Topoclimatic Graph Dataset for Frost Prediction in the Tropical High-Mountain Altiplano Cundiboyacense, Colombia

Frost prediction in tropical high-mountain agricultural regions is difficult because sparse meteorological networks must represent strong terrain-driven microclimatic variability. This article presents a topoclimatic graph dataset for frost prediction in the Altiplano Cundiboyacense, Colombia. The released core dataset contains 23 agricultural weather stations and is seasonally focused on recurrent November–February frost periods rather than year-round continuous monitoring. It includes four consistently available meteorological variables at 30 min resolution: air temperature, relative humidity, dew point temperature, and solar radiation. The data were consolidated from multiple operational sources, harmonized to a common temporal grid, subjected to physical and consistency-based quality control, and completed through temporal and spatial reconstruction with traceability labels. The final release also provides binary frost_event and frost_warning_6h labels, point-based topographic descriptors, 1 km buffer-based raster summaries, land-cover proportions, station-level static feature vectors, and graph products including edge lists and adjacency matrices. These data products support graph-based deep learning, multimodal spatiotemporal analysis, and frost early warning experiments in a tropical mountain agroecosystem. The dataset offers a reproducible framework for integrating heterogeneous environmental observations into graph-ready representations while preserving sufficient environmental context for benchmarking frost prediction methods in data-sparse regions.