The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
H. A. R. Bruin - One of the best experts on this subject based on the ideXlab platform.
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monin obukhov similarity functions of the Structure Parameter of temperature and turbulent kinetic energy dissipation rate in the stable boundary layer
Boundary-Layer Meteorology, 2005Co-Authors: O K Hartogensis, H. A. R. BruinAbstract:The Monin–Obukhov similarity theory (MOST) functions fe and f T , of the dissipation rate of turbulent kinetic energy (TKE). e, and the Structure Parameter of temperature, C T 2 , were determined for the stable atmospheric surface layer using data gathered in the context of CASES-99. These data cover a relatively wide stability range, i.e. ζ=z/L of up to 10, where z is the height and L the Obukhov length. The best fits were given by fe = 0.8 + 2.5ζ and f T = 4.7[ 1+1.6(ζ)2/3], which differ somewhat from previously published functions. e was obtained from spectra of the longitudinal wind velocity using a time series model (ARMA) method instead of the traditional Fourier transform. The neutral limit fe =0.8 implies that there is an imbalance between TKE production and dissipation in the simplified TKE budget equation. Similarly, we found a production-dissipation imbalance for the temperature fluctuation budget equation. Correcting for the production-dissipation imbalance, the ‘standard’ MOST functions for dimensionless wind speed and temperature gradients (φ m and φ m ) were determined from fe and f T and compared with the φ m and φ h formulations of Businger and others. We found good agreement with the Beljaars and Holtslag [J. Appl. Meteorol. 30, 327–341 (1991)] relations. Lastly, the flux and gradient Richardson numbers are discussed also in terms of fe and f T .
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A verification of some methods to determine the fluxes of momentum, sensible heat, and water vapour using standard deviation and Structure Parameter of scalar meteorological quantities
Boundary-Layer Meteorology, 1993Co-Authors: H. A. R. Bruin, W. Kohsiek, B. J. J. M. HurkAbstract:A set of micro-meteorological data collected over a horizontal, uniform terrain (the plain of La Crau, France) in June 1987 is analysed. Conditions were predominantly sunny and arid, while due to the “ Mistral ” the wind speed could exceed 10 m/s. Verification of several methods to evaluate surface fluxes of heat, momentum and water vapour from the standard deviation of temperature, wind and specific humidity is presented. Also, a similar approach using the Structure Parameter of temperature is considered. These methods are all based on Monin-Obukhov (M-O) similarity theory. It is found that the standard deviation of temperature, vertical and horizontal wind speed as well as the Structure Parameter for temperature behave according to M-O similarity. It is shown that the sensible heat flux and friction velocity can be determined from a fast response thermometer and a cup anemometer. Also, it appears that the analytic solution of the set of governing equations as derived by the first author yields good results. M-O theory does not appear to work for the standard deviation of specific humidity. This may be due to the relative importance of large eddies.
Jens Bange - One of the best experts on this subject based on the ideXlab platform.
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Observations of the Temperature and Humidity Structure Parameter Over Heterogeneous Terrain by Airborne Measurements During the LITFASS-2003 Campaign
Boundary-Layer Meteorology, 2017Co-Authors: Andreas Platis, Frank Beyrich, Arnold F. Moene, Daniel Martínez Villagrasa, David Tupman, Jens BangeAbstract:The turbulent Structure Parameters of temperature ( $$C_T^2$$ C T 2 ) and humidity ( $$C_Q^2$$ C Q 2 ), and their cross-Structure Parameter ( $$C_{QT}$$ C Q T ), are investigated using data collected with the airborne-measurement platform Helipod during the LITFASS-2003 campaign. The flights took place within the atmospheric surface layer over heterogeneous terrain including forests, a lake and farmland. We find variability in $$C_T^2$$ C T 2 along such flight legs, with values of $$C_T^2$$ C T 2 over forested surfaces one order of magnitude larger than over farmland, and two orders of magnitude larger than over the lake. However, a quantitative relationship between the magnitude of $$C_Q^2$$ C Q 2 and the surface type is not found, most likely due to a similar surface latent heat flux between the land-use types. However, when the different flight legs are taken together and data grouped by land-use type, values of $$C_Q^2$$ C Q 2 are significantly lower over the lake than over the other surfaces. A classification of $$C_{QT}$$ C Q T is only possible between water and land surfaces, with lower values over water. We find the correlation coefficient $$R_{QT}$$ R Q T in the range of 0.4–1.0, which is less than unity, and thus violates the assumption of unity in Monin–Obukhov similarity theory.
