Real-time estimation of airflow vector based on lidar observations for preview control

Abstract. As part of control techniques, gust-alleviation systems using airborne Doppler lidar technology are expected to enhance aviation safety by significantly reducing the risk of turbulence-related accidents. Accurate measurement and estimation of the vertical wind velocity are very important in the successful implementation of such systems. An estimation algorithm for the airflow vector based on data from airborne lidars is proposed and investigated for preview control to prevent turbulence-induced aircraft accidents in flight. An existing technique – simple vector conversion – assumes that the wind field between the lidars is homogeneous, but this assumption fails when turbulence occurs due to a large wind-velocity fluctuation. The proposed algorithm stores the line-of-sight (LOS) wind data at every moment and uses recent and past LOS wind data to estimate the airflow vector and to extrapolate the wind field between the airborne twin lidars without the assumption of homogeneity. Two numerical experiments – using the ideal vortex model and numerical weather prediction, respectively – were conducted to evaluate the estimation performance of the proposed method. The proposed method has much better performance than simple vector conversion in both experiments, and it can estimate accurate two-dimensional wind-field distributions, unlike simple vector conversion. The estimation performance and the computational cost of the proposed method can satisfy the performance demand for preview control.

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Real-Time Estimation of Airflow Vector based on Lidar Observations for Preview Control

10.5194/amt-2020-106 ◽

2020 ◽

Author(s):

Ryota Kikuchi ◽

Takashi Misaka ◽

Shigeru Obayashi ◽

Hamaki Inokuchi

Keyword(s):

Wind Velocity ◽

Wind Field ◽

Numerical Experiments ◽

Wind Data ◽

Control Technique ◽

Successful Implementation ◽

Doppler Lidar ◽

Preview Control ◽

Estimation Performance ◽

Gust Alleviation

Abstract. The control technique in a gust alleviation system by using the airborne Doppler Lidar technology is expected to enhance aviation safety to minimize the risks of turbulence-related accidents. Accurate measurement and estimation of the vertical wind velocity are very important in the successful implementation of a gust alleviation system by using the airborne Doppler Lidar technology. An estimation algorithm of airflow vector based on the airborne Lidars is proposed and investigated for preview control to prevent turbulence-induced aircraft accidents in flight. The use of the simple vector conversion method, which is an existing technique, assumes that the wind field between the Lidars is homogeneous. The assumption of a homogeneous field would be wrong when turbulence occurs due to large wind velocity fluctuation. The proposed algorithm stores the line-of-sight (LOS) wind data with each passing moment and uses recent and past LOS wind data in order to estimate the airflow vector. The recent and past LOS wind data are used to extrapolate the wind field between the airborne twin Lidars. Two numerical experiments – ideal vortex model and numerical weather prediction – were conducted to evaluate the estimation performance of the proposed method. The proposed method has much better performance than simple vector conversion in the two numerical experiments, and it can estimate accurate two-dimensional wind field distributions unlike simple vector conversion. The estimation performance and the computational cost of the proposed method can satisfy the performance demand for preview control.

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A new module for the tracking of radar-derived precipitation with model-derived winds

Advances in Geosciences ◽

10.5194/adgeo-10-77-2007 ◽

2007 ◽

Vol 10 ◽

pp. 77-83 ◽

Cited By ~ 8

Author(s):

T. Winterrath ◽

W. Rosenow

Keyword(s):

Wind Field ◽

Lead Time ◽

Weather Prediction ◽

Dynamical Evolution ◽

Linear Displacement ◽

Wind Data ◽

Horizontal Wind ◽

Time Range ◽

Wind Fields ◽

Precipitation Field

Abstract. A new approach for the nowcasting of precipitation has been developed at the German Weather Service combining extrapolation techniques and Numerical Weather Prediction (NWP) for a lead time range of several hours. Radar-derived precipitation fields serve as input data for a tracking algorithm using model-derived wind data. The composite precipitation field is derived from the precipitation scans which are performed every five minutes at the 16 German radar stations. The data are corrected from clutter and shading effects. The tracking of this radar-derived precipitation field is performed using the temporally and spatially resolved horizontal wind fields at different pressure levels provided by the Local Model Europe (LME). The optimal wind field is derived from minimization of the least-squares difference between a linear combination of model wind data from different pressure levels and the linear displacement vectors calculated via pattern recognition from previous radar measurements. An area-preserving displacement of the precipitation fields is realized by eliminating the wind field divergence and by omitting the dynamical evolution of the precipitation fields. Advection is performed using the fourth-order Bott scheme. Forecasted data comprise precipitation rates for every five minutes lead time as well as hourly sums of precipitation. The verification of a case study's results against radar precipitation measurements lead to a mean Equitable Threat Score (ETS) of 70%, 46%, and 38% for the first, second, and third forecast hour, respectively.

