Clustering the Diurnal Cycle of Precipitation Using Global Satellite Data

Abstract The diurnal cycle of precipitation (DCP) is a fundamental component of the precipitation cycles across various temporal scales. However, the absence of a comprehensive and intuitive clustering description of the DCP, including precipitation amount, frequency, intensity, and seasonality on the global scale, has impeded understanding of climate impacts on society. This study investigates the diurnal cycle and its seasonal differences in global precipitation using hierarchical clustering and high‐resolution satellite precipitation data. Results indicate that regions with coastal and topographic complexity exhibit intricate clustering patterns globally. Significant inter‐cluster differences suggest that the precipitation amount and frequency have stronger diurnal cycles than that of the precipitation intensity. Seasonal cluster transitions indicate that the global DCP exhibits significant seasonal differences. This study provides multiple sets of information‐rich and user‐friendly maps of the DCP, enhancing research on sub‐daily precipitation mechanisms and their climatic impacts.

Authors

Institutions

Publication Details

Journal
Geophysical Research Letters
Published
2024-12-04
DOI
https://doi.org/10.1029/2024gl111513
Citations
7
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
1.98

Funders

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

Clustering the Diurnal Cycle of Precipitation Using Global Satellite Data

Junguo Liu, Shuiqing Yin, Aifang Chen, Penghan Chen et al.
7 citations
Geophysical Research Letters
Climate variability and models
1.98
article

Clustering the Diurnal Cycle of Precipitation Using Global Satellite Data

Junguo Liu, Shuiqing Yin, Aifang Chen, Penghan Chen, Yuxin Li
article en
7 citations

Abstract

Abstract The diurnal cycle of precipitation (DCP) is a fundamental component of the precipitation cycles across various temporal scales. However, the absence of a comprehensive and intuitive clustering description of the DCP, including precipitation amount, frequency, intensity, and seasonality on the global scale, has impeded understanding of climate impacts on society. This study investigates the diurnal cycle and its seasonal differences in global precipitation using hierarchical clustering and high‐resolution satellite precipitation data. Results indicate that regions with coastal and topographic complexity exhibit intricate clustering patterns globally. Significant inter‐cluster differences suggest that the precipitation amount and frequency have stronger diurnal cycles than that of the precipitation intensity. Seasonal cluster transitions indicate that the global DCP exhibits significant seasonal differences. This study provides multiple sets of information‐rich and user‐friendly maps of the DCP, enhancing research on sub‐daily precipitation mechanisms and their climatic impacts.

Geophysical Research LettersVol. 51(23)
North China University of Water Resources and Electric Power (CN), Beijing Normal University (CN), Dongguan University of Technology (CN), Southern University of Science and Technology (CN), University of Hong Kong (HK)
National Natural Science Foundation of China, Southern University of Science and Technology
Climate action
Openalex Percentile: Top 14%
Climate variability and models
1.98
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.

Clustering the Diurnal Cycle of Precipitation Using Global Satellite Data — Junguo Liu, Shuiqing Yin, et al. · Geophysical Research Letters (2024) | TGRS Research Map | TGRS