SB
S. Basu
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1
Free-space optical communication (FSOC) is a key technology to meet the growing demand for high-bandwidth, secure, and energy-efficient data links.
However, the atmospheric channel introduces a major challenge: optical turbulence (OT). Turbulent fluctuations of the refractive index, driven by wind shear and buoyancy in the atmosphere, distort the propagating optical beam, degrading communication performance. This dissertation investigates the modelling of optical turbulence, quantified through the refractive index structure parameter Cn2, along two complementary avenues: traditional numerical weather prediction (NWP) using mesoscale models and machine learning (ML) techniques.
The first part establishes the state of the art in mesoscale Cn2 modelling for FSOC. Chapter 2 presents a systematic intercomparison of the three main classes of Cn2 parameterizations -- flux-based, gradient-based, and variance-based -- applied to both observed data and output from a numerical mesoscale model.
Evaluated against scintillometer observations, the variance-based parameterization yields the best overall performance and, unlike the other two, is not restricted to the atmospheric surface layer. Building on this foundation, chapter 3 proposes an end-to-end framework that translates Cn2 estimates into FSOC link performance metrics, specifically turbulence-induced losses and a theoretically achievable information rate (generalized mutual information, GMI).
Applied to an urban example link, the framework reveals that the sensitivity of the estimated FSOC performance to Cn2 errors strongly depends on the specific link. Reporting Cn2 model errors alone is therefore considered insufficient for FSOC, and end-to-end assessments of link performance are needed.
Running mesoscale models is computationally expensive, and the traditional parameterizations used to obtain Cn2 -- while physically motivated -- are often limited in their applicability or accuracy. Machine learning offers a promising alternative by learning complex relationships directly from data, as explored in the second part of this dissertation. Chapter 4 introduces OTCliM, a gradient boosting-based methodology that relates globally available ERA5 reanalysis data to observed near-surface Cn2. Across 17 diverse stations in New York State, OTCliM trained on just one year of observations accurately extrapolates Cn2 over four held-out years. However, the resulting models are limited to near-surface Cn2, which is sufficient for terrestrial links but not for satellite-to-ground applications that require vertical profiles. To extend modelling into the vertical, chapter 5 introduces Π-ML, a physics-inspired ML framework that combines automated dimensional analysis with gradient boosting. By expressing inputs and outputs as non-dimensional groups grounded in physical principles, Π-ML derives an interpretable, data-driven similarity theory for Cn2 in the atmospheric surface layer. The framework achieves high accuracy under both stable and unstable atmospheric conditions and identifies scalings that are physically consistent with established theories. Finally, chapter 6 addresses the full atmospheric column by developing OTProf. This deep-learning-based model estimates Cn2 profiles of high vertical resolution from low-resolution ERA5 pressure-level inputs. Trained on one year of mesoscale simulations, the model substantially outperforms the commonly used Hufnagel-Valley analytical model.
The estimated profiles exhibit a physically realistic vertical structure, although ML-typical smoothing leads to some underestimation of integrated turbulence parameters, such as the Fried parameter and the scintillation index.
Nevertheless, OTProf is regarded as a computationally efficient and considerably more accurate approach compared to traditional analytical models.
Taken together, this dissertation demonstrates that both numerical mesoscale modelling and machine learning deliver practical advances for optical turbulence estimation and FSOC applications. The overarching conclusion, however, is that the greatest potential lies in combining the two paradigms, which is viewed as a promising direction for future research. ...
However, the atmospheric channel introduces a major challenge: optical turbulence (OT). Turbulent fluctuations of the refractive index, driven by wind shear and buoyancy in the atmosphere, distort the propagating optical beam, degrading communication performance. This dissertation investigates the modelling of optical turbulence, quantified through the refractive index structure parameter Cn2, along two complementary avenues: traditional numerical weather prediction (NWP) using mesoscale models and machine learning (ML) techniques.
The first part establishes the state of the art in mesoscale Cn2 modelling for FSOC. Chapter 2 presents a systematic intercomparison of the three main classes of Cn2 parameterizations -- flux-based, gradient-based, and variance-based -- applied to both observed data and output from a numerical mesoscale model.
Evaluated against scintillometer observations, the variance-based parameterization yields the best overall performance and, unlike the other two, is not restricted to the atmospheric surface layer. Building on this foundation, chapter 3 proposes an end-to-end framework that translates Cn2 estimates into FSOC link performance metrics, specifically turbulence-induced losses and a theoretically achievable information rate (generalized mutual information, GMI).
Applied to an urban example link, the framework reveals that the sensitivity of the estimated FSOC performance to Cn2 errors strongly depends on the specific link. Reporting Cn2 model errors alone is therefore considered insufficient for FSOC, and end-to-end assessments of link performance are needed.
Running mesoscale models is computationally expensive, and the traditional parameterizations used to obtain Cn2 -- while physically motivated -- are often limited in their applicability or accuracy. Machine learning offers a promising alternative by learning complex relationships directly from data, as explored in the second part of this dissertation. Chapter 4 introduces OTCliM, a gradient boosting-based methodology that relates globally available ERA5 reanalysis data to observed near-surface Cn2. Across 17 diverse stations in New York State, OTCliM trained on just one year of observations accurately extrapolates Cn2 over four held-out years. However, the resulting models are limited to near-surface Cn2, which is sufficient for terrestrial links but not for satellite-to-ground applications that require vertical profiles. To extend modelling into the vertical, chapter 5 introduces Π-ML, a physics-inspired ML framework that combines automated dimensional analysis with gradient boosting. By expressing inputs and outputs as non-dimensional groups grounded in physical principles, Π-ML derives an interpretable, data-driven similarity theory for Cn2 in the atmospheric surface layer. The framework achieves high accuracy under both stable and unstable atmospheric conditions and identifies scalings that are physically consistent with established theories. Finally, chapter 6 addresses the full atmospheric column by developing OTProf. This deep-learning-based model estimates Cn2 profiles of high vertical resolution from low-resolution ERA5 pressure-level inputs. Trained on one year of mesoscale simulations, the model substantially outperforms the commonly used Hufnagel-Valley analytical model.
The estimated profiles exhibit a physically realistic vertical structure, although ML-typical smoothing leads to some underestimation of integrated turbulence parameters, such as the Fried parameter and the scintillation index.
Nevertheless, OTProf is regarded as a computationally efficient and considerably more accurate approach compared to traditional analytical models.
Taken together, this dissertation demonstrates that both numerical mesoscale modelling and machine learning deliver practical advances for optical turbulence estimation and FSOC applications. The overarching conclusion, however, is that the greatest potential lies in combining the two paradigms, which is viewed as a promising direction for future research. ...
Free-space optical communication (FSOC) is a key technology to meet the growing demand for high-bandwidth, secure, and energy-efficient data links.
However, the atmospheric channel introduces a major challenge: optical turbulence (OT). Turbulent fluctuations of the refractive index, driven by wind shear and buoyancy in the atmosphere, distort the propagating optical beam, degrading communication performance. This dissertation investigates the modelling of optical turbulence, quantified through the refractive index structure parameter Cn2, along two complementary avenues: traditional numerical weather prediction (NWP) using mesoscale models and machine learning (ML) techniques.
The first part establishes the state of the art in mesoscale Cn2 modelling for FSOC. Chapter 2 presents a systematic intercomparison of the three main classes of Cn2 parameterizations -- flux-based, gradient-based, and variance-based -- applied to both observed data and output from a numerical mesoscale model.
Evaluated against scintillometer observations, the variance-based parameterization yields the best overall performance and, unlike the other two, is not restricted to the atmospheric surface layer. Building on this foundation, chapter 3 proposes an end-to-end framework that translates Cn2 estimates into FSOC link performance metrics, specifically turbulence-induced losses and a theoretically achievable information rate (generalized mutual information, GMI).
Applied to an urban example link, the framework reveals that the sensitivity of the estimated FSOC performance to Cn2 errors strongly depends on the specific link. Reporting Cn2 model errors alone is therefore considered insufficient for FSOC, and end-to-end assessments of link performance are needed.
