Diagnostic Wind Model Research Articles

The accuracy of pollutant concentration calculations on the microscale using a modified Lagrangian advanced micro-scale model

The influence of data determining the initial wind field in a diagnostic wind model with spatial subdomains on the accuracy of concentrations calculated by Lagrangian Advanced Micro-scale Model was analysed. Calculations were carried out for an idealised built environment. Three input datasets were considered: (I) a single inlet wind profile, (II) a single inlet wind profile with additional parametrisation of wind flow around buildings and (III) full wind tunnel wind data. Results show that: in case (I) it is not possible to properly map the position of the pollutant plume; in case (II) the application of additional parametrisation leads to increased accuracy of the mapping of the position of the pollutant plume, in particular in the close vicinity of the source; in case (III) correct mapping of the shape and location of the plume within the entire area was achieved.

QUIC-URB and QUIC-fire extension to complex terrain: Development of a terrain-following coordinate system

Ensemble-based approaches to prescribed fire planning cannot be supported by CFD-based models like FIRETEC and WFDS because they are too computationally expensive and cannot leverage LES approaches like CAWFE and WRF-SFIRE because too coarse of resolution. QUIC-Fire was developed to fill this gap but it cannot currently address complex terrain, typical for instance of the Western United States. In this paper, we describe the extension of the diagnostic wind model QUIC-URB, the wind engine of QUIC-Fire, to a terrain-following coordinate system. In particular, the paper presents the mathematical derivation of the wind solver leading to a linear system of equations that are solved through the successive over-relaxation method. The model is validated against a standard test used in previous works (the Askervein Hill) and against a new dataset from measurements in the Socorro Mountains, New Mexico. The terrain-following implementation captured the correct phenomenology for the isolated Askervein Hill, with a wind speed up at the top of the hill. The model agreed well with measurements on the upwind side of the peak, but overestimated speed-up on the downwind side of the hill. This is due to the inability of the model to generate flow separation and wake-eddy dynamics. On a common laptop, the divergence-free wind field was obtained in 6 s, making the solver appealing for coupled fire–atmosphere simulations. The Socorro Mountain was highly complex, with many cliff faces, peaks, and valleys. Although the model captures the magnitude and direction of inlet and outlet areas of the domain, it performs rather poorly in the valley region and in the regions near the steep cliffs. Hence, the model shows good agreement with data in areas of open sloped terrain but lacks in areas where flow separation and thermally driven effects may be present (neither effect was addressed in this work). Results highlight that future work should focus on the implementation of parameterizations of wake-eddies, similar to QUIC-URB’s building parameterizations, and on thermodynamic-driven flow.

Statistical post-processing of reanalysis wind speeds at hub heights using a diagnostic wind model and neural networks

Abstract. The correct representation of wind speeds at hub height (e.g., 100 m above ground) is becoming more and more important with respect to the expansion of renewable energy. In this study, a post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8 compared to COSMO-REA6 and considers different vertical atmospheric stabilities. In the second step, a statistical correction is performed using a neural network, as well as a generalized linear model based on different variables of the reanalysis. Although only a few measurements by masts or lidars are available at hub height, an improvement of the wind speed in the root-mean-squared error of almost 30 % can be achieved. A final comparison with radiosonde observations confirms the added value of combining the physical and statistical approaches in post-processing the wind speed.

Improvement of a Diagnostic Urban Wind Model for Flow Fields around a Single Rectangular Obstacle in Micrometeorology Simulation

In general, computational fluid dynamics (CFD) models incur high computational costs when dealing with realistic and complicated flows. In contrast, the mass-consistent flow (MASCON) field model provides a three-dimensional flow field at reasonable computational cost. Unfortunately, some weaknesses in simulating the flow of the wake zone exist because the momentum equations are not considered in the MASCON field model. In the present study, a new set of improved algebraic models to provide initial flow fields for the MASCON field model are proposed to overcome these weaknesses by considering the effect of momentum diffusion in the wake zone. Specifically, these models for the wake region are developed on the basis of the wake models used in well-recognized Gaussian plume models, ADMS-build and PRIME. The MASCON fields provided by the new set of wake zone models are evaluated against wind-tunnel experimental data on flow around a wall-mounted rectangular obstacle. Each MASCON field is compared with the experimental results, focusing on the positions of the vortex core and saddle points of the vortex formed in the near-wake zone and the vertical velocity distribution in the far-wake zone. The set of wake zone models developed in the present study better reproduce the experimental results in both the wake zones compared to the previously proposed models. In particular, the complicated recirculation flow which is formed by the union of the sidewall recirculation zone and the near-wake zone is reproduced by the present wake zone model using the PRIME model that includes the parameterization of the sidewall recirculation zones.

