Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real time, recently developed by the authors (Schlager et al., 2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains eleven meteorological stations at elevations from about 600 m to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 minutes from the WegenerNet JBT, mean wind and wind gust fields at 10 m and 50 m height levels with a high spatial resolution of 100 × 100 m and a temporal resolution of 30 minutes. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. A main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance with somewhat better values for the strong wind conditions. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from five-year climate data of the WegenerNet JBT in comparison to nine-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al., (2017)In general, the five-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with and evaluation of wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

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Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

Atmospheric Measurement Techniques ◽

10.5194/amt-11-5607-2018 ◽

2018 ◽

Vol 11 (10) ◽

pp. 5607-5627 ◽

Cited By ~ 1

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger

Keyword(s):

High Resolution ◽

Climate Models ◽

Meteorological Station ◽

Strong Wind ◽

Mountainous Region ◽

Climate Data ◽

Wind Fields ◽

Station Network ◽

Weather And Climate ◽

Wind Events

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real-time, recently developed by the authors Schlager et al. (2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains 11 meteorological stations at elevations from about 600 to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 min from the WegenerNet JBT, mean wind and wind gust fields at 10 and 50 m height levels with a high spatial resolution of 100 m × 100 m and a temporal resolution of 30 min. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. The main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance. Due to the spatially more homogeneous wind speeds and directions for strong wind events in this mountainous region, the results show somewhat better performance for these events. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from 5-year climate data of the WegenerNet JBT in comparison to 9-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al. (2017). In general, the 5-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with, and evaluation of, wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

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Generation of High-Resolution Wind Fields from the WegenerNet Dense Meteorological Station Network in Southeastern Austria

Weather and Forecasting ◽

10.1175/waf-d-16-0169.1 ◽

2017 ◽

Vol 32 (4) ◽

pp. 1301-1319 ◽

Cited By ~ 3

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Jürgen Fuchsberger

Keyword(s):

Wind Speed ◽

Meteorological Station ◽

High Density ◽

Weather Data ◽

Strong Wind ◽

Wind Fields ◽

Near Surface ◽

Station Network ◽

Wind Speeds ◽

Weather And Climate

Abstract An operational weather diagnostics application for automatic generation of wind fields in near–real time from observations delivered by the high-density WegenerNet meteorological station network in the Feldbach region of Austria is introduced. The purpose of the application is to empirically provide near-surface wind fields of very high spatial and temporal resolution for evaluating convection-permitting climate models as well as investigating weather and climate variability on a local scale. The diagnostic California Meteorological Model (CALMET) is used as the core tool. This model computes 3D wind fields based on observational weather data, a digital elevation model, and land-use categories. The application first produces the required input files from the WegenerNet stations and subsequently runs the CALMET model based on this input. In a third step the modeled wind fields are stored in the WegenerNet data archives every 30 min with a spatial resolution of 100 m × 100 m, while also generating averaged weather and climate products during postprocessing. The performance of the modeling against station observations, for which wind speeds were classified into weak and strong wind speeds, is evaluated and reasonably good results were found for both wind speed classes. The statistical agreement for the vector-mean wind speed is slightly better for weak wind speeds than for strong ones while the difference between modeled and observed wind directions is smaller for strong wind speeds than for weak ones. The application is also a valuable tool for other high-density networks.

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A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

10.5194/gmd-2018-238 ◽

2018 ◽

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger ◽

Alexander Kann ◽

Heimo Truhetz

Keyword(s):

