Real Observational Data Research Articles

Climatic Study of the Marine Surface Wind Field over the Greek Seas with the Use of a High Resolution RCM Focusing on Extreme Winds

The marine surface wind field (10 m) over the Greek seas is analyzed in this study using The RegCM. The model’s spatial resolution is dynamically downscaled to 10 km × 10 km, in order to simulate more efficiently the complex coastlines and the numerous islands of Greece. Wind data for the 1980–2000 and 2080–2100 periods are produced and evaluated against real observational data from 15 island and coastal meteorological stations in order to assess the model’s ability to reproduce the main characteristics of the surface wind fields. RegCM model shows a higher simulating skill to project seasonal wind speeds and direction during summer and the lowest simulating skill in the cold period of the year. Extreme wind speed thresholds were estimated using percentiles indices and three Peak Over Threshold (POT) techniques. The mean threshold values of the three POT methods are used to examine the inter-annual distribution of extreme winds in the study region. The highest thresholds were observed in three poles; the northeast, the southeast, and the southwest of Aegean Sea. Future changes in extreme speeds show a general increase in the Aegean Sea, while lower thresholds are expected in the Ionian Sea. Return levels for periods of 20, 50, 100, and 200 years are estimated.

A synthetic, spatially decorrelating solar irradiance generator and application to a LV grid model with high PV penetration

Residential photovoltaic (PV) technology is expected to have mass global deployment. With widespread PV in the electricity distribution grids, the variable nature of the solar resource must be understood to facilitate reliable operation. This research demonstrates that synthetic, 1-min resolution irradiance time series that vary on a spatial dimension can be generated based on the following inputs: mean hourly meteorological observations of okta, wind speed, cloud height and atmospheric pressure.The synthetic time series temporally validate against observed 1-min irradiance data for four locations—Cambourne, UK; Lerwick, UK; San Diego, CA USA; and Oahu, HI USA—when analysing 4 metrics of variability indices, ramp-rate size, irradiance magnitude frequency and clear-sky index frequency. Each metric is calculated for the modelled and observed data at each location and CDF profile correlation compared as well as applying the Kolmogorov-Smirnov (K–S) test with 99% confidence limits. CDF correlation coefficients of each metric are all above R⩾0.908, and a minimum of 90.96% of daily irradiance time series passed the K–S test. A spatial validation was performed comparing the model outputs to real observation data. The spatial correlation coefficient regression with site separation was successfully recreated with MAPE=0.865%, RMSE=0.01 and R=0.955. The spatial instantaneous correlation was shown to behave anisotropically when using fixed cloud direction, with different correlation in along and cross wind directions. Cloud cover states of 40–60% showed the most spatial decorrelation while 0% and 100% had the least.The model outputs are applied to a distribution grid impact model using the IEEE-8500 node test feeder. PV scenarios of 25%,50%, and 75% uptake were modelled across a 1.5×1.5km grid. The magnitude and frequency of severe tap changing events are found to be significantly higher when using a single irradiance time series for all PV systems versus individually assigning spatially decorrelating time series.

An investigation of pulsar searching techniques with the fast folding algorithm

Here we present an in-depth study of the behaviour of the Fast Folding Algorithm, an alternative pulsar searching technique to the Fast Fourier Transform. Weaknesses in the Fast Fourier Transform, including a susceptibility to red noise, leave it insensitive to pulsars with long rotational periods (P > 1 s). This sensitivity gap has the potential to bias our understanding of the period distribution of the pulsar population. The Fast Folding Algorithm, a time-domain based pulsar searching technique, has the potential to overcome some of these biases. Modern distributed-computing frameworks now allow for the application of this algorithm to all-sky blind pulsar surveys for the first time. However, many aspects of the behaviour of this search technique remain poorly understood, including its responsiveness to variations in pulse shape and the presence of red noise. Using a custom CPU-based implementation of the Fast Folding Algorithm, ffancy, we have conducted an in-depth study into the behaviour of the Fast Folding Algorithm in both an ideal, white noise regime as well as a trial on observational data from the HTRU-S Low Latitude pulsar survey, including a comparison to the behaviour of the Fast Fourier Transform. We are able to both confirm and expand upon earlier studies that demonstrate the ability of the Fast Folding Algorithm to outperform the Fast Fourier Transform under ideal white noise conditions, and demonstrate a significant improvement in sensitivity to long-period pulsars in real observational data through the use of the Fast Folding Algorithm.

