Errors In Flux Estimates Research Articles

An Inverse Modeling Approach for Retrieving High-Resolution Surface Fluxes of Greenhouse Gases from Measurements of Their Concentrations in the Atmospheric Boundary Layer

This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations and airflow properties over complex terrain with high spatial resolution. Our approach is based on a three-dimensional hydrodynamic model capable of determining the airflow parameters that affect the spatial distribution of GHG concentrations within the atmospheric boundary layer. The model is primarily designed to solve the forward problem of calculating the steady-state distribution of GHG concentrations and fluxes at different levels over an inhomogeneous land surface within the model domain. The inverse problem deals with determining the unknown surface GHG fluxes by minimizing the difference between measured and modeled GHG concentrations at two selected levels above the land surface. Several numerical experiments were conducted using surrogate data that mimicked UAV observations of varying accuracies and density of GHG concentration measurements to test the robustness of the approach. Our primary modeling target was a 6 km2 forested area in the foothills of the Greater Caucasus Mountains in Russia, characterized by complex topography and mosaic vegetation. The numerical experiments show that the proposed inverse modeling approach can effectively solve the inverse problem, with the resulting flux distribution having the same spatial pattern as the required flux. However, the approach tends to overestimate the mean value of the required flux over the domain, with the maximum errors in flux estimation associated with areas of maximum steepness in the surface topography. The accuracy of flux estimates improves as the number of points and the accuracy of the concentration measurements increase. Therefore, the density of UAV measurements should be adjusted according to the complexity of the terrain to improve the accuracy of the modeling results.

Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data

Satellite retrievals of thermal infrared data from the Earth’s surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture as another key constraint (TSEB-SM) improves model performance and utility. Here, high resolution satellite soil moisture data from SMAP (Soil Moisture Active Passive) were used to provide soil water boundary conditions for the TSEB-SM model. This SMAP driven TSEB-SM approach was applied in the Heihe river basin in China across a wide range of soil moisture conditions using MODIS thermal infrared land surface temperature (LST) data. The ET estimates from TSEB-SM were validated using flux tower measurements over the main land cover types in the study area and compared with the results from the original TSEB model. The comparison showed that the TSEB-SM approach reduced the RMSE error in latent heat flux estimates by 5 % to 20 % at satellite overpass time, and TSEB-SM outperformed the original TSEB model in daily ET estimates (RMSE values of 1.0 mm/d versus 1.5 mm/d, respectively) across all validation sites. The magnitude of the TSEB-SM improvements were larger early and late in the growing season and in arid areas, consistent with soil moisture improving estimates of ET particularly under times of higher water stress. The use of SMAP satellite soil moisture data with the TSEB-SM model increased the reliability of ET estimation and provides an improved capacity for continuos monitoring of drought effects on the global water cycle.

Flux Observation Transfer-Based Inductance Identification Technique for Precise FCS-MPCC Used in Surface-Mounted PMSMs

To eliminate the side impacts of inductance mismatch on the accuracy of finite control set model predictive current control (FCS-MPCC) scheme used for surface-mounted permanent magnet synchronous motors (SPMSM), this paper proposes a new flux observation transfer-based inductance identification strategy. First, a flux observer based on sliding mode theory is developed, with its stability analyzed. Second, the flux estimation errors are discussed, obtaining the relationship between the estimated flux and the inductance variations. On this basis, the inductance is detected using the flux information. Then, the estimated inductance is used to achieve the precise FCS-MPCC method. Finally, experiment is conducted on a three-phase SPMSM prototype to validate the proposed techniques.

Self-starting control strategy of variable speed pumped storage unit based on adaptive full order state observer

When the variable speed pumped storage unit operates in the electrical state, the self-starting control strategy is generally used to start the process from zero speed to grid connection, which saves the starting device of the traditional constant speed unit and saves the construction cost of the unit. For the self-starting control process, the traditional open-loop flux calculation method will produce cumulative errors in the amplitude and angle of the flux when the motor parameters change. To solve this problem, an adaptive full order state observer is designed to observe the motor stator flux, which has stronger parameter robustness. Based on the flux observation, the speed adaptive law is designed to estimate the rotor speed and position angle, and the speed sensorless vector control of variable speed pumped storage unit is accomplished. The simulation results show that compared with the open-loop flux calculation method, the full order state observer can greatly reduce the flux estimation error and accurately estimate the rotor speed and position angle.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

A Hybrid Sensorless Observer for the Robust Global Asymptotic Flux Reconstruction of Permanent Magnet Synchronous Machines

We propose a hybrid sensorless observer for permanent magnet synchronous machines with global asymptotic stability guarantees. Exploiting the constraint of the rotor flux on a circle of unknown radius, we design an integrator system with periodic jumps triggered by a clock to generate a linear regression containing the flux estimation error. Then, a normalized projected gradient descent identifier provides the observer estimates. For the closed-loop system, it is shown that there exists a robustly globally asymptotically stable compact attractor, which, additionally, ensures zero estimation error if appropriate Persistency of Excitation (PE) conditions are satisfied. In this respect, sufficient conditions ensuring PE are provided for the angular speed and the clock period.

