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607 results for “wind data”

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zenodo32/100

Hourly wind speed, solar radiation and load demand data

Hourly data for wind velocity, solar radiation and load demand as time series of 10 years length, used within the simulation of a hybrid renewable energy system in the island of Sifnos, Greece.

opencc-by-4.0Jun 2023View details →

zenodo32/100

Data for the publication: Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients

The files contain the datasets shown in the publication "Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients" to appear in J. Geophys. Res. Atmos. (revised manuscript submitted). The files are xmgrace plot files containing the research data (ASCII) shown in all figures in the main text and Appendix A.

opencc-by-4.0Aug 2023View details →

dryad32/100

Data from: Hierarchical power control of a large-scale wind farm by using a data-driven optimization method

With the participation in automatic generation control (AGC), a large-scale wind farm should distribute the real-time AGC signal to numerous wind turbines (WTs). This easily leads to an expensive computation for a high-quality dispatch scheme, especially considering the wake effect among WTs. To address this problem, a hierarchical power control (HPC) is constructed based on the geographical layout and electrical connection of all the WTs. Firstly, the real-time AGC signal of the whole wind farm is distributed to multiple decoupled groups in proportion of their regulation capacities. Secondly, the AGC signal of each group is distributed to multiple WTs via the data-driven surrogate-assisted optimization, which can dramatically reduce the computation time with a small number of time-consuming objective evaluations. Besides, a high-quality dispatch scheme can be acquired by the efficient local search based on the dynamic surrogate. The effectiveness of the proposed technique is thoroughly verified with different AGC signals under different wind speeds and directions.

opencc-zeroAug 2023View details →

zenodo32/100

Data accompanying the paper "Improving the near-surface wind and turbulence at the edge of the orographic drag grey zone by tuning the roughness length"

This repository contains outputs from simulations (with observations) launched with the ALARO-HR18 model utilizing the TOUCANS two-energy turbulence scheme and different roughness length configurations. The results are provided in three separate files, i.e., "acf_data.zip", "fluxes.zip", and "verif_data.zip". 1) "acf_data.zip" contains four ASCII files with the name "acf_DATE_TYPE.txt", where DATE=YYYYMMDD and TYPE=mountain or flat. Each file includes the model data from surroundings of approx. 200 x 200 km around the chosen point (flat terrain or mountain), and provides data on longitude (LON), latitude (LAT), zonal wind (U-WIND), meridional wind (V-WIND) and wind speed (W). The data included in this file were used to compute the 2D spatial autocorrelation function for different cases and locations shown in Fig.3. 2) "fluxes.zip" contains individual ASCII files with the name "FTYPE_EXP.txt", where FTYPE=uw_flux or vw_flux, and EXP is one of OBS, ORL, REF, EXP1, EXP2, EXP3 and EXP4. Each file contains turbulent momentum flux data at four vertical levels (5 m; UW005 and VW005, 60 m; UW060 and VW060; 100 m; UW100 and VW100 and 180 m; UW180 and VW180). The description of EXP is following: OBS; observed momentum fluxes from the Cabauw tower, the Netherlands, corresponding to five different cases, i.e., matching with 3D model simulations starting at 00 UTC on 6, 10 and 16th February 2020, as well as 16 and 25th August 2020. Each column corresponds to one vertical level, while the rows represent hourly values (up to +72 hours in advance), added case by case. ORL; predicted fluxes obtained with the old roughness length configuration described in the paper. REF; predicted fluxes obtained with the new roughness length (NRL) configuration as described in the paper and without tuning the vegetation roughness length (C1=0.25, C2=1.0, C3=6, FA=0 and OGWD scheme on). EXP1; is the same as REF but with C2=1.5. EXP2; is the same as REF but with C2=1.875. EXP3; is the same as REF but with C2=1.875 and C3=3. EXP4; is the same as REF but with C2=1.875 and C3=1.5. The data included in this file were used to prepare inputs to Table 2. NOTE: The measurements taken at the Cabauw tower are under the care of "Koninklijk Nederlands Meteorologisch Instituut" (KNMI). They are added to this repository only to confirm related results presented in the paper. For any personal use of these data, contact the responsible KNMI staff. 3) "verif_data.zip" contains individual ASCII files used to validate the impact of various tunable parameters and final settings of the NRL configuration on 10-m wind, i.e., for preparation of Fig. 6-8. and Fig. 9-12. Individual ASCII files are named "WS_EXP_SDATE_ENDDATE_all", where: <ul> <li>a) EXP is one of C1s, C1f, C2s, C2f, C3s, C3f, ORL, NRLs, NRLf, FAs, FAf, GWDon and GWDoff. "s" and "f" correspond to the starting and the final setup related to sensitivity studies performed in the paper (Fig. 6-8.) or starting and final version of the NRL configuration (Fig. 10-12). The results from FAs, FAf, GWDon and GWDoff are only briefly commented on within the paper but not shown.</li> <li>b) SDATE=YYYYMMDD (starting date of the validation period), and</li> <li>c) EDATE=YYYYMMDD (ending date of the validation period).</li> </ul> Each ASCII file contains the following fields (column-wise): SYNOP number of station (location; for explanation cf. "synop_list.txt"), latitude (lat), longitude (lon), altitude of the station (h), date to which data refers to (date), forecast lead time to which data refers to (lead time), predicted 10-m wind (fcst) and measured 10-m wind (obs). CAUTION: Be aware that all times are given in UTC and that they are consistently used where predicted and observed data are used. In case you have any questions, contact the corresponding author.

opencc-by-4.0Aug 2023View details →

zenodo32/100

Data set used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koopman Dynamic Mode Decomposition Approach

Step-wise yaw deflection in 2 wind turbines in SOWFA. More information in the article.

opencc-by-4.0Jun 2021View details →

zenodo32/100

Data and R code for "Negative effects of wind on plant hydraulics at the global scale"

To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources. Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET. Simple linear regression was used to examine the relationships between two variables, utilizing the 'lm' function in R software. Partial regression analysis was conducted using the R package VISREG to investigate the relationships between wind speed and plant hydraulics while controlling for other variables. This analysis helped to illustrate the independent effect of wind on plant hydraulics. The Random Forest machine-learning algorithm (implemented using the R package randomForest) was utilized to assess the relative importance of environmental variables for each plant hydraulic trait. The Mean Decrease in Gini was calculated as the average of a variable's total decrease in node impurity, taking into account the proportion of samples that reach that node in each individual decision tree in the random forest. This provides a measure of a variable's importance in estimating the value of the target variable across all of the trees in the forest. A higher Mean Decrease in Gini value indicates greater importance of the variable. Multiple regression analyses were performed to develop predictive equations for plant hydraulic traits using environmental variables. To test for hydraulic traits-wind speed slope directions and differences among species groups in different climatic regions, we used standardized major axis (SMA) analyses. The R package SMATR was employed for these analyses. We considered p < 0.05 as the threshold for statistical significance in all models.

opencc-by-4.0Aug 2023View details →

dryad32/100