Improving short-term wind power forecasts during ramp events by including nacelle wind speed observations

<p>Wind energy is and will be one of the key technologies for a transition to green electricity. However, the smooth integration of the generated wind energy into the electrical grid depends on reliable power forecasts. Rapid changes in power generation, so-called ramps, are not always reflected properly in NWP data and pose a challenge for power predictions and, therefore, grid operation. While contributions to the topic of ramp forecasting increased in the recent years, this work approaches the mitigation of deviations from the forecast more directly.</p> <p>The power forecast tool used here is based on an artificial neural network, trained and evaluated on multiple years of data. It is applied in comparison to power generation data for a 44 MW wind farm in Brandenburg. For short-term wind power forecasts, NWP wind speeds in this power forecast tool are replaced with recent Doppler Lidar wind profiles and nacelle wind speed observations from ultra-sonic anemometers, aiming to provide an easy-to-implement way to reduce negative impacts of ramps. Compared to NWP input data, this persistence approach with observational data aims to improve the forecast quality especially during the time of wind ramps.</p> <p>Different ramp definitions and forecast horizons are explored. In general, the number of ramps detected increases dramatically when using wind speed observations instead of the (too smooth) NWP model data. In addition, the mean deviation between power forecast and actual power generation around ramp events decreases, indicating a reduced need for balancing efforts.</p>

Machbarkeitsstudie zur Bestimmung des sensiblen Wärmestroms von Gründächern

<p>Die Eddy-Kovarianz-Methode ist ein bewährtes Verfahren zur Erfassung des fühlbaren Wärmeströms mit Hilfe dreidimensionaler Sonic Anemometer. <br />Diese Methode eignet sich jedoch nicht für kleinere Flächen wie Gründächer, da diese nicht mit den räumlichen Dimensionen des  <br />entsprechenden Footprints übereinstimmen. </p> <p>Als eine alternative Methode wird ein kürzlich konstruiertes akustisches Anemometer (Ly-ATOM) getestet, <br />das horizontal mit einer Ausdehnung von ca. 1 m und einer Datenerfassungsfrequenz von 1 Hz arbeitet. <br />Das Ly-ATOM ist in der Lage sowohl die akustische virtuelle Temperatur als auch die horizontalen Windkomponenten<br />eines dreidimensionalen Sonic Anemometers zuverlässig zu reproduzieren. <br />Da das Ly-ATOM viel näher am Boden angebracht werden kann, kann die Größe des Footprint erheblich reduziert werden (um den Faktor 25).</p> <p>Zwei Methoden werden verwendet, um den fühlbaren Wärmestrom aus den Schwankungen der Temperatur und <br />der horizontalen Windkomponenten, die vom Ly-ATOM aufgezeichnet wurden, zu ermitteln:<br />Die Kombination der Fluss-Varianz-Ähnlichkeits-Methode und der alternativen Fluss-Varianz-Methode <br />für die Anwendung bei labiler bzw. stabiler Schichtung führt zu guten Ergebnissen für die Sonic-Messungen. <br />Daher können diese Methoden auch auf duie Messungen des Ly-ATOMs angewandt werden.<br />Bei der Untersuchung der Senisitivität zur Detektion veränderter Oberflächeneigenschaften,<br /> insbesondere erhöhter Evapotranspiration und verringerter Oberflächenalbedo Albedo, <br />erweist sich das Ly-ATOM-Gerät als geeigneter im Vergleich zum Sonic Anemometer, welches vertikal weiter <br />von der zu untersuchenden Oberfläche entfernt ist.</p>

High-frequency observation during sand and dust storms at the Qingtu Lake Observatory

Partially due to global climate change, sand and dust storms (SDSs) have occurred more and more frequently, yet a detailed measurement of SDS events at different heights is still lacking. Here we provide a high-frequency observation from the Qingtu Lake Observation Array (QLOA), China. The wind and dust information were measured simultaneously at different wall-normal heights during the SDS process. The datasets span the period from 17 March to 9 June 2016. The wind speed and direction are recorded by a sonic anemometer with a sampling frequency of 50 Hz, while particulate matter with a diameter of 10 µm or less (PM10) is sampled simultaneously by a dust monitor with a sampling frequency of 1 Hz. The wall-normal array had 11 sonic anemometers and monitors spaced logarithmically from z=0.9 to 30 m, where the spacing is about 2 m between the sonic anemometer and dust monitor at the same height. Based on its nonstationary feature, an SDS event can be divided into three stages, i.e., ascending, stabilizing and descending stages, in which the dynamic mechanism of the wind and dust fields might be different. This is preliminarily characterized by the classical Fourier power analysis. Temporal evolution of the scaling exponent from Fourier power analysis suggests a value slightly below the classical Kolmogorov value of -5/3 for the three-dimensional homogeneous and isotropic turbulence. During the stabilizing stage, the collected PM10 shows a very intermittent pattern, which can be further linked with the burst events in the turbulent atmospheric boundary layer. This dataset is valuable for a better understanding of SDS dynamics and is publicly available in a Zenodo repository at https://doi.org/10.5281/zenodo.5034196 (Li et al., 2021a).