Design of Frequency-Adaptive Flux Observer in PMSM Drives Robust to Discretization Error

This article analyzes the effect of discretization in the frequency-adaptive flux observer (FAO). Although FAO has structural simplicity and ease of implementation, the discrete-time FAO has phase and gain errors in fundamental flux estimation. As a result, the estimated flux might be erroneous, especially in the high-speed region. For accurate flux estimation, this article analyzes how the voltage synthesis in the pulsewidth-modulation affects the stator flux. Through the analysis, the backward Euler method is selected for the primitive stator flux estimation. To mitigate the FAO's discretization error, the proposed FAO utilizes the integrators constructed at the rotor reference frame (RRF). Since the discretization error comes from the approximation error of the continuous-time integrator at the operating frequency, the proposed FAO conducts the fundamental flux extraction at the RRF, where the operating frequency components are treated as a dc signal. Thanks to the coordinate transformation, the FAO can be discretized with no error while keeping the structure of double integrators. The proposed method is verified through the computer simulation and experimental test.

A comparison of radon, heat tracer and head gradient methods to quantify surface water - groundwater exchange in a tidal wetland (Kooragang Island, Newcastle, Australia)

Subsurface flow plays an important role in the functioning of wetlands and in the maintenance of their ecosystem services. Specifically, the transport and exchange of dissolved matter between sediments and surface waters is regulated by subsurface flow, which can strongly affect ecological zonation and productivity. Having a quantitative understanding of this subsurface flow is therefore important. Field techniques based on Darcy’s equation or natural tracers are often used separately to assess flows. Here, radon and heat (both natural groundwater tracers) and Darcy’s equation are used simultaneously to quantify the subsurface flow in a tidal wetland (Kooragang Island, Newcastle, Australia) and the results of the independent methods are compared. A steady-state radon mass balance model indicated an overall net subsurface exfiltration of 10.2 ± 4.2 cm/d while a 1D, vertical fluid heat transport model indicated a net exfiltration of 4.3 ± 2.9 cm/d. Flow estimated from analysis of hydraulic heads indicated an exfiltration rate of 3.2 ± 1.8 cm/d. The difference in flow rates is likely due to the localised measurement of the heat and head methods relative to radon, and therefore, these methods are less likely to capture zones of preferential subsurface flow. The main advantage of radon is that it provides the total subsurface flow regardless of the driving force. While head gradient or heat tracer method have the advantage of temporally quantify infiltration and exfiltration, we highlight that these methods may underestimate subsurface flows in highly dynamic coastal systems, such as tidal wetlands where a large portion of the subsurface flow is recirculated seawater. This could potentially lead to errors in solute flux estimates. This study highlights the importance of employing a multi-tracer approach and has implications towards quantifying the hydrological export of dissolved constituents (e.g., carbon and nitrogen) in coastal wetlands.

In-orbit calibration to the point-spread function of Insight-HXMT

We make the in-orbit calibration to the point-spread functions (PSFs) of the collimators of the Hard X-ray Modulation Telescope with the scanning observation of the Crab. We construct the empirical adjustments to the theoretically calculated geometrical PSFs. The adjustments contain two parts: a rotating matrix to adjust the directional deviation of the collimators and a paraboloidal function to correct the inhomogeneity of the real PSFs. The parameters of the adjusting matrices and paraboloidal functions are determined by fitting the scanning data with lower scanning speed and smaller intervals during the calibration observations. After the PSF calibration, the systematic errors in source localization in the Galactic plane scanning survey are 0∘.010, 0∘.015, 0∘.113 for the Low-Energy Telescope (LE), the Medium-Energy telescope (ME) and the High-Energy telescope (HE), respectively; meanwhile, the systematic errors in source flux estimation are 1.8%, 1.6%, 2.7% for LE, ME and HE, respectively.