Statistical post-processing of forecasts for extremes using bivariate brown-resnick processes with an application to wind gusts

To improve the forecasts of weather extremes, we propose a joint spatial model for the observations and the forecasts, based on a bivariate Brown-Resnick process. As the class of stationary bivariate Brown-Resnick processes is fully characterized by the class of pseudo cross-variograms, we contribute to the theorical understanding of pseudo cross-variograms refining the knowledge of the asymptotic behaviour of all their components and introducing a parsimonious, but flexible parametric model. Both findings are of interest in classical geostatistics on their own. The proposed model is applied to real observation and forecast data for extreme wind gusts at 119 stations in Northern Germany.

On identification of Hammerstein and Wiener model with application to virtualised software system

ABSTRACTThis paper proposes a system identification method for estimating virtualised software system dynamics within the framework of a Hammerstein–Wiener model. Building on the authors’ previous work in identification and control of the software systems, the approach utilises frequency sampling filter structure to describe the linear dynamics and B-spline curve functions for the inverse static output nonlinearity. Furthermore, the issue on parameter selection for B-spline model approximation of scatter data is addressed by using a data clustering method. An experimental test-bed of virtualised software system is established to generate real observational data which are used to confirm the performance of the proposed approach. The identification results have shown that the model efficacy is increased with the proposed approach because the dimension of the nonlinear model can be significantly reduced while maintaining the desired accuracy.

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet-a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite "leakage" during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.

An Improved Geometry-free Three Carrier Ambiguity Resolution Method for the BeiDou Navigation Satellite System

BeiDou satellites transmit triple-frequency signals, which bring substantial benefits to carrier phase Ambiguity Resolution (AR). The traditional geometry-free model Three-Carrier Ambiguity Resolution (TCAR) method looks for a suitable combination of carrier phase and code-range observables by searching and comparing in the integer range, which limits the AR success probability. By analysing the error characteristics of the BeiDou triple-frequency observables, we introduce a new procedure to select the optimal combination of carrier phase and code observables to resolve the resolution of Extra-Wide-Lane (EWL) and Wide-Lane (WL) ambiguity. We also investigate a geometry-free and ionosphere-eliminated method for AR of the Medium-Lane (ML) and Narrow-Lane (NL) observables. In order to evaluate the performance of the improved TCAR method, real BeiDou triple-frequency observation data for different baseline cases were collected and processed epoch-by-epoch. The results show that the improved geometry-free TCAR method increases the single epoch AR success probability by up to 90% for short baseline and 80% for long baseline. The A perfect (100%) AR success probability can also be effortlessly achieved by averaging the float ambiguities over just tens of epochs.

Application of an artificial neural network for a direct estimation of atmospheric instability from a next-generation imager

Atmospheric instability information derived from satellites plays an important role in short-term weather forecasting, especially the forecasting of severe convective storms. For the next generation of weather satellites for Korea’s multi-purpose geostationary satellite program, a new imaging instrument has been developed. Although this imaging instrument is not designed to perform full sounding missions and its capability is limited, its multi-spectral infrared channels provide information on vertical sounding. To take full advantage of the observation data from the much improved spatiotemporal resolution of the imager, the feasibility of an artificial neural network approach for the derivation of the atmospheric instability is investigated. The multi-layer perceptron model with a feed-forward and back-propagation training algorithm shows quite a sensitive response to the selection of the training dataset and model architecture. Through an extensive performance test with a carefully selected training dataset of 7197 independent profiles, the model architectures are selected to be 12, 5000, and 0.3 for the number of hidden nodes, number of epochs, and learning rate, respectively. The selected model gives a mean absolute error, RMSE, and correlation coefficient of 330 J kg-1, 420 J kg-1, and 0.9, respectively. The feasibility is further demonstrated via application of the model to real observation data from a similar instrument that has comparable observation channels with the planned imager.

