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- Author or Editor: Qing Wang x
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Abstract
A five-hole radome pressure probe at the nose of a small two-engine newly instrumented research aircraft was combined with global positioning system (GPS) receivers in differential mode to obtain high frequency measurements of the wind vector in the atmospheric boundary layer with possible accuracy (root-mean-square error) of about 0.1 m s−1. This low cost and simple system can provide wind velocity measurements of sufficient accuracy to estimate turbulent fluctuations. Special aircraft maneuvers above the atmospheric boundary layer were used to calibrate the radome probe. The analysis of these data showed that the static pressure defect has a significant dependence on flow angles and is affected by the propellers when significant thrust is applied. Using a simple method, the authors found that the pressure distribution on the radome deviated from the one expected for airflow incident on a sphere by more than 5%, the authors also detected a problem in the attack angle differential pressure sensor. The calibration of the local attack and sideslip flow angles due to flow distortion by the aircraft was obtained using two different methods. The first method was a least wind variance one assuming a linear form for the calibration of flow angles. This method is easy to use and can be applied in the presence of turbulence, but does not reveal any possible nonlinear dependence or problems in the data. The second method was a direct one that assumes near–zero mean vertical wind velocity above the boundary layer, while an average horizontal wind was estimated using the airstream speed with respect to the aircraft and the aircraft velocity from the differential GPS data. These methods gave similar results and, thus, increased the reliability of the calibration. The performance of the calibration procedure of the whole system was tested by examining the sensitivity of estimated wind components to the aircraft motion (about 5%) and the quality of mean profiles and turbulence statistics in the boundary layer.
Abstract
A five-hole radome pressure probe at the nose of a small two-engine newly instrumented research aircraft was combined with global positioning system (GPS) receivers in differential mode to obtain high frequency measurements of the wind vector in the atmospheric boundary layer with possible accuracy (root-mean-square error) of about 0.1 m s−1. This low cost and simple system can provide wind velocity measurements of sufficient accuracy to estimate turbulent fluctuations. Special aircraft maneuvers above the atmospheric boundary layer were used to calibrate the radome probe. The analysis of these data showed that the static pressure defect has a significant dependence on flow angles and is affected by the propellers when significant thrust is applied. Using a simple method, the authors found that the pressure distribution on the radome deviated from the one expected for airflow incident on a sphere by more than 5%, the authors also detected a problem in the attack angle differential pressure sensor. The calibration of the local attack and sideslip flow angles due to flow distortion by the aircraft was obtained using two different methods. The first method was a least wind variance one assuming a linear form for the calibration of flow angles. This method is easy to use and can be applied in the presence of turbulence, but does not reveal any possible nonlinear dependence or problems in the data. The second method was a direct one that assumes near–zero mean vertical wind velocity above the boundary layer, while an average horizontal wind was estimated using the airstream speed with respect to the aircraft and the aircraft velocity from the differential GPS data. These methods gave similar results and, thus, increased the reliability of the calibration. The performance of the calibration procedure of the whole system was tested by examining the sensitivity of estimated wind components to the aircraft motion (about 5%) and the quality of mean profiles and turbulence statistics in the boundary layer.
Abstract
Flow distortion is a major issue in the measurement of wind turbulence with gust probes mounted on a nose boom, at the radome, or under the wing of research aircraft. In this paper, the effects both of the propellers of a turboprop aircraft and of the aircraft vortex system on the pressure and wind velocity measurements near the nose of the aircraft are examined. It is shown that, for a turboprop aircraft, the sensors mounted near the nose are affected directly (slipstream) or indirectly (lift increase) by the propellers. The propeller effects are more significant for pressure sensors located ahead of the propellers on the fuselage and are less significant for the small local flow angles measured at the nose of the aircraft. The first case is clearly realized during in-flight calibration maneuvers performed by a turboprop aircraft. A major flow distortion, which seriously affects the vertical wind velocity measurements near the nose of an aircraft, is the upwash induced mainly by the wing-bound vortex. Also, low energy of the vertical wind component in the inertial subrange for scales larger than the fuselage diameter is usually observed in aircraft measurements. This is shown to be the result of not taking into account the decrease of the upwash correction with eddy frequency (or no need for such a correction in the inertial subrange) caused by the aerodynamic delay and the response of the wing vortex to turbulence. The level of energy in the inertial subrange of the vertical wind component is significant because it is commonly used for the estimation of the dissipation rate of turbulence kinetic energy. A method to estimate this frequency variable correction and correct the spectra or the time series of the estimated vertical wind component is described. Data from low-level flight legs with a Twin Otter aircraft show that this correction may result in about a 20% correction of the variance of the vertical wind component and a 5% correction of the vertical turbulent fluxes.
