High-resolution Mesoscale Model Research Articles

Assessment of universal thermal climate index (UTCI) using the WRF-UCM model over a metropolitan city in India.

Rapid urbanization increases urban air temperature, considerably affecting health, comfort, and the quality of life in urban spaces. The accurate assessment of outdoor thermal comfort is crucial for urban health. In the present study, a high-resolution mesoscale model coupled with a layer Urban Canopy Model (WRF-UCM) is implemented over the city of Hyderabad (17.3850° N, 78.4867° E) to simulate urban meteorological conditions during the summer and winter period of 2009 and 2019. The universal thermal climate index (UTCI) has been estimated using the model-derived atmospheric variables and a human biometeorology parameter to assess the linkages between the outdoor environment and thermal comfort. Results revealed that during summer, the city experiences nearly 50h of very strong thermal stress, whereas about 120h of slight cold stress are experienced during winter. The urban area in Hyderabad expanded from 5 to 15% during the study period, leading to a 2.5℃ (2.8 ℃) increase in land surface temperature, and a 1.2 (1.9 ℃) rise in air temperature at 2m height and 1.5 (2.5 ℃) UTCI during summer (winter) time. The analysis reveals that the maximum UTCI values were noticed over built-up areas compared to other land classes during daytime and nighttime. The results derived from the present study have shown that the performance of WRF-UCM-derived UTCI reasonably portrayed the significant impact of urbanization on thermal comfort over the city and provided useful insights with regard to urban comfort and welfare.

Storm surge prediction improvement using high resolution meso-scale model products over the Bay of Bengal

Storm surge prediction improvement using high resolution meso-scale model products over the Bay of Bengal

Using the U.S. Climate Reference Network to Identify Biases in Near- and Subsurface Meteorological Fields in the High-Resolution Rapid Refresh (HRRR) Weather Prediction Model

Abstract The ability of high-resolution mesoscale models to simulate near-surface and subsurface meteorological processes is critical for representing land–atmosphere feedback processes. The High-Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous United States from 1 January 2021 to 31 December 2021. We compared the 1-, 3-, 6-, 12-, 18-, 24-, 30-, and 48-h forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR’s performance for different geographic regions and land cover types. We found that the HRRR well simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m3 m−3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the western United States and a negative MBE over the eastern United States. Although caution is needed when applying conclusions regarding HRRR’s biases to locations with subgrid-scale land cover variations, general knowledge of HRRR’s biases will help guide improvements to land surface models used in high-resolution weather forecasting models. Significance Statement Weather forecasters rely upon output from many different models. However, the models’ ability to represent processes happening near the land surface over short time scales is critical for producing accurate weather forecasts. In this study, we evaluated the High-Resolution Rapid Refresh (HRRR) model using observations from the U.S. Climate Reference Network, which currently includes 114 reference climate observing stations in the contiguous United States. These stations provide highly accurate measurements of air temperature, precipitation, soil temperature, and soil moisture. Our findings helped illustrate conditions when the HRRR performs well, but also conditions in which the HRRR can be improved, which we expect will motivate ongoing improvements to the HRRR and other weather forecasting models.

Short-term probabilistic forecasting of meso-scale near-surface urban temperature fields

This paper introduces a probabilistic approach to spatio-temporal high resolution meso-scale modeling of near-surface temperature and applies it to regions of dimension about 150∼200 km, with 1 km grid spacing and 30-min interval. Our probabilistic approach, based on linear Gaussian models and dimensionality reduction, can accurately forecast short-term temperature fields and serve as a computationally less expensive alternative to physics-based models that necessitate high-performance computing. The probabilistic models here are calibrated from simulations of a physics-based model, the Princeton Urban Canopy Model, coupled to the Weather Research and Forecasting Model (WRF-PUCM). We assess the performance of the calibrated models to forecast short-term near-surface temperature in various cases. In the numerical campaign, our models achieve 0.97–1.13 °C root mean squared error (RMSE) for 24-hours ahead forecast; generating three days of forecast takes between 20 and 170 sec on a single processor (Intel Xeon E5-2690 v4@2.60GHz). Hence, the proposed approach provides predictions at relatively high accuracy and low computational cost.