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On the Discrepancy in Simultaneous Observations of the Structure Parameter of Temperature Using Scintillometers and Unmanned Aircraft
Boundary-Layer Meteorology, 2016Co-Authors: Miranda Braam, Sabrina Martin, Frank Beyrich, Jens Bange, Andreas Platis, Björn Maronga, Arnold F. MoeneAbstract:We elaborate on the preliminary results presented in Beyrich et al. (in Boundary-Layer Meteorol 144:83–112, 2012 ), who compared the Structure Parameter of temperature ( $${C_{T}^2}_{}$$ C T 2 ) obtained with the unmanned meteorological mini aerial vehicle ( $$\text{ M }^{2}\text{ AV } $$ M 2 AV ) versus $${C_{T}^2}_{}$$ C T 2 obtained with two large-aperture scintillometers (LASs) for a limited dataset from one single experiment (LITFASS-2009). They found that $${C_{T}^2}_{}$$ C T 2 obtained from the $$\text{ M }^{2}\text{ AV } $$ M 2 AV data is significantly larger than that obtained from the LAS data. We investigate if similar differences can be found for the flights on the other six days during LITFASS-2009 and LITFASS-2010, and whether these differences can be reduced or explained through a more elaborate processing of both the LAS data and the $$\text{ M }^{2}\text{ AV } $$ M 2 AV data. This processing includes different corrections and measures to reduce the differences between the spatial and temporal averaging of the datasets. We conclude that the differences reported in Beyrich et al. can be found for other days as well. For the LAS-derived values the additional processing steps that have the largest effect are the saturation correction and the humidity correction. For the $$\text{ M }^{2}\text{ AV } $$ M 2 AV -derived values the most important step is the application of the scintillometer path-weighting function. Using the true air speed of the $$\text{ M }^{2}\text{ AV } $$ M 2 AV to convert from a temporal to a spatial Structure function rather than the ground speed (as in Beyrich et al.) does not change the mean discrepancy, but it does affect $${C_{T}^2}_{}$$ C T 2 values for individual flights. To investigate whether $${C_{T}^2}_{}$$ C T 2 derived from the $$\text{ M }^{2}\text{ AV } $$ M 2 AV data depends on the fact that the underlying temperature dataset combines spatial and temporal sampling, we used large-eddy simulation data to analyze $${C_{T}^2}_{}$$ C T 2 from virtual flights with different mean ground speeds. This analysis shows that $${C_{T}^2}_{}$$ C T 2 does only slightly depends on the true air speed when averaged over many flights.
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spatially averaged temperature Structure Parameter over a heterogeneous surface measured by an unmanned aerial vehicle
Boundary-Layer Meteorology, 2012Co-Authors: A C Van Den Kroonenberg, Sabrina Martin, Frank Beyrich, Jens BangeAbstract:The Structure Parameter of temperature, \({C_{T}^{2}}\) , in the lower convective boundary layer was measured using the unmanned mini aerial vehicle M2AV. The measurements were carried out on two hot summer days in July 2010 over a heterogeneous land surface around the boundary-layer field site of the Lindenberg Meteorological Observatory—Richard-Asmann-Observatory of the German Meteorological Service. The spatial series of \({C_{T}^{2}}\) showed considerable variability along the flight path that was caused by both temporal variations and surface heterogeneity. Comparison of the aircraft data with \({C_{T}^{2}}\) values derived from tower-based in situ turbulence measurements showed good agreement with respect to the diurnal variability. The decrease of \({C_{T}^{2}}\) with height as predicted by free-convection scaling could be confirmed for the morning and afternoon flights while the flights around noon suggest a different behaviour.
Changjin Lee - One of the best experts on this subject based on the ideXlab platform.
