Kinematic source process models using tele-seismic waveform inversion of three stable continental region (SCR) earthquakes from India (1993 Mw 6.2 Latur, 1997 Mw 5.8 Jabalpur and 2001 Mw 7.6 Bhuj) are presented in this paper to demonstrate the slip evolution and stress drop. These finite-fault models are methodologically unique to constrain the source dimensions, compared with assumptions and quasi-observations around blind faults. Our results show that these events do have a compact zone of singular asperity breakage within the Indian crust. Whereas the Bhuj and Jabalpur events have their rupture majorly restricted within the lower crustal regions. The Latur event ruptured a very shallow crustal asperity. The estimated rupture velocities are in the range of 2.6–3.2 km/s, Bhuj event the slowest and lengthiest. Our results do not favour an updip shallow component of asperity breakage for the Bhuj earthquake, as evident from lack of surface ruptures. Study also indicates that the 1993 Latur event occurred on a fault with high stress concentration which indicates stronger fault asperities or slip within a newer fault. Models on 1997 Jabalpur event imply higher frictional strength and very brittle nature at the lower crustal regions of the Indian slab, indicating a mechanically very strong lower crust. We conclude that an event like Latur can occur anywhere within continental interiors of Indian SCR, or elsewhere globally, and is an underestimated seismic hazard.
Multipath Transmission Control Protocol (MPTCP) is an innovative next-generation transport protocol standardized by the Internet Engineering Task Force (IETF) to overcome the single path limitation of the Transmission Control Protocol (TCP). MPTCP augments TCP with a new set of signaling options for seamless transmission and reception of application data across multiple interlinked TCP connections called subflows. In this paper, we focus on a new security concern associated with the signal exchanging process of MPTCP. To the best of our knowledge, for the first time, this paper exposes that MPTCP signal exchange scheme is vulnerable to a sophisticated packet spoofing technique, which we name as Data Sequence Signal (DSS) manipulation. We implement the vulnerability, create attack scenarios in Linux Kernel and conduct experiments over emulated testbed to demonstrate the existence of the vulnerability and means of exploiting it for powerful attacks. Our results show that DSS manipulation can be tactically exploited, on top of TCP optimistic ACKing, to generate non-responsive traffic like Denial-of-Service (DoS) attack flood. Particularly, we demonstrate two new adverse scenarios, where a MPTCP sender is forced to: (a) transmit at a rate significantly higher than the bottleneck link bandwidth, and (b) induce high intensity and harmful packet bursts at line-rate called Maliciously-induced-Bursts (MiBs). We also show that the non-responsive traffic resulting from the attack can suppress genuine congestion controlled traffic to the extent of causing DoS attack. We capture and analyze the dynamics of important MPTCP parameters, like send buffer occupancy of meta and subflow sockets, congestion window and flightsize to highlight the attack impact. DSS manipulation originates from a fundamental protocol design limitation rather than from any implementation flaw. We also propose a novel technique called data sequence map skipping for detection and countermeasure against DSS manipulation based attacks.
by Toshiro INOUE, Kavirajan RAJENDRAN, Masaki SATOH, Hiroaki MIURA
Abstract: The dual peak semidiurnal variation in surface rainfall rate over the tropics, simulated using a 3.5-km-mesh Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for 26–31 December 2006, is analyzed and compared with data from the 17 year winter precipitation climatology of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Precipitation Radar (PR), and the same 6 day data of Global Satellite Mapping of Precipitation, as well as infrared data from geostationary satellites.
We focus on land areas including southern Africa and the Amazon. Over these land areas, the NICAM simulation captures the primary peak in the afternoon and the secondary peak in the early morning, at similar times to those captured using TRMM data. In the PR observation, the primary peak of rainfall is mainly due to convective rain, whereas the secondary peak is due to stratiform rain. In the NICAM simulation, if a simple method is used for the classification of convective/stratiform rain, convective rain is dominant all day long, and the rainfall rate is generally higher than in the PR observation. Nevertheless, an analysis of deep convection (DC) areas indicates consistency between the observation and NICAM; the primary peak of rainfall rate occurs at the mature stage of the number of DC areas, whereas the secondary peak occurs when the mean size of DC areas is almost at its highest point. However, in the NICAM simulation, the relative magnitudes of the two peaks are not represented well, and the contribution of the stratiform rain is underestimated.
