Evaluation and Visualisation of Unusual Weather in a Compound Framework ‐ a Methodology Based on Hungarian Station Data

ABSTRACT In this paper, we introduce a set of definitions together with a data visualisation platform, which can be used to evaluate past weather conditions and to visualise unusual periods in a simplified, easy‐to‐understand manner. To achieve this, four main objectives are set: (1) the definitions of unusual phenomena must be based on relative thresholds and take into account seasonality; (2) the reference climatology must be relevant spatially; (3) the results should not be bound to specific calendar years or months; (4) unusual phenomena should be visualised simultaneously (hence provide a compound framework). The expression ‘unusual’ is used in this study to describe anomalous values in contrast to the more commonly used ‘extreme’, because we aim to highlight periods with non‐absolute weather extremes. As input, daily meteorological data available at 70 stations across Hungary from 2002 to 2024 are used to create the methodology, where a total of 9 unusual weather phenomena are defined using temperature, precipitation and wind gust as basic variables. The main motivation behind this study is to analyse compound climatic events, which are spatially and/or temporally connected. Additionally, the methodology serves as the meteorological basis for a science communication platform that combines complex information with simplicity. The approach described in this paper can serve as inspiration for further studies aiming to evaluate weather extremes in the past.

Authors

Institutions

Publication Details

Journal
International Journal of Climatology
Published
2025-09-24
DOI
https://doi.org/10.1002/joc.70091
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
1.11

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation and Visualisation of Unusual Weather in a Compound Framework ‐ a Methodology Based on Hungarian Station Data

Márk Zoltán Mikes, Zsuzsanna Dezső, Roland Hollós, Rita Pongrácz
1 citations
International Journal of Climatology
Climate variability and models
1.11
article

Evaluation and Visualisation of Unusual Weather in a Compound Framework ‐ a Methodology Based on Hungarian Station Data

Márk Zoltán Mikes, Zsuzsanna Dezső, Roland Hollós, Rita Pongrácz
article en
1 citations

Abstract

ABSTRACT In this paper, we introduce a set of definitions together with a data visualisation platform, which can be used to evaluate past weather conditions and to visualise unusual periods in a simplified, easy‐to‐understand manner. To achieve this, four main objectives are set: (1) the definitions of unusual phenomena must be based on relative thresholds and take into account seasonality; (2) the reference climatology must be relevant spatially; (3) the results should not be bound to specific calendar years or months; (4) unusual phenomena should be visualised simultaneously (hence provide a compound framework). The expression ‘unusual’ is used in this study to describe anomalous values in contrast to the more commonly used ‘extreme’, because we aim to highlight periods with non‐absolute weather extremes. As input, daily meteorological data available at 70 stations across Hungary from 2002 to 2024 are used to create the methodology, where a total of 9 unusual weather phenomena are defined using temperature, precipitation and wind gust as basic variables. The main motivation behind this study is to analyse compound climatic events, which are spatially and/or temporally connected. Additionally, the methodology serves as the meteorological basis for a science communication platform that combines complex information with simplicity. The approach described in this paper can serve as inspiration for further studies aiming to evaluate weather extremes in the past.

International Journal of ClimatologyVol. 45(14)
Eötvös Loránd University (HU), Czech Academy of Sciences, Global Change Research Institute (CZ), HUN-REN Centre for Agricultural Research (HU)
European Commission
Openalex Percentile: Top 19%
Climate variability and models
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.