Work

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

Year: 2025

Type: article

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.

Source: International Journal of Climatology

DOI: 10.1002/joc.70091

Authors: Márk Zoltán Mikes, Zsuzsanna Dezső, Roland Hollós, Rita Pongrácz

Institutions: Eötvös Loránd University, Czech Academy of Sciences, Global Change Research Institute, HUN-REN Centre for Agricultural Research

Language: English

Topic: Climate variability and models

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $3,920