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

The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but often suffer from inaccuracies, e.g., due to insufficient spatial resolution. In this contribution, we investigate the added value of sDoG, a reanalysis data postprocessing and downscaling tool designed to extend short-term and/or interrupted weather station data from high mountain sites to the baseline climate. sDoG is applied to ERA interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. sDoG training and cross-validation is based on observations from 2002 to 2012. The availability of observations at the Vernagtbach climate monitoring site further back in time allows us to perform a true evaluation: "true evaluation" in contrast to cross-validation, by assessing the performance of the sDoG retrospective forecast for the period 1979 to 2001.We demonstrate the ability of sDoG in modeling air temperature in the true evaluation period for different temporal scales. sDoG adds significant value over a selection of reference data sets, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product (SPARTACUS). However, we identify limitations of sDoG in modeling summer air temperature variations, most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. Comparing the performance of the considered reference data sets reveals that higher resolution data sets do not necessarily add value over data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA interim (80 km resolution) both clearly outperform the higher resolution surface analyses ERA5-Land (11 km resolution), HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences amongst ERA5 and ERA interim, by contrast, are comparably small. The results highlight the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas, both for data users and model developers.

The evaluation of models in general is a non-trivial task. Even a well established model may yield correct results for the wrong reasons, i.e. by a different chain of processes than found in observations. While guidelines and strategies exist to maximize the chances that results match measurements for the right reasons, these are mostly applicable to full-physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is a comparatively novel atmospheric model employed to downscale atmospheric fields. ICAR uses linear mountain wave theory to represent the wind field and advects atmospheric quantities, such as temperature and moisture in this wind field. Additionally a microphysics scheme is applied to represent the formation of clouds and precipitation.We conducted an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to improve its results. We contrast the ICAR simulations to Weather Research and Forecasting (WRF) model simulations and asses the impact of our recommendations with a case study for the South Island of New Zealand.Our results suggest two key aspects relevant for ICAR to obtain the correct results for the right reasons. Firstly, the representation of the wind field within the domain improves when the dry and the moist Brunt-Väisälä frequencies are calculated in accordance to linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Secondly, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. We analysed the causes for the differences between the idealized ICAR and WRF simulations and attribute them to the non-linearities in the WRF wind field and additional simplifications in the governing equations of ICAR. With our recommended ICAR setup applied to the real case study we find an upwind spatial shift of the precipitation maximum in comparison to the results obtained with the original ICAR setup. Additionally our results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such analysis may not reflect the skill of the model in capturing atmospheric processes such as gravity waves and cloud formation.Overall our findings have consequences for the interpretation of past results obtained with ICAR and suggest improvements to ICAR in future studies.

The output of general circulation models is too coarse to adequately capture the features influencing local climate and weather, particularly in complex topography. To asses the long-term impact of a changing global climate in mountainous regions, regional climate models need to be run on a fine spatial and temporal grid. Here the Intermediate Complexity Atmospheric Research (ICAR) model is a computationally frugal and physics based alternative to full physics regional climate models such as the Weather Research and Forecasting (WRF) model. A sizable portion of the computational efficiency of ICAR stems from its application of linear mountain wave theory to determine the wind field in the domain, thereby avoiding a numerical solution of the Navier-Stokes equations of motion. Heat, moisture and other atmospheric quantities are then advected in this wind field. Microphysical conversion processes between water vapor and various hydrometeor species are handled by a complex microphysics scheme. Altogether ICAR does not require measurements and enables computationally cheap downscaling, particularly in mountainous regions with complex topography, yielding a physically consistent set of atmospheric variables. However, in a real-world application and evaluation of ICAR we observed a strong sensitivity of the model performance to the elevation of the model top (Horak et al., 2019).We present three recommendations, derived from idealized simulations, that improve different aspects of ICAR simulations. The simulations constitute an idealized ridge experiment with a non-dimensional mountain height of 0.5. The ridge is specified by a witch of Agnesi function and the sounding characterized by a saturated, horizontally and vertically homogeneous atmosphere with constant and stable stratification. The wind field calculated by ICAR is compared to the exact analytical solution. Furthermore, the water vapor, suspended hydrometeor and precipitating hydrometeor fields are used as proxies to identify inconsistencies in the model output, such as the dependence of the results on the elevation of the model top. To highlight the deviations of ICAR results from a full physics model, resulting from non-linearities in the wind field, the ICAR output was additionally compared to that of a WRF simulation. The results of our investigation strongly suggest that ICAR simulations can be significantly improved by (i) calculating the Brunt-Väisälä frequency from the forcing data set instead of the perturbed state of the atmosphere, (ii) setting the model top to an elevation of at least 11.4 km and, (iii) by applying a zero value boundary condition to the water vapor and hydrometeor species at the model top. To our knowledge none of the preceding studies employing ICAR satisfied these three conditions. Overall our investigation deepens the understanding of the ICAR model sensitivity to crucial model components, thereby increasing the potential of the model as a tool for long-term impact studies in data-sparse regions with complex topography.ReferencesHorak, J., Hofer, M., Maussion, F., Gutmann, E., Gohm, A., and Rotach, M. W. (2019), Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography. Hydrology and Earth System Sciences, 23(6), 2715-2734.