Running mesoscale models is computationally expensive, and the traditional parameterizations used to obtain Cn2 -- while physically motivated -- are often limited in their applicability or accuracy. Machine learning offers a promising alternative by learning complex relationships directly from data, as explored in the second part of this dissertation. Chapter 4 introduces OTCliM, a gradient boosting-based methodology that relates globally available ERA5 reanalysis data to observed near-surface Cn2. Across 17 diverse stations in New York State, OTCliM trained on just one year of observations accurately extrapolates Cn2 over four held-out years. However, the resulting models are limited to near-surface Cn2, which is sufficient for terrestrial links but not for satellite-to-ground applications that require vertical profiles. To extend modelling into the vertical, chapter 5 introduces Π-ML, a physics-inspired ML framework that combines automated dimensional analysis with gradient boosting. By expressing inputs and outputs as non-dimensional groups grounded in physical principles, Π-ML derives an interpretable, data-driven similarity theory for Cn2 in the atmospheric surface layer. The framework achieves high accuracy under both stable and unstable atmospheric conditions and identifies scalings that are physically consistent with established theories. Finally, chapter 6 addresses the full atmospheric column by developing OTProf. This deep-learning-based model estimates Cn2 profiles of high vertical resolution from low-resolution ERA5 pressure-level inputs. Trained on one year of mesoscale simulations, the model substantially outperforms the commonly used Hufnagel-Valley analytical model.
The estimated profiles exhibit a physically realistic vertical structure, although ML-typical smoothing leads to some underestimation of integrated turbulence parameters, such as the Fried parameter and the scintillation index.
Nevertheless, OTProf is regarded as a computationally efficient and considerably more accurate approach compared to traditional analytical models.
Taken together, this dissertation demonstrates that both numerical mesoscale modelling and machine learning deliver practical advances for optical turbulence estimation and FSOC applications. The overarching conclusion, however, is that the greatest potential lies in combining the two paradigms, which is viewed as a promising direction for future research.
However, the atmospheric channel introduces a major challenge: optical turbulence (OT). Turbulent fluctuations of the refractive index, driven by wind shear and buoyancy in the atmosphere, distort the propagating optical beam, degrading communication performance. This dissertation investigates the modelling of optical turbulence, quantified through the refractive index structure parameter Cn2, along two complementary avenues: traditional numerical weather prediction (NWP) using mesoscale models and machine learning (ML) techniques.
The first part establishes the state of the art in mesoscale Cn2 modelling for FSOC. Chapter 2 presents a systematic intercomparison of the three main classes of Cn2 parameterizations -- flux-based, gradient-based, and variance-based -- applied to both observed data and output from a numerical mesoscale model.
Evaluated against scintillometer observations, the variance-based parameterization yields the best overall performance and, unlike the other two, is not restricted to the atmospheric surface layer. Building on this foundation, chapter 3 proposes an end-to-end framework that translates Cn2 estimates into FSOC link performance metrics, specifically turbulence-induced losses and a theoretically achievable information rate (generalized mutual information, GMI).
Applied to an urban example link, the framework reveals that the sensitivity of the estimated FSOC performance to Cn2 errors strongly depends on the specific link. Reporting Cn2 model errors alone is therefore considered insufficient for FSOC, and end-to-end assessments of link performance are needed.
Running mesoscale models is computationally expensive, and the traditional parameterizations used to obtain Cn2 -- while physically motivated -- are often limited in their applicability or accuracy. Machine learning offers a promising alternative by learning complex relationships directly from data, as explored in the second part of this dissertation. Chapter 4 introduces OTCliM, a gradient boosting-based methodology that relates globally available ERA5 reanalysis data to observed near-surface Cn2. Across 17 diverse stations in New York State, OTCliM trained on just one year of observations accurately extrapolates Cn2 over four held-out years. However, the resulting models are limited to near-surface Cn2, which is sufficient for terrestrial links but not for satellite-to-ground applications that require vertical profiles. To extend modelling into the vertical, chapter 5 introduces Π-ML, a physics-inspired ML framework that combines automated dimensional analysis with gradient boosting. By expressing inputs and outputs as non-dimensional groups grounded in physical principles, Π-ML derives an interpretable, data-driven similarity theory for Cn2 in the atmospheric surface layer. The framework achieves high accuracy under both stable and unstable atmospheric conditions and identifies scalings that are physically consistent with established theories. Finally, chapter 6 addresses the full atmospheric column by developing OTProf. This deep-learning-based model estimates Cn2 profiles of high vertical resolution from low-resolution ERA5 pressure-level inputs. Trained on one year of mesoscale simulations, the model substantially outperforms the commonly used Hufnagel-Valley analytical model.
The estimated profiles exhibit a physically realistic vertical structure, although ML-typical smoothing leads to some underestimation of integrated turbulence parameters, such as the Fried parameter and the scintillation index.
Nevertheless, OTProf is regarded as a computationally efficient and considerably more accurate approach compared to traditional analytical models.
Taken together, this dissertation demonstrates that both numerical mesoscale modelling and machine learning deliver practical advances for optical turbulence estimation and FSOC applications. The overarching conclusion, however, is that the greatest potential lies in combining the two paradigms, which is viewed as a promising direction for future research.
Can fourier neural operators replicate the intrinsic predictability of spatiotemporal chaos?
For the Kuramoto-Sivashinsky system
The use of deep learning in global weather forecasting has shown significant promise in improving both forecasting accuracy and speed. Traditional numerical weather prediction models have gradually improved forecasting skills but at the cost of increased computational complexity. In contrast, new deep learning models, trained directly on reanalysis data, have demonstrated significant gains in forecasting accuracy, achieving competitive levels of performance.
However, the potential of deep learning in predicting spatiotemporal chaotic systems, such as weather patterns, remains unexplored. To address this gap, we investigate the efficacy of a data-driven Fourier neural operator Markovian forecaster to replicate the intrinsic predictability and the characteristic Lyapunov spectrum of the Kuramoto-Sivashinsky system.
FNO reproduces intrinsic predictability and precisely estimates the characteristic Lyapunov spectrum, even with a small dataset.
They cannot represent one of the invariant symmetries, a zero characteristic Lyapunov exponent in the spectrum.
Our findings suggest that deep learning can not only enhance the speed and accuracy of traditional numerical forecasting models but also replicate the weather's chaotic nature.
This has significant implications for generating large ensembles and improving overall probabilistic forecasts.
The research is limited to a deterministic system defined on a single process time scale surrogated by FNO.
The future merits a similar study to investigate if FNO models can perform similarly on larger, stochastic, coupled, and/or multiple time-scale spatiotemporal chaotic systems. ...
However, the potential of deep learning in predicting spatiotemporal chaotic systems, such as weather patterns, remains unexplored. To address this gap, we investigate the efficacy of a data-driven Fourier neural operator Markovian forecaster to replicate the intrinsic predictability and the characteristic Lyapunov spectrum of the Kuramoto-Sivashinsky system.
FNO reproduces intrinsic predictability and precisely estimates the characteristic Lyapunov spectrum, even with a small dataset.
They cannot represent one of the invariant symmetries, a zero characteristic Lyapunov exponent in the spectrum.
Our findings suggest that deep learning can not only enhance the speed and accuracy of traditional numerical forecasting models but also replicate the weather's chaotic nature.
This has significant implications for generating large ensembles and improving overall probabilistic forecasts.
The research is limited to a deterministic system defined on a single process time scale surrogated by FNO.
The future merits a similar study to investigate if FNO models can perform similarly on larger, stochastic, coupled, and/or multiple time-scale spatiotemporal chaotic systems. ...
The use of deep learning in global weather forecasting has shown significant promise in improving both forecasting accuracy and speed. Traditional numerical weather prediction models have gradually improved forecasting skills but at the cost of increased computational complexity. In contrast, new deep learning models, trained directly on reanalysis data, have demonstrated significant gains in forecasting accuracy, achieving competitive levels of performance.
However, the potential of deep learning in predicting spatiotemporal chaotic systems, such as weather patterns, remains unexplored. To address this gap, we investigate the efficacy of a data-driven Fourier neural operator Markovian forecaster to replicate the intrinsic predictability and the characteristic Lyapunov spectrum of the Kuramoto-Sivashinsky system.
FNO reproduces intrinsic predictability and precisely estimates the characteristic Lyapunov spectrum, even with a small dataset.