A coordinate-independent characterization of a black hole shadow

A large international effort is under way to assess the presence of a shadow in the radio emission from the compact source at the centre of our Galaxy, Sagittarius A$^*$ (Sgr A$^*$). If detected, this shadow would provide the first direct evidence of the existence of black holes and that Sgr A$^*$ is a supermassive black hole. In addition, the shape of the shadow could be used to learn about extreme gravity near the event horizon and to determine which theory of gravity better describes the observations. The mathematical description of the shadow has so far used a number of simplifying assumptions that are unlikely to be met by the real observational data. We here provide a general formalism to describe the shadow as an arbitrary polar curve expressed in terms of a Legendre expansion. Our formalism does not presume any knowledge of the properties of the shadow, e.g. the location of its centre, and offers a number of routes to characterize the distortions of the curve with respect to reference circles. These distortions can be implemented in a coordinate independent manner by different teams analysing the same data. We show that the new formalism provides an accurate and robust description of noisy observational data, with smaller error variances when compared to previous approaches for the measurement of the distortion.

A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models

For the evaluation of the dynamic behavior of biological processes, e.g., gene regulatory sequences, we typically utilize nonlinear differential equations within a state space model in the context of genomic data assimilation. For the estimation of the parameter values for such systems, the particle filter can be a strong approach in terms of obtaining their theoretically exact posterior distributions of the parameter values. However, it has some drawbacks for dealing with biological processes in practice: (i) the number of unique particles decreases rapidly since the dimension of the parameter vector and the number of observed time points are higher than its capability, (ii) it cannot be applied when the likelihood function is analytically intractable, and (iii) the prior distributions of the parameter values are often arbitrary determined. To address these problems, we propose a novel method that utilizes the approximate Bayesian computation in filtering the data and self-organizing ensemble Kalman filter in constructing the prior distributions of the parameter values. Simulation studies show that the proposed method can overcome these problems; thus, it can estimate the posterior distributions of the parameter values with automatically setting prior distributions even for the cases that the particle filter cannot perform good results. Finally, we apply the method to real observation data in rat circadian oscillation and demonstrate the usefulness in practical situations.

Selected properties of GPS and Galileo-IOV receiver intersystem biases in multi-GNSS data processing

Two overlapping frequencies—L1/E1 and L5/E5a—in GPS and Galileo systems support the creation of mixed double-differences in a tightly combined relative positioning model. On the other hand, a tightly combined model makes it necessary to take into account receiver intersystem bias, which is the difference in receiver hardware delays. This bias is present in both carrier-phase and pseudorange observations. Earlier research showed that using a priori knowledge of earlier-calibrated ISB to correct GNSS observations has significant impact on ambiguity resolution and, therefore, precise positioning results. In previous research concerning ISB estimation conducted by the authors, small oscillations in phase ISB time series were detected. This paper investigates this effect present in the GPS–Galileo-IOV ISB time series. In particular, ISB short-term temporal stability and its dependence on the number of Galileo satellites used in the ISB estimation was examined. In this contribution we investigate the amplitude and frequency of the detected ISB time series oscillations as well as their potential source. The presented results are based on real observational data collected on a zero baseline with the use of different sets of GNSS receivers.

Solving inverse problems of groundwater-pollution-source identification using a differential evolution algorithm

In this study, an accurate model was developed for solving problems of groundwater-pollution-source identification. In the developed model, the numerical simulations of flow and pollutant transport in groundwater were carried out using MODFLOW and MT3DMS software. The optimization processes were carried out using a differential evolution algorithm. The performance of the developed model was tested on two hypothetical aquifer models using real and noisy observation data. In the first model, the release histories of the pollution sources were determined assuming that the numbers, locations and active stress periods of the sources are known. In the second model, the release histories of the pollution sources were determined assuming that there is no information on the sources. The results obtained by the developed model were found to be better than those reported in literature.