Investigation of watershed nutrient export affected by extreme events and the corresponding sampling frequency

Although the real-time monitoring technique has been widely applied due to the improvement of sensors, development of traditional sampling methods is still worth of being discussed due to the economically feasibility. Currently, extreme events (e.g. extreme rainfall caused by climate change) play a relatively important role in nutrient export. However, impacts of extreme events on the optimization of sampling strategy is still not well addressed despite the uncertainty of different frequency sampling programs has been sufficiently discussed in previous studies. Therefore, the corresponding impact of extreme events impact on the optimization of sampling strategy was investigated by examining temporal (i.e., inter-annual and seasonal) variations of available data. Uncertainty of nutrient flux estimates under different sampling frequencies was explored by subsampling daily monitoring data. Results showed that uncertainty in flux estimates differed between nitrogen and phosphorus. The relative error (RE) in annual TN flux estimates ranged from −4.2% to 2.4% (once per three-day) to −21.4–31.1% (monthly sampling), while the RE in annual TP flux estimates varied from −14.1% to 8.2% (once per three-day) to −65.9%–163.4% (monthly sampling). Biweekly and weekly sampling routines are considered the optimal sampling program for total nitrogen (TN) and for total phosphorus (TP) when the extreme events impact were not been considered. The uncertainty of flux estimates with different sampling frequencies increased with the increasing extreme events. High level of uncertainty occurred in years with the most extreme events in 2012 (RE: 21.4–69.0% for TN, 33.3–96.6% for TP), while the lowest can be found in 2011 (RE: 0–20.7% for TN, 0–48.3% for TP) (with the fewest extreme events). In addition, uncertainty in TN and TP flux estimates was generally greater during summer season than during other seasons. These results highlighted the important role of extreme events in nutrient export. Approximately half of the annual TN and TP flux occurred in some extreme days that only accounted for less than 20% in the same year. The onset of these extremes of nutrient export was likely due to the stormflow with addition of external fertilizer and the direct discharge of surface ponding water from paddy fields during special periods of time. These results would be helpful for the optimization of sampling strategy.

A Very-Low-Speed Sensorless Control Induction Motor Drive with Online Rotor Resistance Tuning by Using MRAS Scheme

Abstract A sensorless indirect stator-flux-oriented control (ISFOC) induction motor drive at very low frequencies is presented herein. The model reference adaptive system (MRAS) scheme is used to estimate the speed and the rotor resistance simultaneously. However, the error between the reference and the adjustable models, which are developed in the stationary stator reference frame, is used to drive a suitable adaptation mechanism that generates the estimates of speed and the rotor resistance from the stator voltage and the machine current measurements. The stator flux components in the stationary reference frame are estimated through a pure integration of the back electro-motive force (EMF) of the machine. When the machine is operated at low speed, the pure integration of the back EMF introduces an error in flux estimation which affects the performance torque and speed control. To overcome this problem, pure integration is replaced with a programmable cascaded low-pass filter (PCLPF). The stability analysis method of the MRAS estimator is verified in order to show the robustness of the rotor resistance variations. Experimental results are presented to prove the effectiveness and validity of the proposed scheme of sensorless ISFOC induction motor drive.

Stochastic modeling of 3-D compositional distribution in the crust with Bayesian inference and application to geoneutrino observation in Japan

Geoneutrino observations, first achieved by KamLAND in 2005 and followed by Borexino in 2010, have accumulated statistics and improved sensitivity for more than ten years. The uncertainty of the geoneutrino flux at the surface is now reduced to a level small enough to set useful constraints on U and Th abundances in the Bulk Silicate Earth (BSE). However, in order to make inferences on earth’s compositional model, the contributions from the local crust need to be understood within a similar uncertainty. Here we develop a new method to construct a stochastic crustal composition model utilizing Bayesian inference. While the methodology has general applicability, it incorporates all the local uniqueness in its probabilistic framework. Unlike common approaches for this type of problem, our method does not depend on crustal segmentation into upper, (middle) and lower, whose classification and boundaries are not always well defined. We also develop a new modeling method to infer rock composition distributions that conserve mass balance and therefore do not bias the results. Combined with a new vast collection of geochemical data for rock samples in the Japan arc, we apply this method to geoneutrino observation at Kamioka, Japan. Currently a difficulty remains in the handling of correlations in the flux integration; we conservatively assume maximum correlation, which leads to large flux estimation errors of 60–70%. Despite the large errors, this is the first local crustal model for geoneutrino flux prediction with probabilistic error estimation in a reproducible way.

