40,619 research outputs found
Spectral absorption of biomass burning aerosol determined from retrieved single scattering albedo during ARCTAS
Actinic flux, as well as aerosol chemical and optical properties, were measured aboard the NASA DC-8 aircraft during the ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) mission in Spring and Summer 2008. These measurements were used in a radiative transfer code to retrieve spectral (350-550 nm) aerosol single scattering albedo (SSA) for biomass burning plumes encountered on 17 April and 29 June. Retrieved SSA values were subsequently used to calculate the absorption Angstrom exponent (AAE) over the 350-500 nm range. Both plumes exhibited enhanced spectral absorption with AAE values that exceeded 1 (6.78 ± 0.38 for 17 April and 3.34 ± 0.11 for 29 June). This enhanced absorption was primarily due to organic aerosol (OA) which contributed significantly to total absorption at all wavelengths for both 17 April (57.7%) and 29 June (56.2%). OA contributions to absorption were greater at UV wavelengths than at visible wavelengths for both cases. Differences in AAE values between the two cases were attributed to differences in plume age and thus to differences in the ratio of OA and black carbon (BC) concentrations. However, notable differences between AAE values calculated for the OA (AAEOA) for 17 April (11.15 ± 0.59) and 29 June (4.94 ± 0.19) suggested differences in the plume AAE values might also be due to differences in organic aerosol composition. The 17 April OA was much more oxidized than the 29 June OA as denoted by a higher oxidation state value for 17 April (+0.16 vs. -0.32). Differences in the AAEOA, as well as the overall AAE, were thus also possibly due to oxidation of biomass burning primary organic aerosol in the 17 April plume that resulted in the formation of OA with a greater spectral-dependence of absorption. © Author(s) 2012. CC Attribution 3.0 License
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A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling
HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning
Analyzing Herd Behavior in Global Stock Markets: An Intercontinental Comparison
Herd behavior is an important economic phenomenon, especially in the context
of the recent financial crises. In this paper, herd behavior in global stock
markets is investigated with a focus on intercontinental comparison. Since most
existing herd behavior indices do not provide a comparative method, we propose
a new herd behavior index and demonstrate its desirable properties through
simple theoretical models. As for empirical analysis, we use global stock
market data from Morgan Stanley Capital International to study herd behavior
especially during periods of financial crises in detail
Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation
Image correlation remote sensing monitoring techniques are becoming key tools for
providing effective qualitative and quantitative information suitable for natural hazard assessments,
specifically for landslide investigation and monitoring. In recent years, these techniques have
been successfully integrated and shown to be complementary and competitive with more standard
remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry.
The objective of this article is to apply the proposed in-depth calibration and validation analysis,
referred to as the Digital Image Correlation technique, to measure landslide displacement.
The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized
by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS
(Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models
and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide
displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the
landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive
sensitivity analyses and statistics-based processing approaches are used to identify the role of the
background noise that affects the whole dataset. This noise has a directly proportional relationship to
the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy
of the environmental-instrumental background noise evaluation allowed the actual displacement
measurements to be correctly calibrated and validated, thereby leading to a better definition of
the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability
(ranging from 1/10 to 8/10 pixel) for each processed dataset
Neural Natural Language Inference Models Enhanced with External Knowledge
Modeling natural language inference is a very challenging task. With the
availability of large annotated data, it has recently become feasible to train
complex models such as neural-network-based inference models, which have shown
to achieve the state-of-the-art performance. Although there exist relatively
large annotated data, can machines learn all knowledge needed to perform
natural language inference (NLI) from these data? If not, how can
neural-network-based NLI models benefit from external knowledge and how to
build NLI models to leverage it? In this paper, we enrich the state-of-the-art
neural natural language inference models with external knowledge. We
demonstrate that the proposed models improve neural NLI models to achieve the
state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201
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