Testing gas dispersion modelling: A case study at La Soufrière volcano (Guadeloupe, Lesser Antilles)

Volcanic gas dispersal can be a serious threat to people living near active volcanoes since it can have short- and long-term effects on human health, and severely damage crops and agricultural land. In recent decades, reliable computational models have significantly advanced, and now they may represent a valuable tool to make quantitative and testable predictions, supporting gas dispersal forecasting and hazard assessments for public safety. Before applying a specific modelling tool into hazard quantification, its calibration and its sensitivity to initial and boundary conditions should be carefully tested against available data, in order to produce unbiased hazard quantifications. In this study, we provided a number of prototypical tests aimed to validate the modelling of gas dispersal from a hazard perspective. The tests were carried out at La Soufrière de Guadeloupe volcano, one of the most active gas emitters in the Lesser Antilles.La Soufrière de Guadeloupe has shown quasi-permanent degassing of a low-temperature hydrothermal nature since its last magmatic eruption in 1530 CE, when the current dome was emplaced. We focused on the distribution of CO2 and H2S discharged from the three main present-day fumarolic sources at the summit, using the measurements of continuous gas concentrations collected in the period March–April 2017. We developed a new probabilistic implementation of the Eulerian code DISGAS-2.0 for passive gas dispersion coupled with the mass-consistent Diagnostic Wind Model, using local wind measurements and atmospheric stability information from a local meteorological station and ERA5 reanalysis data. We found that model outputs were not significantly affected by the type of wind data but rather upon the relative positions of fumaroles and measurement stations. Our results reproduced the statistical variability in daily averages of observed data over the investigated period within acceptable ranges, indicating the potential usefulness of DISGAS-2.0 as a tool for reproducing the observed fumarolic degassing and for quantifying gas hazard at La Soufrière. The adopted testing procedure allows for an aware application of simulation tools for quantifying the hazard, and thus we think that this kind of testing should actually be the first logical step to be taken when applying a simulator to assess (gas) hazard in any other volcanic contexts.

JRII special sonic anemometer study: A first comparison of building wakes measurements with different levels of numerical modelling approaches

After the summer 2015 chlorine releases during the Jack Rabbit II field experiment at Dugway Proving Ground, a special sonic anemometer study was carried out in October 2015 and March 2016. The goal was to provide documented wind fields in similar conditions to the actual releases and to study building wakes without risk for the instruments. This study included 30 sonic anemometers that were placed around these “buildings” to best capture the recirculating regions. Building wakes are important for the dispersion of pollutants in cities and around industrial plants and therefore we have selected two time periods during March 2016, in order to present model to model and model to data comparisons. There are four models involved in the comparison ranging from a diagnostic wind model with wake parameterization and Lagrangian particle dispersion and a CFD model with a k-ε turbulence closure and solving the Eulerian dispersion equation. Detailed comparisons are carried out for the wind speed and for the turbulence level. Six virtual releases are added in the simulations to investigate how the wake resolving method can affect the dispersion. For the wind speed comparison, the models give fair to good comparison with the sonic measurements. However, for the turbulence comparison the results clearly show a need for improvement, even for the k-ε closure, which still provides the best results. For the concentrations, for which there is no corresponding measurements, we find that the simulations are much closer than for the turbulence comparison.

Modeling Wind Direction Distributions Using a Diagnostic Model in the Context of Probabilistic Fire Spread Prediction

With emerging research into the dynamics of extreme fire behaviour, it is increasingly important for wind models used in operational fire prediction to accurately capture areas of complex flow across rugged terrain. Additionally, the emergence of ensemble and stochastic modelling frameworks has led to the discussion of uncertainty in fire prediction. To capture the uncertainty of modelled fire outputs, it is necessary to recast uncertain inputs in probabilistic terms. WindNinja is the diagnostic wind model currently applied within a number of operational fire prediction frameworks across the world. For computational efficiency, allowing for real-time or faster than real-time prediction, the physical equations governing wind flow across complex terrain are often simplified. The model has a number of well documented limitations, for instance, it is known to perform least well on leeward slopes. This study first aims to understand these limitations in a probabilistic context by comparing individual deterministic predictions to observed distributions of wind direction. Secondly, a novel application of the deterministic WindNinja model is presented and shown to enable prediction of wind direction distributions that capture some of the variability of complex wind flow. Recasting wind fields in terms of probability distributions enables better understanding of variability across the landscape, and provides the probabilistic information required to capture uncertainty through ensemble or stochastic fire modelling. The comparisons detailed in this study indicate the potential for WindNinja to predict multimodal wind direction distributions that represent complex wind behaviours, including recirculation regions on leeward slopes. However, the limitations of using deterministic models within probabilistic frameworks are also highlighted. To enhance fire prediction and better understand uncertainty, it is recommended that statistical approaches also be developed to complement existing physics-based deterministic wind models.

