scholarly journals Validation of GNSS data about the integrated water vapor in Europe using sun photometers

2019 ◽  
Vol 55 (4) ◽  
pp. 58-63
Author(s):  
V. V. Kalinnikov ◽  
O. G. Khutorova
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Standard Deviation ◽  
Seasonal Course ◽  
Standard Deviations ◽  
Gnss Data

In the article the comparison of time series of integrated water vapor (IWV) for 2015-2017 at 8 pair stations of GNSS and solar photometers of AERONET network in Europe is carried out. The distance between pairs of stations didn’t exceed 20 km. It is shown that bias and standard deviations of divergences have the seasonal course. In the winter GNSS-photometer bias was from –0.61 to 0.34 mm. In the summer the GNSS overestimates IWV relative to photometers by values from 0.52 to 2.26 mm. The standard deviation is maximal in summer and is from 1.31 to 1.64 mm, in winter it decreases to 0.49-0.86 mm that is 5-6% of IWV.

scholarly journals Estimating trends in atmospheric water vapor and temperature time series over Germany

2017 ◽  
Author(s):  
Fadwa Alshawaf ◽  
Kyriakos Balidakis ◽  
Galina Dick ◽  
Stefan Heise ◽  
Jens Wickert
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Temporal Trends ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Surface Measurements ◽  
Standard Deviations ◽  
Gnss Time Series

Abstract. Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data and meteorological measurements. We aim at evaluating climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim reanalysis data, and 3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods, the first applies least squares to seasonally-adjusted time series and the second using the Theil-Sen estimator. The trends estimated at 113 GNSS sites, with 10 and 19 year temporal coverage, varies between −1.5 and 2 mm/decade with standard deviations below 0.25 mm/decade. These values depend on the length and the variations of the time series. Therefore, we estimated the PWV trends using ERA-Interim and surface measurements spanning from 1991 to 2016 (26 years) at synoptic 227 stations over Germany. The former shows positive PWV trends below 0.5 mm/decade while the latter shows positive trends below 0.9 mm/decade with standard deviations below 0.03 mm/decade. The estimated PWV trends correlate with the temperature trends.

scholarly journals Estimating and Comparing Dam Deformations Using Classical and GNSS Techniques

2018 ◽  
Author(s):  
Riccardo Barzaghi ◽  
Noemi Emanuela Cazzaniga ◽  
Carlo Iapige De Gaetani ◽  
Livio Pinto ◽  
Vincenza Tornatore
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Data Processing ◽  
Time Span ◽  
Analytical Models ◽  
Monitoring Applications ◽  
Gnss Receivers ◽  
The One ◽  
Gnss Data ◽  
Dam Monitoring

GNSS receivers are nowadays commonly used in monitoring applications, e.g., in estimating crustal and infrastructure deformations. This is basically due to the recent improvements in GNSS instruments and methodologies that allow high precision positioning, 24 h availability and semiautomatic data processing. In this paper, GNSS estimated deformations on a dam structure have been analyzed and compared with pendulum data. This study has been carried out for the Eleonora D’Arborea (Cantoniera) dam, which is in the Sardinia Island. Time series of pendulum and GNSS over a time span of 2.5 years have been aligned so to be comparable. Analytical models fitting these time series have been estimated and compared. Those models were able to properly fit pendulum data and GNSS data, with standard deviation of residuals smaller than one millimeter. This encouraging results led to the conclusion that GNSS technique can be profitably applied to dam monitoring allowing a denser description, both in space and time, of the dam displacements than the one based on pendulum observations.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

scholarly journals The Systematic Bias of Entropy Calculation in the Multi-Scale Entropy Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 659
Author(s):  
Jue Lu ◽  
Ze Wang
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Coarse Graining ◽  
Sample Entropy ◽  
Systematic Bias ◽  
Original Time Series ◽  
Multi Scale ◽  
Calculation Results ◽  
Surrogate Measure ◽  
Original Time

Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the “logarithmic likelihood” that a small section (within a window of a length m) of the data “matches” with other sections will still “match” the others if the section window length increases by one. “Match” is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

METEOROLOGICAL GNSS APPLICATION FOR HEAVY RAIN ON DECEMBER 31, 2019 IN THE JAKARTA AND SURROUNDING AREAS

2021 ◽  
pp. 273
Author(s):  
Syachrul Arief ◽  
Ihsan Muhamad Muafiry
Keyword(s):  
Water Vapor ◽  
Data Processing ◽  
Average Distance ◽  
Heavy Rain ◽  
Research Area ◽  
High Rainfall ◽  
Interesting Pattern ◽  
Surrounding Areas ◽  
Gnss Data ◽  
Second Peak

