Overview of Belgingur's research

Belgingur has a long history of collaboration with domestic and international universities and research institutions. Examples of ongoing research is the “Measurement of gasses and dust using UAV” project, led by Iceland Geosurvey and research project done in collaboration with Menapia Ltd. in the UK where we investigate the effects of assimilating atmospheric profiles, collected by automatic weather drones, on the simulated flow. In both of these projects the focus is on the utilization of unmanned aerial vehicles to collect in-situ measurements that can be of vital importance to improve weather forecasting. Our most recent research project, called “High resolution machine learning weather forecast model for Iceland” focuses on the potential of using Large Language Models (LLMs) to create forecasts for a limited area.

In the aftermath of the horrific earthquake that devastated Haiti in January 2010 Belgingur got a request from the Icelandic Urban Search and Rescue (SAR) Team (the first international SAR team to arrive in Haiti) to set up and run high-resolution forecasts, in operational mode, for the region. This request was fulfilled in less than two hours with the forecasts being updated four times per day over the next six months. With the latest version of the forecast system, a similar request can now be handled in a matter of minutes. Parallel to the development of an operational forecasting system Belgingur developed a cloud-based, on-demand, high-resolution forecasting system, tailored to the needs of Search and Rescue operators, named SARWeather. This system came on-line in the fall of 2010 and later became the backbone of our current operational and on-demand forecast system. As of 2011, Belgingur is a certified service provider for the Global Disaster Alerts and Coordination System through the SARWeather forecasting system.

In 2015 Belgingur provided services to the United Nations Economic Commission for Africa via provision of the operational and on demand WOD weather forecasting systems for the NMHS’s of Cabo Verde and the Seychelles. The operational forecasting system was installed on in-house hardware at these NMHS’s and ran until 2021 and 2022, respectively, when culminating hardware malfunctions rendered the systems non-operational. In 2022 the system was installed at the Faroe Islands meteorological agency, again on in-house hardware.

Peer reviewed articles

  • Rögnvaldsson, Ó., P. Crochet and H. Ólafsson, 2004. Mapping of precipitation in Iceland using numerical simulations and statistical modeling. Meteorolog. Z., Vol. 13, No. 3, 209-219 (June 2004).
  • Rögnvaldsson, Ó., J.F. Jónsdóttir and H. Ólafsson, 2007. Numerical Simulations of Precipitation in the Complex Terrain of Iceland – Comparison with Glaciological and Hydrological Data. Meteorolog. Z., Vol. 16, No. 1, 71-85 (February 2007).
  • Rögnvaldsson, Ó., J-W. Bao and H. Ólafsson, 2007. Sensitivity Simulations of Orographic Precipitation with MM5 and Comparison with Observations in Iceland during the Reykjanes EXperiment. Meteorolog. Z., Vol. 16, No. 1, 87-98 (February 2007).
  • Elíasson, J., Ó. Rögnvaldsson and T. Jónsson, 2009. Extracting statistical parameters of extreme precipitation from a NWP model. Hydrol. Earth Syst. Sci., 13, 2233-2240.
  • Arason, T., Ó. Rögnvaldsson and H. Ólafsson, 2010. Validation of Numerical Simulations of Precipitation in Complex Terrain at high Temporal Resolution. Hydro. Res., 41.3-4, 164-170.
  • Rögnvaldsson, Ó., J.F. Jónsdóttir and H. Ólafsson, 2010. Dynamical Downscaling of Precipitation in Iceland 1961-2006. Hydro. Res., 41.3-4, 153-163.
  • Rögnvaldsson, Ó., J.-W. Bao, H. Ágústsson, and H. Ólafsson, 2011. Downslope windstorm in Iceland – WRF/MM5 model comparison. Atmos. Chem. Phys., 11, 103- 120, doi:10.5194/acp-11-103-2011. Available on-line:
  • Joachim R., M. Ablinger, H. Ágústsson, P. Brisset, S. Brynjólfsson, M. Garhammer, T. Jóhannesson, M.O. Jonassen, R. Kühnel, S. Lämmlein, T. de Lange, C. Lindenberg, S. Malardel, S. Mayer, M. Müller, H. Ólafsson, Ó. Rögnvaldsson, W. Schäper, T. Spengler, G. Zängl and J. Egger, 2011.
  • FLOHOF 2007: An overview of the mesoscale meteorological field campaign at Hofsjökull, Central Iceland. Meteorol. Atmos. Phys., doi: 10.1007/s00703-010-0118-4, January 2011. Available on-line:
  • Jonassen, M.O., H. Ólafsson, H. Ágústsson, Ó. Rögnvaldsson, and J. Reuder, 2012. Improving high resolution numerical weather simulations by assimilating data from an unmanned aerial system. Mon. Weather Rev., 140(11): 3734– 3756. Available on-line: 11- 00344.1journalCode=mwre.
  • Rögnvaldsson, Ó., 2013. Numerical simulations of winds and precipitation in Iceland – Die zweite Aufgabe der theoretischen Meteorologie. Dissertation for the degree of philosophiae doctor. University of Bergen, Norway. ISBN: 978-82-308-2225-8. Available on-line:
  • Ágústsson, H., H. Ólafsson, M.O. Jonassen and Ó. Rögnvaldsson, 2014. The impact of assimilating data from a remotely piloted aircraft on simulations of weak-wind orographic flow. Tellus A 2014, 66, 25421,
  • Hackerott, J.A., and Ó. Rögnvaldsson, 2019. Avaliação e validação da previsão de geração eólica do Nordeste brasileiro do modelo da Tempo Ok. Wind Power Brazil. 39- 54 p. Available on-line: windpower/pt/2020/ebook/AnuarioTrabalhosTecnicosBWP2019.pdf
  • Lopes, V., J.H. Hackerott, J.C.M. VAZ, and Ó. Rögnvaldsson, 2020. Previsão de geração eólica no Nordeste brasileiro pelo modelo da Tempo Ok. Wind Power Brazil. 97-116 p. Available on-line:
Scroll to Top