High Resolution ◽

Wind Speed ◽

Climate Model ◽

Strong Wind ◽

Wind Fields ◽

Dynamical Modeling ◽

Thermally Induced ◽

Wind Speeds ◽

Error Measures ◽

Wind Events

Abstract. Empirical high-resolution surface wind fields, automatically generated by a weather diagnostic application, the WegenerNet Wind Product Generator (WPG), were intercompared with wind field analysis data from the Integrated Nowcasting through Comprehensive Analysis (INCA) system and with dynamical climate model wind field data from the non-hydrostatic climate model COSMO-CLM. The INCA analysis fields are available at a horizontal grid spacing of 1 km x 1 km, whereas the COSMO model fields are from simulations at a 3 km x 3 km grid. The WPG, developed by Schlager et al. (2017, 2018), generates diagnostic fields at a high resolution grid of 100 m x 100 m, using observations from two dense meteorological station networks: The WegenerNet Feldbach Region (FBR) and its alpine sister network, the WegenerNet Johnsbachtal (JBT). The high-density WegenerNet FBR is located in southeastern Styria, Austria, a region predominated by a hilly terrain and small differences in altitude. The network consists of more than 150 meteorological stations. The WegenerNet JBT contains eleven meteorological stations at elevations ranging from about 600 m to 2200 m in a mountainous region in northern Styria. The wind fields of these different empirical/dynamical modeling approaches were intercompared for thermally induced and strong wind events, using hourly temporal resolutions as supplied by the WPG, with the focus on evaluating spatial differences and displacements between the different datasets. For this comparison, a novel neighborhood-based spatial wind verification methodology based on fractions skill socres (FSS) is used to estimate the modeling performances. All comparisons show an increasing FSS with increasing neighborhood size. In general, the spatial verification indicates a better statistical agreement for the hilly WegenerNet FBR than for the mountainous WegenerNet JBT. The results for the WegenerNet FBR show a better agreement between INCA and WegenerNet than between COSMO and WegenerNet wind fields, especially for large scales (neighborhoods). In particular, COSMO-CLM clearly underperforms in case of thermally induced wind events. For the JBT region, all spatial comparisons indicate little overlap at small neighborhood sizes and in general large biases of wind vectors occur between the dynamical (COSMO) and analysis (INCA) fields and the diagnostic (WegenerNet) reference dataset. Furthermore, gridpoint-based error measures were calculated for the same evaluation cases. The statistical agreement, estimated for the vector-mean wind speed and wind directions show again a better agreement for the WegenerNet FBR than for the WegenerNet JBT region. In general, the difference between modeled and observed wind directions is smaller for strong wind speed events than for thermally induced ones. A combined examination of all spatial and gridpoint-based error measures shows that COSMO-CLM with its limited horizontal resolution of 3 km x 3 km and hence, a too smoothed orography, is not able to represent small-scale wind patterns. The results for the JBT region indicate that the INCA analysis fields generally overestimate wind speeds in the summit regions. For strong wind speed events the wind speed in the valleys is underestimated by INCA, however. Regarding the WegenerNet diagnostic wind fields, the statistics show decent performance in the FBR and somewhat overestimated wind speeds for strong wind speed events in the Enns valley of the JBT region.

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A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

Geoscientific Model Development ◽

10.5194/gmd-12-2855-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 2855-2873

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger ◽

Alexander Kann ◽

Heimo Truhetz

Keyword(s):

High Resolution ◽

Wind Speed ◽

Climate Model ◽

Grid Point ◽

Regional Climate ◽

Small Scale ◽

Strong Wind ◽

Wind Fields ◽

Thermally Induced ◽

Wind Events

Abstract. Empirical high-resolution surface wind fields, automatically generated by a weather diagnostic application, the WegenerNet Wind Product Generator (WPG), were intercompared with wind field analysis data from the Integrated Nowcasting through Comprehensive Analysis (INCA) system and with regional climate model wind field data from the Consortium for Small Scale Modeling Model in Climate Mode (CCLM). The INCA analysis fields are available at a horizontal grid spacing of 1 km × 1 km, whereas the CCLM fields are from simulations at a 3 km × 3 km grid. The WPG, developed by Schlager et al. (2017, 2018), generates diagnostic fields on a high-resolution grid of 100 m × 100 m, using observations from two dense meteorological station networks: the WegenerNet Feldbach Region (FBR), located in a region predominated by a hilly terrain, and its Alpine sister network, the WegenerNet Johnsbachtal (JBT), located in a mountainous region. The wind fields of these different empirical–dynamical modeling approaches were intercompared for thermally induced and strong wind events, using hourly temporal resolutions as supplied by the WPG, with the focus on evaluating spatial differences and displacements between the different datasets. For this comparison, a novel neighborhood-based spatial wind verification methodology based on fractions skill scores (FSSs) is used to estimate the modeling performances. All comparisons show an increasing FSS with increasing neighborhood size. In general, the spatial verification indicates a better statistical agreement for the hilly WegenerNet FBR than for the mountainous WegenerNet JBT. The results for the WegenerNet FBR show a better agreement between INCA and WegenerNet than between CCLM and WegenerNet wind fields, especially for large scales (neighborhoods). In particular, CCLM clearly underperforms in the case of thermally induced wind events. For the JBT region, all spatial comparisons indicate little overlap at small neighborhood sizes, and in general large biases of wind vectors occur between the regional climate model (CCLM) and analysis (INCA) fields and the diagnostic (WegenerNet) reference dataset. Furthermore, grid-point-based error measures were calculated for the same evaluation cases. The statistical agreement, estimated for the vector-mean wind speed and wind directions again show better agreement for the WegenerNet FBR than for the WegenerNet JBT region. A combined examination of all spatial and grid-point-based error measures shows that CCLM with its limited horizontal resolution of 3 km × 3 km, and hence too smoothed an orography, is not able to represent small-scale wind patterns. The results for the JBT region indicate significant biases in the INCA analysis fields, especially for strong wind speed events. Regarding the WegenerNet diagnostic wind fields, the statistics show acceptable performance in the FBR and somewhat overestimated wind speeds for strong wind speed events in the Enns valley of the JBT region.