Volcanogenic SO2, a natural pollutant: Measurements, modeling and hazard assessment at Vulcano Island (Aeolian Archipelago, Italy)

Sulfur dioxide (SO2) is a major component of magmatic gas discharges. Once emitted in the atmosphere it can affect the air and land environment at different spatial and temporal scales, with harmful effects on human health and plant communities.We used a dense dataset of continuous SO2 flux and meteorological measurements collected at Vulcano over an 8-year period spanning from May 2008 to February 2016 to model air SO2 concentrations over the island. To this end, we adopted the DISGAS (DISpersion of GAS) numerical code coupled with the Diagnostic Wind Model (DWM). SO2 concentrations in air were determined for three different SO2 emission rates: a reference SO2 flux of ∼18 t/d (the median of more than 800 measurements), an enhanced SO2 flux of 40 t/d (average of all measurements plus 1 σ), and a maximum SO2 flux of 106 t/d (maximum value measured in the investigated period). Maximum SO2 concentrations in air were estimated at the crater, near the high-T fumarole field that is the source of the gas, and ranged from 2000 ppb to ∼24,000 ppb for the reference flux, from 2000 ppb to 51,000 ppb for the enhanced flux and from 5000 ppb to 136,000 ppb for the maximum flux, with peak values in limited areas at the bottom of the crater. These concentrations pose a hazard for people visiting the crater, for sensitive individuals in particular. Based on estimated SO2 concentrations in air, we also consider the phytotoxic effects of SO2 on local vegetation.

Enhanced air dispersion modelling at a typical Chinese nuclear power plant site: Coupling RIMPUFF with two advanced diagnostic wind models

An enhanced air dispersion modelling scheme is proposed to cope with the building layout and complex terrain of a typical Chinese nuclear power plant (NPP) site. In this modelling, the California Meteorological Model (CALMET) and the Stationary Wind Fit and Turbulence (SWIFT) are coupled with the Risø Mesoscale PUFF model (RIMPUFF) for refined wind field calculation. The near-field diffusion coefficient correction scheme of the Atmospheric Relative Concentrations in the Building Wakes Computer Code (ARCON96) is adopted to characterize dispersion in building arrays. The proposed method is evaluated by a wind tunnel experiment that replicates the typical Chinese NPP site. For both wind speed/direction and air concentration, the enhanced modelling predictions agree well with the observations. The fraction of the predictions within a factor of 2 and 5 of observations exceeds 55% and 82% respectively in the building area and the complex terrain area. This demonstrates the feasibility of the new enhanced modelling for typical Chinese NPP sites.

Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM–LUR model using 93 biweekly observations of NOx at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NOx. We built a linear regression model for NOx, using a stepwise forward selection of covariates. The resulting model predicted observed NOx (R2=0.89) better than the DM without covariates (R2=0.68, P-interaction <0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NOx levels (routine urban NOx, less routine rural NOx). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data.

Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

Atmospheric dispersion of natural carbon dioxide emissions on Vulcano Island, Italy

AbstractLa Fossa quiescent volcano and its surrounding area on the Island of Vulcano (Italy) are characterized by intensive, persistent degassing through both fumaroles and diffuse soil emissions. Periodic degassing crises occur, with marked increase in temperature and steam and gas output (mostly CO2) from crater fumaroles and in CO2 soil diffuse emission from the crater area as well as from the volcano flanks and base. The gas hazard of the most inhabited part of the island, Vulcano Porto, was investigated by simulating the CO2 dispersion in the atmosphere under different wind conditions. The DISGAS (DISpersion of GAS) code, an Eulerian model based on advection‐diffusion equations, was used together with the mass‐consistent Diagnostic Wind Model. Numerical simulations were validated by measurements of air CO2 concentration inside the village and along the crater's rim by means of a Soil CO2 Automatic Station and a Tunable Diode Laser device. The results show that in the village of Vulcano Porto, the CO2 air concentration is mostly due to local soil degassing, while the contribution from the crater gas emission is negligible at the breathing height for humans and always remains well below the lowest indoor CO2 concentration threshold recommended by the health authorities (1000 ppm). Outdoor excess CO2 maxima up to 200 ppm above local background CO2 air concentration are estimated in the center of the village and up to 100 ppm in other zones. However, in some ground excavations or in basements the health code threshold can be exceeded. In the crater area, because of the combined effect of fumaroles and diffuse soil emissions, CO2 air concentrations can reach 5000–7000 ppm in low‐wind conditions and pose a health hazard for visitors.

