Prominent advances in the field of artificial intelligence during the past decade and the breakthrough of deep learning would be useful for investigating ionospheric weather using ground and ...space-based ionospheric sensors data. The significance of deep learning algorithms needs to be assessed in forecasting the low latitude ionospheric disturbances (delays) for the global positioning system (GPS) signals. Total electron content (TEC) data sets prepared by taking advantage of GPS satellite radio frequency (RF) signals. This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals. The deep learning models implemented using the vertical TEC (VTEC) time-series data estimated from GPS measurements over Bengaluru, Guntur, and Lucknow GPS stations. The LSTM-CNN model performs well when compared to other ionospheric deep learning forecasting algorithms with minimum root-mean-square error (RMSE) of 1.5 TEC units (TECUs) and a high degree of <inline-formula> <tex-math notation="LaTeX">R^{2} = 0.99 </tex-math></inline-formula>.
In recent years, there has been growing interest in using precipitable water vapor (PWV) derived from global positioning system (GPS) signal delays to predict rainfall. However, the occurrence of ...rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features such as Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal, and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a 4-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.
Slow Slip Events in New Zealand Wallace, Laura M
Annual review of earth and planetary sciences,
05/2020, Letnik:
48, Številka:
1
Journal Article
Recenzirano
Continuously operating global positioning system sites in the North Island of New Zealand have revealed a diverse range of slow motion earthquakes on the Hikurangi subduction zone. These slow slip ...events (SSEs) exhibit diverse characteristics, from shallow (<15 km), short (<1 month), frequent (every 1-2 years) events in the northern part of the subduction zone to deep (>30 km), long (>1 year), less frequent (approximately every 5 years) SSEs in the southern part of the subduction zone. Hikurangi SSEs show intriguing relationships to interseismic coupling, seismicity, and tectonic tremor, and they exhibit a diversity of interactions with large, regional earthquakes. Due to the marked along-strike variations in Hikurangi SSE characteristics, which coincide with changes in physical characteristics of the subduction margin, the Hikurangi subduction zone presents a globally unique natural laboratory to resolve outstanding questions regarding the origin of episodic, slow fault slip behavior.
New Zealand's Hikurangi subduction zone hosts slow slip events with a diverse range of depth, size, duration, and recurrence characteristics.
Hikurangi slow slip events show intriguing relationships with seismicity ranging from small earthquakes and tremor to larger earthquakes.
Slow slip events play a major role in the accommodation of plate motion at the Hikurangi subduction zone.
Many aspects of the Hikurangi subduction zone make it an ideal natural laboratory to resolve the physical processes controlling slow slip.
The variability and trend in global precipitable water vapor (PWV) from 1979 to 2014 are analyzed using the PWV data sets from the ERA-Interim reanalysis of the European Centre for Medium-Range ...Weather Forecasts (ECMWF), reanalysis of the National Centers for Environmental Prediction (NCEP), radiosonde, Global Positioning System (GPS), and microwave satellite observations. PWV data from the ECMWF and NCEP have been evaluated by radiosonde, GPS, and microwave satellite observations, showing that ECMWF has higher accuracy than NCEP. Over the oceans, ECMWF has a much better agreement with the microwave satellite than NCEP. An upward trend in the global PWV is evident in all the five PWV data sets over three study periods: 1979-2014, 1992-2014, and 2000-2014. Positive global PWV trends, defined as percentage normalized by annual average, of 0.61 plus or minus 0.33%decade super(-1), 0.57 plus or minus 0.28%decade super(-1), and 0.17 plus or minus 0.35%decade super(-1), have been derived from the NCEP, radiosonde, and ECMWF, respectively, for the period 1979-2014. It is found that ECMWF overestimates the PWV over the ocean prior to 1992. Thus, two more periods, 1992-2014 and 2000-2014, are studied. Increasing PWV trends are observed from all the five data sets in the two periods: 1992-2014 and 2000-2014. The linear relationship between PWV and surface temperature is positive over most oceans and the polar region. Steep positive/negative regression slopes are generally found in regions where large regional moisture flux divergence/convergence occurs. Key Points * ECMWF PWV shows a better performance than NCEP one as evaluated by radiosonde, GPS, and microwave satellite * Upward global PWV trends are observed from five types of data sets in the period of 1979-2014 and become more apparent in recent years * Positive PWV-temperature regression slopes are observed over most oceans, and negative values are found over many continents
Determining accurate latitude and longitude positions in GPS-denied environments is a long-standing issue in the fields of navigation and positioning. Much of the ongoing research in these fields ...centers on costly, evermore sophisticated sensor and algorithm development. Yet, several applications exist, which do not require high levels of precision or investment. This article describes a simple and cost-effective solution developed to map generalized, georeferenced bathymetry underneath piers using an uncrewed surface vessel (USV) with the minimum number of instruments. Under-pier areas are challenging environments constrained by tides, ship movements, varying pier architectures, and sporadic or nonexistent GPS signals. Working within these constraints, we used a small, remotely operated USV with an integrated single-beam sonar system (for depth, z measurements) and geographic positioning system (GPS; for some latitude/longitude, x,y positions) and also used an ultrashort baseline (USBL) acoustic positioning system to determine x,y positions when GPS was denied under the pier. We developed data processing steps to correct the positional and bathymetric estimates and assessed the accuracy of these values. We found that our quality-controlled USBL positions were reasonably precise compared with GPS positions (1.2 and 0.6 m average standard deviation, respectively), although there was also an apparent horizontal offset between USBL and GPS positions that averaged about 3.25 m. However, comparing the sediment volume under piers estimated using this low-cost USV method with that calculated from sidescan sonar-generated bathymetric maps, we found that these volume estimates agreed closely, within <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula> 0.6%. This manuscript presents the methods developed, including the approach used to integrate these different data streams, to allow other researchers to collect and process similar data sets in constrained environments.