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A new pan-European dataset for gridded daily average wind speed based on in-situ observations

10.5194/ems2021-358 ◽

2021 ◽

Author(s):

Jouke de Baar ◽

Gerard van der Schrier ◽

Irene Garcia-Marti ◽

Else van den Besselaar

Keyword(s):

Spatial Distribution ◽

High Resolution ◽

Cross Validation ◽

Data Sets ◽

Optimal Prediction ◽

Auxiliary Variables ◽

Climate Data ◽

Ensemble Spread ◽

Wind Fields

ObjectiveThe purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to in-situ observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.Research question&#160;We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?ApproachIn our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a &#8216;background&#8217; for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.Novelty&#160;&#160;&#160;The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.

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The WegenerNet 3D weather and climate research facility: A unique open-air laboratory for high-resolution precipitation studies

10.5194/egusphere-egu2020-1417 ◽

2020 ◽

Author(s):

Jürgen Fuchsberger ◽

Gottfried Kirchengast ◽

Christoph Bichler

Keyword(s):

High Resolution ◽

Precipitable Water ◽

Heavy Precipitation ◽

Satellite System ◽

Doppler Velocity ◽

Time Sampling ◽

Volume Data ◽

Station Network ◽

Climate Research ◽

Weather And Climate

The WegenerNet Feldbach Region is a unique weather and climate observation facility comprising 155 meteorological stations measuring temperature, humidity, precipitation, and other parameters, in a tightly spaced grid within a core area of 22 km &#215; 16 km centered near the city of Feldbach (46.93&#176;N, 15.90&#176;E). With its stations every about two square-km (area of about 300 square-km in total), and each station with 5-min time sampling, the network provides regular measurements since January 2007. In 2020, the station network will be expanded by three major new components, converting it from a 2D ground station network into a 3D open-air laboratory for weather and climate research at very high resolution. The following new observing components will start operations by spring 2020:<ol><li>A polarimetric X-band Doppler weather radar for studying precipitation parame- ters in the troposphere above the ground network, such as rain rate, hydrometeor classification, Doppler velocity, and approximate drop size and number. It can provide 3D volume data (at about 1 km &#215; 1 km horizontal and 500 m vertical res- olution, and 5-min time sampling) for moderate to strong precipitation. Together with the dense ground network this allows detailed studies of heavy precipitation events at high accuracy.</li> <li>An azimuth-steerable microwave/IR radiometer for vertical profiling of temperature, humidity, and cloud liquid water in the troposphere (with 200 m to 1 km vertical resolution, and 5-min time sampling), also capable of measuring integrated water vapor (IWV) along line-of-sight paths towards Global Navigation Satellite System (GNSS) satellites.</li> <li>A water vapor mapping high-resolution GNSS station network, named GNSS StarNet, comprising six ground stations, spatially forming two star-shaped subnets (one with &#8764;10 km interstation distance, and one embedded with &#8764;5 km distance), for providing slant IWV, vertical IWV, and precipitable water, among other parame- ters, at 5-min time sampling.</li> </ol>We will present a detailed overview of the new components, their location, specifica- tion, and output data products.

