
Belgingur is a meteorological company with over two decades of research experience, specializing in numerical simulations of atmospheric flow and orography effects. They offer operational weather forecasts and re-analysis of past weather through their proprietary 'Weather On Demand' (WOD) framework. This framework is cloud-deployable and customizable for any location worldwide, utilizing ensemble 3D/4D-variational and observation nudging data assimilation methods, complemented by novel machine learning for post-processing. Belgingur focuses on high-resolution, detailed, and reliable forecasts of precipitation, winds, and temperature, with a recent emphasis on sub-seasonal forecasts for renewable energy and agrology sectors, and the use of Machine Learning models for weather prediction. They have provided services to entities like the United Nations Economic Commission for Africa and installed their systems in meteorological agencies in Cabo Verde, Seychelles, and the Faroe Islands.

Belgingur is a meteorological company with over two decades of research experience, specializing in numerical simulations of atmospheric flow and orography effects. They offer operational weather forecasts and re-analysis of past weather through their proprietary 'Weather On Demand' (WOD) framework. This framework is cloud-deployable and customizable for any location worldwide, utilizing ensemble 3D/4D-variational and observation nudging data assimilation methods, complemented by novel machine learning for post-processing. Belgingur focuses on high-resolution, detailed, and reliable forecasts of precipitation, winds, and temperature, with a recent emphasis on sub-seasonal forecasts for renewable energy and agrology sectors, and the use of Machine Learning models for weather prediction. They have provided services to entities like the United Nations Economic Commission for Africa and installed their systems in meteorological agencies in Cabo Verde, Seychelles, and the Faroe Islands.
Founded: 2001
Headquarters: Reykjavík, Iceland
Core product: Weather On Demand (WOD) forecasting framework
Specialization: High-resolution numerical weather prediction and ML post-processing
Team size (reported): 4 employees
Last recorded funding: Grant, March 2013
Accurate, high-resolution weather forecasting and re-analysis tailored to complex terrain and sector-specific needs (e.g., wind energy, agrology, emergency rescue).
2001
Meteorology / Weather forecasting
Last recorded funding event listed as a grant in March 2013.