Application of nonlinear dynamical methods in abrupt climate change detection

The research of abrupt climate change is an important field in the climate change. The rapid and accurate detection of the abrupt climate change has important practical significance and major economic-social costs, which will help us understand climate change and forecast the future evolutionary trend of the climate system. The detection results of most traditional abrupt climate change depend on the selection of the time scale concerned, which may result in the fact that we cannot identify an abrupt climate change until the event has been past for a long time. Moreover, these detection methods cannot extract the dynamical changes from the observational data of the climate system. As the rapid development in nonlinear science, the abrupt climate change detection technology has also been improved gradually. This article briefly reviews several new progresses in abrupt dynamical detection methods developed on the basis of recent nonlinear technologies, and some applications in the real observational data. These new methods mainly contain the technologies based on the long-range correlation of climate systems, such as moving detrended fluctuation analysis, moving cut data-detrended fluctuation analysis, moving cut data-R/S analysis, degenerate fingerprinting, and red noise. Moreover, some abrupt dynamical detection methods developed by the complexity of the time series, namely, entropy, such as approximate entropy, moving cutting data-approximate entropy, Fisher information, and wavelet Fisher's information measure. Furthermore, there are some other abrupt dynamical detection methods based on the theory of phase space, such as the dynamics exponent Q. Climate system is a complex dynamical system with nonlinear and interactive nature, which has long-range persistence in spatio-temporal variation, thus the abrupt detection method on spatial field change is pointed out to be a promising direction for further research in future. Because the spatial field contains abundance of information about the evolution of climate system which is much more than that in a time series in single meteorological station, the detecting methods on spatial field will greatly help us detect an abrupt climate change as soon as possible. And then we will have enough time to take action and make preparations for the new challenges due to the abrupt climate change.

Examination of possible energy conservation in a biological water treatment process using a multiple regression model

This study examined various controls and operating parameters of a sewage treatment plant by performing multiple regression analyses on the operating parameters and the effluent quality of the sewage treatment plant. For the examination, operating data from April to November, 2012, was collected from a sewage treatment plant using the media anaerobic-anoxic-oxic method. The chemical oxygen demand (CODMn) and total nitrogen (T-N) forecasting models for the secondary sedimentation basin effluent were built through multiple regression analysis and showed Mean Absolute Percentage Error (MAPE) 6.6–9.3 and 16.0–23.7, respectively. All models showed similar results to real observational data. When controlling the operating parameters of CODMn and T-N, by using the regression model and the standardization regression coefficients without violating legal water quality criteria, operating conditions can be set that save an average of 23% of power consumption. Using this result, an operating guide for low energy consumption can be provided to the operators of sewage treatment plants.

Application of the Schumann resonance spectral decomposition in characterizing the main African thunderstorm center

AbstractIn this paper we present a new method for quantifying the main tropical thunderstorm regions based on extremely low frequency (ELF) electromagnetic wave measurements from a single station—the Hylaty ELF station in Central Europe. Our approach is based on Schumann resonance (SR) measurements, which we apply as an example to thunderstorms in Africa. By solving the inverse problem, using the SR power spectrum templates derived analytically, we calculate distances to the most powerful thunderstorm centers and present simplified 1‐D thunderstorm lightning activity “maps” in absolute units C2m2/s. We briefly describe our method of SR power spectrum analysis and present how this method is used with real observational data. We obtained the monthly lightning activity maps of the African storm centers with a spatial resolution of 1° and temporal resolution of 10 min for January and August 2011. This allowed us to study the varying location and intensities of the African storm centers in different seasons of the year. A cross check of the obtained lightning activity maps with Tropical Rainfall Measuring Mission satellite data recorded by the Lightning Imaging Sensor and the derived correlation coefficients between SR and optical data were used to validate the proposed method. We note that modeling a maximum possible number of resonance modes in the SR power spectra (in our case, seven resonances) is essential in application of the proposed approach.

ASSESSMENT OF PROPAGATION CHARACTERISTICS FOR TSUNAMI WAVE ASCENDING RIVER

Tsunami wave ascending rivers is one of the important phenomena in river and coastal engineering. It is regarded as a potential disaster due to the tsunami propagation. The features of the tsunami wave in the river can be explained by physical parameters. This study focuses on the verification of an estimation method for the tsunami parameters. A numerical experiment was used to evaluate the suggested method using measured water level data. The method applied to real observation data during the 2011 Tohoku Tsunami. The tsunami characteristics along the river were confirmed by the calculated river discharge and flow velocity induced by the tsunami.