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TopoSCALE: deriving surface fluxes from gridded climate data

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-3381-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 3381-3426 ◽

Cited By ~ 1

Author(s):

J. Fiddes ◽

S. Gruber

Keyword(s):

High Resolution ◽

Land Surface ◽

Climate Models ◽

Good Description ◽

Longwave Radiation ◽

Surface Fluxes ◽

Swiss Alps ◽

Climate Data ◽

Heterogeneous Terrain ◽

Reference Methods

Abstract. Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by e.g. topography and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that leverages the good description of the atmospheric column provided by climate models, together with high resolution DEM's, to derive a consistent topography-based, scaling of coarse grid climate variables to fine-scale. We test the method together with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure-levels: air temperature, relative humidity, wind speed and incoming longwave radiation. It is expected that this method can be used to improve inputs to numerical simulations in complex and/or remote terrain especially when statistical methods are not possible due to lack of observations i.e. remote areas or future periods.

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Comparison of Collocated Automated (NCECONet) and Manual (COOP) Climate Observations in North Carolina

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1873.1 ◽

2006 ◽

Vol 23 (5) ◽

pp. 671-682 ◽

Cited By ~ 19

Author(s):

Christopher Holder ◽

Ryan Boyles ◽

Ameenulla Syed ◽

Dev Niyogi ◽

Sethu Raman

Keyword(s):

North Carolina ◽

Climate Models ◽

Daily Rainfall ◽

Maximum Temperature ◽

Spatial Density ◽

Climate Data ◽

Data Resource ◽

Weather And Climate ◽

Observation Times ◽

Mean Square Errors

Abstract The National Weather Service's Cooperative Observer Program (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where 13 of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product–moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm day−1. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root-mean-square errors are reduced by up to 71% with the day-shift and hourly corrections. This study shows that COOP and automated data [such as from the North Carolina Environment and Climate Observing Network (NCECONet)] can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.

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An online trajectory module (version 1.0) for the non-hydrostatic numerical weather prediction model COSMO

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-1223-2013 ◽

2013 ◽

Vol 6 (1) ◽

pp. 1223-1257

Author(s):

A. K. Miltenberger ◽

S. Pfahl ◽

H. Wernli

Keyword(s):

High Resolution ◽

Wind Field ◽

Model Simulation ◽

Weather Prediction ◽

Limited Area ◽

Model Output ◽

Wind Fields ◽

Time Step ◽

Flow Distortion ◽

Cosmo Model

Abstract. A module to calculate online trajectories has been implemented into the non-hydrostatic limited-area weather prediction and climate model COSMO. Whereas offline trajectories are calculated with wind fields from model output, which is typically available every one to six hours, online trajectories use the simulated wind field at every model time step (typically less than a minute) to solve the trajectory equation. As a consequence, online trajectories much better capture the short-term temporal fluctuations of the wind field, which is particularly important for mesoscale flows near topography and convective clouds, and they do not suffer from temporal interpolation errors between model output times. The numerical implementation of online trajectories in the COSMO model is based upon an established offline trajectory tool and takes full account of the horizontal domain decomposition that is used for parallelization of the COSMO model. Although a perfect workload balance cannot be achieved for the trajectory module (due to the fact that trajectory positions are not necessarily equally distributed over the model domain), the additional computational costs are fairly small for high-resolution simulations. Various options have been implemented to initialize online trajectories at different locations and times during the model simulation. As a first application of the new COSMO module an Alpine North Föhn event in summer 1987 has been simulated with horizontal resolutions of 2.2 km, 7 km, and 14 km. It is shown that low-tropospheric trajectories calculated offline with one- to six-hourly wind fields can significantly deviate from trajectories calculated online. Deviations increase with decreasing model grid spacing and are particularly large in regions of deep convection and strong orographic flow distortion. On average, for this particular case study, horizontal and vertical positions between online and offline trajectories differed by 50–190 km and 150–750 m, respectively, after 24 h. This first application illustrates the potential for Lagrangian studies of mesoscale flows in high-resolution convection-resolving simulations using online trajectories.

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Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Geoscientific Model Development ◽

10.5194/gmd-14-1267-2021 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1267-1293

Author(s):

Sara Top ◽

Lola Kotova ◽

Lesley De Cruz ◽

Svetlana Aniskevich ◽

Leonid Bobylev ◽

...

Keyword(s):

High Resolution ◽

Central Asia ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Maximum Temperature ◽

The Tibetan Plateau ◽

Climate Information ◽

Climate Data

Abstract. To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

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