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Estimation and Modeling of Fluctuating Wind Amplitude and Phase Spectrum Using APES Algorithm Based on Field Monitored Data

Shock and Vibration ◽

10.1155/2018/6038159 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14

Author(s):

Dan-hui Dan ◽

Xiang-jie Wang ◽

Xing-fei Yan ◽

Wei Cheng

Keyword(s):

Wind Velocity ◽

Wind Field ◽

Spectral Estimation ◽

Amplitude Spectrum ◽

Estimation Algorithm ◽

Phase Spectrum ◽

Velocity Data ◽

Field Estimation ◽

Local Wind Field ◽

Fluctuating Wind

The fluctuating wind power spectrum (FWPS) in given specifications could only represent the second-order probabilistic characteristic, which indicates that it is not capable of fully expressing the stochastic wind field. Estimation and modeling of the fluctuating wind amplitude spectrum (FWAS) as well as the fluctuating wind phase spectrum (FWPhS) by using measured wind velocity data can make up for the deficiencies mentioned above. A high-resolution nonparametric spectral estimation algorithm—amplitude and phase estimation (APES)—is used to estimate the FWAS and the FWPhS, using the field measured wind velocity data of a certain cable-stayed bridge in Shanghai, China. An empirical expression (eFWAS) is introduced by dimensional analysis to model the random FWAS, and its specific Davenport form is proposed according to field measured data. The parameters of the Davenport eFWAS model are estimated by using the above measured FWAS, and three specific applications of this model are put forward when different known conditions are met. Compared with the measured FWAS, the stochastic Davenport eFWAS model proposed in this paper can accurately describe the statistical properties of the local wind field and improve the modeling accuracy of the FWAS, which is important in antiwind structural design and safety assessment.

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A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

Sensors ◽

10.3390/s21124068 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4068

Author(s):

Zheshuo Zhang ◽

Jie Zhang ◽

Jiawen Dai ◽

Bangji Zhang ◽

Hengmin Qi

Keyword(s):

Unscented Kalman Filter ◽

Time Estimation ◽

Estimation Algorithm ◽

Estimation Methods ◽

Fusion Algorithm ◽

State Variable ◽

Current Estimation ◽

Real Time Estimation ◽

And Control ◽

Independent Parameters

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.

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Low-Computational Cost Estimation Algorithm for Adaptive Optics Systems*

IFAC Proceedings Volumes ◽

10.3182/20110828-6-it-1002.02709 ◽

2011 ◽

Vol 44 (1) ◽

pp. 5573-5578

Author(s):

M. Abbas Turki ◽

D. Esqueda Merino ◽

K. Kasper ◽

C. Durieu

Keyword(s):

Adaptive Optics ◽

Cost Estimation ◽

Computational Cost ◽

Estimation Algorithm ◽

Low Computational Cost

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Proactive Quality Control: Observing System Simulation Experiments with the Lorenz ’96 Model

Monthly Weather Review ◽

10.1175/mwr-d-18-0138.1 ◽

2019 ◽

Vol 147 (1) ◽

pp. 53-67 ◽

Cited By ~ 5

Author(s):

Tse-Chun Chen ◽

Eugenia Kalnay

Keyword(s):

Quality Control ◽

Weather Prediction ◽

Computational Cost ◽

Network Size ◽

Simulation Experiments ◽

Systematic Testing ◽

Kalman Gain ◽

Lorenz 96 ◽

First Time ◽

Shed Light

Proactive quality control (PQC) is a fully flow-dependent QC for observations based on the ensemble forecast sensitivity to observations technique (EFSO). It aims at reducing the forecast skill dropout events suffered in operational numerical weather prediction by rejecting observations identified as detrimental by EFSO. Past studies show that individual dropout cases from the Global Forecast System (GFS) were significantly improved by noncycling PQC. In this paper, we perform for the first time cycling PQC experiments in a controlled environment with the Lorenz model to provide a systematic testing of the new method and possibly shed light on the optimal configuration of operational implementation. We compare several configurations and PQC update methods. It is found that PQC improvement is insensitive to the suboptimal configurations in DA, including ensemble size, observing network size, model error, and the length of DA window, but the improvements increase with the flaws in observations. More importantly, we show that PQC improves the analysis and forecast even in the absence of flawed observations. The study reveals that reusing the exact same Kalman gain matrix for PQC update not only provides the best result but requires the lowest computational cost among all the tested methods.

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Evaluation of shortwave radiation fluxes in the multi-layer SPARTACUS-Urban scheme using DART

10.5194/egusphere-egu21-16076 ◽

2021 ◽

Author(s):

Megan Stretton ◽

William Morrison ◽

Robin Hogan ◽

Sue Grimmond

Keyword(s):

Radiative Transfer ◽

Urban Form ◽

Climate Models ◽

Weather Prediction ◽

Computational Cost ◽

Three Dimensional ◽

Shortwave Radiation ◽

Radiative Transfer Model ◽

Transfer Model ◽

Urban Structure

<p>The heterogenous structure of cities impacts radiative exchanges (e.g. albedo and heat storage). Numerical weather prediction (NWP) models often characterise the urban structure with an infinite street canyon &#8211; but this does not capture the three-dimensional urban form. SPARTACUS-Urban (SU) - a fast, multi-layer radiative transfer model designed for NWP - is evaluated using the explicit Discrete Anisotropic Radiative Transfer (DART) model for shortwave fluxes across several model domains &#8211; from a regular array of cubes to real cities .</p><p>SU agrees with DART (errors < 5.5% for all variables) when the SU assumptions of building distribution are fulfilled (e.g. randomly distribution). For real-world areas with pitched roofs, SU underestimates the albedo (< 10%) and shortwave transmission to the surface (< 15%), and overestimates wall-plus-roof absorption (9-27%), with errors increasing with solar zenith angle. SU should be beneficial to weather and climate models, as it allows more realistic urban form (cf. most schemes) without large increases in computational cost.</p>

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