Flow In Complex Terrain Research Articles

Field assessments on the impact of CO2 concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests

Abstract. CO2 storage (Fs) is the cumulation or depletion in CO2 amount over a period in an ecosystem. Along with the eddy covariance flux and wind-stream advection of CO2, it is a major term in the net ecosystem CO2 exchange (NEE) equation. The CO2 storage dominates the NEE equation under a stable atmospheric stratification when the equation is used for forest ecosystems over complex terrains. However, estimating Fs remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows that lead to tremendous difficulties in capturing the true trend of CO2 changes for use in storage estimation from eddy covariance along with atmospheric profile techniques. Using measurements from Qingyuan Ker Towers equipped with NEE instrument systems separately covering mixed broad-leaved, oak, and larch forest towers in a mountain watershed, this study investigates gust periods and CO2 fluctuation magnitudes and examines their impact on Fs estimation in relation to the terrain complexity index (TCI). The gusts induce CO2 fluctuations for numerous periods of 1 to 10 min over 2 h. Diurnal, seasonal, and spatial differences (P < 0.01) in the maximum amplitude of CO2 fluctuations (Am) range from 1.6 to 136.7 ppm, and these differences range from 140 to 170 s in a period (Pm) at the same significance level. Am and Pm are significantly correlated to the magnitude of and random error in Fs with diurnal and seasonal differences. These correlations decrease as CO2 averaging time windows become longer. To minimize the uncertainties in Fs, a constant [CO2] averaging time window for the Fs estimates is not ideal. Dynamic averaging time windows and a decision-level fusion model can reduce the potential underestimation of Fs by 29 %–33 % for temperate forests in complex terrain. In our study, the relative contribution of Fs to the 30 min NEE observations ranged from 17 % to 82 % depending on turbulent mixing and the TCI. The study's approach is notable as it incorporates the TCI and utilizes three flux towers for replication, making the findings relevant to similar regions with a single tower.

Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions

Abstract. Inverse modeling is a widely used top-down method to infer greenhouse gas (GHG) emissions and their spatial distribution based on atmospheric observations. The errors associated with inverse modeling have multiple sources, such as observations and a priori emission estimates, but they are often dominated by the transport model error. Here, we utilize the Lagrangian particle dispersion model (LPDM) FLEXPART (FLEXible PARTicle Dispersion Model), driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main sources of errors in LPDMs are the turbulence diffusion parameterization and the meteorological fields. The latter are outputs of an Eulerian model. Recently, we introduced an improved parameterization scheme of the turbulence diffusion in FLEXPART, which significantly improves FLEXPART-COSMO simulations at 1 km resolution. We exploit F-gas measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens), and we conduct both high-resolution (1 km) and low-resolution (7 km) FLEXPART transport simulations that are then used in a Bayesian analytical inversion to estimate spatial emission distributions. Our results for four F-gases (HFC-134a, HFC-125, HFC-32, SF6) indicate that both high-resolution inversions and a dense measurement network significantly improve the ability to estimate spatial distribution of the emissions. Furthermore, the total emission estimates from the high-resolution inversions (351 ± 44 Mg yr−1 for HFC-134a, 101 ± 21 Mg yr−1 for HFC-125, 50 ± 8 Mg yr−1 for HFC-32, 9.0 ± 1.1 Mg yr−1 for SF6) are significantly higher compared to the low-resolution inversions (20 %–40 % increase) and result in total a posteriori emission estimates that are closer to national inventory values as reported to the UNFCCC (10 %–20 % difference between high-resolution inversion estimates and inventory values compared to 30 %–40 % difference between the low-resolution inversion estimates and inventory values). Specifically, we attribute these improvements to a better representation of the atmospheric flow in complex terrain in the high-resolution model, partly induced by the more realistic topography. We further conduct numerous sensitivity inversions, varying different parameters and variables of our Bayesian inversion framework to explore the whole range of uncertainty in the inversion errors (e.g., inversion grid, spatial distribution of a priori emissions, covariance parameters like baseline uncertainty and spatial correlation length, temporal resolution of the assimilated observations, observation network, seasonality of emissions). From the abovementioned parameters, we find that the uncertainty of the mole fraction baseline and the spatial distribution of the a priori emissions have the largest impact on the a posteriori total emission estimates and their spatial distribution. This study is a step towards mitigating the errors associated with the transport models and better characterizing the uncertainty inherent in the inversion error. Improvements in the latter will facilitate the validation and standardization of national GHG emission inventories and support policymakers.

Laboratory investigation of nominally two-dimensional anabatic flow on symmetric double slopes

We investigated the dynamics of highly turbulent thermally driven anabatic (upslope) flow on a physical model inside a large water tank using particle image velocimetry (PIV) and a thermocouple grid. The results showed that the flow exhibited pronounced variations in velocity and temperature and, importantly, could not be accurately modeled as a two-dimensional quasi-steady flow. Five significant findings are presented to underscore the three-dimensional nature of the flow, namely, the B-shaped mean velocity profiles, B-shaped turbulent flux profiles, synthetic streaks that revealed particles flowing perpendicular to the laser sheet, average vorticity maps revealing helical structure splitting, and identified vortices shooting away from the boundary toward the apex plume. Collectively, these findings offer novel insights into the flow behavior patterns of thermally driven complex terrain flows, which influence local weather and microclimates and are responsible for scalar transport, e.g., pollution.

Multi-scale modeling of a wind turbine wake in complex terrain

Comparing numerical simulations of wind turbine wakes in neutral atmospheric boundary layer (ABL) flow over complex terrains with full-scale experiments is not always straightforward. Pure neutral ABL conditions are rarely found in the atmosphere as the characteristics of the ABL change during the diurnal cycle. This study presents some insights into how a single wind turbine (WT) and its wake behave under near-neutral ABL conditions in complex terrain. The Perdigão Valley in Portugal was chosen as the test case as it is an excellent case study of three-dimensional flow in complex terrain to validate numerical simulations of WT wake with experimental data due to the availability of extensively deployed remote sensing equipment (e.g., German Aerospace Center (DLR) and Technical University of Denmark (DTU) multi-Doppler lidars). The Weather Research and Forecasting (WRF) [1] model is utilized in large-eddy simulation (LES) mode in order to simulate the period of interest with a multi-scale modeling approach. Five nested domains, with the finest domain having a spatial resolution of 5 m, are used to dynamically downscale mesoscale flow features to microscale. A generalized actuator line (GAL) WT parameterization is used to model the wind turbine-flow interaction. WRF-LES-GAL (hereinafter referred to as WLG) results compare quite well with the experimental data obtained from lidars and meteorological mast, with minor biases between the simulated and observed data. Due to insufficient buoyancy generation from the WRF-LES model, the simulated track wake was found to have lower vertical deflection compared to the lidar data; hence, no recirculation zone is observed in the valley. Overall, the WLG model is able to reproduce the wake characteristics observed on the first ridge top into the valley, as well as the power and thrust generation, and can be used for further analysis of other stability conditions.

“Gray Zone” Simulations Using a Three-Dimensional Planetary Boundary Layer Parameterization in the Weather Research and Forecasting Model

Abstract Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δx) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δx ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional mesoscale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. Significance Statement The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface–atmosphere interaction studies.

Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations

AbstractGlobal atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real‐geography coarse‐grid model (FV3GFS with a 200 km grid) to be closer to those of a 40‐day reference simulation using X‐SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea‐surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse‐grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine‐grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time‐mean precipitation patterns are improved 30% by applying the ML correction. Adding machine‐learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time‐mean upper tropospheric temperature and zonal wind patterns thereafter.

Distinguishing Time Scales of Katabatic Flow in Complex Terrain

To examine spatial and temporal scales of katabatic flow, a distributed temperature sensing (DTS) optical fiber was deployed 2 km down a mild slope irregularly interrupted by small-scale drainage features as part of the Mountain Terrain Atmospheric Modeling and Observation (MATERHORN) experiment conducted at the U.S. Army Dugway Proving Ground, Utah. The fiber was suspended at two heights near the surface, enabling measurement of variations in lapse rate near the surface at meter-scale spatial resolution with 1-min temporal resolution. Experimental results derived from the DTS and tower-mounted instrumentation indicate that airflow through small-scale drainage features regulated the local cooling rate whereas topographic slope and distance along the drainage strongly influenced the larger-scale cooling rate. Empirical results indicate that local cooling rate decays exponentially after local sunset and basin-wide cooling rate decreases linearly with time. The difference in the functional form for cooling rate between local and basin-wide scales suggests that small-scale features have faster timescales that manifests most strongly shortly after local sunset. More generally, partitioning drainage flow by scale provides insight and a methodology for improved understanding of drainage flow in complex terrain.

Driving Mechanisms of Double-Nosed Low-Level Jets during MATERHORN Experiment

AbstractIn the realm of boundary layer flows in complex terrain, low-level jets (LLJs) have received considerable attention, although little literature is available for double-nosed LLJs that remain not well understood. To this end, we use the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) dataset to demonstrate that double-nosed LLJs developing within the planetary boundary layer (PBL) are common during stable nocturnal conditions and present two possible mechanisms responsible for their formation. It is observed that the onset of a double-nosed LLJ is associated with a temporary shape modification of an already-established LLJ. The characteristics of these double-nosed LLJs are described using a refined version of identification criteria proposed in the literature, and their formation is classified in terms of two driving mechanisms. The wind-driven mechanism encompasses cases where the two noses are associated with different air masses flowing one on top of the other. The wave-driven mechanism involves the vertical momentum transport by an inertial–gravity wave to generate the second nose. The wave-driven mechanism is corroborated by the analysis of nocturnal double-nosed LLJs, where inertial–gravity waves are generated close to the ground by a sudden flow perturbation.

Role of the Rheological Parameters in Debris Flow Modelling

Nowadays, the debris flow model has become an essential part of risk analysis and impact engineering. Coupled with field observations and historical records, these models have proven powerful tools to understand the behaviour of debris flow in complex terrain. However, their application poses several new challenges to scholars and engineers. A detailed understanding of the debris flow phenomena requires a sound knowledge of the shallow water equation and rheological model used to simulate the debris flow hazard. In this study, important rheological models used to analyse the debris flow process and their limitations have been highlighted. Furthermore, the suitability of the Voellmy-Salm rheological model has been studied for 2D pyroclastic flow taking different combinations of the coefficient of friction namely coulomb friction coefficient \\i,and turbulent coefficient of friction £ using IMEX SFLOW 2D dynamic continuum model. It was found that velocity and runout distances are significantly influenced by the variation of the coefficient of the turbulent friction © at a large scale. It is then concluded that the identification of a suitable rheological model is necessary to simulate the precise behaviour of complex and heterogeneous debris flow

GANs enabled super-resolution reconstruction of wind field

Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this paper, we demonstrate a novel approach to address this issue through a combination of fast coarse scale physics based simulator and a family of advanced machine learning algorithm called the Generative Adversarial Networks. The physics-based simulator generates a coarse wind field in a real wind farm and then ESRGANs enhance the result to a much finer resolution. The method outperforms state of the art bicubic interpolation methods commonly utilized for this purpose.

Preface: Special issue on the MATERHORN program and complex terrain flows

Preface: Special issue on the MATERHORN program and complex terrain flows

A variational multiscale framework for atmospheric turbulent flows over complex environmental terrains

A residual-based variational multi-scale (VMS) modeling framework is applied to simulate atmospheric flow over complex environmental terrains. The VMS framework is verified and validated using two test cases using linear finite element (FEM) and quadratic non-uniform rational B-spline (NURBS) discretizations. First, the flow over the 3D, axisymmetric Gaussian hill (normally distributed surface) is used to compare the results with FEM and NURBS discretization. Next, the actual terrain of the Bolund hill is used to demonstrate the efficacy of the framework. Good agreement with published data, coming from simulations and field measurements, is achieved, with the NURBS discretization showing much better per-degree-of-freedom accuracy compared with linear FEM. The paper includes a comprehensive review of experimental and numerical methods, and the corresponding challenges, for complex-terrain flows, which provides a proper context for the developments presented in this work.

Analysis of flow in complex terrain using multi-Doppler lidar retrievals

Abstract. Strategically placed Doppler lidars (DLs) offer insights into flow processes that are not observable with meteorological towers. For this study we use intersecting range height indicator (RHI) scans of scanning DLs to create four virtual towers. The measurements were performed during the Perdigão experiment, which set out to study atmospheric flows in complex terrain and to collect a high-quality dataset for the validation of meso- and microscale models. Here we focus on a period of 6 weeks from 1 May 2017 through 15 June 2017. During this Intensive Observation Period (IOP) data of six intersecting RHI scans are used to calculate wind speeds at four virtual towers located along the valley at Perdigão with a temporal resolution of 15 min. While meteorological towers were only up to 100 m tall, the virtual towers cover heights from 50 to 600 m above the valley floor. Thus, they give additional insights into the complex interactions between the flow inside the valley and higher up across the ridges. Along with the wind speed and direction, uncertainties of the virtual-tower retrieval were analyzed. A case study of a nighttime stable boundary layer flow with wave features in the valley is presented to illustrate the usefulness of the virtual towers in analyzing the spatially complex flow over the ridges during the Perdigão campaign. This study shows that, despite having uncoordinated scans, the retrieved virtual towers add value in observing flow in and above the valley. Additionally, the results show the virtual towers can more accurately capture the flow in areas where the assumptions for more traditional DL scan strategies break down.