変分法による風速場推定モデルの区画内気流性状解析への応用 - 平行平板間の立方体周りの流れ場における検証

変分法による風速場推定モデルの区画内気流性状解析への応用 - 平行平板間の立方体周りの流れ場における検証

Diagnostic Wind Model Initialization over Complex Terrain Using the Airborne Doppler Wind Lidar Data~!2010-03-01~!2010-04-29~!2010-08-07~!

An initialization method using airborne Doppler wind lidar data was developed and evaluated for a mass- consistent diagnostic wind model over complex terrain. The wind profiles were retrieved from the airborne lidar using a conical scanning scheme and a signal processing algorithm specifically designed for the airborne lidar system. An objective data analysis method in complex terrain was then applied to those wind profiles to produce a three-dimensional wind field for model initialization. The model results using the lidar data initialization were compared with independent surface weather observational data and profiles from a microwave radar wind profiler. The model was previously run for a small domain with simple terrain where comparisons with a surface observation array showed that the model performed well in a strong wind condition. For the more complex terrain in the Salinas valley, the model evaluation with a limited number of observations indicated that the diagnostic wind model with airborne Doppler lidar data also produced a reasonably good wind field in moderate to strong wind conditions. However, caution must be stressed for weak wind conditions in which the flow is thermally driven as the mass-consistent diagnostic wind model is not equipped to handle such cases. The effect of the lidar wind profile density over a simulation domain was also investigated for practical applications. The results indicate that about a half dozen lidar wind profiles would be adequate for a 20 by 20 km complex terrain domain with fairly uniform and moderate wind conditions.

An Evaluation of a Diagnostic Wind Model (CALMET)

Abstract A U.S. Environmental Protection Agency (EPA)-approved diagnostic wind model [California Meteorological Model (CALMET)] was evaluated during a typical lake-breeze event under fair weather conditions in the Chicago region. The authors focused on the performance of CALMET in terms of simulating winds that were highly variable in space and time. The reference winds were generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) assimilating system, with which CALMET results were compared. Statistical evaluations were conducted to quantify overall model differences in wind speed and direction over the domain. Below 850 m above the surface, relative differences in (layer averaged) wind speed were about 25%–40% during the simulation period; wind direction differences generally ranged from 6° to 20°. Above 850 m, the differences became larger because of the limited number of upper-air stations near the studied domain. Analyses implied that model differences were dependent on time because of time-dependent spatial variability in winds. Trajectory analyses were made to examine the likely spatial dependence of CALMET deviations from the reference winds within the domain. These analyses suggest that the quality of CALMET winds in local areas depended on their proximity to the lake-breeze front position. Large deviations usually occurred near the front area, where observations cannot resolve the spatial variability of wind, or in the fringe of the domain, where observations are lacking. Results simulated using different datasets and model options were also compared. Differences between CALMET and the reference winds tended to be reduced with data sampled from more stations or from more uniformly distributed stations. Suggestions are offered for further improving or interpreting CALMET results under complex wind conditions in the Chicago region, which may also apply to other regions.

Parameterisation of the Planetary Boundary Layer for Diagnostic Wind Models

The planetary boundary-layer (PBL) parameterization is a key issue for the definition of initial wind flow fields in diagnostic models. However, PBL theories usually treat separately stable, neutral, and convective stability conditions, so that their implementation in diagnostic wind models is not straightforward. In the present paper, an attempt is made to adopt a comprehensive PBL parameterisation, covering stable/neutral and unstable atmospheric conditions, which appears suitable to diagnostic models. This parameterisation is implemented into our diagnostic mass-consistent code. A validation of the consistency between the implemented PBL parameterisations has been checked through an analysis of the sensitivity of the vertical wind profiles to atmospheric stability.

