arXiv (All)
open access
Borrowing Space, Clustering Temporal Dynamics: Bayesian Regime Mapping and Spatial Prediction for European PM10
Daily particulate matter measurements from European monitoring networks vary both through spatially shared fluctuations and through differences in local temporal dynamics. Characterising both features is important for identifying groups of monitoring locations with similar temporal behaviour and for
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations#1249194
- Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study#1312667
- MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations#463238
- Study on the spatiotemporal evolution characteristics and simulation prediction of urban metabolic efficiency in China's urban agglomerations.#15223
- Machine-Learning-Driven Reconstruction of Organic Aerosol Sources across Dense Monitoring Networks in Europe#430395
Record · ID 1386671
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive