An atmospheric surface layer study: The Idealized horizontal Planar Array experiment for Quantifying Surface Heterogeneity (IPAQS)

2020 ◽  
Author(s):  
Travis Morrison ◽  
Marc Calaf ◽  
Eric Pardyjak ◽  
Marcus Hultmark ◽  
Chad Higgins ◽  
...  
Keyword(s):  
Surface Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Heterogeneity ◽  
Internal Boundary ◽  
Valuable Insight ◽  
Sonic Anemometers ◽  
Wide Range ◽  
Planar Array ◽  
Scale Turbulence

<p>Numerical weather prediction models rely heavily on boundary-layer theories, which poorly capture the interactions between the Earth’s heterogeneous surface and the internal boundary layers aloft. Further, in relation to these theories, there remains outstanding questions that still require new understanding, such as the closure of the surface energy balance, advection quantification, and surface-flux interaction. We hypothesize that under certain conditions of unstable and neutral stratification, surface thermal heterogeneities can significantly influence the flow structure and alter momentum and scalar transport. To be able to access this hypothesis, we designed the Idealized horizontal Planar Array experiment for Quantifying Surface heterogeneity (IPAQS). IPAQS took place during the summers of 2018 and 2019 at the Great Salt Lake Desert playa in western Utah at the U.S. Army Dugway Proving Ground’s Surface Layer Turbulence and Environmental Test (SLTEST) facility. The site is characterized by a long uninterrupted fetch with uniform surface roughness and large thermal and moisture heterogeneities covering a wide range of scales. Observations were made with an array of 2-m high, temporally-synchronized, fast-response sonic anemometers, and finewire thermocouples, which were deployed on a coarse grid covering an area of 800 m x 800 m with 200-m spacing. Results provide valuable insight into the spatial and temporal evolution of the flow. Fine-scale turbulence was measured using Nano-Scale Thermal Anemometry Probes (NSTAP). Meanwhile, larger-scale turbulence was captured with Doppler wind LiDARs. Presented is an overview of the experiment and initial results.</p>

The NREL Large-Scale Turbine Inflow and Response Experiment: Preliminary Results

2002 ◽  
Author(s):  
Neil Kelley ◽  
Maureen Hand ◽  
Scott Larwood ◽  
Ed McKenna
Keyword(s):  
Wind Turbine ◽  
Large Scale ◽  
Structural Response ◽  
Technology Center ◽  
Operating Environments ◽  
Sonic Anemometers ◽  
Wide Range ◽  
Scaling Parameters ◽  
Planar Array ◽  
Turbulence Scaling

The accurate numerical dynamic simulation of new large-scale wind turbine designs operating over a wide range of inflow environments is critical because it is usually impractical to test prototypes in a variety of locations. Large turbines operate in a region of the atmospheric boundary layer that currently may not be adequately simulated by present turbulence codes. In this paper, we discuss the development and use of a 42-m (137-ft) planar array of five, high-resolution sonic anemometers upwind of a 600-kW wind turbine at the National Wind Technology Center (NWTC). The objective of this experiment is to obtain simultaneously collected turbulence information from the inflow array and the corresponding structural response of the turbine. The turbulence information will be used for comparison with that predicted by currently available codes and establish any systematic differences. These results will be used to improve the performance of the turbulence simulations. The sensitivities of key elements of the turbine aeroelastic and structural response to a range of turbulence-scaling parameters will be established for comparisons with other turbines and operating environments. In this paper, we present an overview of the experiment, and offer examples of two observed cases of inflow characteristics and turbine response collected under daytime and nighttime conditions, and compare their turbulence properties with predictions.

scholarly journals Data Assimilation as Synchronization of Truth and Model: Experiments with the Three-Variable Lorenz System*

2006 ◽  
Vol 63 (9) ◽  
pp. 2340-2354 ◽  
Author(s):  
Shu-Chih Yang ◽  
Debra Baker ◽  
Hong Li ◽  
Katy Cordes ◽  
Morgan Huff ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Chaos Synchronization ◽  
Prediction Models ◽  
Partial Information ◽  
Coupling Parameter ◽  
Weather Prediction ◽  
Adaptive Synchronization ◽  
Accurate Analysis ◽  
Wide Range ◽  
Bred Vectors

Abstract The potential use of chaos synchronization techniques in data assimilation for numerical weather prediction models is explored by coupling a Lorenz three-variable system that represents “truth” to another that represents “the model.” By adding realistic “noise” to observations of the master system, an optimal value of the coupling strength was clearly identifiable. Coupling only the y variable yielded the best results for a wide range of higher coupling strengths. Coupling along dynamically chosen directions identified by either singular or bred vectors could improve upon simpler chaos synchronization schemes. Generalized synchronization (with the parameter r of the slave system different from that of the master) could be easily achieved, as indicated by the synchronization of two identical slave systems coupled to the same master, but the slaves only provided partial information about regime changes in the master. A comparison with a standard data assimilation technique, three-dimensional variational analysis (3DVAR), demonstrated that this scheme is slightly more effective in producing an accurate analysis than the simpler synchronization scheme. Higher growth rates of bred vectors from both the master and the slave anticipated the location and size of error spikes in both 3DVAR and synchronization. With less frequent observations, synchronization using time-interpolated observational increments was competitive with 3DVAR. Adaptive synchronization, with a coupling parameter proportional to the bred vector growth rate, was successful in reducing episodes of large error growth. These results suggest that a hybrid chaos synchronization–data assimilation approach may provide an avenue to improve and extend the period for accurate weather prediction.

scholarly journals Improved observations of turbulence dissipation rates from wind profiling radars

2017 ◽  
Vol 10 (7) ◽  
pp. 2595-2611 ◽  
Author(s):  
Katherine McCaffrey ◽  
Laura Bianco ◽  
James M. Wilczak
Keyword(s):  
Boundary Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Spectral Width ◽  
High Spectral Resolution ◽  
Dissipation Rates ◽  
Sonic Anemometers ◽  
Processing Algorithms ◽  
Processing Techniques ◽  
Numerical Weather Prediction Models

Abstract. Observations of turbulence dissipation rates in the planetary boundary layer are crucial for validation of parameterizations in numerical weather prediction models. However, because dissipation rates are difficult to obtain, they are infrequently measured through the depth of the boundary layer. For this reason, demonstrating the ability of commonly used wind profiling radars (WPRs) to estimate this quantity would be greatly beneficial. During the XPIA field campaign at the Boulder Atmospheric Observatory, two WPRs operated in an optimized configuration, using high spectral resolution for increased accuracy of Doppler spectral width, specifically chosen to estimate turbulence from a vertically pointing beam. Multiple post-processing techniques, including different numbers of spectral averages and peak processing algorithms for calculating spectral moments, were evaluated to determine the most accurate procedures for estimating turbulence dissipation rates using the information contained in the Doppler spectral width, using sonic anemometers mounted on a 300 m tower for validation. The optimal settings were determined, producing a low bias, which was later corrected. Resulting estimations of turbulence dissipation rates correlated well (R2 = 0. 54 and 0. 41) with the sonic anemometers, and profiles up to 2 km from the 449 MHz WPR and 1 km from the 915 MHz WPR were observed.

Structure of weather prediction errors in stably-stratified atmospheric conditions

2020 ◽  
Author(s):  
Igor Esau ◽  
Stephen Outten ◽  
Mikhail Tolstykh
Keyword(s):  
Surface Layer ◽  
Mixed Layer ◽  
Turbulent Mixing ◽  
Prediction Models ◽  
Numerical Models ◽  
Temporal Structure ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Prediction Errors ◽  
Atmospheric Conditions

<p>Stably-stratified atmospheric conditions still challenge numerical weather forecast, especially in high latitudes where they are frequently observed all year around. In stably-stratified atmosphere, surface is colder than air above. Such conditions suppress vertical turbulent mixing and may lead to surface layer decoupling in numerical models. Enhanced mixing could prevent decoupling but being implemented without sufficient care results in damped response of the surface layer meteorological variables on fluctuations of the weather conditions. In this study, we investigate weather prediction errors related to such a damped response. We run a group of operational prediction models (HIRLAM-HARMONIE, SL-AV) with a set of different turbulence parametrizations that includes HARATU, TOUCANS, and pTKE schemes. The results are compared with real weather observations and idealized GABLS setups proposed for a high latitude domain. We found that the systematic warm temperature bias in the models is caused by too slow response of the modelled temperature on the implied cooling. The largest (and quickly growing) errors are found over the first few hours of cooling, whereas in longer perspective the errors diminish as the model equilibrates with more stationary weather conditions. We develop a theory that may explain the observed structure of weather prediction errors. The explanation is based on the well-known coupling between the turbulent mixing intensity and the thickness of the mixed layer embedded into the parametrization of the mixing length scale. The required enhanced mixing could be provided by the energy-flux balance scheme by Zilitinkevich et al., but it does not reduce the warm bias as it makes the mixed deeper and less responsive. We propose more accurate limitations on the mixed layer thickness to improve the temporal structure of the surface layer temperature response in the weather prediction models.</p>

Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging

2013 ◽  
Vol 141 (6) ◽  
pp. 2107-2119 ◽  
Author(s):  
J. McLean Sloughter ◽  
Tilmann Gneiting ◽  
Adrian E. Raftery
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Prediction Models ◽  
Weather Prediction ◽  
Model Averaging ◽  
Wind Vector ◽  
Bivariate Distributions ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Wide Range

Abstract Probabilistic forecasts of wind vectors are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Unlike other common forecasting problems, which deal with univariate quantities, statistical approaches to wind vector forecasting must be based on bivariate distributions. The prevailing paradigm in weather forecasting is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty can then be assessed through ensemble forecasts, where multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic predictions. Ensemble forecasts are often uncalibrated, however, and Bayesian model averaging (BMA) is a statistical way of postprocessing these forecast ensembles to create calibrated predictive probability density functions (PDFs). It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights reflect the forecasts’ relative contributions to predictive skill over a training period. In this paper the authors extend the BMA methodology to use bivariate distributions, enabling them to provide probabilistic forecasts of wind vectors. The BMA method is applied to 48-h-ahead forecasts of wind vectors over the North American Pacific Northwest in 2003 using the University of Washington mesoscale ensemble and is shown to provide better-calibrated probabilistic forecasts than the raw ensemble, which are also sharper than probabilistic forecasts derived from climatology.

scholarly journals Vertical Velocity Variance Measurements from Wind Profiling Radars

2016 ◽  
Author(s):  
Katherine McCaffrey ◽  
Laura Bianco ◽  
Paul Johnston ◽  
James M. Wilczak
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Planetary Boundary Layer ◽  
Vertical Velocity ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Stable Conditions ◽  
Sonic Anemometers ◽  
Velocity Variance

Abstract. Observations of turbulence in the planetary boundary layer are critical for developing and evaluating boundary layer parameterizations in mesoscale numerical weather prediction models. These observations, however, are expensive, and rarely profile the entire boundary layer. Using optimized configurations for 449 MHz and 915 MHz wind profiling radars during the eXperimental Planetary boundary layer Instrumentation Assessment, improvements have been made to the historical methods of measuring vertical velocity variance through the time series of vertical velocity, as well as the Doppler spectral width. Using six heights of sonic anemometers mounted on a 300-m tower, correlations of up to R2 = 0.74 are seen in measurements of the large-scale variances from the radar time series, and R2 = 0.79 in measurements of small-scale variance from radar spectral widths. The total variance, measured as the sum of the small- and large-scales agrees well with sonic anemometers, with R2 = 0.79. Correlation is higher in daytime, convective boundary layers than nighttime, stable conditions when turbulence levels are smaller. With the good agreement with the in situ measurements, highly-resolved profiles up to 2 km can be accurately observed from the 449 MHz radar, and 1 km from the 915 MHz radar. This optimized configuration will provide unique observations for the verification and improvement to boundary layer parameterizations in mesoscale models.

scholarly journals Improved Observations of Turbulence Dissipation Rates from Wind Profiling Radars

2016 ◽  
Author(s):  
Katherine McCaffrey ◽  
Laura Bianco ◽  
James M. Wilczak
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Spectral Width ◽  
High Spectral Resolution ◽  
Spectral Moments ◽  
Dissipation Rates ◽  
Sonic Anemometers ◽  
Processing Algorithms ◽  
Processing Techniques ◽  
Numerical Weather Prediction Models

Abstract. Observations of turbulence in the planetary boundary layer are crucial for validation of parameterizations in numerical weather prediction models. However, these observations are sparse. For this reason, demonstrating the ability of commonly-used wind profiling radars (WPRs) to measure turbulence dissipation rates would be greatly beneficial. During the XPIA field campaign at the Boulder Atmospheric Observatory, two WPRs operated in an optimized configuration, using high spectral resolution for increased accuracy of Doppler spectral width, specifically chosen to measure turbulence from a vertically-pointing beam only. Multiple post-processing techniques, including different numbers of spectral averages and peak-processing algorithms for calculating spectral moments, were analyzed to determine the most accurate procedures for measuring turbulence dissipation rates using the information contained in the Doppler spectral width, and compared to sonic anemometers mounted on a 300-meter tower. The optimal settings were determined, producing a constant low bias, which was later corrected. Resulting measurements of turbulence dissipation rates correlated well (R2 = 0.57) with sonic anemometers, and profiles up to 2 km from the 449-MHz WPR and 1 km from the 915-MHz WPR were observed.

Distributed urban drag parameterization in the sub-kilometre scale London Model

2021 ◽  
Author(s):  
Birgit Sützl ◽  
Gabriel Rooney ◽  
Anke Finnenkoetter ◽  
Sylvia Bohnenstengel ◽  
Sue Grimmond ◽  
...  
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Urban Environments ◽  
Internal Boundary ◽  
Average Height ◽  
High Rise ◽  
London Model

<p>Urban environments in numerical weather prediction models are currently parameterised as part of the atmosphere-surface exchange at ground-level. The vertical structure of buildings is represented by the average height, which does not account for heterogeneous building forms at the subgrid-level. The use of city-scale models with sub-kilometre resolutions and growing number of high-rise buildings in cities call for a better vertical representation of urban environments.</p><p>We present the use of a newly developed, height-distributed urban drag parameterization with the London Model, a high-resolution version of the Met Office Unified Model over Greater London and surroundings at approximately 333 m resolution. The distributed drag parameterization requires vertical morphology profiles in form of height-distributed frontal area functions, which capture the full extent and variability of building-heights. These morphology profiles were calculated for Greater London and parameterised by an exponential distribution with the ratio of maximum to mean building-height as parameter.</p><p>A case study with the high-resolution London Model and the new drag parameterization appears to capture more realistic features of the urban boundary layer compared to the standard parameterization. The simulation showed increased horizontal spatial variability in total surface stress, identifying a broad range of morphology features (densely built-up areas, high-rise building clusters, parks and the river). Vertical effects include heterogeneous wind profiles, extended building wakes, and indicate the formation of internal boundary layers. This study demonstrates the potential of height-distributed urban parameterizations to improve urban weather forecasting, albeit research into distribution of heat- and moisture-exchange is necessary for a fully distributed parameterization of urban areas.</p>

scholarly journals The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions

2017 ◽  
Vol 98 (8) ◽  
pp. 1717-1737 ◽  
Author(s):  
Jordan G. Powers ◽  
Joseph B. Klemp ◽  
William C. Skamarock ◽  
Christopher A. Davis ◽  
Jimy Dudhia ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
System Modeling ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Earth System Modeling ◽  
Numerical Weather ◽  
Wide Range ◽  
Weather Research

Abstract Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

scholarly journals Analysis of the Influence of the Length Scales in a Boundary-Layer Model

2021 ◽  
Author(s):  
Enrico Ferrero ◽  
Massimo Canonico
Keyword(s):  
Boundary Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Layer Model ◽  
Length Scales ◽  
Boundary Layer Model ◽  
Low Wind Speed ◽  
Stable Conditions ◽  
Sonic Anemometers ◽  
Numerical Weather Prediction Models

AbstractWe consider the Janjic (NCEP Office Note 437:61, 2001) boundary-layer model, which is one of the most widely used in numerical weather prediction models. This boundary-layer model is based on a number of length scales that are, in turn, obtained from a master length multiplied by constants. We analyze the simulation results obtained using different sets of constants with respect to measurements using sonic anemometers, and interpret these results in terms of the turbulence processes in the atmosphere and of the role played by the different length scales. The simulations are run on a virtual machine on the Chameleon cloud for low-wind-speed, unstable, and stable conditions.

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