They cannot represent one of the invariant symmetries, a zero characteristic Lyapunov exponent in the spectrum.
Our findings suggest that deep learning can not only enhance the speed and accuracy of traditional numerical forecasting models but also replicate the weather's chaotic nature.
This has significant implications for generating large ensembles and improving overall probabilistic forecasts.
The research is limited to a deterministic system defined on a single process time scale surrogated by FNO.
The future merits a similar study to investigate if FNO models can perform similarly on larger, stochastic, coupled, and/or multiple time-scale spatiotemporal chaotic systems.
However, the potential of deep learning in predicting spatiotemporal chaotic systems, such as weather patterns, remains unexplored. To address this gap, we investigate the efficacy of a data-driven Fourier neural operator Markovian forecaster to replicate the intrinsic predictability and the characteristic Lyapunov spectrum of the Kuramoto-Sivashinsky system.
FNO reproduces intrinsic predictability and precisely estimates the characteristic Lyapunov spectrum, even with a small dataset.
They cannot represent one of the invariant symmetries, a zero characteristic Lyapunov exponent in the spectrum.
Our findings suggest that deep learning can not only enhance the speed and accuracy of traditional numerical forecasting models but also replicate the weather's chaotic nature.
This has significant implications for generating large ensembles and improving overall probabilistic forecasts.
The research is limited to a deterministic system defined on a single process time scale surrogated by FNO.
The future merits a similar study to investigate if FNO models can perform similarly on larger, stochastic, coupled, and/or multiple time-scale spatiotemporal chaotic systems.
Novel machine learning methods for short-term solar PV forecasting
Satellite image and PV generation based forecast framework for the German energy market
With the growing global drive to act up on climate change, the adoption of renewable energy sources such as solar photovoltaic (PV) is continuously increasing. This crucial shift poses many economic and environmental benefits, however the variability in solar PV generation may also threaten the stability of our power grid and energy supply. The reliable prediction of this fluctuating power resource on various time scales has been identified as a crucial technology for the continuous massive adoption of solar PV. This study concentrates on the application of convolutional neural networks (CNN) and Long Short Term Memory (LSTM) to process real-time data sources in spatially aggregated solar PV power forecast for Germany, with specifically a forecast horizon of 3 hours and 15-minute interval. Two models are designed to be applicable in a real-time operational setting with a short forecast lag: (1) A LSTM network that leverages on the latest solar PV generation data and a NWP based day-ahead power forecast, and (2) a CNN-LSTM network designed to utilize the latest satellite images and a NWP based day-ahead power forecast. The accuracy of the forecast models are evaluated using one year of solar PV power generation data in Germany (January 2020 through December 2020), and are compared to a persistence model and a NWP based day-ahead and intra-day power forecast provided by the German transmission system operators. The empirical results show that the two proposed models perform equal or better than the benchmark models. An implication for power trading practices is that deep learning models, such as LSTM and CNN-LSTM, shows to be a promising forecasting technique which deserves a place in a comprehensive solar PV power forecasting toolbox.
...
With the growing global drive to act up on climate change, the adoption of renewable energy sources such as solar photovoltaic (PV) is continuously increasing. This crucial shift poses many economic and environmental benefits, however the variability in solar PV generation may also threaten the stability of our power grid and energy supply. The reliable prediction of this fluctuating power resource on various time scales has been identified as a crucial technology for the continuous massive adoption of solar PV. This study concentrates on the application of convolutional neural networks (CNN) and Long Short Term Memory (LSTM) to process real-time data sources in spatially aggregated solar PV power forecast for Germany, with specifically a forecast horizon of 3 hours and 15-minute interval. Two models are designed to be applicable in a real-time operational setting with a short forecast lag: (1) A LSTM network that leverages on the latest solar PV generation data and a NWP based day-ahead power forecast, and (2) a CNN-LSTM network designed to utilize the latest satellite images and a NWP based day-ahead power forecast. The accuracy of the forecast models are evaluated using one year of solar PV power generation data in Germany (January 2020 through December 2020), and are compared to a persistence model and a NWP based day-ahead and intra-day power forecast provided by the German transmission system operators. The empirical results show that the two proposed models perform equal or better than the benchmark models. An implication for power trading practices is that deep learning models, such as LSTM and CNN-LSTM, shows to be a promising forecasting technique which deserves a place in a comprehensive solar PV power forecasting toolbox.
Since the 13th of October 2017, the Tropospheric Monitoring Instrument (TROPOMI) aboard ESA’s Sentinel 5-Precursor (S5-P) satellite enables daily global measurements of carbon monoxide (CO) total column con- centrations at an unprecedented spatial resolution of 7×5.6 km. TROPOMI has the ability to detect distinct pollution plumes, arising from point source emissions, from which emission rates can be derived. We in- vestigate the potential of CO column concentrations observed by TROPOMI to estimate the CO emissions of point sources on an operational level. This study developed a Python framework that for pre-defined point sources automatically detects pollution plumes and from which it estimates CO emissions using a mass bal- ance approach directly from single overpass CO observations. The algorithm is based on concepts from the computer vision to identify the plume and extract the plume center line while respecting the plume orienta- tion. The emission rate is approximated from flux profiles through multiple plume cross-sections following the plume center line. The performance of the developed framework and its potential is demonstrated by the application on 132 identified steel plant facilities over a time period of more than 2.5 years. Currently the lack of accessible and quality-wise good data limits spatial or even temporal comparison of CO emissions from steel plants. Therefore the control and understanding of emission rates could greatly benefit from the proposed approach. In total we obtained 1,774 emission estimates for 97 facilities. Up to 119 measurements per facility are derived where for the majority of the facilities the average number of measurements is around 10. The obtained time series showed large variation in the distribution of measurements over time as well as the emission values itself. For a number of higher emission values, that exceeded up to 2 times the aver- age emission, measured for e.g. the Bhilai Steel Plant, India, the outliers corresponded with interference of another source. Although individual plumes could be identified for two sources (∼35 km apart) in the same Bhilai area, no non-merged plumes were detected for the Schwelgern and Huttenheim sites (∼18 km apart) in Duisburg, Germany. Moreover, we tested the agreement of our measurements with recorded or stated events: i) The emission estimate from the afternoon of the 24th of May 2019, Bhilai site, confirmed the manufacturers statement that the operations had continued that day despite a reported fire in the morning. ii) Our results did not match the significant global drop noted in steel production during the first period of 2020 as a result of the pandemic. The scattered distribution of measurements and their emission values over time seem to limit the representation of a small time frame needed for such analysis. iii) We found a positive correlation with a Pearson Coefficient of 0.76 between the European Pollutant Release Transfer Register (E-PRTR) and our data. For all examined facilities our obtained emissions were greater than reported by the facilities to E-PRTR. This might indicate an underestimation of the data registered. This first evaluation emphasizes the potential of TROPOMI observations to improve our understanding of point source emissions and to compliment existing data such as the E-PRTR. However, to be able to interpret the data from TROPOMI indeed structurally and to develop a reliable validation method extensive data-analysis on plant and area-level is required, especially to be able to rule out interfering factors.
...
Since the 13th of October 2017, the Tropospheric Monitoring Instrument (TROPOMI) aboard ESA’s Sentinel 5-Precursor (S5-P) satellite enables daily global measurements of carbon monoxide (CO) total column con- centrations at an unprecedented spatial resolution of 7×5.6 km. TROPOMI has the ability to detect distinct pollution plumes, arising from point source emissions, from which emission rates can be derived. We in- vestigate the potential of CO column concentrations observed by TROPOMI to estimate the CO emissions of point sources on an operational level. This study developed a Python framework that for pre-defined point sources automatically detects pollution plumes and from which it estimates CO emissions using a mass bal- ance approach directly from single overpass CO observations. The algorithm is based on concepts from the computer vision to identify the plume and extract the plume center line while respecting the plume orienta- tion. The emission rate is approximated from flux profiles through multiple plume cross-sections following the plume center line. The performance of the developed framework and its potential is demonstrated by the application on 132 identified steel plant facilities over a time period of more than 2.5 years. Currently the lack of accessible and quality-wise good data limits spatial or even temporal comparison of CO emissions from steel plants. Therefore the control and understanding of emission rates could greatly benefit from the proposed approach. In total we obtained 1,774 emission estimates for 97 facilities. Up to 119 measurements per facility are derived where for the majority of the facilities the average number of measurements is around 10. The obtained time series showed large variation in the distribution of measurements over time as well as the emission values itself. For a number of higher emission values, that exceeded up to 2 times the aver- age emission, measured for e.g. the Bhilai Steel Plant, India, the outliers corresponded with interference of another source. Although individual plumes could be identified for two sources (∼35 km apart) in the same Bhilai area, no non-merged plumes were detected for the Schwelgern and Huttenheim sites (∼18 km apart) in Duisburg, Germany. Moreover, we tested the agreement of our measurements with recorded or stated events: i) The emission estimate from the afternoon of the 24th of May 2019, Bhilai site, confirmed the manufacturers statement that the operations had continued that day despite a reported fire in the morning. ii) Our results did not match the significant global drop noted in steel production during the first period of 2020 as a result of the pandemic. The scattered distribution of measurements and their emission values over time seem to limit the representation of a small time frame needed for such analysis. iii) We found a positive correlation with a Pearson Coefficient of 0.76 between the European Pollutant Release Transfer Register (E-PRTR) and our data. For all examined facilities our obtained emissions were greater than reported by the facilities to E-PRTR. This might indicate an underestimation of the data registered. This first evaluation emphasizes the potential of TROPOMI observations to improve our understanding of point source emissions and to compliment existing data such as the E-PRTR. However, to be able to interpret the data from TROPOMI indeed structurally and to develop a reliable validation method extensive data-analysis on plant and area-level is required, especially to be able to rule out interfering factors.
This research attempts to investigate the coastal flow structure horizontally. The flow cases with different flow directions, including perpendicular and parallel to the shoreline directions, are selected by analyzing the New Europe Wind Atlas (NEWA) reanalysis data. After the cases are determined, high-resolution Weather Research and Forecasting Model (WRF) are performed under Yonsei University (YSU) Planetary Boundary Layer (PBL) scheme with ERA5 forcing data. The results are validated against the measurements from long-range wind scanner WindCube 400S. The validated data is analyzed for quantifying the orthogonal wind speed gradient over the study domain. As a result, the WRF, being a mesoscale modelling tool, it captures the orthogonal coastal wind speed gradients reasonably accurate within a 6km range compared with observations. Generally, WRF performs better in the alongshore flow cases, especially in the flow case 2018-10-08 when the stable condition occurs most frequently during the case. The Root Mean Square Error (RMSE) for this case is 0.9m/s. However, the simulation results lost correlations with the observational data in some epochs when the fluctuation of wind speed occurs frequently. To be concrete, during flow case 2018-12-10 when the flow direction is around 330◦ advecting onshore, the WRF simulation can predict the fluctuations correct in magnitude but with shifting in time. For the coastal wind speed gradient, WRF predicts an absolute wind speed difference of 0.5 m/s daily averaged at the typical hub-height over a 1.4km distance for the alongshore flow cases. This difference can enlarge to 1m/s or higher while the low-level jets take place. The long-range wind scanning LiDAR provides a great possibility for observing the flow condition over a wind farm sized domain. Especially for the nearshore wind farm, it compensates for lack of observations on the water.
...
This research attempts to investigate the coastal flow structure horizontally. The flow cases with different flow directions, including perpendicular and parallel to the shoreline directions, are selected by analyzing the New Europe Wind Atlas (NEWA) reanalysis data. After the cases are determined, high-resolution Weather Research and Forecasting Model (WRF) are performed under Yonsei University (YSU) Planetary Boundary Layer (PBL) scheme with ERA5 forcing data. The results are validated against the measurements from long-range wind scanner WindCube 400S. The validated data is analyzed for quantifying the orthogonal wind speed gradient over the study domain. As a result, the WRF, being a mesoscale modelling tool, it captures the orthogonal coastal wind speed gradients reasonably accurate within a 6km range compared with observations. Generally, WRF performs better in the alongshore flow cases, especially in the flow case 2018-10-08 when the stable condition occurs most frequently during the case. The Root Mean Square Error (RMSE) for this case is 0.9m/s. However, the simulation results lost correlations with the observational data in some epochs when the fluctuation of wind speed occurs frequently. To be concrete, during flow case 2018-12-10 when the flow direction is around 330◦ advecting onshore, the WRF simulation can predict the fluctuations correct in magnitude but with shifting in time. For the coastal wind speed gradient, WRF predicts an absolute wind speed difference of 0.5 m/s daily averaged at the typical hub-height over a 1.4km distance for the alongshore flow cases. This difference can enlarge to 1m/s or higher while the low-level jets take place. The long-range wind scanning LiDAR provides a great possibility for observing the flow condition over a wind farm sized domain. Especially for the nearshore wind farm, it compensates for lack of observations on the water.
In the well-known process of the turbine design, turbulence intensity (TI) plays a vital role in prediction of the power output and loads on the turbine's structure. TI is believed to be an important statistical parameter of the wind speed that can be extracted from the signals recorded by the dedicated sensors in the wind energy area. Despite the limitations of the mast-mounted sensors, they are probably more popular than LIDARs in wind energy applications. Although the sonic anemometers are reference tools in measuring turbulent features of wind, they are expensive instruments to be employed in a large-scale. In this regard, the cup anemometers appear to be the most commonly used instruments in the wind energy community. Accordingly, it would be tremendously advantageous if the 1-Hz cup anemometer data can be employed with the synthetic down-scaling idea to build the turbulence-like velocity signal fields. In this research, small-scale fluctuations are constructed via the Fractal Interpolation (FI) technique. In addition, this study aims to assess the compatibility of the FI technique in enhancing the cup anemometer data. The analysis has been carried out for the data collected in September 2018. Through this analysis, it is deduced that the cup anemometer data can be improved using the FI method. Subsequently, by applying the FI method, in most of the cases, the standard deviation values of the cup anemometer data are increased.
...
In the well-known process of the turbine design, turbulence intensity (TI) plays a vital role in prediction of the power output and loads on the turbine's structure. TI is believed to be an important statistical parameter of the wind speed that can be extracted from the signals recorded by the dedicated sensors in the wind energy area. Despite the limitations of the mast-mounted sensors, they are probably more popular than LIDARs in wind energy applications. Although the sonic anemometers are reference tools in measuring turbulent features of wind, they are expensive instruments to be employed in a large-scale. In this regard, the cup anemometers appear to be the most commonly used instruments in the wind energy community. Accordingly, it would be tremendously advantageous if the 1-Hz cup anemometer data can be employed with the synthetic down-scaling idea to build the turbulence-like velocity signal fields. In this research, small-scale fluctuations are constructed via the Fractal Interpolation (FI) technique. In addition, this study aims to assess the compatibility of the FI technique in enhancing the cup anemometer data. The analysis has been carried out for the data collected in September 2018. Through this analysis, it is deduced that the cup anemometer data can be improved using the FI method. Subsequently, by applying the FI method, in most of the cases, the standard deviation values of the cup anemometer data are increased.
Mesoscale Modelling of Waterspouts
An Offshore Wind Energy Perspective
Wind energy is becoming an important renewable energy source. An increased number of offshore wind farms are constructed due to the relatively higher wind speeds. Besides, compared with the land, the ocean areas offer more empty space for the installation of wind turbines. In recent years, several governments in Europe have the plan to expand their countries’ wind farms over the North Sea area. With this surge in the development of offshore wind farms, extreme weather events over the sea pose threats to the installations. A waterspout is one of such phenomenon of concern.
In this study, we simulated and characterized the atmospheric conditions associated with two waterspout events observed recently over the North Sea. These cases were selected from the European Severe Weather Database. Various types of observational data, including radiosondes, radar reflectivities, satellite imageries, lighting maps, and floating liar-based wind profiles, were utilized for detailed characterization. Atmospheric circulation patterns associated with waterspouts were deduced from surface-level and upper-air synoptic charts. A mesoscale model, called the Weather Research and Forecasting model, was used for simulations with a high spatial resolution of 1 km. We used five different parameterizations of varying complexities to quantify the sensitivity of the simulated results with respect to cloud microphysics. A number of meteorological variables and indices (e.g., thermodynamic indices, wind shear, vertical velocity, reflectivity) are extracted from the simulations and compared with the observational data. In general, our results are in agreement with the findings from previous studies. For instance, we have found that a double moment microphysics parameterization produces more realistic results in comparison with a single moment one. However, we have noticed that our simulated results fall outside the range specified by the so-called Szilagyi waterspout nomogram. This nomogram was initially proposed based on observational data from the Great Lakes region and is widely used by the operational meteorologists. Based on the results, updating this nomogram is needed with additional observational and simulated data from the North Sea region. ...
In this study, we simulated and characterized the atmospheric conditions associated with two waterspout events observed recently over the North Sea. These cases were selected from the European Severe Weather Database. Various types of observational data, including radiosondes, radar reflectivities, satellite imageries, lighting maps, and floating liar-based wind profiles, were utilized for detailed characterization. Atmospheric circulation patterns associated with waterspouts were deduced from surface-level and upper-air synoptic charts. A mesoscale model, called the Weather Research and Forecasting model, was used for simulations with a high spatial resolution of 1 km. We used five different parameterizations of varying complexities to quantify the sensitivity of the simulated results with respect to cloud microphysics. A number of meteorological variables and indices (e.g., thermodynamic indices, wind shear, vertical velocity, reflectivity) are extracted from the simulations and compared with the observational data. In general, our results are in agreement with the findings from previous studies. For instance, we have found that a double moment microphysics parameterization produces more realistic results in comparison with a single moment one. However, we have noticed that our simulated results fall outside the range specified by the so-called Szilagyi waterspout nomogram. This nomogram was initially proposed based on observational data from the Great Lakes region and is widely used by the operational meteorologists. Based on the results, updating this nomogram is needed with additional observational and simulated data from the North Sea region. ...
Wind energy is becoming an important renewable energy source. An increased number of offshore wind farms are constructed due to the relatively higher wind speeds. Besides, compared with the land, the ocean areas offer more empty space for the installation of wind turbines. In recent years, several governments in Europe have the plan to expand their countries’ wind farms over the North Sea area. With this surge in the development of offshore wind farms, extreme weather events over the sea pose threats to the installations. A waterspout is one of such phenomenon of concern.
In this study, we simulated and characterized the atmospheric conditions associated with two waterspout events observed recently over the North Sea. These cases were selected from the European Severe Weather Database. Various types of observational data, including radiosondes, radar reflectivities, satellite imageries, lighting maps, and floating liar-based wind profiles, were utilized for detailed characterization. Atmospheric circulation patterns associated with waterspouts were deduced from surface-level and upper-air synoptic charts. A mesoscale model, called the Weather Research and Forecasting model, was used for simulations with a high spatial resolution of 1 km. We used five different parameterizations of varying complexities to quantify the sensitivity of the simulated results with respect to cloud microphysics. A number of meteorological variables and indices (e.g., thermodynamic indices, wind shear, vertical velocity, reflectivity) are extracted from the simulations and compared with the observational data. In general, our results are in agreement with the findings from previous studies. For instance, we have found that a double moment microphysics parameterization produces more realistic results in comparison with a single moment one. However, we have noticed that our simulated results fall outside the range specified by the so-called Szilagyi waterspout nomogram. This nomogram was initially proposed based on observational data from the Great Lakes region and is widely used by the operational meteorologists. Based on the results, updating this nomogram is needed with additional observational and simulated data from the North Sea region.
In this study, we simulated and characterized the atmospheric conditions associated with two waterspout events observed recently over the North Sea. These cases were selected from the European Severe Weather Database. Various types of observational data, including radiosondes, radar reflectivities, satellite imageries, lighting maps, and floating liar-based wind profiles, were utilized for detailed characterization. Atmospheric circulation patterns associated with waterspouts were deduced from surface-level and upper-air synoptic charts. A mesoscale model, called the Weather Research and Forecasting model, was used for simulations with a high spatial resolution of 1 km. We used five different parameterizations of varying complexities to quantify the sensitivity of the simulated results with respect to cloud microphysics. A number of meteorological variables and indices (e.g., thermodynamic indices, wind shear, vertical velocity, reflectivity) are extracted from the simulations and compared with the observational data. In general, our results are in agreement with the findings from previous studies. For instance, we have found that a double moment microphysics parameterization produces more realistic results in comparison with a single moment one. However, we have noticed that our simulated results fall outside the range specified by the so-called Szilagyi waterspout nomogram. This nomogram was initially proposed based on observational data from the Great Lakes region and is widely used by the operational meteorologists. Based on the results, updating this nomogram is needed with additional observational and simulated data from the North Sea region.
In current study, several fundamental and inherent problems in original Deardorff subgrid model are identified under stably stratified condition. It is found that the mixing length parameterization in this subgrid model is at the root of a long trouble problem of grid size sensitivity in large-eddy simulation (LES). A new formulation of mixing length is proposed under the consideration of some basic elements including the presence of surface, the dependence of grid size Δ and a smoothing interpolation. The performance of this modified scheme is remarkable regarding the improvement of the simulation quality and accuracy. In other words, not only is the convergence of the simulated results from a range of grid size achieved but also in the precise intensity of physical variables are modelled. The only discrepancies display in the variance of temperature in the middle of boundary layer and high turbulent kinetic energy near the surface.
To further experiment the performance of the new scheme under different scenarios, the cases of different stability condition, an independent LES code with same modification, the cases of different advection schemes and different prescribed parameters are explored. In very stable condition, the first order variables from the modified scheme are in reasonable range but with some spreads compared to the results from a dynamic code. The deviation of second order statistics shows that the proposed formulation of mixing length meets limitations due to the complex interaction between the surface and turbulent flow in shallower boundary layer. The modified scheme is model system independent based on the similar improvement of simulation results in an independent LES code system. The sensitivity of advection schemes is surprisingly hardly found in new proposed SGS model. The cases of tested parameters further verifies the limitation of original Deardroff subgrid model. ...
To further experiment the performance of the new scheme under different scenarios, the cases of different stability condition, an independent LES code with same modification, the cases of different advection schemes and different prescribed parameters are explored. In very stable condition, the first order variables from the modified scheme are in reasonable range but with some spreads compared to the results from a dynamic code. The deviation of second order statistics shows that the proposed formulation of mixing length meets limitations due to the complex interaction between the surface and turbulent flow in shallower boundary layer. The modified scheme is model system independent based on the similar improvement of simulation results in an independent LES code system. The sensitivity of advection schemes is surprisingly hardly found in new proposed SGS model. The cases of tested parameters further verifies the limitation of original Deardroff subgrid model. ...
In current study, several fundamental and inherent problems in original Deardorff subgrid model are identified under stably stratified condition. It is found that the mixing length parameterization in this subgrid model is at the root of a long trouble problem of grid size sensitivity in large-eddy simulation (LES). A new formulation of mixing length is proposed under the consideration of some basic elements including the presence of surface, the dependence of grid size Δ and a smoothing interpolation. The performance of this modified scheme is remarkable regarding the improvement of the simulation quality and accuracy. In other words, not only is the convergence of the simulated results from a range of grid size achieved but also in the precise intensity of physical variables are modelled. The only discrepancies display in the variance of temperature in the middle of boundary layer and high turbulent kinetic energy near the surface.
To further experiment the performance of the new scheme under different scenarios, the cases of different stability condition, an independent LES code with same modification, the cases of different advection schemes and different prescribed parameters are explored. In very stable condition, the first order variables from the modified scheme are in reasonable range but with some spreads compared to the results from a dynamic code. The deviation of second order statistics shows that the proposed formulation of mixing length meets limitations due to the complex interaction between the surface and turbulent flow in shallower boundary layer. The modified scheme is model system independent based on the similar improvement of simulation results in an independent LES code system. The sensitivity of advection schemes is surprisingly hardly found in new proposed SGS model. The cases of tested parameters further verifies the limitation of original Deardroff subgrid model.
To further experiment the performance of the new scheme under different scenarios, the cases of different stability condition, an independent LES code with same modification, the cases of different advection schemes and different prescribed parameters are explored. In very stable condition, the first order variables from the modified scheme are in reasonable range but with some spreads compared to the results from a dynamic code. The deviation of second order statistics shows that the proposed formulation of mixing length meets limitations due to the complex interaction between the surface and turbulent flow in shallower boundary layer. The modified scheme is model system independent based on the similar improvement of simulation results in an independent LES code system. The sensitivity of advection schemes is surprisingly hardly found in new proposed SGS model. The cases of tested parameters further verifies the limitation of original Deardroff subgrid model.
Wind Classification using Unsupervised Learning
In support of the Olympic Sailing Competition in Tokyo, Japan
During the preparation for the Olympic Sailing Competition, held in 2021 in Tokyo, Japan, the Dutch National Sailing Team encountered days with unpredicted wind behaviour. To gain more understanding in the wind patterns occurring, a deep learning based approach is taken. The goal of this research is to find out if unsupervised learning methods can contribute to wind pattern classification. It can then be investigated if the classification can increase understanding in specific wind patterns. The input data for the unsupervised learning model consists of 40 years of reanalysis wind speed data of an area including Japan. To classify the wind patterns, the dimensionality of the input data is reduced using different autoencoders. This reduced dimensional form is then clustered using K-means clustering. The results of the K-means algorithm are compared and the best autoencoder is chosen. The resulting clusters are analyzed for extreme wind patterns, such as typhoons. It is expected that these wind patterns will be clustered together. To check this, the cluster containing typhoon Jebi, the typhoon which caused the highest insurance cost ever in Japan, is analyzed. If this cluster contains typhoons, unsupervised learning is able to provide useful information regarding wind patterns. The best working autoencoder used in this research is the 3D CNN autoencoder. Using the 3D CNN autoencoder, some clusters with specific wind patterns are found. The cluster containing typhoon Jebi consists of 95.8% of typhoons, from which it can be concluded that unsupervised learning is a valid method for wind pattern classification.
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During the preparation for the Olympic Sailing Competition, held in 2021 in Tokyo, Japan, the Dutch National Sailing Team encountered days with unpredicted wind behaviour. To gain more understanding in the wind patterns occurring, a deep learning based approach is taken. The goal of this research is to find out if unsupervised learning methods can contribute to wind pattern classification. It can then be investigated if the classification can increase understanding in specific wind patterns. The input data for the unsupervised learning model consists of 40 years of reanalysis wind speed data of an area including Japan. To classify the wind patterns, the dimensionality of the input data is reduced using different autoencoders. This reduced dimensional form is then clustered using K-means clustering. The results of the K-means algorithm are compared and the best autoencoder is chosen. The resulting clusters are analyzed for extreme wind patterns, such as typhoons. It is expected that these wind patterns will be clustered together. To check this, the cluster containing typhoon Jebi, the typhoon which caused the highest insurance cost ever in Japan, is analyzed. If this cluster contains typhoons, unsupervised learning is able to provide useful information regarding wind patterns. The best working autoencoder used in this research is the 3D CNN autoencoder. Using the 3D CNN autoencoder, some clusters with specific wind patterns are found. The cluster containing typhoon Jebi consists of 95.8% of typhoons, from which it can be concluded that unsupervised learning is a valid method for wind pattern classification.
Master thesis
(2020)
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C.C. van Wirdum, S. Basu, C.M.H. Unal, G. Lenderink, Andreas Sterl, Hylke de Vries
Severe wind gusts associated with mid-latitude convective storms contribute to an increasing amount of natural hazard related losses in Europe. Modifications of the atmosphere associated with anthropogenic climate change are projected to increase the frequency of favorable conditions for convective storms, and it is absolutely critical to understand the implications of these changes to prepare for a resilient future. To this end, the fate of convective gusts in Europe in a future warmer climate is addressed through the output of two high resolution regional climate models (RCMs). One RCM includes convection permitting (CP) physics, and the other assumes hydrostatic conditions in which deep convection is parameterized. It is found that the magnitude and characteristics of extreme straight line gusts from mesoscale convective systems are well resolved in the CP-RCM but not in the hydrostatic RCM.
The RCMs are forced with a high carbon emission scenario for the end of the 21st century, and the CP-RCM shows an increase in the frequency and magnitude of extreme convective gusts over mainland Europe. These changes are likely related to an increase in conditions where strong wind shear (≥15 m/s) simultaneously occurs with unstable environments (lifted index ≤-2). However, the inherent low frequency of extreme convective storms requires continued investigation to draw more robust conclusions. In addition, the environmental conditions in which the severe gusts take place indicate the importance of addressing gusts separately from the more commonly studied effects of climate change on extreme convective precipitation. This thesis provides an important bridge to understand the fate of extreme gusts from convective storms in a future warmer climate and the high potential of CP-RCMs in such studies. ...
The RCMs are forced with a high carbon emission scenario for the end of the 21st century, and the CP-RCM shows an increase in the frequency and magnitude of extreme convective gusts over mainland Europe. These changes are likely related to an increase in conditions where strong wind shear (≥15 m/s) simultaneously occurs with unstable environments (lifted index ≤-2). However, the inherent low frequency of extreme convective storms requires continued investigation to draw more robust conclusions. In addition, the environmental conditions in which the severe gusts take place indicate the importance of addressing gusts separately from the more commonly studied effects of climate change on extreme convective precipitation. This thesis provides an important bridge to understand the fate of extreme gusts from convective storms in a future warmer climate and the high potential of CP-RCMs in such studies. ...
Severe wind gusts associated with mid-latitude convective storms contribute to an increasing amount of natural hazard related losses in Europe. Modifications of the atmosphere associated with anthropogenic climate change are projected to increase the frequency of favorable conditions for convective storms, and it is absolutely critical to understand the implications of these changes to prepare for a resilient future. To this end, the fate of convective gusts in Europe in a future warmer climate is addressed through the output of two high resolution regional climate models (RCMs). One RCM includes convection permitting (CP) physics, and the other assumes hydrostatic conditions in which deep convection is parameterized. It is found that the magnitude and characteristics of extreme straight line gusts from mesoscale convective systems are well resolved in the CP-RCM but not in the hydrostatic RCM.
The RCMs are forced with a high carbon emission scenario for the end of the 21st century, and the CP-RCM shows an increase in the frequency and magnitude of extreme convective gusts over mainland Europe. These changes are likely related to an increase in conditions where strong wind shear (≥15 m/s) simultaneously occurs with unstable environments (lifted index ≤-2). However, the inherent low frequency of extreme convective storms requires continued investigation to draw more robust conclusions. In addition, the environmental conditions in which the severe gusts take place indicate the importance of addressing gusts separately from the more commonly studied effects of climate change on extreme convective precipitation. This thesis provides an important bridge to understand the fate of extreme gusts from convective storms in a future warmer climate and the high potential of CP-RCMs in such studies.
The RCMs are forced with a high carbon emission scenario for the end of the 21st century, and the CP-RCM shows an increase in the frequency and magnitude of extreme convective gusts over mainland Europe. These changes are likely related to an increase in conditions where strong wind shear (≥15 m/s) simultaneously occurs with unstable environments (lifted index ≤-2). However, the inherent low frequency of extreme convective storms requires continued investigation to draw more robust conclusions. In addition, the environmental conditions in which the severe gusts take place indicate the importance of addressing gusts separately from the more commonly studied effects of climate change on extreme convective precipitation. This thesis provides an important bridge to understand the fate of extreme gusts from convective storms in a future warmer climate and the high potential of CP-RCMs in such studies.