An Improved Sensorless Control Strategy of Ship IPMSM at Full Speed Range

This paper proposed an improved sensorless control strategy for ship electric propulsion which bases on I/f control and back electromotive force integral (EMF), suppressing the speed fluctuation during algorithm switching, improving the flux estimation accuracy of internal permanent magnet synchronous motors (IPMSMs) and achieving the stable operation of the motor. Firstly, this strategy was adopted for controlling the stator current during the switching process to improve speed fluctuation. Then, a simple flux estimation method is presented by rewriting the mathematical model of IPMSM. To achieve the result which overcomes the problem of flux estimation and current measurement errors and improved LPF was developed which solved the phase delay and DC-offset problem of flux estimation. Finally, the propeller load platform is under construction to verify the effectiveness of the control strategy. Experimental results show that the system based on this method works smoothly and it has better speed tracking performance.

Extending Low-Speed Self-Sensing via Flux Tracking With Volt–Second Sensing

This paper introduces an enhanced speed self-sensing method that tracks the stator flux linkage in induction machines at very-low speeds. Volt–second sensing technology is used to mitigate the inverter nonlinearity, which is the major source of flux estimation error in the very-low-speed range. By integrating volt–second sensing in the Gopinath style closed-loop flux observer, the rotor speed information with more precision and lower noise is extracted from the estimated stator flux. Experimental results show that the proposed self-sensing method extends the low-speed operating range, reduces the speed and torque ripple, and increases the disturbance rejection capability compared with the traditional self-sensing method based on the voltage integration method and the back electromotive force (EMF) tracking method.

Recent T2K flux predictions with NA61/SHINE thin graphite target measurements

The T2K neutrino beam predictions require precise knowledge of the hadron production in proton-carbon interactions at 31 GeV/c. Currently one of the largest errors in flux estimates comes from the poor constraints of particle yields off the target. The NA61/SHINE experiment addresses this source of uncertainty for T2K with hadron production measurements obtained using data taken in 2007 and 2009 with a thin graphite target of 2 cm length (about 0.04 of an interaction length λI). Measurements of π± and K+ yields from the 2007 dataset were already used by the T2K experiment to tune neutrino beam simulations and reduce uncertainties. New results on π±, K±, p, and production from the NA61/SHINE 2009 data analyses with smaller statistical and systematic errors were recently published. They allow for further reduction of neutrino and antineutrino flux uncertainties in T2K. Finally we discuss the total T2K beam uncertainty for neutrino and antineutrino mode with detailed decomposition of sources of errors that affect the flux predictions.

Characterizing the Effect of Vegetation Dynamics on the Bulk Heat Transfer Coefficient to Improve Variational Estimation of Surface Turbulent Fluxes

Abstract Estimation of turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into a variational data assimilation (VDA) framework has been the subject of numerous studies. The VDA approaches are focused on the estimation of two key parameters that regulate the partitioning of available energy between sensible and latent heat fluxes. These parameters are neutral bulk heat transfer coefficient CHN and evaporative fraction (EF). The CHN mainly depends on the roughness of the surface and varies on the time scale of changing vegetation phenology. The existing VDA methods assumed that the variations in vegetation phenology over the period of one month are negligible and took CHN as a monthly constant parameter. However, during the growing season, bare soil may turn into a fully vegetated surface within a few weeks. Thus, assuming a constant CHN may result in a significant error in the estimation of surface fluxes, especially in regions with a high temporal variation in vegetation cover. In this study the VDA approach is advanced by taking CHN as a function of leaf area index (LAI). This allows the characterization of the dynamic effect of vegetation phenology on CHN. The performance of the new VDA model is tested over three sites in the United States and one site in China. The results show that the new model outperforms the previous one and reduces the root-mean-square error (and bias) in sensible and latent heat flux estimates across the four sites on average by 31% (61%) and 21% (37%), respectively.

Switching losses minimization by using direct torque control of induction motor

Direct torque control is becoming the industrial standards for induction motor torque control. This paper presents switching loss minimization technique of improved direct torque control (DTC) of induction motor. Direct torque control (DTC) of an induction motor supplied by a voltage source inverter is a simple scheme that does not need long computation time, can be implanted without speed sensors and is insensitive to parameter variations. In principle, the motor terminal voltages and currents are used to estimate the motor flux and torque. Based on the instantaneous errors in torque and stator flux magnitude and estimates of the flux position, a voltage vector is selected to limit the flux and torque errors within their flux and torque hysteresis bands. In the conventional DTC, the selected voltage vector applies for the whole switching period, irrespective of the magnitude of the torque error. DTC drive gives variable switching frequency and high torque ripple. DTC gives torque and flux ripples because no any VSI states are capable to generate the exact voltage vector from switching table required to make zero both the torque electromagnetic error and the stator flux error. To minimize this problem, a torque hysteresis band with variable amplitude fuzzy logic controller is proposed. The fuzzy logic controller is used to reduce the flux and torque ripples and it improves performance DTC especially at low speed. A duty ratio control scheme for an inverter-fed induction machine using DTC method is presented in this article. The use of the duty ratio control gives improved steady state torque response, with less torque ripple than the conventional DTC. Fuzzy logic control (FLC) used to implement the duty ratio controller. Total harmonic distortion (THD) calculation of electromagnetic torque, rotor speed and stator current of DTC and DTC with fuzzy has done successfully in this article. With the help of FLC with duty ratio, 8% THD in torque, speed and stator current have minimized compared with DTC (Uddin and Hafeez, 2012). In this paper, switching loses minimization technique through THD minimization. Switching losses are minimized because the transistors are only switched when it is needed to keep torque and flux within their hysteresis bounds, improve efficiency & reduced losses. Direct torque control with the fuzzy logic controller has verified by MATLAB SIMULINK and experimentally.