Residence times of airborne pollutants in the urban environment

This study investigates the applicability and relevance of the residence time to the characterisation of airborne pollutant removal in the urban environment. Using large-eddy simulations of flow over an idealised building array and a realistic urban area, the statistical and spatial distributions of residence times are analysed for different source locations and wind conditions. For the idealised building array, the mean residence times in the avenue and intersection units depend on the mean flow, turbulent kinetic energy and wind direction. The probability density function (PDF) shows distinct early and late regimes when there are pollutant removal processes associated with different boundaries. For the realistic urban domain, the residence times are significantly shorter and stronger fluctuations are observed in the PDF, especially when the inflow winds are driven by the output from a high-resolution mesoscale model. The residence time is especially useful for applications to assessment and planning, when information about the timescale of pollutant removal is required and the flow is inhomogeneous and unsteady.

Sensitivity studies of cloud microphysical schemes of WRF-ARW model in the simulation of trailing stratiform squall lines over the Gangetic West Bengal region

In this paper an attempt has been made to investigate the impact of different microphysical (MP) parameterization schemes in simulation of leading convective line with trailing stratiform (TS) squall lines observed over the Gangetic West Bengal (GWB) region of India during the pre-monsoon season of March–May using Advanced Research Weather Research and Forecasting (WRF-ARW). The model is employed to study the features associated with three TS squall line (May 15, 2009, May 5, 2010 and May 7, 2010) observed over the study region. The model sensitivity is evaluated with four different microphysics schemes of the model (Goddard, WDM6, Thompson and Morrison). Model simulations are carried out with three two-way nested domains with horizontal resolution of 18, 6 and 2 km grid spacing and explicit convection in the innermost domain. The model simulated results are compared with available observations (Surface data, rainfall, hydrometeors and Doppler Weather Radar products). The analysis demonstrates that the simulation of TS squall lines are very sensitive to the parameterization of microphysical processes of different MP schemes, in spite of using similar initial and boundary conditions and model configuration. The model provides better simulations of rainfall, maximum reflectivity and vertical profiles of hydrometeors with the Morrison MP Scheme. The study reveals that snow and graupel particles has significant impacts on the simulation of stratiform precipitation region of the TS squall line. The study suggests that simulation of TS squall lines using a high-resolution mesoscale model with suitable cloud microphysics scheme is useful for the prediction of such severe events.

Quasi-operational forecast guidance of extremely severe cyclonic storm Fani over the Bay of Bengal using high-resolution mesoscale models

Tropical cyclone (TC) “Fani” (April 26–May 04, 2019) over the Bay of Bengal (BoB) is the first extreme severe cyclonic storm (ESCS) of the season that struck Puri coast with a maximum sustained wind speed of 110 knots. In the past 130 years, TC Fani is the first of its kind with the peculiarity of being persistent with the intensity of severe cyclonic storm (SCS) over land for more than 24 h, thus causing substantial casualties in the major cities of Odisha. Two high-resolution mesoscale modeling systems, (1) state-of-the-art advanced research version of weather research and forecasting (WRF-ARW) model with a single fixed domain (9 km horizontal resolution) and (2) TC specific hurricane WRF (HWRF) with three domains (27/9/3 km horizontal resolution) are used for real-time prediction of Fani in quasi-operational setup. Storms surge prediction is also carried out using IIT Delhi dynamical storm surge model. Both the models, WRF and HWRF, were able to predict the system from its genesis stage (5 days in advance of landfall) with reasonable accuracy in position and strength. The tracks from both models are in good agreement with the observed track. The average track errors for WRF and HWRF are 101 km and 85 km, respectively. The landfall time and position are well predicted, with 60 h of lead time. HWRF model performance in predicting landfall time, position, and intensity is significant. Along with the statistical analysis, structural and vertical analysis of wind, enthalpy flux, and thermal distribution are carried out by considering two represented initial conditions from both WRF and HWRF models. Maximum reflectivity and precipitable water diagnostic studies depict that both the models show asymmetricity at the peak intensity of ESCS. The rainfall during the landfall day from both the models is comparable with the gauge-satellite merged observed rainfall datasets. Along with TC track, intensity, and structural forecasts associated with storm surge at different vulnerable coastal cities are attempted to predict with reasonable accuracy.

