scholarly journals Assessment of the Ionospheric and Tropospheric Effects in Location Errors of Data Collection Platforms in Equatorial Region during High and Low Solar Activity Periods

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
Áurea Aparecida da Silva ◽  
Wilson Yamaguti ◽  
Hélio Koiti Kuga ◽  
Cláudia Celeste Celestino
Keyword(s):  
Solar Activity ◽  
Data Collection ◽  
Signal Propagation ◽  
Equatorial Region ◽  
Environmental Data ◽  
High Solar Activity ◽  
Atmospheric Effects ◽  
Climatic Data ◽  
Ionospheric Effects ◽  
Location Errors

The geographical locations of data collection platforms (DCP) in the Brazilian Environmental Data Collection System are obtained by processing Doppler shift measurements between satellites and DCP. When the signals travel from a DCP to a satellite crossing the terrestrial atmosphere, they are affected by the atmosphere layers, which generate a delay in the signal propagation, and cause errors in its final location coordinates computation. The signal propagation delay due to the atmospheric effects consists, essentially, of the ionospheric and tropospheric effects. This work provides an assessment of ionospheric effects using IRI and IONEX models and tropospheric delay compensation using climatic data provided by National Climatic Data Center. Two selected DCPs were used in this study in conjunction with SCD-2 satellite during high and low solar activity periods. Results show that the ionospheric effects on transmission delays are significant (about hundreds of meters) in equatorial region and should be considered to reduce DCP location errors, mainly in high solar activity periods, while in those due to tropospheric effects the zenith errors are about threemeters. Therefore it is shown that the platform location errors can be reduced when the ionospheric and tropospheric effects are properly considered.

scholarly journals Evaluating the Performance of IRI-2016 Using GPS-TEC Measurements over the Equatorial Region

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1243
Author(s):  
Nouf Abd Elmunim ◽  
Mardina Abdullah ◽  
Siti Aminah Bahari
Keyword(s):  
Solar Activity ◽  
Early Morning ◽  
Equatorial Region ◽  
Activity Period ◽  
High Solar Activity ◽  
Electron Content ◽  
Dual Frequency ◽  
Iri Model ◽  
Total Electron ◽  
Solar Activity Period

Total electron content (TEC) is an important parameter in the ionosphere that is extensively used to study the variability of the ionosphere as it significantly affects radio wave propagations, causing delays on GPS signals. Therefore, evaluating the performance of ionospheric models is crucial to reveal the variety of ionospheric behaviour in different solar activity periods during geomagnetically quiet and disturbed periods for further improvements of the IRI model performance over the equatorial region. This research aimed to investigate the variations of ionospheric VTEC and observe the improvement in the performance of the IRI-2016 (IRI-2001, IRI01-corr, and NeQuick). The IRI-2016 was evaluated with the IRI-2012 using NeQuick, IRI-2001, and IRI01-corr topside electron density options. The data were obtained using a dual-frequency GPS receiver installed at the Universiti Utara Malaysia Kedah (UUMK) (geographic coordinates 4.62° N–103.21° E, geomagnetic coordinates 5.64° N–174.98° E), Mukhtafibillah (MUKH) (geographic coordinates 6.46° N–100.50° E, geomagnetic coordinates 3.32° S–172.99° E), and Tanjung Pengerang (TGPG) (geographic coordinates 1.36° N–104.10°E, geomagnetic coordinates 8.43° S–176.53° E) stations, during ascending to high solar activity at the geomagnetically quiet and disturbed periods in October 2011, March 2012, and March 2013. The maximum hourly ionospheric VTEC was observed during the post-noon time, while the minimum was during the early morning time. The ionospheric VTEC modelled by IRI-2016 had a slight improvement from the IRI-2012. However, the differences were observed during the post-noon and night-time, while the modelled VTEC from both IRI models were almost similar during the early morning time. Regarding the daily quiet and disturbed period’s prediction capability of the IRI-2016 and IRI-2012, IRI-2016 gave better agreement with the measured VTEC. The overall results showed that the model’s prediction performance during the high solar activity period in 2013 was better than the one during the ascending solar activity period. The results of the comparison between IRI-2016 and IRI-2012 in high solar activity exhibited that during quiet periods, all the IRI models showed better agreement with the measured VTEC compared to the disturbed periods.

scholarly journals Spatially and temporally distributed data foraging decisions in disciplinary field science

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Cristina G. Wilson ◽  
Feifei Qian ◽  
Douglas J. Jerolmack ◽  
Sonia Roberts ◽  
Jonathan Ham ◽  
...  
Keyword(s):  
Data Collection ◽  
Simulated Data ◽  
Environmental Data ◽  
Distributed Data ◽  
Field Science ◽  
In Situ Data ◽  
Disciplinary Field ◽  
Variable Space ◽  
Query Selection ◽  
Fitting Error

AbstractHow do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions.

MEDIUM-TERM OSCILLATIONS OF THE SOLAR ACTIVITY

2021 ◽  
Vol 44 ◽  
pp. 85-91
Author(s):  
V.N. Obridko ◽  
◽  
D.D. Sokoloff ◽  
V.V. Pipin ◽  
A.S. Shibalova ◽  
...  
Keyword(s):  
Solar Activity ◽  
Activity Cycle ◽  
Solar Interior ◽  
Local Fields ◽  
Lower Atmosphere ◽  
High Solar Activity ◽  
Dynamo Model ◽  
Sunspot Activity ◽  
Geomagnetic Variations ◽  
Near Surface