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reinforcement Structure Parameter learning for neural network based fuzzy logic control systems
IEEE International Conference on Fuzzy Systems, 1993Co-Authors: Chinteng Lin, Changjin LeeAbstract:The authors propose a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. RNN-FLCS is best applied to learning environments where obtaining exact training data is expensive. It is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLCs), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller implements a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, the RNN-FLCs can construct a fuzzy logic control system automatically and dynamically through a reward-penalty signal or through very simple fuzzy information feedback. Structure learning and Parameter learning are performed simultaneously in the two NN-FLCs. Simulation results are presented. >
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real time supervised Structure Parameter learning for fuzzy neural network
IEEE International Conference on Fuzzy Systems, 1992Co-Authors: Chinteng Lin, Changjin LeeAbstract:The authors propose a real-time supervised Structure and Parameter learning algorithm for constructing fuzzy neural networks (FNNs) automatically and dynamically. This algorithm combines the backpropagation learning scheme for the Parameter learning and a novel fuzzy similarity measure for the Structure learning. The fuzzy similarity measure is a new tool to determine the degree to which two fuzzy sets are equal. The FNN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist Structure which has distributed learning abilities. The Structure learning decides the proper connection types and the number of hidden units which represent fuzzy logic rules and the number of fuzzy partitions. The Parameter learning adjusts the node and link Parameters which represent the membership functions. The proposed supervised learning algorithm provides an efficient way of constructing a FNN in real time. Simulation results are presented to illustrate the performance and applicability of the proposed learning algorithm. >
Chinteng Lin - One of the best experts on this subject based on the ideXlab platform.
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reinforcement Structure Parameter learning for neural network based fuzzy logic control systems
IEEE Transactions on Fuzzy Systems, 1994Co-Authors: Chinteng Lin, C S G LeeAbstract:This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC's), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC performs as a fuzzy predictor, and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, both Structure learning and Parameter learning are performed simultaneously in the two NN-FLC's using the fuzzy similarity measure. The proposed RNN-FLCS can construct a fuzzy logic control and decision-making system automatically and dynamically through a reward/penalty signal or through very simple fuzzy information feedback such as "high," "too high," "low," and "too low." The proposed RNN-FLCS is best applied to the learning environment, where obtaining exact training data is expensive. It also preserves the advantages of the original NN-FLC, such as the ability to find proper network Structure and Parameters simultaneously and dynamically and to avoid the rule-matching time of the inference engine. Computer simulations were conducted to illustrate its performance and applicability. >
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reinforcement Structure Parameter learning for neural network based fuzzy logic control systems
IEEE International Conference on Fuzzy Systems, 1993Co-Authors: Chinteng Lin, Changjin LeeAbstract:The authors propose a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. RNN-FLCS is best applied to learning environments where obtaining exact training data is expensive. It is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLCs), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller implements a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, the RNN-FLCs can construct a fuzzy logic control system automatically and dynamically through a reward-penalty signal or through very simple fuzzy information feedback. Structure learning and Parameter learning are performed simultaneously in the two NN-FLCs. Simulation results are presented. >
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real time supervised Structure Parameter learning for fuzzy neural network
IEEE International Conference on Fuzzy Systems, 1992Co-Authors: Chinteng Lin, Changjin LeeAbstract:The authors propose a real-time supervised Structure and Parameter learning algorithm for constructing fuzzy neural networks (FNNs) automatically and dynamically. This algorithm combines the backpropagation learning scheme for the Parameter learning and a novel fuzzy similarity measure for the Structure learning. The fuzzy similarity measure is a new tool to determine the degree to which two fuzzy sets are equal. The FNN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist Structure which has distributed learning abilities. The Structure learning decides the proper connection types and the number of hidden units which represent fuzzy logic rules and the number of fuzzy partitions. The Parameter learning adjusts the node and link Parameters which represent the membership functions. The proposed supervised learning algorithm provides an efficient way of constructing a FNN in real time. Simulation results are presented to illustrate the performance and applicability of the proposed learning algorithm. >
Frank Beyrich - One of the best experts on this subject based on the ideXlab platform.
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Observations of the Temperature and Humidity Structure Parameter Over Heterogeneous Terrain by Airborne Measurements During the LITFASS-2003 Campaign
Boundary-Layer Meteorology, 2017Co-Authors: Andreas Platis, Frank Beyrich, Arnold F. Moene, Daniel Martínez Villagrasa, David Tupman, Jens BangeAbstract:The turbulent Structure Parameters of temperature ( $$C_T^2$$ C T 2 ) and humidity ( $$C_Q^2$$ C Q 2 ), and their cross-Structure Parameter ( $$C_{QT}$$ C Q T ), are investigated using data collected with the airborne-measurement platform Helipod during the LITFASS-2003 campaign. The flights took place within the atmospheric surface layer over heterogeneous terrain including forests, a lake and farmland. We find variability in $$C_T^2$$ C T 2 along such flight legs, with values of $$C_T^2$$ C T 2 over forested surfaces one order of magnitude larger than over farmland, and two orders of magnitude larger than over the lake. However, a quantitative relationship between the magnitude of $$C_Q^2$$ C Q 2 and the surface type is not found, most likely due to a similar surface latent heat flux between the land-use types. However, when the different flight legs are taken together and data grouped by land-use type, values of $$C_Q^2$$ C Q 2 are significantly lower over the lake than over the other surfaces. A classification of $$C_{QT}$$ C Q T is only possible between water and land surfaces, with lower values over water. We find the correlation coefficient $$R_{QT}$$ R Q T in the range of 0.4–1.0, which is less than unity, and thus violates the assumption of unity in Monin–Obukhov similarity theory.