A New Horizontal Length Scale for a Three-Dimensional Turbulence Parameterization in Mesoscale Atmospheric Modeling over Highly Complex Terrain

AbstractThe correct simulation of the atmospheric boundary layer (ABL) in highly complex terrain is a challenge for mesoscale numerical weather prediction models. An improvement in model performance is possible if horizontal contributions to turbulence kinetic energy (TKE) production, such as horizontal shear production, are implemented in the model’s turbulence parameterization. However, 3D turbulence parameterizations often only have a constant horizontal length scale that depends on the horizontal grid spacing. This is unphysical for mesoscale applications, because such parameterizations were initially developed for much smaller model grid spacings (e.g., for large-eddy simulations). In this study, we develop a new physically based horizontal length scale for the high-resolution mesoscale model COSMO. We analyze days dominated by thermally driven circulations (valley wind days) in the Inn Valley, Austria. Results show that the new horizontal length scale improves TKE simulations in the valley, when horizontal shear processes contribute to the overall TKE budget. Vertical profiles of TKE and transects across the valley indicate that the model simulates the ABL in a more realistic way than standard turbulence schemes, because the new scheme is able to account for terrain inhomogeneities. A model validation with 88 stations in Austria for four case study days indicates no change in the mean surface fields of temperature, relative humidity, and wind speed by the new turbulence parameterization.

How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

A High-Resolution Mesoscale Model Approach to Reproduce Super Typhoon Maysak (2015) Over Northwestern Pacific Ocean

In this study, an attempt is made to simulate super typhoon Maysak, which occurred over the northwest Pacific Ocean in 2015 and made landfall on the Philippines coast. The aim of the present study is to assess the various atmospheric conditions during the life cycle of Maysak to explore the associated dynamics and behavior over the ocean. For this purpose, the advanced research core of the weather research and forecasting (WRF) mesoscale model is adopted. The model is simulated using 27-km horizontal grid resolution with National Centers for Environmental Prediction global Final analyses (FNL) initial conditions. The relevant parameters, namely storm track, intensity, wind–vorticity, rainfall, minimum sea level pressure, relative humidity, and maximum reflectivity etc., were analyzed. The model is able to perform reasonably well when available observations over the region compared with the simulated values of these parameters. The present study is able to demonstrate the capability of WRF in simulating and predicting the relevant characteristic features of typhoons over the northwest Pacific Ocean region through the case of Maysak.

Supercomputer Technologies as a Tool for High-resolution Atmospheric Modelling towards the Climatological Timescales

Estimation of the recent and future climate changes is the most important challenge in the modern Earth sciences. Numerical climate models are an essential tool in this field of research. However, modelling results are highly sensitive to the spatial resolution of the model. The most of the climate change studies utilize the global atmospheric models with a grid cell size of tens of kilometres or more. High-resolution mesoscale models are much more detailed, but require significantly more computational resources. Applications of such high-resolution models in climate studies are usually limited by regional simulations and by relatively short timespan. In this paper we consider the experience of the long-term regional climate studies based on the mesoscale modelling. On the examples of urban climate studies and extreme wind assessments, we demonstrate the principle advantage of long-term high-resolution simulations, which were carried out on the modern supercomputers.

Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

Aircraft-based observations are a promising source of above-surface observations for assimilation into mesoscale model simulations. The Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations have potential advantages over some other aircraft observations including the presence of water vapor observations. The impact of assimilating TAMDAR observations via observation nudging in 1 km horizontal grid spacing Weather Research and Forecasting model simulations is evaluated using five cases centered over California. Overall, the impact of assimilating the observations is mixed, with the layer with the greatest benefit being above the surface in the lowest 1000 m above ground level and the variable showing the most consistent benefit being temperature. Varying the nudging configuration demonstrates the sensitivity of the results to details of the assimilation, but does not clearly demonstrate the superiority of a specific configuration.

Prediction of heavy rainfall over Chennai Metropolitan City, Tamil Nadu, India: Impact of microphysical parameterization schemes

This study attempts to investigate the real-time prediction of a heavy rainfall event over the Chennai Metropolitan City, Tamil Nadu, India that occurred on 01 December 2015 using Advanced Research Weather Research and Forecasting (WRF-ARW) model. The study evaluates the impact of six microphysical (Lin, WSM6, Goddard, Thompson, Morrison and WDM6) parameterization schemes of the model on prediction of heavy rainfall event. In addition, model sensitivity has also been evaluated with six Planetary Boundary Layer (PBL) and two Land Surface Model (LSM) schemes. Model forecast was carried out using nested domain and the impact of model horizontal grid resolutions were assessed at 9km, 6km and 3km. Analysis of the synoptic features using National Center for Environmental Prediction Global Forecast System (NCEP-GFS) analysis data revealed strong upper-level divergence and high moisture content at lower level were favorable for the occurrence of heavy rainfall event over the northeast coast of Tamil Nadu. The study signified that forecasted rainfall was more sensitive to the microphysics and PBL schemes compared to the LSM schemes. The model provided better forecast of the heavy rainfall event using the logical combination of Goddard microphysics, YSU PBL and Noah LSM schemes, and it was mostly attributed to timely initiation and development of the convective system. The forecast with different horizontal resolutions using cumulus parameterization indicated that the rainfall prediction was not well represented at 9km and 6km. The forecast with 3km horizontal resolution provided better prediction in terms of timely initiation and development of the event. The study highlights that forecast of heavy rainfall events using a high-resolution mesoscale model with suitable representations of physical parameterization schemes are useful for disaster management and planning to minimize the potential loss of life and property.

