impact on various sectors, including
agriculture, public health and natural resource
management. The ability to accurately predict the E3CI is
critical to mitigating the adverse effects of these
extreme events, allowing decision-makers to implement
effective prevention and response strategies.
It is from the code presented in this repository that the
results of the last chapter of the Master's thesis have
been extracted.
version: '1.0'
date-released: '2024-06-24'