In addition to the well-known 11-year cycle, longer and shorter characteristic periods can be isolated in variations of the parameters of helio-geophysical activity. Periods of about 36 and 60 years were revealed in variations of the geomagnetic activity and an approximately 60-year periodicity, in the evolution of correlation between the pressure in the lower atmosphere and the solar activity. Similar periods are observed in the cyclonic activity. Such periods in the parameters of the solar activity are difficult to identify because of a limited database available; however, they are clearly visible in variations of the asymmetry of the sunspot activity in the northern and southern solar hemispheres. In geomagnetic variations, one can also isolate oscillations with the characteristic periods of 5-6 years (QSO) and 2-3 years (QBO). We have considered 5-6-year periodicities (about half the main cycle) observed in variations of the sunspot numbers and the intensity of the dipole component of the solar magnetic field. A comparison with different magnetic dynamo models allowed us to determine the possible origin of these oscillations. A similar result can be reproduced in a dynamo model with nonlinear parameter variations. In this case, the activity cycle turns out to be anharmonic and contains other periodicities in addition to the main one. As a result of the study, we conclude that the 5-6-year activity variations are related to the processes of nonlinear saturation of the dynamo in the solar interior. Quasi-biennial oscillations are actually separate pulses related little to each other. Therefore, the methods of the spectral analysis do not reveal them over large time intervals. They are a direct product of local fields, are generated in the near-surface layers, and are reliably recorded only in the epochs of high solar activity.

Unmanned vehicles for environmental data collection

2010 ◽  
Vol 13 (2) ◽  
pp. 369-380 ◽  
Author(s):  
J. Borges de Sousa ◽  
G. Andrade Gonçalves
Keyword(s):  
Data Collection ◽  
Unmanned Vehicles ◽  
Environmental Data

scholarly journals A Practical and Adaptive Approach to Predicting Indoor CO2

2021 ◽  
Vol 11 (22) ◽  
pp. 10771
Author(s):  
Giacomo Segala ◽  
Roberto Doriguzzi-Corin ◽  
Claudio Peroni ◽  
Tommaso Gazzini ◽  
Domenico Siracusa
Keyword(s):  
Data Collection ◽  
Prediction Accuracy ◽  
Network Models ◽  
Window Size ◽  
Environmental Data ◽  
Neural Network Models ◽  
Using Data ◽  
Carbon Dioxide Co2 ◽  
Long Term Performance ◽  
Term Performance

COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO2) represents one of the pollutants that most affects environmental health. Therefore, forecasting future indoor CO2 plays a central role in taking preventive measures to keep CO2 level as low as possible. Unlike other research that aims to maximize the prediction accuracy, typically using data collected over many days, in this work we propose a practical approach for predicting indoor CO2 using a limited window of recent environmental data (i.e., temperature; humidity; CO2 of, e.g., a room, office or shop) for training neural network models, without the need for any kind of model pre-training. After just a week of data collection, the error of predictions was around 15 parts per million (ppm), which should enable the system to regulate heating, ventilation and air conditioning (HVAC) systems accurately. After a month of data we reduced the error to about 10 ppm, thereby achieving a high prediction accuracy in a short time from the beginning of the data collection. Once the desired mobile window size is reached, the model can be continuously updated by sliding the window over time, in order to guarantee long-term performance.

scholarly journals A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly

2021 ◽  
Vol 13 (22) ◽  
pp. 4559
Author(s):  
Marjolijn Adolfs ◽  
Mohammed Mainul Hoque
Keyword(s):  
Neural Network ◽  
Solar Activity ◽  
Geomagnetic Latitude ◽  
Machine Learning Techniques ◽  
High Solar Activity ◽  
Electron Content ◽  
Solar Cycles ◽  
Total Electron ◽  
The Neural Network ◽  
Winter Anomaly

With the availability of fast computing machines, as well as the advancement of machine learning techniques and Big Data algorithms, the development of a more sophisticated total electron content (TEC) model featuring the Nighttime Winter Anomaly (NWA) and other effects is possible and is presented here. The NWA is visible in the Northern Hemisphere for the American sector and in the Southern Hemisphere for the Asian longitude sector under solar minimum conditions. During the NWA, the mean ionization level is found to be higher in the winter nights compared to the summer nights. The approach proposed here is a fully connected neural network (NN) model trained with Global Ionosphere Maps (GIMs) data from the last two solar cycles. The day of year, universal time, geographic longitude, geomagnetic latitude, solar zenith angle, and solar activity proxy, F10.7, were used as the input parameters for the model. The model was tested with independent TEC datasets from the years 2015 and 2020, representing high solar activity (HSA) and low solar activity (LSA) conditions. Our investigation shows that the root mean squared (RMS) deviations are in the order of 6 and 2.5 TEC units during HSA and LSA period, respectively. Additionally, NN model results were compared with another model, the Neustrelitz TEC Model (NTCM). We found that the neural network model outperformed the NTCM by approximately 1 TEC unit. More importantly, the NN model can reproduce the evolution of the NWA effect during low solar activity, whereas the NTCM model cannot reproduce such effect in the TEC variation.

scholarly journals An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

2019 ◽  
Vol 9 (1S6) ◽  
pp. 52-61
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Energy Efficient ◽  
Residual Energy ◽  
Environmental Data ◽  
Energy Balancing ◽  
Control Centre

WSN consist of set of Sensing points which are responsible for collecting the detected information and then send the packets towards control centre which is responsible for processing of data. The applications of WSN include environmental data analysis, defence data collection and information. The survey of algorithms is done for the improvement of lifetime ratio. Four different algorithms namely Random, Random-CGT, EGT-Random and GTEB algorithms. The four algorithms are compared and then it is proved GTEB exhibits best behaviour with respect to energy consumed, number of non-holes, number of holes, Non-Hole to Hole ratio, residual energy, overhead and throughput.

Sign in / Sign up

Export Citation Format

Share Document