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Alfonso Ladino-Rincon

Alfonso Ladino-Rincon

Data Scientist

3 posts

Radar DataTree: Transforming thousands of scans into a single cohesive model

Radar DataTree: Transforming thousands of scans into a single cohesive model

From structure to scale, radar needs a model that organizes complete collections as time-aware, cloud-native datasets. In our second post, we looked at how new standards and open-source tools are transforming weather radar from raw binary blobs into structured, metadata-rich datasets. FM-301—an offi

Alfonso Ladino-Rincon
Alfonso Ladino-Rincon

Data Scientist

From Files to Datasets: FM-301 and the Future of Radar Interoperability

From Files to Datasets: FM-301 and the Future of Radar Interoperability

At Earthmover, we’re interested in weather radar data for two reasons: - First, radar data are uniquely valuable for our customers thanks to their ability to characterize precipitation, atmospheric turbulence, and phenomena like tornadoes and hurricanes in real time with fine spatial and temporal re

Alfonso Ladino-Rincon
Alfonso Ladino-Rincon

Data Scientist

The Untapped Promise of Weather Radar Data

The Untapped Promise of Weather Radar Data

Weather radar is one of the most powerful observational tools in atmospheric science. Every few minutes, it captures reflectivity, among other variables, in a sample volume that is rotated forming a high-resolution, four-dimensional dataset (x,y,z,t) that tracks storms in real time, revealing fine-s

Alfonso Ladino-Rincon
Alfonso Ladino-Rincon

Data Scientist