Interaction of Sea Breeze and Deep Convection over the Northeastern Adriatic Coast: An Analysis of Sensitivity Experiments Using a High-Resolution Mesoscale Model

This study investigates the sensitivity of a high-resolution mesoscale atmospheric model in the model reproduction of thermally induced local wind (i.e., sea breezes, SB) on the development of deep convection (Cb). The three chosen cases are simulated by the Weather and Research Forecasting (WRF-ARW) model at three (nested) model domains, whereas the area of the interest is Istria (peninsula in the northeastern Adriatic). The sensitivity tests are accomplished by modifying (1) the model setup, (2) the model topography and (3) the sea surface temperature (SST) distribution. The first set of simulations (over the three 1.5-day periods during summer) is conducted by modifying the model setup, i.e., microphysics and the boundary layer parameterizations. The same events are simulated with the modified topography where the mountain heights in Istria are reduced to 30% of their initial height. The SST distribution has two representations in the model: a constant SST field from the ECMWF skin temperature analysis and a varying SST field, which is provided by hourly geostationary satellite data. A comprehensive set of numerical experiments is statistically analyzed through several different approaches (i.e., the standard statistical measures, the spectral method and the image moment analysis). The overall model evaluation of each model setup revealed certain advantages of one model setup over the others. The numerical tests with the modified topography showed the influence of reducing the mountains heights on the pre-thunderstorm characteristics due to: (1) decrease of sensible heat flux and mid-tropospheric moisture and (2) change of slope-SB wind system. They consequently affect the evolution and dimensions of SBs and the features of the thunderstorm itself: timing, location and intensity (weaker storm). The implementation of the varying SST field in the model have an impact on the characteristics and dynamics of the SB and finally on the accuracy of Cb evolution, duration and the intensity. SST variations emphasized the importance of the phase matching in both daytime cycles of SB and Cb due to their extremely strong nonlinear relationship.

Contributions of Moist Convection and Internal Gravity Waves to Building the Atmospheric −5/3 Kinetic Energy Spectra

Abstract With high-resolution mesoscale model simulations, the authors have confirmed a recent study demonstrating that convective systems, triggered in a horizontally homogeneous environment, are able to generate a background mesoscale kinetic energy spectrum with a slope close to −5/3, which is the observed value for the kinetic energy spectrum at mesoscales. This shallow slope can be identified at almost all height levels from the lower troposphere to the lower stratosphere in the simulations, implying a strong connection between different vertical levels. The present study also computes the spectral kinetic energy budget for these simulations to further analyze the processes associated with the creation of the spectrum. The buoyancy production generated by moist convection, while mainly injecting energy in the upper troposphere at small scales, could also contribute at larger scales, possibly as a result of the organization of convective cells into mesoscale convective systems. This latter injected energy is then transported by energy fluxes (due to gravity waves and/or convection) both upward and downward. Nonlinear interactions, associated with the velocity advection term, finally help build the approximate −5/3 slope through upscale and/or downscale propagation at all levels.

Simulation of Storm Surges in the Bay of Bengal Using One-Way Coupling Between NMM-WRF and IITD Storm Surge Model

