111 research outputs found
Reliability Integrated Intrusion Detection System for Isolating Black Hole Attack in MANET
905-908Mobile ad hoc network (MANET) is a temporary network which can be utilized for emergency applications. It is easy to deploy the attackers in the network. The network performance may get degraded due to the presence of attackers. Black hole attack is the major attack which will totally violate the network rules and degrade the routing process. In this research, the Reliability Integrated Intrusion Detection System (RIIDS) is used for isolating the black hole attacks. It contains three phases. In first phase, the node forwarding ration is estimated to provide node reliability. In second phase, route reliability metric is evaluated to obtain the effective routes which can withstand the attackers. In third phase, objective function with effective routing strategy is adopted to detect attackers and isolate them by discovering alternate routes. The simulation results are analyzed using AODV protocol in terms of various performance metrics i.e. attacker detection ratio, queuing delay, packet delivery ratio and confidentiality
Reliability Integrated Intrusion Detection System for Isolating Black Hole Attack in MANET
Mobile ad hoc network (MANET) is a temporary network which can be utilized for emergency applications. It is easy to deploy the attackers in the network. The network performance may get degraded due to the presence of attackers. Black hole attack is the major attack which will totally violate the network rules and degrade the routing process. In this research, the Reliability Integrated Intrusion Detection System (RIIDS) is used for isolating the black hole attacks. It contains three phases. In first phase, the node forwarding ration is estimated to provide node reliability. In second phase, route reliability metric is evaluated to obtain the effective routes which can withstand the attackers. In third phase, objective function with effective routing strategy is adopted to detect attackers and isolate them by discovering alternate routes. The simulation results are analyzed using AODV protocol in terms of various performance metrics i.e. attacker detection ratio, queuing delay, packet delivery ratio and confidentiality
Constraining Aerosol Vertical Profile in the Boundary Layer Using Hyperspectral Measurements of Oxygen Absorption
This study attempts to infer aerosol vertical structure in the urban boundary layer using passive hyperspectral measurements. A spectral sorting technique is developed to retrieve total aerosol optical depth (AOD) and effective aerosol layer height (ALH) from hyperspectral measurements in the 1.27‐μm oxygen absorption band by the mountaintop Fourier Transform Spectrometer at the California Laboratory for Atmospheric Remote Sensing instrument (1,673 m above sea level) overlooking the LA basin. Comparison to AOD measurements from Aerosol Robotic Network and aerosol backscatter profile measurements from a Mini MicroPulse Lidar shows agreement, with coefficients of determination (r^2) of 0.74 for AOD and 0.57 for effective ALH. On average, the AOD retrieval has an error of 24.9% and root‐mean‐square error of 0.013, while the effective ALH retrieval has an error of 7.8% and root‐mean‐square error of 67.01 m. The proposed method can potentially be applied to existing and future satellite missions with hyperspectral oxygen measurements to constrain aerosol vertical distribution on a global scale
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Constraints on methane emissions in North America from future geostationary remote-sensing measurements
The success of future geostationary (GEO) satellite observation missions
depends on our ability to design instruments that address their key
scientific objectives. In this study, an Observation System Simulation
Experiment (OSSE) is
performed to quantify the constraints on methane (CH4) emissions in North
America obtained from shortwave infrared (SWIR), thermal infrared (TIR), and
multi-spectral (SWIR+TIR) measurements in geostationary orbit and from
future SWIR low-Earth orbit (LEO) measurements. An efficient stochastic
algorithm is used to compute the information content of the inverted
emissions at high spatial resolution (0.5° × 0.7°)
in a variational framework using the GEOS-Chem chemistry-transport model and
its adjoint. Our results show that at sub-weekly timescales, SWIR
measurements in GEO orbit can constrain about twice as many independent flux
patterns than in LEO orbit, with a degree of freedom for signal (DOF) for the
inversion of 266 and 115, respectively. Comparisons between TIR GEO and SWIR
LEO configurations reveal that poor boundary layer sensitivities for the TIR
measurements cannot be compensated for by the high spatiotemporal sampling of
a GEO orbit. The benefit of a multi-spectral instrument compared to current
SWIR products in a GEO context is shown for sub-weekly timescale constraints,
with an increase in the DOF of about 50 % for a 3-day inversion. Our
results further suggest that both the SWIR and multi-spectral measurements on
GEO orbits could almost fully resolve CH4 fluxes at a spatial resolution
of at least 100 km × 100 km over source hotspots (emissions
> 4 × 105 kg day−1). The sensitivity of the optimized
emission scaling factors to typical errors in boundary and initial conditions
can reach 30 and 50 % for the SWIR GEO or SWIR LEO configurations,
respectively, while it is smaller than 5 % in the case of a multi-spectral
GEO system. Overall, our results demonstrate that multi-spectral measurements
from a geostationary satellite platform would address the need for higher
spatiotemporal constraints on CH4 emissions while greatly mitigating the
impact of inherent uncertainties in source inversion methods on the inferred
fluxes
The ACOS CO_2 retrieval algorithm – Part 1: Description and validation against synthetic observations
This work describes the NASA Atmospheric CO_2 Observations from Space (ACOS) X_(CO_2) retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small
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The United States' Next Generation of Atmospheric Composition and Coastal Ecosystem Measurements: NASA's Geostationary Coastal and Air Pollution Events (GEO-CAPE) Mission
