High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Abstract. A two-dimensional variational retrieval (2DVAR) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer-2 (AMSR2) are explicitly simulated to attempt retrieval of near surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters shown by analysis of 2DVAR averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2DVAR for cloud-free retrievals, and opens the possibility of standalone 3DVAR retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

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A spectral analysis of near-surface wind speed and possible sources of predictability in the Iberian Peninsula

10.5194/ems2021-7 ◽

2021 ◽

Author(s):

Eduardo Utrabo-Carazo ◽

Cesar Azorin-Molina ◽

Enric Aguilar ◽

Manola Brunet

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Wind Speed ◽

Iberian Peninsula ◽

Southern Oscillation ◽

Statistical Significance ◽

Surface Wind ◽

Surface Wind Speed ◽

Near Surface ◽

Mean Values

Conventional time series analysis of observed near-surface wind speed (SWS) have focused both on mean values and on the sign, magnitude and statistical significance of trends. Specifically, a decrease in the SWS has been detected in continental surfaces of the planet's mid-latitudes from 1979 to 2010 approximately, the so-called stilling phenomenon; and an increase from 2010 until now, the reversal phenomenon. However, although various hypotheses have been proposed in the scientific literature, the mechanisms behind these phenomena and what evolution this parameter will follow in the future are still understudied, mainly because the response of a variable dependent on atmospheric circulation, such as wind speed, to a warming climate is uncertain. This study aims to use spectral analysis (Fourier and wavelet) to determine the most significant frequency modes associated with the SWS time series in the Iberian Peninsula (IP), for both mean wind speed and daily peak wind gusts, as well as its temporal evolution for 1961-2019. Subsequently, this study will also attempt to relate these modes to those corresponding to various modes of ocean-atmosphere variability such as the El Ni&#241;o-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) or, due to the influence of sudden stratospheric warming (SSW) in the European troposphere, the polar vortex. The ultimate goal will be to explore possible sources of predictability in the medium-long term for SWS across the IP, which would have direct applications in areas such as: wind-power generation, agriculture, air quality, insurance and fishing industries, among many others socioeconomic and environmental issues.&#160;

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Nonlinear analysis of the near-surface wind speed time series

2012 5th International Congress on Image and Signal Processing ◽

10.1109/cisp.2012.6470023 ◽

2012 ◽

Cited By ~ 4

Author(s):

Ming Zeng ◽

Haiyan Jia ◽

Qinghao Meng ◽

Tiemao Han ◽

Zhengcun Liu

Keyword(s):

Time Series ◽

Wind Speed ◽

Nonlinear Analysis ◽

Surface Wind ◽

Surface Wind Speed ◽

Near Surface

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Projected Near-Surface Wind Speed Trends in Lithuania

Energies ◽

10.3390/en14175425 ◽

2021 ◽

Vol 14 (17) ◽

pp. 5425

Author(s):

Justė Jankevičienė ◽

Arvydas Kanapickas

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Observational Data ◽

Surface Wind ◽

Surface Wind Speed ◽

Green Energy ◽

Near Surface ◽

Wind Speeds ◽

Global Circulation ◽

Global Circulation Models

Developing wind energy in Lithuania is one of the most important ways to achieve green energy goals. Observational data show that the decline in wind speeds in the region may pose challenges for wind energy development. This study analyzed the long-term variation of the observed 2006–2020 and projected 2006–2100 near-surface wind speed at the height of 10 m over Lithuanian territory using data of three models included in the Coupled Model Intercomparison Project phase 5 (CMIP5). A slight decrease in wind speeds was found in the whole territory of Lithuania for the projected wind speed data of three global circulation models for the scenarios RCP2.6, RCP4.5, and RCP8.5. It was found that the most favorable scenario for wind energy production is RCP2.6, and the most unfavorable is the RCP4.5 scenario under which the decrease in wind speed may reach 12%. At the Baltic Sea coastal region, the decline was smaller than in the country’s inner regions by the end of the century. The highest reduction in speed is characteristic of the most severe RCP8.5 scenario. Although the analysis of wind speeds projected by global circulation models (GCM) confirms the downward trends in wind speeds found in the observational data, the projected changes in wind speeds are too small to significantly impact the development of wind farms in Lithuania.

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Multiscale multifractal analysis of near-surface wind speed time series

2016 12th World Congress on Intelligent Control and Automation (WCICA) ◽

10.1109/wcica.2016.7578757 ◽

2016 ◽

Author(s):

Ming Zeng ◽

Xiaonei Zhang ◽

Jinghai Li ◽

Qinghao Meng

Keyword(s):

Time Series ◽

Wind Speed ◽

Multifractal Analysis ◽

Surface Wind ◽

Surface Wind Speed ◽

Near Surface

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An experimental 2D-Var retrieval using AMSR2

Atmospheric Measurement Techniques ◽

10.5194/amt-12-6341-2019 ◽

2019 ◽

Vol 12 (12) ◽

pp. 6341-6359

Author(s):

David Ian Duncan ◽

Patrick Eriksson ◽

Simon Pfreundschuh

Keyword(s):

Wind Speed ◽

Spatial Resolution ◽

Spatial Scales ◽

Surface Wind ◽

Optimal Estimation ◽

Surface Wind Speed ◽

Model Errors ◽

Near Surface ◽

Noise Characteristics ◽

Microwave Imager

Abstract. A two-dimensional variational retrieval (2D-Var) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are explicitly simulated to attempt retrieval of near-surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters judged by analysis of 2D-Var averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2D-Var for cloud-free retrievals and opens the possibility of stand-alone 3D-Var retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

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Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

Journal of Central South University ◽

10.1007/s11771-017-3470-4 ◽

2017 ◽

Vol 24 (3) ◽

pp. 692-698 ◽

Cited By ~ 6

Author(s):

Ming Zeng ◽

Jing-hai Li ◽

Qing-hao Meng ◽

Xiao-nei Zhang

Keyword(s):

Time Series ◽

Wind Speed ◽

Correlation Analysis ◽

Cross Correlation ◽

Surface Wind ◽

Surface Wind Speed ◽

Near Surface ◽

Cross Correlation Analysis

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Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1605 ◽

2014 ◽

Vol 599-601 ◽

pp. 1605-1609 ◽

Cited By ~ 1

Author(s):

Ming Zeng ◽

Zhan Xie Wu ◽

Qing Hao Meng ◽

Jing Hai Li ◽

Shu Gen Ma

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Recurrence Plot ◽

Sampling Frequency ◽

Near Surface ◽

Gas Leakage ◽

Time Period ◽

Characteristics Analysis ◽

Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

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