Abstract
Flow distortion is a major issue in the measurement of wind turbulence with gust probes mounted on a nose boom, at the radome, or under the wing of research aircraft. In this paper, the effects both of the propellers of a turboprop aircraft and of the aircraft vortex system on the pressure and wind velocity measurements near the nose of the aircraft are examined. It is shown that, for a turboprop aircraft, the sensors mounted near the nose are affected directly (slipstream) or indirectly (lift increase) by the propellers. The propeller effects are more significant for pressure sensors located ahead of the propellers on the fuselage and are less significant for the small local flow angles measured at the nose of the aircraft. The first case is clearly realized during in-flight calibration maneuvers performed by a turboprop aircraft. A major flow distortion, which seriously affects the vertical wind velocity measurements near the nose of an aircraft, is the upwash induced mainly by the wing-bound vortex. Also, low energy of the vertical wind component in the inertial subrange for scales larger than the fuselage diameter is usually observed in aircraft measurements. This is shown to be the result of not taking into account the decrease of the upwash correction with eddy frequency (or no need for such a correction in the inertial subrange) caused by the aerodynamic delay and the response of the wing vortex to turbulence. The level of energy in the inertial subrange of the vertical wind component is significant because it is commonly used for the estimation of the dissipation rate of turbulence kinetic energy. A method to estimate this frequency variable correction and correct the spectra or the time series of the estimated vertical wind component is described. Data from low-level flight legs with a Twin Otter aircraft show that this correction may result in about a 20% correction of the variance of the vertical wind component and a 5% correction of the vertical turbulent fluxes.
Abstract
During the Dynamics of Madden–Julian Oscillation (DYNAMO) Experiment in 2011, airborne expendable conductivity–temperature–depth (AXCTD) probes and airborne expendable bathythermographs (AXBTs) were deployed using NOAA’s WP-3D Orion aircraft over the southern tropical Indian Ocean. From initial analysis of the AXCTD data, about 95% of profiles exhibit double mixed layer structures. The presence of a mixed layer from some of these profiles were erroneous and were introduced because of the AXCTD processing software not being able to correctly identify the starting point of the probe descent. This work reveals the impact of these errors in data processing and presents an objective method to remove such erroneous data from the profiles using spectrograms from raw audio files. Reconstructed AXCTD/AXBT profiles are compared with collocated shipborne conductivity–temperature–depth (CTD) and expendable bathythermograph (XBT) profiles and are found to be in good agreement.
Abstract
During the Dynamics of Madden–Julian Oscillation (DYNAMO) Experiment in 2011, airborne expendable conductivity–temperature–depth (AXCTD) probes and airborne expendable bathythermographs (AXBTs) were deployed using NOAA’s WP-3D Orion aircraft over the southern tropical Indian Ocean. From initial analysis of the AXCTD data, about 95% of profiles exhibit double mixed layer structures. The presence of a mixed layer from some of these profiles were erroneous and were introduced because of the AXCTD processing software not being able to correctly identify the starting point of the probe descent. This work reveals the impact of these errors in data processing and presents an objective method to remove such erroneous data from the profiles using spectrograms from raw audio files. Reconstructed AXCTD/AXBT profiles are compared with collocated shipborne conductivity–temperature–depth (CTD) and expendable bathythermograph (XBT) profiles and are found to be in good agreement.
A Method for Identifying Kolmogorov’s Inertial Subrange in the Velocity Variance SpectrumAbstract
Kolmogorov’s inertial subrange is one of the most recognized concepts in fluid turbulence. However, the practical application of this theory to turbulent flows requires identifying subrange bandwidth. In the atmospheric boundary layer, decades of investigation support Kolmogorov’s theory, but the techniques used to identify the subrange vary and no systematic approach has emerged. The algorithm for robust identification of the inertial subrange (ARIIS) has been developed to facilitate empirical studies of the turbulence cascade. ARIIS systematically and robustly identifies the most probable subrange bandwidth in a given velocity variance spectrum. The algorithm is a novel approach in that it directly uses the expected 3/4 ratio between streamwise and transverse velocity components to locate the onset and extent of the inertial subrange within a single energy density spectrum. Furthermore, ARIIS does not assume a −5/3 power law but instead uses a robust, iterative statistical fitting technique to derive the slope over the identified range. This algorithm was tested using a comprehensive micrometeorological dataset obtained from the Floating Instrument Platform (FLIP). The analysis revealed substantial variation in the inertial subrange bandwidth and spectral slope, which may be driven, in part, by mechanical wind–wave interactions. Although demonstrated using marine atmospheric data, ARIIS is a general approach that can be used to study the energy cascade in other turbulent flows.
Abstract
Kolmogorov’s inertial subrange is one of the most recognized concepts in fluid turbulence. However, the practical application of this theory to turbulent flows requires identifying subrange bandwidth. In the atmospheric boundary layer, decades of investigation support Kolmogorov’s theory, but the techniques used to identify the subrange vary and no systematic approach has emerged. The algorithm for robust identification of the inertial subrange (ARIIS) has been developed to facilitate empirical studies of the turbulence cascade. ARIIS systematically and robustly identifies the most probable subrange bandwidth in a given velocity variance spectrum. The algorithm is a novel approach in that it directly uses the expected 3/4 ratio between streamwise and transverse velocity components to locate the onset and extent of the inertial subrange within a single energy density spectrum. Furthermore, ARIIS does not assume a −5/3 power law but instead uses a robust, iterative statistical fitting technique to derive the slope over the identified range. This algorithm was tested using a comprehensive micrometeorological dataset obtained from the Floating Instrument Platform (FLIP). The analysis revealed substantial variation in the inertial subrange bandwidth and spectral slope, which may be driven, in part, by mechanical wind–wave interactions. Although demonstrated using marine atmospheric data, ARIIS is a general approach that can be used to study the energy cascade in other turbulent flows.