Over the years, the Leipzig Wind Profile observed under near neutral condition has been considered as an essential benchmark for idealized friction layer models. However, the general weather condition for the Leipzig Wind Profile still remains a mystery after nearly 90 years. In order to simulate this event and try to recreate the weather conditions at that time, the WRF model driven by two types of reanalysis data setting up with two domains is launched. The model captures the wind physics well, except for a small deviation around 800 meters. Additionally, the simulated surface friction velocity and surface heat flux at Leipzig are close to the validation value. The simulated temperature slope at Lindenberg has some deviation compared to the documented value, which may be caused by the vertical coarse resolution and sensitive temperature fluctuations over the height. Moreover, there may have been rain or drizzle when the observation took place and this may have contributed to the near neutral condition feature. A finer resolution simulation could be run to investigate this further
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Over the years, the Leipzig Wind Profile observed under near neutral condition has been considered as an essential benchmark for idealized friction layer models. However, the general weather condition for the Leipzig Wind Profile still remains a mystery after nearly 90 years. In order to simulate this event and try to recreate the weather conditions at that time, the WRF model driven by two types of reanalysis data setting up with two domains is launched. The model captures the wind physics well, except for a small deviation around 800 meters. Additionally, the simulated surface friction velocity and surface heat flux at Leipzig are close to the validation value. The simulated temperature slope at Lindenberg has some deviation compared to the documented value, which may be caused by the vertical coarse resolution and sensitive temperature fluctuations over the height. Moreover, there may have been rain or drizzle when the observation took place and this may have contributed to the near neutral condition feature. A finer resolution simulation could be run to investigate this further
At the mercy of strong winds, high wind shear, unstable boundary layer and anomalous atmospheric conditions, stands a wind turbine designed to produce sustainable power under harsh conditions. The field of wind energy is a promising prospect for a sustainable future. Diverse research towards the improvement of a wind turbine’s capability and cost is currently the focus of the wind energy industry. With higher wind turbines being designed every day, various challenges and limitations of the current state-of-the-art surface; anomalous atmospheric conditions, structural integrity and cost.
The goal of this thesis is to extend the approach to design a site-specific wind turbine considering an anomalous atmospheric condition. By coupling a mesoscale model with a stochastic turbulence function, a wind field capable of depicting a particular atmospheric condition is created. Using an aeroelastic solver the resulting loads on a wind turbine can be simulated. The methodology uses Weather, Research and Forecasting (WRF) model to re-create an event of low-level jet identified at the met mast of FINO-1, off coast Germany. The wind profile is coupled with a stochastic turbulence function designed at FINO-1 to be used as wind field for the aeroelastic solver, FAST.
A literature survey identified a multitude of approaches used for simulating a low-level jet and analyse the loads on a wind turbine, the majority of which indicate high computational costs and contrived re-creations of the event. Thus, the challenge was to identify a near-realistic event creation with low computational costs. Therefore, coupling a low-resolution mesoscale model to create the event with a site-specific stochastic turbulence function is used to analyse loads on a wind turbine.
Meteorological data analysis at FINO-1 led to the identification of three case studies of low-level jets under varied stability conditions of the atmosphere. The case studies are compared with the International Electrotechnical Commission (IEC) standard’s, IEC – 61400 – Ed3; IEC Kaimal and IEC Great Planes Low Level Jet (GPLLJ) spectrum. For cases with high stability, on an average proposed model predicts 21% higher stress on blade root and 27% higher at the tower top and base in comparison to IEC GPLLJ and 15% and 30% lower in comparison to IEC Kaimal, respectively. Similarly, under unstable conditions, proposed model predicts similar loads on the blade root, 7% lower loads at the tower top and base in comparison to IEC GPLLJ and 30% higher loads for blade root and tower top and base in comparison to IEC Kaimal. Comparing these results with literature on high stability loading higher loads are expected under these conditions.
Concluding, this project developed a model framework to analyse anomalous atmospheric phenomena on a wind turbine specific to a site with low computational costs. While the capabilities of the model have been successfully showcased, only a partial validation on a benchmark case has been carried out. Therefore, going forward a full physical validation of the model for its accuracy for its target applications is recommended.
...
The goal of this thesis is to extend the approach to design a site-specific wind turbine considering an anomalous atmospheric condition. By coupling a mesoscale model with a stochastic turbulence function, a wind field capable of depicting a particular atmospheric condition is created. Using an aeroelastic solver the resulting loads on a wind turbine can be simulated. The methodology uses Weather, Research and Forecasting (WRF) model to re-create an event of low-level jet identified at the met mast of FINO-1, off coast Germany. The wind profile is coupled with a stochastic turbulence function designed at FINO-1 to be used as wind field for the aeroelastic solver, FAST.
A literature survey identified a multitude of approaches used for simulating a low-level jet and analyse the loads on a wind turbine, the majority of which indicate high computational costs and contrived re-creations of the event. Thus, the challenge was to identify a near-realistic event creation with low computational costs. Therefore, coupling a low-resolution mesoscale model to create the event with a site-specific stochastic turbulence function is used to analyse loads on a wind turbine.
Meteorological data analysis at FINO-1 led to the identification of three case studies of low-level jets under varied stability conditions of the atmosphere. The case studies are compared with the International Electrotechnical Commission (IEC) standard’s, IEC – 61400 – Ed3; IEC Kaimal and IEC Great Planes Low Level Jet (GPLLJ) spectrum. For cases with high stability, on an average proposed model predicts 21% higher stress on blade root and 27% higher at the tower top and base in comparison to IEC GPLLJ and 15% and 30% lower in comparison to IEC Kaimal, respectively. Similarly, under unstable conditions, proposed model predicts similar loads on the blade root, 7% lower loads at the tower top and base in comparison to IEC GPLLJ and 30% higher loads for blade root and tower top and base in comparison to IEC Kaimal. Comparing these results with literature on high stability loading higher loads are expected under these conditions.
Concluding, this project developed a model framework to analyse anomalous atmospheric phenomena on a wind turbine specific to a site with low computational costs. While the capabilities of the model have been successfully showcased, only a partial validation on a benchmark case has been carried out. Therefore, going forward a full physical validation of the model for its accuracy for its target applications is recommended.
...
At the mercy of strong winds, high wind shear, unstable boundary layer and anomalous atmospheric conditions, stands a wind turbine designed to produce sustainable power under harsh conditions. The field of wind energy is a promising prospect for a sustainable future. Diverse research towards the improvement of a wind turbine’s capability and cost is currently the focus of the wind energy industry. With higher wind turbines being designed every day, various challenges and limitations of the current state-of-the-art surface; anomalous atmospheric conditions, structural integrity and cost.
The goal of this thesis is to extend the approach to design a site-specific wind turbine considering an anomalous atmospheric condition. By coupling a mesoscale model with a stochastic turbulence function, a wind field capable of depicting a particular atmospheric condition is created. Using an aeroelastic solver the resulting loads on a wind turbine can be simulated. The methodology uses Weather, Research and Forecasting (WRF) model to re-create an event of low-level jet identified at the met mast of FINO-1, off coast Germany. The wind profile is coupled with a stochastic turbulence function designed at FINO-1 to be used as wind field for the aeroelastic solver, FAST.
A literature survey identified a multitude of approaches used for simulating a low-level jet and analyse the loads on a wind turbine, the majority of which indicate high computational costs and contrived re-creations of the event. Thus, the challenge was to identify a near-realistic event creation with low computational costs. Therefore, coupling a low-resolution mesoscale model to create the event with a site-specific stochastic turbulence function is used to analyse loads on a wind turbine.
Meteorological data analysis at FINO-1 led to the identification of three case studies of low-level jets under varied stability conditions of the atmosphere. The case studies are compared with the International Electrotechnical Commission (IEC) standard’s, IEC – 61400 – Ed3; IEC Kaimal and IEC Great Planes Low Level Jet (GPLLJ) spectrum. For cases with high stability, on an average proposed model predicts 21% higher stress on blade root and 27% higher at the tower top and base in comparison to IEC GPLLJ and 15% and 30% lower in comparison to IEC Kaimal, respectively. Similarly, under unstable conditions, proposed model predicts similar loads on the blade root, 7% lower loads at the tower top and base in comparison to IEC GPLLJ and 30% higher loads for blade root and tower top and base in comparison to IEC Kaimal. Comparing these results with literature on high stability loading higher loads are expected under these conditions.
Concluding, this project developed a model framework to analyse anomalous atmospheric phenomena on a wind turbine specific to a site with low computational costs. While the capabilities of the model have been successfully showcased, only a partial validation on a benchmark case has been carried out. Therefore, going forward a full physical validation of the model for its accuracy for its target applications is recommended.
The goal of this thesis is to extend the approach to design a site-specific wind turbine considering an anomalous atmospheric condition. By coupling a mesoscale model with a stochastic turbulence function, a wind field capable of depicting a particular atmospheric condition is created. Using an aeroelastic solver the resulting loads on a wind turbine can be simulated. The methodology uses Weather, Research and Forecasting (WRF) model to re-create an event of low-level jet identified at the met mast of FINO-1, off coast Germany. The wind profile is coupled with a stochastic turbulence function designed at FINO-1 to be used as wind field for the aeroelastic solver, FAST.