Atmospheric Boundary Layers: Nature, Theory and Applications to Environmental Modelling and Security

Atmospheric boundary layers: nature, theory and applications to environmental modelling and security.- Some modern features of boundary-layer meteorology: a birthday tribute for Sergej Zilitinkevich.- Energy- and flux-budget (EFB) turbulence closure model for stably stratified flows. Part I: steady-state, homogeneous regimes.- Similarity theory and calculation of turbulent fluxes at the for the stably stratified atmospheric boundary layer.- Application of a large-eddy simulation database to optimisation of first-order closures for neutral and stably stratified boundary layers.- The effect of mountainous topography on moisture exchange between the surface and the free atmosphere.- The influence of nonstationarity on the turbulent flux-gradient relationship for stable stratification.- Chemical perturbations in the planetary boundary layer and their relevance for chemistry transport modelling.- Theoretical considerations of meandering winds in simplified conditions.- Aerodynamic roughness of the sea at high winds.- Modelling dust distributions in the atmospheric boundary layer on Mars.- On the turbulent Prandtl number in the stable atmospheric boundary layer.- Micrometeorological observations of a microburst in southern Finland.- Role of land-surface temperature feedback on model performance for the stable boundary layer.- Katabatic flow with Coriolis effect and gradually varying eddy diffusivity.- Parameterisation of the planetary boundary layer for diagnostic wind models.

Evaluating a Hybrid Prognostic–Diagnostic Model That Improves Wind Forecast Resolution in Complex Coastal Topography

Abstract The results from a hybrid approach that combines the forecasts of a mesoscale model with a diagnostic wind model to produce high-resolution wind forecasts in complex coastal orography are evaluated. The simple diagnostic wind model [Winds on Critical Streamline Surfaces (WOCSS)] was driven with forecasts (on a 9-km grid) from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) to obtain detailed near-surface wind forecasts with 3-km horizontal spacing. Forecasts were produced by this hybrid model for four cold-season cases—two frontal and two nonfrontal—over the central California coastal region. They were compared with 3-km forecasts from the innermost COAMPS full physics model nest and with winds observed at 35 surface sites scattered throughout the study domain. The evaluation sought to determine the conditions for which the hybrid approach performs well and those for which it does not. The performance (in terms of bias and root-mean-square error) was evaluated 1) when there were and were not fronts and 2) for the early (6–18 h) and late (21–36 h) periods of the mesoscale model forecasts. The geographic distribution of performance was also examined to see if forecasts were affected by mountains and oceans. The hybrid approach performed best during stable, nonfrontal conditions. There were no clearly defined geographic effects on hybrid performance. The computation requirements of the full physics mesoscale model nested down to 3 km are substantially greater than those of the hybrid approach. Suggestions are given for further improvements.

An evaluation of three diagnostic wind models (CALMET, MCSCIPUF, and SWIFT) with wind data from the Dipole Pride 26 field experiments

AbstractThis paper describes the evaluation of three diagnostic wind models by direct comparison with wind field data. The models are the California Meteorological Model (CALMET), the Mass Consistent model (MCSCIPUF) associated with the Second Order Closure Integrated Puff (SCIPUFF) transport/dispersion model, and the Stationary Wind Field and Turbulence (SWIFT) model. The evaluation follows previous works by Chang, Franzese &amp; Hanna, who compared the same three models, and by Bradley &amp; Mazzola who evaluated SWIFT coupled with SCIPUFF. As with SWIFT, MCSCIPUF is incorporated in the Hazard Prediction and Assessment Capability (HPAC), while CALMET is linked with the California Puff model (CALPUFF), another transport and dispersion model. The Dipole Pride 26 (DP26) experiments, performed at the US Department of Energy (DOE) Nevada Test Site, are used as the source of the wind data. They provide a comprehensive set of meteorological data with wide‐ranging atmospheric stability conditions over a complex terrain. Model calculations were compared with measured data in two phases. The first phase uses complete sets of data from eight locations (the 8M phase) as model inputs, and thus tests the ability of the models to reproduce input conditions. In the second phase, five of the measured wind sites are withheld from input, and instead used for validation of model calculations (the 3M phase). In the first phase, the errors were found (with some exceptions) to be quite small. In the second phase, mean absolute errors were found to be of the order of 1ms−1 and 30°, with only small differences among models in terms of performance. Copyright © 2005 Royal Meteorological Society

In search of mountain sites suitable for wind farms in the Mediterranean area using a diagnostic wind model

AbstractA mesoscale diagnostic wind model, based on mass conservation, is utilised to develop a wind climatology for the southern Appennines in Italy, with the aim of identifying areas suitable for wind energy production plants. The model includes the kinematic effects of the high ground, anabatic and katabatic flows along mountain slopes as well as blocking effects. The climatological analysis carried out appears quite effective in selecting favourable areas where a more detailed investigation based on boundary layer models and observational field studies may lead to the final specification of locations suitable for mountain wind farms. Copyright © 2001 Royal Meteorological Society