scholarly journals The Impact of Tropospheric Anomalies on Sea-Based JPALS Integrity

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2579
Author(s):  
Yue Zhang ◽  
Zhipeng Wang
Keyword(s):  
Kalman Filter ◽  
Calculation Method ◽  
Least Squares Method ◽  
Meteorological Data ◽  
Maximum Error ◽  
Double Difference ◽  
Weather Forecasts ◽  
Dependent Model ◽  
Seasonal Characteristic ◽  
The Impact

The Joint Precision Approach Landing System (JPALS) addresses tropospheric errors through double-difference and tropospheric model correction. Large residuals occur with two types of tropospheric anomalies: the vertical duct and horizontal non-nominal troposphere. Through analyzing 8 years of meteorological data from the European Center for Medium-Range Weather Forecasts (ECMWF), we find that the two types of anomalies can occur simultaneously. In addition, the existing vertical protection level (VPL) calculation method under tropospheric anomalies is based on the least squares method, which is not applicable to Sea-Based JPALS using the Kalman filter. Therefore, we start by calculating the zenith duct error by numerical integration. The maximum error observed is 45.64 mm, and the error seasonal characteristic is analyzed. For the non-nominal troposphere, the worst meteorological conditions in the Chinese surrounding sea areas are used to calculate the non-nominal errors, which are fitted to a satellite-elevation-dependent model. Then, a VPL calculation method based on the Kalman filter under tropospheric anomalies is proposed. Finally, a multiple approach simulation is conducted. The results show that the average VPL increments introduced by the duct and non-nominal troposphere anomalies are 0.082 m and 0.211 m, respectively, with growth percentages of 12.903% and 30.857%, respectively. The increment under simultaneous anomalies is 0.272 m with a growth of 40.427%. Furthermore, the average availability under normal conditions is 100%. Considering the duct and the non-nominal troposphere anomalies, the availability loss is 0.017% and 3.674%, respectively. Under simultaneous anomalies, this loss is 4.743%.

scholarly journals A Robust and Adaptive Complementary Kalman Filter Based on Mahalanobis Distance for Ultra Wideband/Inertial Measurement Unit Fusion Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3435 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Mahalanobis Distance ◽  
Inertial Measurement Unit ◽  
Least Squares Method ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Observation Error ◽  
System State ◽  
Inertial Measurement ◽  
The Impact

Ultra wideband (UWB) has been a popular technology for indoor positioning due to its high accuracy. However, in many indoor application scenarios UWB measurements are influenced by outliers under non-line of sight (NLOS) conditions. To detect and eliminate outlying UWB observations, we propose a UWB/Inertial Measurement Unit (UWB/IMU) fusion filter based on a Complementary Kalman Filter to track the errors of position, velocity and direction. By using the least squares method, the positioning residual of the UWB observation is calculated, the robustness factor of the observation is determined, and an observation weight is dynamically set. When the robustness factor does not exceed a pre-defined threshold, the observed value is considered trusted, and adaptive filtering is used to track the system state, while the abnormity of system state, which might be caused by IMU data exceptions or unreasonable noise settings, is detected by using Mahalanobis distance from the observation to the prior distribution. When the robustness factor exceeds the threshold, the observed value is considered abnormal, and robust filtering is used, whereby the impact of UWB data exceptions on the positioning results is reduced by exploiting Mahalanobis distance. Experimental results show that the observation error can be effectively estimated, and the proposed algorithm can achieve an improved positioning accuracy when affected by outlying system states of different quantity as well as outlying observations of different proportion.

scholarly journals On the Impact and Benefits of AMDAR Observations in Operational Forecasting—Part I: A Review of the Impact of Automated Aircraft Wind and Temperature Reports

2016 ◽  
Vol 97 (4) ◽  
pp. 585-602 ◽  
Author(s):  
Ralph Alvin Petersen
Keyword(s):  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Cost Effective ◽  
Forecast Errors ◽  
Commercial Aircraft ◽  
Weather Forecasts ◽  
Fuel Savings ◽  
Temperature Forecast ◽  
The Impact

Abstract This paper reviews the impact of World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) observations on operational numerical weather prediction (NWP) forecasts at both regional and global scales that support national and local weather forecast offices across the globe. Over the past three decades, data collected from commercial aircraft have helped reduce flight-level wind and temperature forecast errors by nearly 50%. Improvements are largest in 3–48-h forecasts and in regions where the automated reports 1) are most numerous, 2) cover a broad area, and 3) are available at multiple levels (e.g., made during aircraft ascent and descent). Improvements in weather forecasts due to these data have already had major impacts on a variety of aspects of airline operations, ranging from fuel savings from improved wind and temperature forecasts used in flight planning to passenger comfort and safety due to better awareness of en route and near-terminal weather hazards. Aircraft wind and temperature observations now constitute the third most important dataset for global NWP and, in areas of ample reports, have become the single most important dataset for use in shorter-term, regional NWP applications. Automated aircraft reports provide the most cost-effective data source for improving NWP, being more than 5 times more cost effective than any other major-impact observing system. They also present an economical alternative for obtaining tropospheric profiles both in areas of diminishing conventional observation and as a supplement to existing datasets, both in time and space. An evaluation of moisture observations becoming available from an increasing number of AMDAR-equipped aircraft will be presented in Part II of this paper, including examples of the use of the full array of AMDAR observations in a variety of forecasting situations.