TRAFFIC TIME SERIES FORECASTING BY FEEDFORWARD NEURAL NETWORK: A CASE STUDY BASED ON TRAFFIC DATA OF MONROE

Abstract. Short time prediction is one of the most important factors in intelligence transportation system (ITS). In this research, the use of feed forward neural network for traffic time-series prediction is presented. In this paper, the traffic in one direction of the road segment is predicted. The input of the neural network is the time delay data exported from the road traffic data of Monroe city. The time delay data is used for training the network. For generating the time delay data, the traffic data related to the first 300 days of 2008 is used. The performance of the feed forward neural network model is validated using the real observation data of the 301st day.

Combining Datasets of Satellite-Retrieved Products. Part I: Methodology and Water Budget Closure

Abstract This study addresses in general terms the problem of the optimal combination of multiple observation datasets. Only satellite-retrieved geophysical parameter datasets are considered here (not the raw satellite observations). This study focuses on the terrestrial water cycle and presents methodologies to obtain a coherent dataset of four water cycle key components: precipitation, evapotranspiration, runoff, and terrestrial water storage. Various innovative “integration” methodologies are introduced: simple weighting (SW), constrained linear (CL), optimal interpolation (OI), and neural networks (NN). The term “integration” will be used here, not “assimilation,” as no model will be included in the data fusion process. A simple postprocessing filtering (PF) step can be used to impose the water cycle budget closure after the integration method. It is shown that this constraint actually improves the estimation of the water cycle components. The integration techniques are tested using real observation data over the Mississippi and Niger basins from satellite and in situ measurements. A Monte Carlo experiment with a synthetic uncertainty perturbation model is used to measure the ability of the SW, OI, and NN, with or without the PF step, to retrieve the four water cycle components. Once the PF closure constraint is added, the methodologies have equivalent accuracies. The need for these types of methodologies should increase in the future since multiple observation datasets are now available and the climate community needs to combine them into a unique, optimal, and coherent dataset of multiple parameters. A companion paper will test these methodologies on satellite observation datasets at the basin and global scales.

Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

Abstract. A four-dimensional ensemble variational (4D-En-Var) data assimilation has been developed for a limited area model. The integration of tangent linear and adjoint models, as applied in standard 4D-Var, is replaced with the use of an ensemble of non-linear model states to estimate four-dimensional background error covariances over the assimilation time window. The computational costs for 4D-En-Var are therefore significantly reduced in comparison with standard 4D-Var and the scalability of the algorithm is improved. The flow dependency of 4D-En-Var assimilation increments is demonstrated in single simulated observation experiments and compared with corresponding increments from standard 4D-Var and Hybrid 4D-Var ensemble assimilation experiments. Real observation data assimilation experiments carried out over a 6-week period show that 4D-En-Var outperforms standard 4D-Var as well as Hybrid 4D-Var ensemble data assimilation with regard to forecast quality measured by forecast verification scores.

The performance of bds relative positioning usage with real observation data

With the first phase of COMPASS/BeiDou-2 (BDS) completed, the assessment of positioning performance and the characterization of its system are analyzed and presented. Pseudo-range and carrier phase measurements modulated on B1 and B2 have been collected in Shanghai, from 00:00 to 24:00 on 28 December, 2012. Compared with GPS, visibility and measurement quality of BDS's GEO, IGSO and MEO satellites are analyzed. DOP during the whole orbital period is also analyzed the results demonstrate that BDS's HDOP is better than GPS's one, but VDOP opposite. Furthermore, the result of positioning is also presented and analyzed. Short baselines are estimated by standalone BDS and GPS's carrier phase measurement, respectively, using 48 segmentations of observations during a whole day (24 hours, each segmentation, is about 30 minutes observation). The analysis of static relative positioning demonstrates that BDS could achieve to millimeter level, corresponding to GPS. Kinematic result is produced by double differenced carrier phase observations with the ambiguities fixed under the constraint of precise short baseline.The result shows that the centimeter accuracy could be achieved. When comparing the results of kinematic baseline solutions, performance of BDS is worse than GPS on North and Up components, but oppositely on the component of East in the kinematic baseline processing.