The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission was recommended by the National Research Council's (NRC's) Earth Science Decadal Survey to measure tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit, providing continuous observations within the field of view. To fulfill the mandate and address the challenge put forth by the NRC, two GEO-CAPE Science Working Groups (SWGs), representing the atmospheric composition and ocean color disciplines, have developed realistic science objectives using input drawn from several community workshops. The GEO-CAPE mission will take advantage of this revolutionary advance in temporal frequency for both of these disciplines. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high-frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean. These observations are to be achieved from a vantage point near 95°–100°W, providing a complete view of North America as well as the adjacent oceans. The SWGs have also endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. GEO-CAPE will join the global constellation of geostationary atmospheric chemistry and coastal ocean color sensors planned to be in orbit in the 2020 time frame.Earth and Planetary SciencesEngineering and Applied Science
THE UNITED STATES’ NEXT GENERATION OF ATMOSPHERIC COMPOSITION AND COASTAL ECOSYSTEM MEASUREMENTS
Change of the NRC report. The U.S. National Research Council (NRC), at the request of the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Geological Survey, conducted an Earth Science Decadal Survey review to assist in planning the next generation of Earth science satellite missions [NRC 2007; commonly referred to as the “Decadal Survey” (“DS”)]. The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission measuring tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit was one of 17 recommended missions. Satellites in geostationary orbit provide continuous observations within their field of view, a revolutionary advance for both atmosphere and ocean science disciplines. The NRC placed GEO-CAPE within the second tier of missions, recommended for launch within the 2013–16 time frame. In addition to providing information for addressing scientific questions, the NRC advised that increasing the societal benefits of Earth science research should be a high priority for federal science agencies and policy makers
Constraining Aerosol Vertical Profile in the Boundary Layer Using Hyperspectral Measurements of Oxygen Absorption
This study attempts to infer aerosol vertical structure in the urban boundary layer using passive hyperspectral measurements. A spectral sorting technique is developed to retrieve total aerosol optical depth (AOD) and effective aerosol layer height (ALH) from hyperspectral measurements in the 1.27‐μm oxygen absorption band by the mountaintop Fourier Transform Spectrometer at the California Laboratory for Atmospheric Remote Sensing instrument (1,673 m above sea level) overlooking the LA basin. Comparison to AOD measurements from Aerosol Robotic Network and aerosol backscatter profile measurements from a Mini MicroPulse Lidar shows agreement, with coefficients of determination (r^2) of 0.74 for AOD and 0.57 for effective ALH. On average, the AOD retrieval has an error of 24.9% and root‐mean‐square error of 0.013, while the effective ALH retrieval has an error of 7.8% and root‐mean‐square error of 67.01 m. The proposed method can potentially be applied to existing and future satellite missions with hyperspectral oxygen measurements to constrain aerosol vertical distribution on a global scale
The United States' next generation of atmospheric composition and coastal ecosystem measurements : NASA's Geostationary Coastal and Air Pollution Events (GEO-CAPE) Mission
Author Posting. © American Meteorological Society, 2012. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 93 (2012): 1547–1566, doi:10.1175/BAMS-D-11-00201.1.The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission was recommended by the National Research Council's (NRC's) Earth Science Decadal Survey to measure tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit, providing continuous observations within the field of view. To fulfill the mandate and address the challenge put forth by the NRC, two GEO-CAPE Science Working Groups (SWGs), representing the atmospheric composition and ocean color disciplines, have developed realistic science objectives using input drawn from several community workshops. The GEO-CAPE mission will take advantage of this revolutionary advance in temporal frequency for both of these disciplines. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high-frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean. These observations are to be achieved from a vantage point near 95°–100°W, providing a complete view of North America as well as the adjacent oceans. The SWGs have also endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. GEO-CAPE will join the global constellation of geostationary atmospheric chemistry and coastal ocean color sensors planned to be in orbit in the 2020 time frame.Funding for GEO-CAPE
definition activities is provided by the Earth Science Division
of the National Aeronautics and Space Administration.2013-04-0
Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm
Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2)
satellite has been taking measurements of reflected solar spectra and using
them to infer atmospheric carbon dioxide levels. This work provides details
of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the
column-averaged dry air mole fraction of atmospheric CO2
(XCO2) for the roughly 100 000 cloud-free measurements recorded
by OCO-2 each day. The algorithm is based on the Atmospheric Carbon
Observations from Space (ACOS) algorithm which has been applied to
observations from the Greenhouse Gases Observing SATellite (GOSAT) since
2009, with modifications necessary for OCO-2. Because high accuracy,
better than 0.25 %, is required in order to accurately infer carbon
sources and sinks from XCO2, significant errors and regional-scale
biases in the measurements must be minimized. We discuss efforts to filter
out poor-quality measurements, and correct the remaining good-quality
measurements to minimize regional-scale biases. Updates to the radiance
calibration and retrieval forward model in version 8 have improved many
aspects of the retrieved data products. The version 8 data appear to have
reduced regional-scale biases overall, and demonstrate a clear improvement
over the version 7 data. In particular, error variance with respect to TCCON
was reduced by 20 % over land and 40 % over ocean between versions 7
and 8, and nadir and glint observations over land are now more consistent.
While this paper documents the significant improvements in the ACOS
algorithm, it will continue to evolve and improve as the CO2 data
record continues to expand.</p
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