In vehicles that are parked, no ventilation and/or air conditioning takes place. If a vehicle is exposed to direct solar radiation, an immediate temperature rise occurs. The high cabin air temperature can threaten children and animals that are left unattended in vehicles. In the USA, lethal heat strokes cause a mean death rate of 37 children per year. In addition, temperature-sensitive goods (e.g. drugs in ambulances and veterinary vehicles) can be adversely affected by high temperatures. To calculate the rise of the cabin air temperature, a dynamic model was developed that is driven by only three parameters, available at standard meteorological stations: air temperature, global radiation and wind velocity. The transition from the initial temperature to the constant equilibrium temperature depends strongly on the configuration of the vehicle, more specifically on insulation, window area and transmission of the glass, as well as on the meteorological conditions. The comparison of the model with empirical data showed good agreement. The model output can be applied to assess the heat load of children and animals as well as temperature-sensitive goods, which are transported and/or stored in a vehicle.

The coarse grid spacing of global circulation models necessitates the application of climate downscaling to investigate the local impact of a changing global climate. Difficulties arise for data sparse regions in complex topography which are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high quality observational data. The Intermediate Complexity Atmospheric Research Model (ICAR) is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multi-year precipitation time series generated with ICAR on a 4 x 4 km² grid for the South Island of New Zealand for the eleven-year period from 2007 to 2017. It focuses on complex topography and evaluates ICAR at 16 weather stations, eleven of which are situated in the Southern Alps between 700 m MSL and 2150 m MSL. ICAR is diagnosed with standard skill scores and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation, with the highest scores obtained for 4 km above topography. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set for alpine weather stations, improving mean squared errors (MSE) up to 53%. It performs similarly during all seasons with an MSE minimum during winter, while flow of higher linearity and atmospheric stability were found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results show the skill of ICAR in a real-world application, and may be improved upon by further observational tuning or bias correction techniques. Based on these findings ICAR shows the potential to generate downscaled fields for long term impact studies in data sparse regions with complex topography.

In this work, the energy transfer from intense continuous-wave laser beams with a wavelength of 1070 nm, a power in the kilowatt range, and with diameters in the millimeter and centimeter range to metal samples is investigated. While the absorptivity of iron and steel samples is almost constant for laser intensities below 3.4 kW/cm2, a decrease in the absorptivity is observed for higher intensities which is attributed to the formation of a vapor plume in the interaction zone. The dynamics of the formation and expansion as well as the emission of light in the visible spectral range from the vapor plume are further characterized with a fixed beam diameter of 2.6 mm at a laser power of 10 kW in detail for iron and aluminum samples. The analysis of high speed video sequences yields expansion velocities of the vapor plume of 5.0 m/s for the iron and 0.29 m/s for the aluminum samples. In the spectra from the aluminum samples, emission lines from atomic aluminum as well as emission bands from molecular aluminum monoxide are identified and allow for the estimation of the basic thermodynamic parameters. A special focus is on the investigation of the effect of vapor and plasma formation on the energy transfer from the laser to the sample and on the analysis of the role of inverse bremsstrahlung in this process. The measurements indicate that the metal vapor is partially ionized and that there is a significant contribution of inverse bremsstrahlung to the absorption of laser energy in the partially ionized vapor plume.

The phenomena occurring during laser-metal interaction depend on a variety of parameters such as laser wavelength, intensity, and thermophysical and optical properties of the irradiated material. This work comprises a detailed study of the perforation process of thin iron and steel plates under CW laser radiation at a wavelength of 1.07 mm. Experiments were carried out over a wide range of intensities between 0.1 and 100 kW/cm2, and with beam radii in the millimeter and centimeter range. Additionally, we describe a method applying contact-free radiative temperature measurements, which allows the measurement of melt-through times without an exact knowledge of the plate's spectral surface emissivity. A detailed numerical model is presented and employed to analyze, interpret, and predict data gathered from the experiments. The model is shown to produce accurate predictions and is in good agreement with experimental data. Furthermore, our results indicate that in the investigated regime perforation is governed by gravitational and surface forces. Based on this hypothesis, a criterion for the onset of the perforation explaining the observed local minimum in the perforation time versus beam radius curve is presented.