Abstract
Harmonic analysis of 10 yr of Ocean Topography Experiment (TOPEX)/Poseidon (T/P) along-track altimetry is performed to derive the semidiurnal and diurnal tides (M 2, S 2, N 2, K 2, K 1, O 1, P 1, and Q 1) near Hawaii. The T/P solutions are evaluated through intercomparison for crossover points of the ascending and descending tracks and comparison with the data of tidal stations, which show that the T/P solutions in the study area are reliable. By using a suitable order polynomial to fit the T/P solutions along every track, the harmonic constants of any point on T/P tracks are acquired. A new fitting method, which is characterized by applying the harmonics from T/P tracks to produce directly empirical cotidal charts, is developed. The harmonic constants derived by this fitting method show good agreement with the data of tidal stations, the results of National Astronomical Observatory 99b (NAO.99b), TOPEX/Poseidon 7.2 (TPXO7.2), and Finite Element Solutions 2004 (FES2004) models, which suggests that the fitting method is reasonable, and the highly accurate cotidal chart could be directly acquired from T/P altimetry data by this fitting method.
Abstract
Harmonic analysis of 10 yr of Ocean Topography Experiment (TOPEX)/Poseidon (T/P) along-track altimetry is performed to derive the semidiurnal and diurnal tides (M 2, S 2, N 2, K 2, K 1, O 1, P 1, and Q 1) near Hawaii. The T/P solutions are evaluated through intercomparison for crossover points of the ascending and descending tracks and comparison with the data of tidal stations, which show that the T/P solutions in the study area are reliable. By using a suitable order polynomial to fit the T/P solutions along every track, the harmonic constants of any point on T/P tracks are acquired. A new fitting method, which is characterized by applying the harmonics from T/P tracks to produce directly empirical cotidal charts, is developed. The harmonic constants derived by this fitting method show good agreement with the data of tidal stations, the results of National Astronomical Observatory 99b (NAO.99b), TOPEX/Poseidon 7.2 (TPXO7.2), and Finite Element Solutions 2004 (FES2004) models, which suggests that the fitting method is reasonable, and the highly accurate cotidal chart could be directly acquired from T/P altimetry data by this fitting method.
Abstract
To investigate the optimum length of time series (TS) for harmonic analysis (HA) in the simulation of multiple constituents, a two-dimensional tidal model is used to simulate the M2, S2, K1, and O1 constituents in the Bohai and Yellow Seas. By analyzing the HA results of several nonoverlapping TS of the same length, which varies from 15 to 365 days, a field-average deviation of HA results is calculated. A deviation that is sufficiently small means that HA results are independent of the choice of TS, and the corresponding TS length is regarded as the optimum. Results indicate that the range of 180–195 days is the optimum length of TS for HA in the simulation of the four principal constituents. To investigate what determines the optimum length, experiments with different computed area and model settings are carried out. Results indicate that the optimum length is independent of advection, nodal corrections, and computed area, and only depends on bottom friction. Nonlinear bottom friction results in the appearance of higher harmonics and explains why the optimum length of TS for HA is 180–195 days.
Abstract
To investigate the optimum length of time series (TS) for harmonic analysis (HA) in the simulation of multiple constituents, a two-dimensional tidal model is used to simulate the M2, S2, K1, and O1 constituents in the Bohai and Yellow Seas. By analyzing the HA results of several nonoverlapping TS of the same length, which varies from 15 to 365 days, a field-average deviation of HA results is calculated. A deviation that is sufficiently small means that HA results are independent of the choice of TS, and the corresponding TS length is regarded as the optimum. Results indicate that the range of 180–195 days is the optimum length of TS for HA in the simulation of the four principal constituents. To investigate what determines the optimum length, experiments with different computed area and model settings are carried out. Results indicate that the optimum length is independent of advection, nodal corrections, and computed area, and only depends on bottom friction. Nonlinear bottom friction results in the appearance of higher harmonics and explains why the optimum length of TS for HA is 180–195 days.
Deriving AMVs from Geostationary Satellite Images Using Optical Flow Algorithm Based on Polynomial ExpansionAbstract
An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.
Abstract
An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.
Quantitative Precipitation Estimation with Operational Polarimetric Radar Measurements in Southern China: A Differential Phase–Based Variational ApproachAbstract
Quantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (K DP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (Z H) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.
Abstract
Quantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (K DP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (Z H) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.
