Topographic Amplification of Pressure Gradients Across Atmospheric States in East Asia

This repository presents a **preliminary** state-based analysis of ERA5 sea-level pressure (SLP) over East Asia. The main focus is how terrain systematically amplifies atmospheric pressure gradients across different large-scale circulation states. The study uses principal component analysis (PCA) for dimensionality reduction, K-means clustering to identify discrete atmospheric states, and spatial gradient analysis to examine the structural relationship between large-scale pressure patterns and topography. **This is Version 1.0**, based only on one year (2025) of daily ERA5 data. Future versions will extend the temporal coverage to multiple decades, replace proxy masks with actual elevation (orography) data, and apply metric-based gradient calculations. The current results demonstrate a consistent topographic amplification of pressure gradients (ratio ≈ 2.33) across identified atmospheric states. ### Data Sources**ERA5 Reanalysis** Sea-level pressure data from the European Centre for Medium-Range Weather Forecasts (ECMWF) via Copernicus Climate Data Store (CDS). License: CC-BY 4.0 **NASA POWER** (optional) https://power.larc.nasa.gov/api/temporal/daily/point

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-26
DOI
https://doi.org/10.5281/zenodo.19231184
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Topographic Amplification of Pressure Gradients Across Atmospheric States in East Asia

K.D. Pom
Zenodo (CERN European Organization for Nuclear Research)
Climate variability and models
article

Topographic Amplification of Pressure Gradients Across Atmospheric States in East Asia

K.D. Pom
article en

Abstract

This repository presents a **preliminary** state-based analysis of ERA5 sea-level pressure (SLP) over East Asia. The main focus is how terrain systematically amplifies atmospheric pressure gradients across different large-scale circulation states. The study uses principal component analysis (PCA) for dimensionality reduction, K-means clustering to identify discrete atmospheric states, and spatial gradient analysis to examine the structural relationship between large-scale pressure patterns and topography. **This is Version 1.0**, based only on one year (2025) of daily ERA5 data. Future versions will extend the temporal coverage to multiple decades, replace proxy masks with actual elevation (orography) data, and apply metric-based gradient calculations. The current results demonstrate a consistent topographic amplification of pressure gradients (ratio ≈ 2.33) across identified atmospheric states. ### Data Sources**ERA5 Reanalysis** Sea-level pressure data from the European Centre for Medium-Range Weather Forecasts (ECMWF) via Copernicus Climate Data Store (CDS). License: CC-BY 4.0 **NASA POWER** (optional) https://power.larc.nasa.gov/api/temporal/daily/point

Zenodo (CERN European Organization for Nuclear Research)
Climate action, Life below water
Openalex Percentile: Top 5%
Climate variability and models
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.