scholarly journals Empirical Stochastic Model of Multi-GNSS Measurements

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4566
Author(s):  
Dominik Prochniewicz ◽  
Kinga Wezka ◽  
Joanna Kozuchowska
Keyword(s):  
Stochastic Model ◽  
Density Ratio ◽  
Functional Model ◽  
Optimal Solution ◽  
Global Navigation Satellite Systems ◽  
Unknown Parameters ◽  
Short Baseline ◽  
Stochastic Parameters ◽  
Dependent Model ◽  
The Impact

The stochastic model, together with the functional model, form the mathematical model of observation that enables the estimation of the unknown parameters. In Global Navigation Satellite Systems (GNSS), the stochastic model is an especially important element as it affects not only the accuracy of the positioning model solution, but also the reliability of the carrier-phase ambiguity resolution (AR). In this paper, we study in detail the stochastic modeling problem for Multi-GNSS positioning models, for which the standard approach used so far was to adopt stochastic parameters from the Global Positioning System (GPS). The aim of this work is to develop an individual, empirical stochastic model for each signal and each satellite block for GPS, GLONASS, Galileo and BeiDou systems. The realistic stochastic model is created in the form of a fully populated variance-covariance (VC) matrix that takes into account, in addition to the Carrier-to-Noise density Ratio (C/N0)-dependent variance function, also the cross- and time-correlations between the observations. The weekly measurements from a zero-length and very short baseline are utilized to derive stochastic parameters. The impact on the AR and solution accuracy is analyzed for different positioning scenarios using the modified Kalman Filter. Comparing the positioning results obtained for the created model with respect to the results for the standard elevation-dependent model allows to conclude that the individual empirical stochastic model increases the accuracy of positioning solution and the efficiency of AR. The optimal solution is achieved for four-system Multi-GNSS solution using fully populated empirical model individual for satellite blocks, which provides a 2% increase in the effectiveness of the AR (up to 100%), an increase in the number of solutions with errors below 5 mm by 37% and a reduction in the maximum error by 6 mm compared to the Multi-GNSS solution using the elevation-dependent model with neglected measurements correlations.

scholarly journals The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Zhang ◽  
Yujie Meng ◽  
Hejia Song ◽  
Ran Niu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
High Temperature ◽  
Air Pollutants ◽  
Meteorological Data ◽  
Effect Of Temperature ◽  
Distributed Lag ◽  
Modification Effect ◽  
Incidence Risk ◽  
The Impact ◽  
The Relationship

Abstract Background Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Methods The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. Results Overall, a 10 μg/m3 increment of O3, PM2.5, PM10 and NO2 could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution. Conclusion Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals Understanding the seasonality of performance resilience to climate volatility in Mediterranean dairy sheep

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Valentina Tsartsianidou ◽  
Vanessa Varvara Kapsona ◽  
Enrique Sánchez-Molano ◽  
Zoitsa Basdagianni ◽  
Maria Jesús Carabaño ◽  
...  
Keyword(s):  
Genetic Control ◽  
Milk Production ◽  
Eastern Mediterranean ◽  
Meteorological Data ◽  
Genetic Correlations ◽  
Farm Animals ◽  
Seasonal Effects ◽  
Cold Weather ◽  
Adverse Weather ◽  
The Impact

AbstractAs future climate challenges become increasingly evident, enhancing performance resilience of farm animals may contribute to mitigation against adverse weather and seasonal variation, and underpin livestock farming sustainability. In the present study, we develop novel seasonal resilience phenotypes reflecting milk production changes to fluctuating weather. We evaluate the impact of calendar season (autumn, winter and spring) on animal performance resilience by analysing 420,534 milk records of 36,908 milking ewes of the Chios breed together with relevant meteorological data from eastern Mediterranean. We reveal substantial seasonal effects on resilience and significant heritable trait variation (h2 = 0.03–0.17). Resilience to cold weather (10 °C) of animals that start producing milk in spring was under different genetic control compared to autumn and winter as exemplified by negative genetic correlations (− 0.09 to − 0.27). Animal resilience to hot weather (25 °C) was partially under the same genetic control with genetic correlations between seasons ranging from 0.43 to 0.86. We report both favourable and antagonistic associations between animal resilience and lifetime milk production, depending on calendar season and the desirable direction of genetic selection. Concluding, we emphasise on seasonal adaptation of animals to climate and the need to incorporate the novel seasonal traits in future selective breeding programmes.