The global positioning system (GPS) has become an indispensable navigation sensor for field operations with unmanned surface vehicles (USVs) in marine environments. However, GPS may not always be ...available, even in open outdoor areas, because it is vulnerable to natural interference and malicious jamming attacks. Thus, an alternative navigation system is required when the use of GPS is restricted or prohibited. In such circumstances, a marine radar, which is a standard sensor in a marine vehicle including USV, can be used for localization in coastal areas. The marine radar can extract landmark features of the surrounding coastlines. These features can be utilized for relative navigation with respect to the detected coastlines. However, coastline maps based on radar signatures may be unavailable in unexplored areas, and they may be unreliable in coastal areas with high tidal elevations. In this study, the relative navigation with respect to the surrounding coastlines is performed in the framework of simultaneous localization and mapping (SLAM) for a USV operation in coastal waters. In particular, coastline features are parameterized by using B-splines for efficient map management, instead of the conventional point cloud representation. To verify and demonstrate the performance of the proposed coastal SLAM algorithm, field experiments were conducted in actual coastal environments. The results are presented and discussed in this paper.
A new algorithm to retrieve water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) channels using the ensemble-based empirical regression model, which was ...developed based on the North Hemisphere (western North America) data, was for the first time applied and validated to the South Hemisphere, mainly the Australia and its surrounding regions. By employing the empirical regression algorithm to retrieve water vapor from MODIS Level 1 reflectance data, the wet bias of MODIS product has been significantly reduced. Validation against global positioning system (GPS) water vapor observations over the period January 1, 2017 to December 31, 2019 in and around Australia shows that the root mean square error (RMSE) of water vapor data obtained from MODIS/Terra has reduced by 58.53% from 5.712 to 2.369 mm when using two-channel ratio transmittance and has reduced by 56.14% to 2.505 mm when using three-channel ratio transmittance. For the data obtained from MODIS/Aqua, the RMSE has reduced by 49.17% from 5.170 to 2.628 mm using two-channel ratio transmittance and has reduced by 46.60% to 2.761 mm using three-channel ratio transmittance, respectively. In addition, validations of the retrieved water vapor results over such a large research area (0°-55°S in latitude and 95°-180°E in longitudes) also show no temporal or spatial dependence, implying that the algorithm is homogeneous, accurate, and robust.
The availability of a reliable and accurate indoor positioning system (IPS) for emergency responders during on-duty missions is regarded as an essential tool to improve situational awareness of both ...the emergency responders and the incident commander. This tool would facilitate the mission planning, coordination, and accomplishment, as well as decrease the number of on-duty deaths. Due to the absence of global positioning system signal in indoor environments, many other signals and sensors have been proposed for indoor usage. However, the challenging scenarios faced by emergency responders imply explicit restrictions and requirements on the design of an IPS, making the use of some technologies, techniques, and methods inadequate on these scenarios. This survey identifies the specific requirements of an IPS for emergency responders and provides a tutorial coverage of the localization techniques and methods, highlighting the pros and cons of their use. Then, the existing IPSs specifically developed for emergency scenarios are reviewed and compared with a focus on the design choices, requirements, and additional features. By doing so, an overview of current IPS schemes as well as their performance is given. Finally, we discuss the main issues of the existing IPSs and some future directions.
We use surface deformation measurements including Interferometric Synthetic Aperture Radar data acquired by the ALOS‐2 mission of the Japanese Aerospace Exploration Agency and Global Positioning ...System (GPS) data to invert for the fault geometry and coseismic slip distribution of the 2015 Mw 7.8 Gorkha earthquake in Nepal. Assuming that the ruptured fault connects to the surface trace of the Main Frontal Thrust (MFT) fault between 84.34°E and 86.19°E, the best fitting model suggests a dip angle of 7°. The moment calculated from the slip model is 6.08 × 1020 Nm, corresponding to the moment magnitude of 7.79. The rupture of the 2015 Gorkha earthquake was dominated by thrust motion that was primarily concentrated in a 150 km long zone 50 to 100 km northward from the surface trace of the Main Frontal Thrust (MFT), with maximum slip of ∼ 5.8 m at a depth of ∼8 km. Data thus indicate that the 2015 Gorkha earthquake ruptured a deep part of the seismogenic zone, in contrast to the 1934 Bihar‐Nepal earthquake, which had ruptured a shallow part of the adjacent fault segment to the east.
Key Points
The geodetic moment is in good agreement with the seismic moment
Rupture likely occurred on the Main Frontal Thrust (MFT) that dips at an angle of 7°
The shallow part of the MFT was not ruptured by the 2015 earthquake and poses future seismic hazard
In recent studies, precipitable water vapor (PWV) has caught the interest of researchers in predicting rainfall. However, rainfall depends on several other atmospheric factors that play a vital role ...in its initiation. With only one atmospheric parameter, the false prediction is high, especially for long-term prediction. In this article, a new method for rainfall forecasting is proposed using horizontal tropospheric gradient and atmospheric residual that are important weather features. It is observed that the gradient orientation defines the weather front for a larger region, and the gradient slope, gradient magnitude, and atmospheric residual play a crucial role in rainfall prediction. The algorithm is based on global positioning system (GPS) PWV data from stations in the tropical region. This proposed algorithm obtains average false alarm (FA) and true detection (TD) rates of 36.6% and 87%, respectively, for a prediction window of 6 h. The proposed threshold values are found to be similar for three different tropical stations that make the algorithm location independent. The comparison of this approach with several other data suggests that this algorithm is suitable in the practical scenario for a long-term rainfall prediction with a better TD rate and a minimal FA rate.
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