Adaptive Thermal Image Velocimetry of spatial wind movement on landscapes using near target infrared cameras

Thermal Image Velocimetry (TIV) is a near-target remote sensing technique for estimating two- dimensional near-surface wind velocity based on spatiotemporal displacement of fluctuations in surface brightness temperature captured by an infrared camera. The addition of an automated parameterization and the combination of ensemble TIV results into one output made the method more suitable to changing meteorological conditions and less sensitive to noise stemming from the airborne sensor platform. Three field campaigns were carried out to evaluate the algorithm over turf, dry grass and wheat stubble. The derived velocities were validated with independently acquired observations from fine wire thermocouples and sonic anemometers. It was found that the TIV technique correctly derives atmospheric flow patterns close to the ground. Moreover, the modified method resolves wind speed statistics close to the surface at a higher resolution than the traditional measurement methods. Adaptive Thermal Image Velocimetry (A-TIV) is capable of providing contact-less spatial information about near-surface atmospheric motion and can help to be a useful tool in researching turbulent transport processes close to the ground.

A field study to estimate the impact of noise barriers on mitigation of near road air pollution

Air pollution associated with vehicle emissions from roadways has been linked to a variety of adverse health effects. Wind tunnel and tracer studies show that noise barriers mitigate the impact of this pollution up to distances of 30 times the barrier height. Data from these studies have been used to formulate dispersion models that account for this mitigating effect. Before these models can be incorporated into Federal and State regulations, it is necessary to demonstrate their applicability under real-world conditions. This paper describes a comprehensive field study conducted in Riverside, CA, in 2019 to collect the data required to evaluate the performance of these models. Eight vehicles fitted with SF6 tracer release systems were driven in a loop on a 2-km stretch of Interstate 215 that had a 5-m tall noise barrier on the downwind side. The tracer, SF6, was sampled at over 40 locations at distances ranging from 5 to 200 m from the barrier. Meteorological data were measured with several 3-D sonic anemometers located upwind and downwind of the highway. The data set, corresponding to 10 h collected over 4 days, consists of information on emissions, tracer concentrations, and micrometeorological variables that can be used to evaluate barrier effects in dispersion models. An analysis of the data using a dispersion model indicates that current models are likely to overestimate concentrations, or underestimate the mitigation from barriers, at low wind speeds. We suggest an approach to correct this problem.

Characterization of Turbulence in the Neutral and Stable Surface Layer at Jang Bogo Station, Antarctica

The availability of 5-year time series of velocity and temperature data from two sonic anemometers installed at Jang Bogo Station, Antarctica, allowed a systematic investigation of the turbulence features in a stable layer affected by submeso motions and characterized by the vertical divergence of some second-order moments for a large fraction of time (quite a non-ideal surface layer). The investigation of the effect of the averaging time interval on the statistics of the second-order moments showed that this is greater for the variances of the velocity components with respect to that for the vertical fluxes. This corresponds to a greater contribution from low-frequency motions. The turbulence statistics were investigated and compared with current literature results in terms of vertical structure, share of energy between horizontal and vertical components, skewness of the vertical velocity and turbulent velocities. As a general result, all the normalized second-order moments show a clear change passing from neutral to stable conditions, passing through the range of bulk Richardson number equal to 0.1–1.

