Fast simulation of high resolution urban wind fields at city scale

Urban Climate ◽  
2021 ◽  
Vol 39 ◽  
pp. 100941
Author(s):  
Songlin Xiang ◽  
Jingcheng Zhou ◽  
Xiangwen Fu ◽  
Leyi Zheng ◽  
Yuqing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Fast Simulation ◽  
Wind Fields

scholarly journals Comparison of high-resolution wind fields extracted from TerraSAR-X SAR imagery with predictions from the WRF mesoscale model

2012 ◽  
Vol 117 (C2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Donald R. Thompson ◽  
Jochen Horstmann ◽  
Alexis Mouche ◽  
Nathaniel S. Winstead ◽  
Raymond Sterner ◽  
...  
Keyword(s):  
High Resolution ◽  
Mesoscale Model ◽  
Wind Fields ◽  
Sar Imagery

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

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

<p><strong>Objective</strong></p><p>The 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 <em>in-situ</em> 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.</p><p><strong>Research question</strong> </p><p>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?</p><p><strong>Approach</strong></p><p>In 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 ‘background’ 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.</p><p>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.</p><p><strong>Novelty</strong>   </p><p>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.</p><p>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.</p><p>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.</p>

scholarly journals Using wind fields from a high-resolution atmospheric model for simulating snow dynamics in mountainous terrain

2009 ◽  
Vol 23 (7) ◽  
pp. 1064-1075 ◽  
Author(s):  
M. Bernhardt ◽  
G. Zängl ◽  
G. E. Liston ◽  
U. Strasser ◽  
W. Mauser
Keyword(s):  
High Resolution ◽  
Atmospheric Model ◽  
Mountainous Terrain ◽  
Wind Fields

scholarly journals Retrieval of High-Resolution Wind Fields over the Southern Korean Peninsula Using the Doppler Weather Radar Network

2009 ◽  
Vol 24 (1) ◽  
pp. 87-103 ◽  
Author(s):  
S-G. Park ◽  
Dong-Kyou Lee
Keyword(s):  
High Resolution ◽  
Korean Peninsula ◽  
General Structure ◽  
Three Dimensional ◽  
Small Scale ◽  
Wind Fields ◽  
Entire Area ◽  
Radar Network ◽  
Strong Winds ◽  
Rain Events

Abstract The performance of a radar network for retrieving high-resolution wind fields over South Korea is examined. The network consists of a total of 18 operational radars. All of the radars possess the Doppler capability and carry out plan position indicator (PPI) volume scans comprising 6–15 elevation steps at every 6 or 10 min. An examination of the coverage of the radar network reveals that the radar network allows the retrieval of three-dimensional high-resolution wind fields over the entire area of the southern Korean Peninsula as well as nearby oceans above a height of approximately 3 km. After the quality control procedures of the radar measurements, the high-resolution wind fields (a few kilometers) are extracted using multiple-Doppler wind synthesis in the Custom Editing and Display of Reduced Information in Cartesian Space (CEDRIC) package developed by NCAR. The radar-retrieved winds are evaluated using the following two rain events: 1) Typhoon Ewiniar in 2006, which resulted in strong winds and heavy rainfall over the entire southern Korean Peninsula, and 2) a well-developed hook echo with a relatively small-scale diameter of about 30 km. The wind fields retrieved from the radar network exhibit counterclockwise rotation around the typhoon center and a general structure around a hook echo such as a cyclonically rotating updraft (i.e., mesocyclone). Comparisons with the wind measurements from four UHF wind profilers for the typhoon case reveal that the u- and υ-wind components retrieved from the radar network deviate by standard deviations of 3.6 and 4.5 m s−1 over ranges from −30 to 20 m s−1 and from 0 to 40 m s−1, respectively. Therefore, it is concluded that the operational radar network has the potential to provide three-dimensional high-resolution wind fields within the mesoscale precipitation systems over almost the entire area of the southern Korean Peninsula.

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

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.

scholarly journals Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications

2018 ◽  
Vol 27 (4) ◽  
pp. 257 ◽  
Author(s):  
O. Rios ◽  
W. Jahn ◽  
E. Pastor ◽  
M. M. Valero ◽  
E. Planas
Keyword(s):  
High Resolution ◽  
Surface Wind ◽  
Computation Time ◽  
Data Driven ◽  
Wind Fields ◽  
Near Surface ◽  
Finite Set ◽  
Multiple Resolutions ◽  
Feasible Alternative ◽  
Wildfire Spread

Local wind fields that account for topographic interaction are a key element for any wildfire spread simulator. Currently available tools to generate near-surface winds with acceptable accuracy do not meet the tight time constraints required for data-driven applications. This article presents the specific problem of data-driven wildfire spread simulation (with a strategy based on using observed data to improve results), for which wind diagnostic models must be run iteratively during an optimisation loop. An interpolation framework is proposed as a feasible alternative to keep a positive lead time while minimising the loss of accuracy. The proposed methodology was compared with the WindNinja solver in eight different topographic scenarios with multiple resolutions and reference – pre-run– wind map sets. Results showed a major reduction in computation time (~100 times once the reference fields are available) with average deviations of 3% in wind speed and 3° in direction. This indicates that high-resolution wind fields can be interpolated from a finite set of base maps previously computed. Finally, wildfire spread simulations using original and interpolated maps were compared showing minimal deviations in the fire shape evolution. This methodology may have an important effect on data assimilation frameworks and probabilistic risk assessment where high-resolution wind fields must be computed for multiple weather scenarios.

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

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.

scholarly journals Multi-scale snowdrift-permitting modelling of mountain snowpack

2020 ◽  
Author(s):  
Vincent Vionnet ◽  
Christopher B. Marsh ◽  
Brian Menounos ◽  
Simon Gascoin ◽  
Nicholas E. Wayand ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Depth ◽  
Large Scale ◽  
Small Scale ◽  
Topographic Effects ◽  
Blowing Snow ◽  
Wind Fields ◽  
Canopy Interception ◽  
Multi Scale ◽  
Temporal And Spatial

Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modeling approach is proposed to simulate the temporal and spatial evolution of high mountain snowpacks using the Canadian Hydrological Model (CHM), a multi-scale, spatially distributed modelling framework. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing snow redistribution and sublimation, avalanching, forest canopy interception and sublimation and snowpack melt. Short-term, km-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM, and were downscaled to the unstructured mesh scale using process-based procedures. In particular, a new wind downscaling strategy combines meso-scale HRDPS outputs and micro-scale pre-computed wind fields to allow for blowing snow calculations. HRDPS-CHM was applied to simulate snow conditions down to 50-m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (~1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne Light Detection and Ranging (LiDAR) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both blowing snow and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of wind-blown snow on leeward slopes and associated snow-cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture leeside flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.

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