The present study indicates that a high-resolution global nonhydrostatic model like NICAM has the potential to overcome the limitations of coarse-resolution general circulation models by reproducing the semidiurnal variation of DC, although there is room for improvement.
by Kantha Rao Bhimala, Krushna Chandra Gouda & S. Himesh
The present study evaluates the skill of the Weather Research and Forecasting (WRF) model to simulate high-resolution rainfall, 2-m air temperature (T2m), and 2-m relative humidity (RH2m) over the metropolitan city of Bangalore, India. The novelty of the present study is that the WRF model simulations were carried out for ten different rain intensities during the monsoon season and compared with in situ observations from a high-density rain gauge network (81 rain gauge stations) and automatic weather stations (AWS) located over Bangalore. Our analysis shows that the model underestimated (bias score < 1) rainfall for most (87%) of the stations, and the model accuracy in the forecasting of rainfall was more than 70% for 16% of stations in the city. The RMSE values ranged between 18 and 28 mm/day for most of the rainfall events. Our analysis also found that the underestimation of the convective available potential energy (CAPE < 2000 J/kg) may be a possible reason for the simulation of low-intensity rainfall (< 10 mm/day) in most of the stations in Bangalore. In the case of T2m and RH2m simulations, the model closely matched the observed values [bias: T2m (−1 °C to 1 °C), Rh2m (0–10%)] for most of the AWS, while the model showed cold (−4.5 °C) and moist bias (19%) for the industrial area of Begur station. Proper representation of the urban morphology, air pollution, and anthropogenic heat data in the WRF modeling system may improve the model skill to capture the spatial variability in rainfall, T2m, and RH2m over highly urbanized cities in India.
Ionospheric perturbations induced by tsunamis and earthquakes can be used for tsunami early warning and remote sensing of earthquakes, provided the perturbations are characterized properly to distinguish them from the ones caused by other sources. The ionospheric perturbations are increasingly being obtained from Global Positioning System (GPS) based Total Electron Content (TEC) measurements sampled at uniform time intervals. However, the sampling is not uniform in space. The nonuniform spatial sampling along the GPS satellite tracks introduces aliasing if it is not accounted while computing the ionospheric perturbations. All the methods hitherto used to detect the co-seismic and tsunamigenic ionospheric perturbations did not account the nonuniform spatial sampling while computing these perturbations. In addition, the residual approach used to obtain the perturbations by detrending the TEC time series using high-order polynomial fit introduces artifacts. These aliasing and artifacts corrupt amplitude, Signal-to-Noise Ratio (SNR), phase, and frequency of ionospheric perturbations which are vital to distinguish the perturbations induced by tsunamis and earthquakes from the rest. We show that Spatio-Periodic Leveling Algorithm (SPLA) successfully removes such aliasing and artifacts. The efficiency of SPLA in removing the aliases and artifacts is validated under two simulated scenarios, and using GPS observations carried out during two natural disasters – the 2004 Indian Ocean tsunami and the 2015 Nepal-Gorkha earthquake. We, further, studied the severity of aliasing and artifacts on co-seismic and tsunamigenic perturbations by analyzing its characteristics employing SNR, spatiotemporal, and wavelet analyses. The results reveal that removal of aliasing and artifacts using SPLA i) increases the SNR up to ∼149% compared to the residual method and ∼39% compared to the differential method, ii) distinctly resolves signals from sharp static variations, and iii) detects 50% more co-seismic ionospheric perturbations and 25% more tsunami-induced ionospheric perturbations in the two events studied. Cross-correlation of the perturbation time series obtained using the residual method and SPLA reveals that aliasing and artifacts shift the time of occurrence by −7.64 minutes to +4.21 minutes. Further, the results show that the SPLA efficiently detects the ionospheric perturbations at low elevation angles, thereby removes the need of applying elevation cut-off and increases the area of ionospheric exploration of a GPS receiver.
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