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

<p>In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a “predictive control” scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the „Long short-term memory“ architecture.</p><p>To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.</p><p>Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.</p><p>As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.</p><p>To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.</p><p>In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.</p>

Does corporate social responsibility affect dividend policy? Empirical evidence in the French context

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Olfa Ben Salah ◽  
Anis Ben Amar
Keyword(s):  
Corporate Social Responsibility ◽  
Panel Data ◽  
Social Responsibility ◽  
Dividend Policy ◽  
Least Squares Method ◽  
Positive Impact ◽  
Alternative Measure ◽  
Content Type ◽  
Corporate Social ◽  
The Impact

Purpose The purpose of this paper is to focus on the impact of corporate social responsibility (CSR) on dividend policy in the French context. In addition, the authors seek to determine if the individual components of CSR influence dividend policy. Design/methodology/approach This study uses panel data methodology for a sample of French non-financial firms between 2008 and 2018. Generalized least squares method is used to estimate the models. Findings Using panel data methodology for a sample of 825 observations for the period 2008–2018, this study finds a positive impact of CSR practices on dividend policy. The authors also find that individual components of CSR positively influence dividend policy. To check the robustness of the results, this study further runs a sensitivity tests, including an alternative measure of dividend policy, all of which confirm the findings. Practical implications This study has examined the impact of CSR on dividend policy in France and may have implications for regulatory, investors, analysts and academics. First, the involvement in CSR best practices encourages companies to pay more dividends to investors. Therefore, investors are more motivated to invest in socially responsible firms than socially irresponsible firms. Second, given the association of CSR with the quality of accounting information and financial markets, regulators should step up recommendations relating to the different societal dimensions of CSR. Originality/value While little previous work has focused on the causal link between CSR and dividend policy, this research is the first, to the authors’ knowledge, to have looked at the impact of CSR on dividend policy in France.

Quantifying recent lake level changes in Patagonia (Argentina and Chile): Back to the future?

2021 ◽  
Author(s):  
Daniel Ariztegui ◽  
Clément Pollier ◽  
Andrés Bilmes
Keyword(s):  
Water Level ◽  
Lake Level ◽  
Satellite Images ◽  
Meteorological Data ◽  
Tierra Del Fuego ◽  
Water Level Fluctuations ◽  
Lake Levels ◽  
The Past ◽  
Rainfall Variations ◽  
The Impact

<p>Lake levels in hydrologically closed-basins are very sensitive to climatically and/or anthropogenically triggered environmental changes. Their record through time can provide valuable information to forecast changes that can have substantial economical and societal impact.</p><p>Increasing precipitation in eastern Patagonia (Argentina) have been documented following years with strong El Niño (cold) events using historical and meteorological data. Quantifying changes in modern lake levels allow determining the impact of rainfall variations while contributing to anticipate the evolution of lacustrine systems over the next decades with expected fluctuations in ENSO frequencies. Laguna Carrilaufquen Grande is located in the intermontane Maquinchao Basin, Argentina. Its dimension fluctuates greatly, from 20 to 55 km<sup>2</sup> water surface area and an average water depth of 3 m. Several well-preserved gravelly beach ridges witness rainfall variations that can be compared to meteorological data and satellite images covering the last ~50 years. Our results show that in 2016 lake level was the lowest of the past 44 years whereas the maximum lake level was recorded in 1985 (+11.8 m above the current lake level) in a position 1.6 km to the east of the present shoreline. A five-years moving average rainfall record of the area was calculated smoothing the extreme annual events and correlated to the determined lake level fluctuations. The annual variation of lake levels was up to 1.2 m (e.g. 2014) whereas decadal variations related to humid-arid periods for the interval 2002 to 2016 were up to 9.4 m. These data are consistent with those from other monitored lakes and, thus, our approach opens up new perspectives to understand the historical water level fluctuations of lakes with non-available monitoring data.</p><p> </p><p>Laguna de los Cisnes in the Chilean section of the island of Tierra del Fuego, is a closed-lake presently divided into two sections of 2.2 and 11.9 km<sup>2</sup>, respectively. These two water bodies were united in the past forming a single larger lake. The lake level was  ca. 4 m higher than today as shown by clear shorelines and the outcropping of large Ca-rich microbialites. Historical data, aerial photographs and satellite images indicate that the most recent changes in lake level are the result of a massive decrease of water input during the last half of the 20<sup>th</sup> century triggered by an indiscriminate use of the incoming water for agricultural purposes. The spectacular outcropping of living and fossil microbialites is not only interesting from a scientific point of view but has also initiated the development of the site as a local touristic attraction. However, if the use of the incoming water for agriculture in the catchment remains unregulated the lake water level might drop dangerously and eventually the lake might fully desiccate.</p><p>These two examples illustrate how recent changes in lake level can be used to anticipate the near future of lakes. They show that ongoing climate changes along with the growing demand of natural resources have already started to impact lacustrine systems and this is likely to increase in the decades to come.</p>

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