This study aims to utilize GNSS for meteorology in Indonesia. With the "goGPS" software, the zenith troposphere delay (ZTD) value is estimated. Calculations in rainy conditions, the ZTD value is converted into a water vapor value (PWV). The research area for the phenomenon of heavy rain occurred at the end of 2019 in Jakarta and its surroundings, which caused flooding on January 1, 2020. According to the Geophysical Meteorology and Climatology Agency (BMKG), the flood's primary cause was high rainfall. Meanwhile, the rainfall at Taman Mini and Jatiasih stations was 335 mm/day and 260 mm/day, respectively. We get an interesting pattern of PWV values for this rain phenomenon. GNSS data processing, the PWV value at five GNSS stations around Jakarta, shows the same pattern even though the average distance between GNSS stations is ~ 30 km. The PWV value appears to increase at noon on December 30, 2019, and the peak occurs in the early hours of December 31, 2019. The PWV value suddenly decreases at noon on January 1, 2020. Next, the PWV value increases again but not as high as at the previous peak. Since January 2, 2020, the PWV value has decreased and remained almost constant until January 4, 2020. In that period, there were two events that the PWV value increased. The PWV value at the first peak is ~ 70 mm, and at the second peak ~ 65 mm. The most significant increase in PWV value was recorded at CJKT stations.

scholarly journals Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Caixia Gao ◽  
Enyu Zhao ◽  
Chuanrong Li ◽  
Yonggang Qian ◽  
Lingling Ma ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Vapor ◽  
Standard Deviation ◽  
Land Surface ◽  
Simulated Data ◽  
Mean Square ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Instrument Noise ◽  
Temperature Retrieval

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

The Effects of Ethyl Alcohol on a Driver's Driving Skill, Visual Perception, Risk Acceptance, Choice Reaction Times and Information Processing Rates

1974 ◽  
Vol 18 (2) ◽  
pp. 116-116
Author(s):  
Helmut T. Zwahlen
Keyword(s):  
Information Processing ◽  
Standard Deviation ◽  
Visual Perception ◽  
Reaction Times ◽  
Driving Skill ◽  
Alcohol Condition ◽  
The Mean ◽  
Risk Acceptance ◽  
Standard Deviations ◽  
Choice Reaction Times

Twelve subjects (20–37 years old) were tested in the laboratory and eleven out of these were also tested in a car in the field, first under a no alcohol condition and then under an alcohol condition (approximately 0.10% BAC). In the laboratory the subjects simple and choice reaction times for two uncertainty modes were measured and their information processing rates (3 bits unsertainty) were determined. In the field the subjects driving skill for driving through a gap with 20 inches total clearance at 20 MPH was measured, as well as their static visual perceptual capabilities and risk acceptance decisions for a 46 feet viewing distance using psychophysical experimental methods. Based upon the driving skill measure (standard deviation of centerline deviations in the gap), the mean of the psychometric visual gap perception function and the mean of the psychometric gap risk acceptance function, the “Safety Distance” and the “Driver Safety Index” (DSI) were obtained. Based upon a statistical analysis of the data we may conclude first that the effects of alcohol (approximately 0.10% BAC) vary widely from one subject to another (slighthly improved performance to highly impaired performance) and that the changes in the group averages of the means and standard deviations of the psychometric visual perception and risk acceptance functions, the driving skill distributions, the “Safety Distances” and the DSI's for the subjects (although all changes in the group averages are in the expected direction) are statistically not significant (α = .05). Second, the group average of the means of the choice reaction times for the subjects increased by 5% under the alcohol condition (statistically significant, α = .05), but more important the group average of the standard deviations of the choice reaction times for the subjects increased by 23% (statistically significant, α = .05). The group average of the information processing rates for the subjects decreased by 3% (statistically not significant, α = .05) under the alcohol condition. A system model in which the system demands on the driver are represented in terms of choice reaction times is used to demonstrate that the increase in performance variability (expressed by the standard deviation of choice reaction times) under the influence of alcohol provides a much better explanation for the higher accident involvement than the historically most frequently used rather small increase in average performance (expressed by the mean of choice reaction times).

scholarly journals Synthesis of Plans to Increase the Results of Ordinary National Education Test by All-Round Tutoring Process for Primary School Grade 6

2021 ◽  
Vol 14 (2) ◽  
pp. 63
Author(s):  
Nuttakan Pakprod ◽  
Kanokrat Jirasatjanukul ◽  
Damrong Tumthong ◽  
Prapa Amklad ◽  
Wipa Lekchom
Keyword(s):  
Standard Deviation ◽  
Primary School ◽  
Target Group ◽  
Educational Testing ◽  
School Grade ◽  
National Education ◽  
Standard Deviations ◽  
The Difference ◽  
High Level ◽  
Basic Level

The objective of this research is to study the results of activities to increase the scores of Ordinary National Education Test. Cluster; teachers of Phetchaburi Rajabhat University comparing the results of Ordinary National Education Test in 2017-2018 and studying the satisfaction of the activities. The target group is 49 schools in Phetchaburi and Prachuap Khiri Khan Provinces, data were analyzed using mean and standard deviation. The study found that the difference of the scores of the Ordinary National Education Test was higher in 32 schools and there is a difference in scores of Ordinary National Education Test tests lower by 2 schools, representing 94.12, with the satisfaction of the participation in the activity of increasing the basic educational testing at the basic level is at a high level with an average of 4.22, standard deviations 0.73, which the participants are satisfied with the process. The process of organizing activities was at the highest with an average of 4.28, standard deviations 0.76 and continues organizing activities to increase the scores of Ordinary National Education Test.

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