A literature survey identified a multitude of approaches used for simulating a low-level jet and analyse the loads on a wind turbine, the majority of which indicate high computational costs and contrived re-creations of the event. Thus, the challenge was to identify a near-realistic event creation with low computational costs. Therefore, coupling a low-resolution mesoscale model to create the event with a site-specific stochastic turbulence function is used to analyse loads on a wind turbine.
Meteorological data analysis at FINO-1 led to the identification of three case studies of low-level jets under varied stability conditions of the atmosphere. The case studies are compared with the International Electrotechnical Commission (IEC) standard’s, IEC – 61400 – Ed3; IEC Kaimal and IEC Great Planes Low Level Jet (GPLLJ) spectrum. For cases with high stability, on an average proposed model predicts 21% higher stress on blade root and 27% higher at the tower top and base in comparison to IEC GPLLJ and 15% and 30% lower in comparison to IEC Kaimal, respectively. Similarly, under unstable conditions, proposed model predicts similar loads on the blade root, 7% lower loads at the tower top and base in comparison to IEC GPLLJ and 30% higher loads for blade root and tower top and base in comparison to IEC Kaimal. Comparing these results with literature on high stability loading higher loads are expected under these conditions.
Concluding, this project developed a model framework to analyse anomalous atmospheric phenomena on a wind turbine specific to a site with low computational costs. While the capabilities of the model have been successfully showcased, only a partial validation on a benchmark case has been carried out. Therefore, going forward a full physical validation of the model for its accuracy for its target applications is recommended.
Wind energy is becoming an important source of energy and reliable forecasts for the production of wind energy are needed to improve its integration in the power grid. The increasing height of wind turbines results in higher layers of the atmosphere being reached, where other phenomena than the surface-based ones can be of importance. One of these phenomena that can affect the wind energy production is a ramp up or a ramp down; a sudden increase or decrease in wind speed. One of the causes of a ramp down is a Frontal Low Level Jet (FLLJ), a jet stream which forms just ahead of a cold front. When the front has passed, the wind speed suddenly drops. It is yet unknown how well frontal Low Level Jets (LLJs) are simulated by a numerical weather prediction model. For this research, case studies of occurrences of FLLJs were determined based on Light Detection And Ranging (LiDAR) observations, synopticmaps and coarse-resolutionWeather Research and Forecasting (WRF) simulations. The chosen case studies were simulated with WRF using a finer resolution. These simulations were compared with surface observations from weather stations, wind profiler observations, LiDAR observations and observations from a wind farm. From these comparisons it was concluded that the WRF model performed relatively well in general. The general development and dissolving of the FLLJ were simulated correctly, but the timing and magnitude of these simulations can be improved.
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Wind energy is becoming an important source of energy and reliable forecasts for the production of wind energy are needed to improve its integration in the power grid. The increasing height of wind turbines results in higher layers of the atmosphere being reached, where other phenomena than the surface-based ones can be of importance. One of these phenomena that can affect the wind energy production is a ramp up or a ramp down; a sudden increase or decrease in wind speed. One of the causes of a ramp down is a Frontal Low Level Jet (FLLJ), a jet stream which forms just ahead of a cold front. When the front has passed, the wind speed suddenly drops. It is yet unknown how well frontal Low Level Jets (LLJs) are simulated by a numerical weather prediction model. For this research, case studies of occurrences of FLLJs were determined based on Light Detection And Ranging (LiDAR) observations, synopticmaps and coarse-resolutionWeather Research and Forecasting (WRF) simulations. The chosen case studies were simulated with WRF using a finer resolution. These simulations were compared with surface observations from weather stations, wind profiler observations, LiDAR observations and observations from a wind farm. From these comparisons it was concluded that the WRF model performed relatively well in general. The general development and dissolving of the FLLJ were simulated correctly, but the timing and magnitude of these simulations can be improved.
The Weather Research and Forecasting (WRF) model is used to investigate the horizontal spatial resolution sensitivity by simulating the 1953 Dutch storm, the grid sizes are 27, 9, 3, 1, 0.5 km from domain 1 to domain 5 respectively. Overall, the the probability density functions of wind speed of all time do not show higher resolution corresponding to higher maximum wind speed and more possibility to detect stronger winds. Because finer resolution domains feature a relatively weak model of wind intensity than coarser resolution domains. In the case, simulations are used two PBL scheme, YSU and Shin-Hong scheme. Three measurement stations with historical meteorology observation in 1953 are selected, Schiphol Netherlands, Leeuwarden Netherlands and Bentwaters United Kingdom. By comparison the measured data and all simulated data, the result from Shin-Hong scheme at the inner domain (d05) perform the best wind speed.
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The Weather Research and Forecasting (WRF) model is used to investigate the horizontal spatial resolution sensitivity by simulating the 1953 Dutch storm, the grid sizes are 27, 9, 3, 1, 0.5 km from domain 1 to domain 5 respectively. Overall, the the probability density functions of wind speed of all time do not show higher resolution corresponding to higher maximum wind speed and more possibility to detect stronger winds. Because finer resolution domains feature a relatively weak model of wind intensity than coarser resolution domains. In the case, simulations are used two PBL scheme, YSU and Shin-Hong scheme. Three measurement stations with historical meteorology observation in 1953 are selected, Schiphol Netherlands, Leeuwarden Netherlands and Bentwaters United Kingdom. By comparison the measured data and all simulated data, the result from Shin-Hong scheme at the inner domain (d05) perform the best wind speed.
Master thesis
(2018)
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Sam Koch, Sukanta Basu, Pier Siebesma, Wim Bierbooms, J.W. Wagenaar, Steven Knoop
The height of wind turbines continues to increase, making the need for more and higher wind measurements for wind turbine model calibrations also increase. The Energy-research Centre of the Netherlands (ECN) and the Danish Technical University (DTU) have conducted the ScanFlow campaign in the winter of 2016/2017 to study the inflow wind field of one of ECN's research turbines by deploying multiple lidar instruments. One of the instruments used in this campaign was a SpinnerLidar. A SpinnerLidar is a forward looking, nacelle-mounted, continuous wave wind lidar system. It measures Line-of-Sight components of the wind in a plane 60 meters in front of the turbine, which need to be transformed into 3D wind vectors. To do so necessary assumptions were made about the free inflow periods, namely that a vertical shear is present and that a wind direction misalignment is more likely than horizontal deviations in wind speed. With these applied assumptions the 3D wind components were determined and used in a validation study with a pulsed lidar instrument, the WindCube V2. The proposed method seems robust as a high correlation in wind speeds at hub-height between two distinctly different lidar systems was found. Using the validated SpinnerLidar measurement to find the turbulent characteristics of the free inflow wind field resulted in turbulence intensity plots showing a higher turbulent component in the lower regions of the measurement plane. Also indications for a induction zone are visible in the SpinnerLidar measurements when compared to the WindCube measurements.
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The height of wind turbines continues to increase, making the need for more and higher wind measurements for wind turbine model calibrations also increase. The Energy-research Centre of the Netherlands (ECN) and the Danish Technical University (DTU) have conducted the ScanFlow campaign in the winter of 2016/2017 to study the inflow wind field of one of ECN's research turbines by deploying multiple lidar instruments. One of the instruments used in this campaign was a SpinnerLidar. A SpinnerLidar is a forward looking, nacelle-mounted, continuous wave wind lidar system. It measures Line-of-Sight components of the wind in a plane 60 meters in front of the turbine, which need to be transformed into 3D wind vectors. To do so necessary assumptions were made about the free inflow periods, namely that a vertical shear is present and that a wind direction misalignment is more likely than horizontal deviations in wind speed. With these applied assumptions the 3D wind components were determined and used in a validation study with a pulsed lidar instrument, the WindCube V2. The proposed method seems robust as a high correlation in wind speeds at hub-height between two distinctly different lidar systems was found. Using the validated SpinnerLidar measurement to find the turbulent characteristics of the free inflow wind field resulted in turbulence intensity plots showing a higher turbulent component in the lower regions of the measurement plane. Also indications for a induction zone are visible in the SpinnerLidar measurements when compared to the WindCube measurements.