ABSTRACTThe storm surge associated with severe tropical cyclones (TCs) in the Bay of Bengal (BoB) is a serious concern along the coastal regions of India, Bangladesh, Myanmar, and Sri Lanka. It is one of the most hazardous elements associated with landfalling TCs other than strong winds and heavy precipitation and about 75% of the casualities in this region are attributed to storm surges. Therefore, it is highly essential to predict the storm surges with greater accuracy at least 2 days in advance for effective evacuation. In the present study, an attempt is made to simulate the storm surges associated with severe TCs in the BoB using one-way coupling of the Non-hydrostatic Mesoscale Model core of Weather Research and Forecasting (NMM-WRF) system with the two-dimensional finite-difference storm surge model developed at the Indian Institute of Technology Delhi (IITD). The NMM-WRF model simulated track, pressure drop, and radius of maximum wind are used to calculate the wind-stress through Jelesnianski wind formulation. The results are compared with the observed/estimated values as provided by the operational/meteorological agencies of India, Bangladesh, and Myanmar. This study suggests that using simulated surface meteorological fields of a high-resolution mesoscale model, the storm surge can be predicted at least 2 days in advance of the actual landfall of TCs with reasonable accuracy. This approach will be helpful in providing disastrous storm warning well in advance in a coastal region, which will help with rapid evacuation from the vulnerable coastal region, relocation as well as protection of valuables, disaster mitigation, and coastal zone management.

Comparison of global horizontal irradiance forecasts based on numerical weather prediction models with different spatio‐temporal resolutions

AbstractIn the framework of the IEA SHC Task 46 “Solar Resource Assessment and Forecasting” we compare solar global horizontal irradiance forecasts based on numerical weather predictions for a variety of different models. These include direct model output of several numerical weather prediction models, a rapid update cycle model assimilating satellite derived cloud products as well as radar data, the multi model ensemble prediction system GLAMEPS, and two MOS systems. In order to allow for a transparent and comparable analysis of the different methods we have set up a joint, consistent framework of evaluation. As a basis for the comparisons we have compiled a common data set of hourly measured solar irradiance values for Denmark, Germany, and Switzerland. Local and regional forecasts are analyzed with respect to different properties. In particular we show that spatial and temporal averaging effects have a strong impact on the root mean square error when comparing solar irradiance forecasts of numerical weather prediction models with different output resolutions. Furthermore, we investigate a new approach to evaluate the models' ability to represent and forecast solar irradiance and cloud variability. The benefit of high resolution mesoscale models in this respect is demonstrated. Copyright © 2016 John Wiley & Sons, Ltd.

Role of land state in a high resolution mesoscale model for simulating the Uttarakhand heavy rainfall event over India

In 2013, Indian summer monsoon witnessed a very heavy rainfall event (>30 cm/day) over Uttarakhand in north India, claiming more than 5000 lives and property damage worth approximately 40 billion USD. This event was associated with the interaction of two synoptic systems, i.e., intensified subtropical westerly trough over north India and north-westward moving monsoon depression formed over the Bay of Bengal. The event had occurred over highly variable terrain and land surface characteristics. Although global models predicted the large scale event, they failed to predict realistic location, timing, amount, intensity and distribution of rainfall over the region. The goal of this study is to assess the impact of land state conditions in simulating this severe event using a high resolution mesoscale model. The land conditions such as multi-layer soil moisture and soil temperature fields were generated from High Resolution Land Data Assimilation (HRLDAS) modelling system. Two experiments were conducted namely, (1) CNTL (Control, without land data assimilation) and (2) LDAS, with land data assimilation (i.e., with HRLDAS-based soil moisture and temperature fields) using Weather Research and Forecasting (WRF) modelling system. Initial soil moisture correlation and root mean square error for LDAS is 0.73 and 0.05, whereas for CNTL it is 0.63 and 0.053 respectively, with a stronger heat low in LDAS. The differences in wind and moisture transport in LDAS favoured increased moisture transport from Arabian Sea through a convectively unstable region embedded within two low pressure centers over Arabian Sea and Bay of Bengal. The improvement in rainfall is significantly correlated to the persistent generation of potential vorticity (PV) in LDAS. Further, PV tendency analysis confirmed that the increased generation of PV is due to the enhanced horizontal PV advection component rather than the diabatic heating terms due to modified flow fields. These results suggest that, two different synoptic systems merged by the strong interaction of moving PV columns resulted in the strengthening and further amplification of the system over the region in LDAS. This study highlights the importance of better representation of the land surface fields for improved prediction of localized anomalous weather event over India.

Intense air-sea exchange and heavy rainfall: impact of the northern Adriatic SST

Abstract. Over the northern Adriatic basin, intense air-sea interactions are often associated with heavy precipitation over the mountainous areas surrounding the basin. In this study, a high-resolution mesoscale model is employed to simulate three severe weather events and to evaluate the effect of the sea surface temperature on the intensity and location of heavy rainfall. The sensitivity tests show that the impact of SST varies among the events and it mainly involves the modification of the PBL characteristics and thus the flow dynamics and its interaction with the orography.

High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.