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On the Discrepancy in Simultaneous Observations of the Structure Parameter of Temperature Using Scintillometers and Unmanned Aircraft
Boundary-Layer Meteorology, 2016Co-Authors: Miranda Braam, Sabrina Martin, Frank Beyrich, Jens Bange, Andreas Platis, Björn Maronga, Arnold F. MoeneAbstract:We elaborate on the preliminary results presented in Beyrich et al. (in Boundary-Layer Meteorol 144:83–112, 2012 ), who compared the Structure Parameter of temperature ( $${C_{T}^2}_{}$$ C T 2 ) obtained with the unmanned meteorological mini aerial vehicle ( $$\text{ M }^{2}\text{ AV } $$ M 2 AV ) versus $${C_{T}^2}_{}$$ C T 2 obtained with two large-aperture scintillometers (LASs) for a limited dataset from one single experiment (LITFASS-2009). They found that $${C_{T}^2}_{}$$ C T 2 obtained from the $$\text{ M }^{2}\text{ AV } $$ M 2 AV data is significantly larger than that obtained from the LAS data. We investigate if similar differences can be found for the flights on the other six days during LITFASS-2009 and LITFASS-2010, and whether these differences can be reduced or explained through a more elaborate processing of both the LAS data and the $$\text{ M }^{2}\text{ AV } $$ M 2 AV data. This processing includes different corrections and measures to reduce the differences between the spatial and temporal averaging of the datasets. We conclude that the differences reported in Beyrich et al. can be found for other days as well. For the LAS-derived values the additional processing steps that have the largest effect are the saturation correction and the humidity correction. For the $$\text{ M }^{2}\text{ AV } $$ M 2 AV -derived values the most important step is the application of the scintillometer path-weighting function. Using the true air speed of the $$\text{ M }^{2}\text{ AV } $$ M 2 AV to convert from a temporal to a spatial Structure function rather than the ground speed (as in Beyrich et al.) does not change the mean discrepancy, but it does affect $${C_{T}^2}_{}$$ C T 2 values for individual flights. To investigate whether $${C_{T}^2}_{}$$ C T 2 derived from the $$\text{ M }^{2}\text{ AV } $$ M 2 AV data depends on the fact that the underlying temperature dataset combines spatial and temporal sampling, we used large-eddy simulation data to analyze $${C_{T}^2}_{}$$ C T 2 from virtual flights with different mean ground speeds. This analysis shows that $${C_{T}^2}_{}$$ C T 2 does only slightly depends on the true air speed when averaged over many flights.
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spatially averaged temperature Structure Parameter over a heterogeneous surface measured by an unmanned aerial vehicle
Boundary-Layer Meteorology, 2012Co-Authors: A C Van Den Kroonenberg, Sabrina Martin, Frank Beyrich, Jens BangeAbstract:The Structure Parameter of temperature, \({C_{T}^{2}}\) , in the lower convective boundary layer was measured using the unmanned mini aerial vehicle M2AV. The measurements were carried out on two hot summer days in July 2010 over a heterogeneous land surface around the boundary-layer field site of the Lindenberg Meteorological Observatory—Richard-Asmann-Observatory of the German Meteorological Service. The spatial series of \({C_{T}^{2}}\) showed considerable variability along the flight path that was caused by both temporal variations and surface heterogeneity. Comparison of the aircraft data with \({C_{T}^{2}}\) values derived from tower-based in situ turbulence measurements showed good agreement with respect to the diurnal variability. The decrease of \({C_{T}^{2}}\) with height as predicted by free-convection scaling could be confirmed for the morning and afternoon flights while the flights around noon suggest a different behaviour.