Automatic processing of atmospheric CO<sub>2</sub> and CH<sub>4</sub> mole fractions at the ICOS Atmosphere Thematic Centre

Abstract. The Integrated Carbon Observation System Atmosphere Thematic Centre (ICOS ATC) automatically processes atmospheric greenhouse gases mole fractions of data coming from sites of the ICOS network. Daily transferred raw data files are automatically processed and archived. Data are stored in the ICOS atmospheric database, the backbone of the system, which has been developed with an emphasis on the traceability of the data processing. Many data products, updated daily, explore the data through different angles to support the quality control of the dataset performed by the principal operators in charge of the instruments. The automatic processing includes calibration and water vapor corrections as described in the paper. The mole fractions calculated in near-real time (NRT) are automatically revaluated as soon as a new instrument calibration is processed or when the station supervisors perform quality control. By analyzing data from 11 sites, we determined that the average calibration corrections are equal to 1.7 ± 0.3 µmol mol−1 for CO2 and 2.8 ± 3 nmol mol−1 for CH4. These biases are important to correct to avoid artificial gradients between stations that could lead to error in flux estimates when using atmospheric inversion techniques. We also calculated that the average drift between two successive calibrations separated by 15 days amounts to ±0.05 µmol mol−1 and ±0.7 nmol mol−1 for CO2 and CH4, respectively. Outliers are generally due to errors in the instrument configuration and can be readily detected thanks to the data products provided by the ATC. Several developments are still ongoing to improve the processing, including automated spike detection and calculation of time-varying uncertainties.

Aerosol lidar observations of atmospheric mixing in Los Angeles: Climatology and implications for greenhouse gas observations.

Atmospheric observations of greenhouse gases provide essential information on sources and sinks of these key atmospheric constituents. To quantify fluxes from atmospheric observations, representation of transport—especially vertical mixing—is a necessity and often a source of error. We report on remotely sensed profiles of vertical aerosol distribution taken over a 2 year period in Pasadena, California. Using an automated analysis system, we estimate daytime mixing layer depth, achieving high confidence in the afternoon maximum on 51% of days with profiles from a Sigma Space Mini Micropulse LiDAR (MiniMPL) and on 36% of days with a Vaisala CL51 ceilometer. We note that considering ceilometer data on a logarithmic scale, a standard method, introduces, an offset in mixing height retrievals. The mean afternoon maximum mixing height is 770 m Above Ground Level in summer and 670 m in winter, with significant day‐to‐day variance (within season σ = 220m≈30%). Taking advantage of the MiniMPL's portability, we demonstrate the feasibility of measuring the detailed horizontal structure of the mixing layer by automobile. We compare our observations to planetary boundary layer (PBL) heights from sonde launches, North American regional reanalysis (NARR), and a custom Weather Research and Forecasting (WRF) model developed for greenhouse gas (GHG) monitoring in Los Angeles. NARR and WRF PBL heights at Pasadena are both systematically higher than measured, NARR by 2.5 times; these biases will cause proportional errors in GHG flux estimates using modeled transport. We discuss how sustained lidar observations can be used to reduce flux inversion error by selecting suitable analysis periods, calibrating models, or characterizing bias for correction in post processing.

An adaptive flux observer for the permanent magnet synchronous motor

SummaryA gradient descent‐based nonlinear observer for surface‐mount permanent magnet synchronous motors with remarkable stability properties was recently proposed. A key assumption for the derivation of the observer is the knowledge of the electrical parameters, which are usually uncertain. In the present paper, we propose a robust adaptive flux observer adding immersion and invariance parameter adaptation algorithms to estimate the stator resistance. Global boundedness of all signals and convergence to a residual set of the flux estimation error is guaranteed. The performance of the new adaptive observer is assessed with realistic simulations. Copyright © 2015 John Wiley & Sons, Ltd.