Similarity functions and a new [formula omitted] closure for predicting stratified atmospheric surface layer flows in complex terrain

Most of the k−ε closures for modeling stratified surface layer are derived from the classical similarity functions and might fail in complex terrain due to limitations of the classical similarity functions. Despite the classical similarity functions to limit the flux Richardson number Rf, we present new similarity functions estimated from field measurement in full range of Rf and propose a k−ε model using the new similarity functions to improve predictions of stratified surface layer flows in complex terrain. Measurements show that the classical similarity functions are partially valid in complex terrain and the wind shear under strongly stable conditions is constrained at large Rf. According to numerical studies in two areas of complex terrain, models using the classical similarity functions and the new similarity functions both present good predictions of convective airflows in complex terrain. The new similarity functions are shown to significantly improve the k−ε model in predicting stably stratified airflows in complex terrain by constraining the wind shear at large Rf, while the classical similarity functions without limiting the wind shear lead to significantly misestimating the wind speedup factor under stable conditions. Using the proposed model to predict flows in wind farm could benefit wind resource estimation and wind power forecasting.

A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Layout optimization of wind farms constitutes an important and challenging task in complex terrain. This is especially due to the complex interactions of the boundary layer flows in complex terrain and wind turbine wakes, which renders wake modelling in complex terrain difficult. This study tackles this challenge with a new engineering wake model, which is developed by superposing a Gaussian shape wake model on top of the background flow field, assuming that the centerlines of wind turbine wakes follow the streamlines of the background flow field. The model is found to predict wind turbine wakes in complex terrain with good accuracy and at the same time it is computationally cheap to run for optimization applications. Comparisons with high fidelity simulations and field measurements for a real wind farm with 25 turbines in complex terrain demonstrate its effectiveness. A systematic comparison of eight optimization algorithms, which includes two gradient-based and six gradient-free algorithms, is also carried out for the layout optimization problem in complex terrain. To accelerate the optimization process, a double-stage approach is proposed, which optimizes the objective function first neglecting wake effects and then, in the second stage, including them. While all the tested optimization algorithms can improve the original wind farm layout, random search, local search, and pattern search are found to be the top three algorithms in terms of optimization results and computational cost.

The Hydrometeorology Testbed–West Legacy Observing Network: Supporting Research to Applications for Atmospheric Rivers and Beyond

An observing network has been established along the United States west coast that provides up to 20 years of observations to support early warning, preparedness and studies of atmospheric rivers (ARs). The Hydrometeorology Testbed–West Legacy Observing Network, a suite of upper air and surface observing instruments, is now an official National Oceanic and Atmospheric Administration (NOAA) observing system with real-time data access provided via publicly available websites. This regional network of wind profiling radars and co-located instruments also provides observations of boundary layer processes such as complex-terrain flows that are not well depicted in the current operational rawindsonde and radar networks, satellites, or in high-resolution models. Furthermore, wind profiling radars have been deployed ephemerally for projects or campaigns in other areas, some with long records of observations. Current research uses of the observing system data are described as well as experimental products and services being transitioned from research to operations and applications. We then explore other ways in which this network and data library provide valuable resources for the community beyond ARs, including evaluation of high-resolution numerical weather prediction models and diagnosis of systematic model errors. Other applications include studies of gap flows and other terrain-influenced processes, snow level, air quality, winds for renewable energy and the predictability of cloudiness for solar energy industry.

