Photo: Hanneke Luijting

About AROME-Arctic

AROME-Arctic is a regional short-range high-resolution forecasting system for the European Arctic with 2.5 km grid spacing and 65 vertical levels (Müller et al. 2017, Køltzow et al. 2019). The model system is based on the HARMONIE-AROME configuration of the ALADIN-HIRLAM numerical weather prediction system (Bengtsson et al. 2017). The HARMONIE-AROME system is part of the code base of the IFS/ARPEGE system of ECMWF and Meteo-France, and it uses the same non-hydrostatic dynamical core as AROME-France described by Seity et al. (2010).

AROME-Arctic is very similar to the Nordic setup MetCoOp (Müller et al. 2017). Since June 2017, the operational AROME-Arctic is based on the cycle version 40h1.1. It issues deterministic forecasts 4 times a day with a lead time of 66 hours.

Data assimilation

AROME-Arctic utilises a three-dimensional variational data assimilation (described by Brousseau et al. 2011) with 3-hourly cycling to assimilate conventional observations, scatterometer ocean surface winds (Valkonen et al. 2017), satellite radiances  (Randriamampianina et al. 2019) and atmospheric motion vectors (Randriamampianina et al. 2017). Additionally, the operational system includes a so called “large scale mixing” where the large spatial scales are blended with the ECMWF high resolution model (HRES) forecasts to obtain the background field. Data assimilation of surface variables (2 m temperature, 2 m humidity and snow depth) in the AROME-Arctic is based on optimal interpolation (Giard and Bazile, 2000).

Boundary conditions

The ECMWF HRES forecasts with 1-h interval are used as lateral boundary conditions. Sea ice concentration and sea surface temperature are also obtained from the ECMWF. The surface temperature over sea-ice is subsequently modelled by a 1D sea-ice model (Batrak et al. 2018).


The Arctic forecast and research activities at MET Norway focus on the use and further development of the AROME-Arctic system. In addition to the core activities at MET Norway, AROME-Arctic is a part of a variety of research projects, for example, APPLICATE, CARRA, Nansen LEGACY, and Alertness.


Batrak, Y., Kourzeneva, E., and  Homleid, M., 2018: Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1. Geosci. Model Dev. 11, 3347–3368, https://doi.org/10.5194/gmd-11-3347-2018

Bengtsson, L., U. Andrae, T. Aspelien, Y. Batrak, J. Calvo, W. de Rooy, E. Gleeson, B. Hansen-Sass, M. Homleid, M. Hortal, K. Ivarsson, G. Lenderink, S. Niemelä, K.P. Nielsen, J. Onvlee, L. Rontu, P. Samuelsson, D.S. Muñoz, A. Subias, S. Tijm, V. Toll, X. Yang, and M.Ø. Køltzow, 2017: The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System. Mon. Wea. Rev., 145, 1919–1935, https://doi.org/10.1175/MWR-D-16-0417.1

Brousseau, P., L. Berre, F. Bouttier, and G. Desroziers (2011), Background-error covariances for a convective-scale data-assimilation system: AROME-France 3D-Var, Q.J.R.Meteorol. Soc., 137, 409–422, doi: 10.1002/qj.750.

Giard, D.; Bazile, E. Implementation of a new as assimilation scheme for soil and surface variables in a global NWP model. Mon. Weather Rev. 2000, 128, 997–1015.

Køltzow, M., B. Casati, E. Bazile, T. Haiden and T. Valkonen, 2019: A NWP model inter-comparison of surface weather parameters in the European Arctic during the Year of Polar Prediction Special Observing Period Northern Hemisphere 1. Weather and Forecasting. https://doi.org/10.1175/WAF-D-19-0003.1

Müller, M., M. Homleid, K. Ivarsson, M.A. Køltzow, M. Lindskog, K.H. Midtbø, U. Andrae, T. Aspelien, L. Berggren, D. Bjørge, P. Dahlgren, J. Kristiansen, R. Randriamampianina, M. Ridal, and O. Vignes, 2017: AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model. Wea. Forecasting, 32, 609–627, https://doi.org/10.1175/WAF-D-16-0099.1 

Müller, M., Y. Batrak, J. Kristiansen, M.A. Køltzow, G. Noer, and A. Korosov, 2017: Characteristics of a Convective-Scale Weather Forecasting System for the European Arctic. Mon. Wea. Rev., 145, 4771–4787, https://doi.org/10.1175/MWR-D-17-0194.1 

Randriamampianina, R.; Schyberg, H.; Mile, M., 2019, Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model. Remote Sens. 2019, 11(8), 981; https://doi.org/10.3390/rs11080981

Randriamampianina R, T Aspenes, M Mile, and H Schyberg, 2017, Impact of Atmospheric Motion Vectors (AMV) on rapid update cycling (RUC) and rapid-refresh (RR) systems. Metno Report 04-2017. Available from https://www.met.no/publikasjoner/met-report/met-report-2017.

Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, and V. Masson, 2011: The AROME-France Convective-Scale Operational Model. Mon. Wea. Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1

Valkonen, T., H. Schyberg and J. Figa-Saldaña, "Assimilating Advanced Scatterometer Winds in a High-Resolution Limited Area Model Over Northern Europe," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 5, pp. 2394-2405, May 2017. doi: 10.1109/JSTARS.2016.2602889