Comparing the Air Turbulence above Smooth and Rough Surfaces in the Amazon Region

The goal of this work is to compare the main air turbulence characteristics of two common areas in the Amazonian landscape: a dense forest (rough surface) and a water surface (smooth surface). Using wind components data collected at high frequency by sonic anemometers located just above these surfaces, turbulence intensity and power spectra, temporal and length scales of the eddies, as well as the main terms of the TKE budget (TKE = turbulent kinetic energy) were evaluated for each surface type. The results showed that in general, the air turbulence intensity above the forest was higher than above the lake during the daytime, due to the high efficiency of the forest in absorbing the momentum of the turbulent flow. During the nighttime, the situation was reversed, with greater air turbulence intensity above the lake, except in some periods in which intermittent turbulence bursts occured above the forest.

The NY-Ålesund TurbulencE Fiber Optic eXperiment (NYTEFOX): investigating the Arctic boundary layer, Svalbard

The NY-Ålesund TurbulencE Fiber Optic eXperiment (NYTEFOX) was a field experiment at the Ny-Ålesund Arctic site (78.9∘ N, 11.9∘ E) and yielded a unique meteorological data set. These data describe the distribution of heat, airflows, and exchange in the Arctic boundary layer for a period of 14 d from 26 February to 10 March 2020. NYTEFOX is the first field experiment to investigate the heterogeneity of airflow and its transport of temperature, wind, and kinetic energy in the Arctic environment using the fiber-optic distributed sensing (FODS) technique for horizontal and vertical observations. FODS air temperature and wind speed were observed at a spatial resolution of 0.127 m and a temporal resolution of 9 s along a 700 m horizontal array at 1 m above ground level (a.g.l.) and along three 7 m vertical profiles. Ancillary data were collected from three sonic anemometers and an acoustic profiler (minisodar; sodar is an acronym for “sound detection and ranging”) yielding turbulent flow statistics and vertical profiles in the lowest 300 m a.g.l., respectively. The observations from this field campaign are publicly available on Zenodo (https://doi.org/10.5281/zenodo.4756836, Huss et al., 2021) and supplement the meteorological data set operationally collected by the Baseline Surface Radiation Network (BSRN) at Ny-Ålesund, Svalbard.

Enhancing Icing Detection and Characterization using the New York State Mesonet

The accurate detection and monitoring of freezing rain and icing conditions at the surface is a notoriously challenging but important problem. This work attempts to enhance icing detection and characterization utilizing data from the New York State Mesonet (NYSM). NYSM is the first operational network measuring winds at 10 meters from two independent sensors: propeller and sonic anemometers. During and after freezing rain events, large wind speed differences are frequently reported between the two anemometers because the propeller develops a coating of ice, thus either stopping or slowing its rotation. Such errors of propeller data provide a signal for identifying icing conditions. An automated method for identifying “active freezing rain” (AFR) and a continuation of “frozen surface” (FS) conditions is developed. Hourly maps of AFR and FS sites are generated using four criteria: (1) a wind speed difference (sonic – propeller) of > 1 m s-1 or 0 m s-1 propeller wind speed for at least half hour, (2) a temperature threshold of -5°C to 2°C for AFR and less than 2°C for FS, (3) insignificant hourly snow accumulation, and (4) with (or without) significant hourly precipitation accumulation for AFR (or FS). The AFR events detected by the automated method for last four winters (2017-2021) show very good agreements in starting and ending times with that from ASOS data. A case study of the ice storm during 14-16 April 2018 further demonstrates the validity of the methodology and highlights the benefit of NYSM profiler and camera data.

Estimating Turbulence Distribution over a Heterogeneous Path Using Time-Lapse Imagery from Dual Cameras

Knowledge of turbulence distribution along an experimental path can help in effective turbulence compensation and mitigation. Although scintillometers are traditionally used to measure the strength of turbulence, they provide a path-integrated measurement and have limited operational ranges. A technique to profile turbulence using time-lapse imagery of a distant target from spatially separated cameras is presented here. The method uses the turbulence induced differential motion between pairs of point features on a target, sensed at a single camera and between cameras to extract turbulence distribution along the path. The method is successfully demonstrated on a 511 m almost horizontal path going over half concrete and half grass. An array of Light-Emitting Diodes (LEDs) of non-uniform separation is imaged by a pair of cameras, and the extracted turbulence profiles are validated against measurements from 3D sonic anemometers placed along the path. A short-range experiment with a heat source to create local turbulence spike gives good results as well. Because the method is phase-based, it does not suffer from saturation issues and can potentially be applied over long ranges. Although in the present work, a cooperative target has been used, the technique can be used with non-cooperative targets. Application of the technique to images collected over slant paths with elevated targets can aid in understanding the altitude dependence of turbulence in the surface layer.