Comparison of CFD Simulation to UAS Measurements for Wind Flows in Complex Terrain: Application to the WINSENT Test Site

This investigation presents a modelling strategy for wind-energy studies in complex terrains using computational fluid dynamics (CFD). A model, based on an unsteady Reynolds Averaged Navier-Stokes (URANS) approach with a modified version of the standard k-ε model, is applied. A validation study based on the Leipzig experiment shows the ability of the model to simulate atmospheric boundary layer characteristics such as the Coriolis force and shallow boundary layer. By combining the results of the model and a design of experiments (DoE) method, we could determine the degree to which the slope, the leaf area index, and the forest height of an escarpment have an effect on the horizontal velocity, the flow inclination angle, and the turbulent kinetic energy at critical positions. The DoE study shows that the primary contributor at a turbine-relevant height is the slope of the escarpment. In the second step, the method is extended to the WINSENT test site. The model is compared with measurements from an unmanned aircraft system (UAS). We show the potential of the methodology and the satisfactory results of our model in depicting some interesting flow features. The results indicate that the wakes with high turbulence levels downstream of the escarpment are likely to impact the rotor blade of future wind turbines.

Simulation of stably stratified flow in complex terrain: flow structures and dividing streamline

The advanced research version of the weather research and forecasting model was employed to simulate Intense Operational Periods of the field campaigns of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program with a 0.5 km horizontal resolution. The focus was on synoptically dominated stably stratified periods, with the mean flow approaching a strongly non-symmetrical rugged topography—the Granite Mountain of the US Army Dugway Proving Ground, Utah. The model was validated against a comprehensive set of MATERHORN data. The special in-house developed software enabled calculations of energetics and pressure anomalies along individual streamlines and tracing of spatial trajectories of streamlines. Owing to complexities of natural flows, for example, the directional shear (skewed vertical velocity profiles) and irregular topographic shape, the flow patterns depend on multiple parameters, although in idealized cases the flow is described by a single parameter (Froude number or a variant). Streamlines at different altitudes of a given location diverged rapidly, making it difficult to study the dividing streamline concept. The new software allowed identification of the dividing streamline passing over the highest crest (summit) of the mountains and its trajectory. The upstream height of the dividing streamline did not follow the well-known Sheppard’s formula. Three cases of flow patterns are presented, identified based on the presence of lee waves, flow separation, horizontal vortex shedding and hydraulics jumps.

Application of Different Turbulence Models Simulating Wind Flow in Complex Terrain: A Case Study for the WindForS Test Site

A model for the simulation of wind flow in complex terrain is presented based on the Reynolds averaged Navier–Stokes (RANS) equations. For the description of turbulence, the standard k-ε, the renormalization group (RNG) k-ε, and a Reynolds stress turbulence model are applied. Additional terms are implemented in the momentum equations to describe stratification of the Earth’s atmosphere and to account for the Coriolis forces driven by the Earth’s rotation, as well as for the drag force due to forested canopy. Furthermore, turbulence production and dissipation terms are added to the turbulence equations for the two-equation, as well as for the Reynolds stress models, in order to capture different types of land use. The approaches for the turbulence models are verified by means of a homogeneous canopy test case with flat terrain and constant forest height. The validation of the models is performed by investigating the WindForS wind test site. The simulation results are compared with five-hole probe velocity measurements using multipurpose airborne sensor carrier (MASC) systems (unmanned small research aircraft)—UAV at different locations for the main wind regime. Additionally, Reynolds stresses measured with sonic anemometers at a meteorological wind mast at different heights are compared with simulation results using the Reynolds stress turbulence model.

Simulation of stratified flows over a ridge using a lattice Boltzmann model

A three-dimensional thermal lattice Boltzmann model (TLBM) using multi-relaxation time method was used to simulate stratified atmospheric flows over a ridge. The main objective was to study the efficacy of this method for turbulent flows in the atmospheric boundary layer, complex terrain flows in particular. The simulation results were compared with results obtained using a traditional finite difference method based on the Navier–Stokes equations and with previous laboratory results on stably stratified flows over an isolated ridge. The initial density profile is neutral stratification in the boundary layer, topped with a stable cap and stable stratification aloft. The TLBM simulations produced waves, rotors, and hydraulic jumps in the lee side of the ridge for stably stratified flows, depending on the governing stability parameters. The Smagorinsky turbulence parameterization produced typical turbulence spectra for the velocity components at the lee side of the ridge, and the turbulent flow characteristics of varied stratifications were also analyzed. The comparison of TLBM simulations with other numerical simulations and laboratory studies indicated that TLBM is a viable method for numerical modeling of stratified atmospheric flows. To our knowledge, this is the first TLBM simulation of stratified atmospheric flow over a ridge. The details of the TLBM, its implementation of complex boundaries and the subgrid turbulence parameterizations used in this study are also described in this article.