A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1

Abstract. The evaluation of models in general is a nontrivial task and can, due to epistemological practical reasons, never be considered complete. Due this incompleteness, model may yield correct results for the wrong i.e., via different chain processes than found observations. While guidelines strategies exist atmospheric sciences maximize chances that are right these mostly applicable full physics models, such as numerical weather prediction models. Intermediate Complexity Atmospheric Research (ICAR) an employing linear mountain wave theory represent wind field. In field, quantities temperature moisture advected microphysics scheme applied formation clouds precipitation. This study conducts in-depth process-based ICAR, idealized simulations increase understanding develop recommendations probability its reasons. To contrast obtained from linear-theory-based ICAR model, with Weather Forecasting (WRF) conducted. impact developed then demonstrated case South Island New Zealand. investigation suggest three modifications improve aspects simulations. representation field within domain improves when dry moist Brunt–Väisälä frequencies calculated accordance unperturbed base state rather time-dependent perturbed atmosphere. Imposing boundary conditions at upper standard zero-gradient condition shown reduce errors potential water vapor fields. Furthermore, show there lowest possible top elevation should not undercut avoid influences on cloud precipitation domain. method determine both real terrain study. Notable differences between WRF observed across all investigated hydrometeor distributions, distribution indicates maximum by simulation spatially shifted upwind comparison unmodified version ICAR. cause shift skill evaluated statistical metrics based comparisons surface observations only, analysis reflect capturing like gravity waves formation.