Submitted as: model description paper
14 Feb 2023
Submitted as: model description paper |  | 14 Feb 2023
Status: this preprint is currently under review for the journal GMD.

The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.0) Model Enables Fast Dynamic Downscaling to the Hectometer Scale

Dylan Stewart Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott

Abstract. High resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere-atmospheric processes. While this approach has yielded insightful results, studies over annual time-scales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a High-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle-flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 118x fewer computational resources than the WRF atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data is often required at hectometer resolution for whole basins at seasonal time scales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver, and coupling of the model with an intermediate complexity snow model.

Dylan Stewart Reynolds et al.

Status: open (until 11 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-16', Anonymous Referee #1, 07 Mar 2023 reply
  • RC2: 'Comment on gmd-2023-16', Junhong Lee, 12 Mar 2023 reply

Dylan Stewart Reynolds et al.

Model code and software

HICAR Source Code Dylan Reynolds, Ethan Gutmann, and Bert Kruyt

Dylan Stewart Reynolds et al.


Total article views: 383 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
295 78 10 383 7 5
  • HTML: 295
  • PDF: 78
  • XML: 10
  • Total: 383
  • BibTeX: 7
  • EndNote: 5
Views and downloads (calculated since 14 Feb 2023)
Cumulative views and downloads (calculated since 14 Feb 2023)

Viewed (geographical distribution)

Total article views: 382 (including HTML, PDF, and XML) Thereof 382 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 20 Mar 2023
Short summary
The challenge of running geophysical models is often compounded by a question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, which is a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.