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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

A Smoking Gun in the Carina Nebula

The Carina Nebula is one of thc youngest, most active sites of massive star formation in our Galaxy. In this nebula, we have discovered a bright X-ray source that has persisted for approx.30 years. The soft X-ray spectrum. consistent with kT approx.130 eV blackbody radiation with mild extinction, and no counterpart in the near- and mid-infrared wavelengths indicate that it is a, approx. 10(exp 6)-year-old neutron star housed in the Carina Nebula. Current star formation theory does not suggest that the progenitor of the neutron star and massive stars in the Carina Nebula, in particular (eta)Car, are coeval. This result demonstrates that the Carina Nebula experienced at least two major episodes of massive star formation. The neutron star would be responsible for remnants of high energy activity seen in multiple wavelengths.

Hamaguchi, Kenji↗
NASA NTRS2008

Smoke Invigoration Versus Inhibition of Clouds over the Amazon

The effect of anthropogenic aerosols on clouds is one of the most important and least understood aspects of human-induced climate change. Small changes in the amount of cloud coverage can produce a climate forcing equivalent in magnitude and opposite in sign to that caused by anthropogenic greenhouse gases, and changes in cloud height can shift the effect of clouds from cooling to warming. Focusing on the Amazon, we show a smooth transition between two opposing effects of aerosols on clouds: the microphysical and the radiative. We show how a feedback between the optical properties of aerosols and the cloud fraction can modify the aerosol forcing, changing the total radiative energy and redistributing it over the atmospheric column.

Koren, Ilan↗
NASA NTRS2009

Smoke Above Clouds

Aerosols in the atmosphere alter the radiative balance of the Earth by reflecting or absorbing solar radiation. Spaceborne measurements of clouds and aerosols advected over the southeastern Atlantic Ocean indicate that the greater the cloud cover below the aerosols, the more likely the aerosols are to heat the planet.

Remer, Lorraine A.↗
NASA NTRS2012

Satellite-Based Evidence of Wavelength-Dependent Aerosol Absorption in Biomass Burning Smoke Inferred from Ozone Monitoring Instrument

We provide satellite-based evidence of the spectral dependence of absorption in biomass burning aerosols over South America using near-UV measurements made by the Ozone Monitoring Instrument (OMI) during 2005-2007. In the current near-UV OMI aerosol algorithm (OMAERUV), it is implicitly assumed that the only absorbing component in carbonaceous aerosols is black carbon whose imaginary component of the refractive index is wavelength independent. With this assumption, OMI-derived aerosol optical depth (AOD) is found to be significantly over-estimated compared to that of AERONET at several sites during intense biomass burning events (August-September). Other well-known sources of error affecting the near-UV method of aerosol retrieval do not explain the large observed AOD discrepancies between the satellite and the ground-based observations. A number of studies have revealed strong spectral dependence in carbonaceous aerosol absorption in the near-UV region suggesting the presence of organic carbon in biomass burning generated aerosols. A sensitivity analysis examining the importance of accounting for the presence of wavelength-dependent aerosol absorption in carbonaceous particles in satellite-based remote sensing was carried out in this work. The results convincingly show that the inclusion of spectrally-dependent aerosol absorption in the radiative transfer calculations leads to a more accurate characterization of the atmospheric load of carbonaceous aerosols.

Jethva, H.↗
NASA NTRS2012

Derivation of a New Smoke Emissions Inventory using Remote Sensing, and Its Implications for Near Real-Time Air Quality Applications

A new emissions inventory of particulate matter (PM) is being derived mainly from remote sensing data using fire radiative power (FRP) and aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, as well as wind data from the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis dataset, which spans the satellite era. This product is generated using a coefficient of emission, C(sub e), that has been produced on a 1x1 degree global grid such that, when it is multiplied with satellite measurements of FRP or its time-integrated equivalent fire radiative energy (FRE) retrieved over a given area and time period, the corresponding PM emissions are estimated. This methodology of using C(sub e) to derive PM emissions is relatively new and advantageous for near real-time air quality applications compared to current methods based on post-fire burned area that may not provide emissions in a timely manner. Furthermore, by using FRP to characterize a fire s output, it will represent better accuracy than the use of raw fire pixel counts, since fires in individual pixels can differ in size and strength by orders of magnitude, resulting in similar differences in emission rates. Here we will show examples of this effect and how this new emission inventory can properly account for the differing emission rates from fires of varying strengths. We also describe the characteristics of the new emissions inventory, and propose the process chain of incorporating it into models for air quality applications.

Ellison, Luke↗
NASA NTRS2016

Airborne Spectral BRDF of Various Surface Types (Ocean, Vegetation, Snow, Desert, Wetlands, Cloud Decks, Smoke Layers) for Remote Sensing Applications

In this paper we describe measurements of the bidirectional reflectance-distribution function (BRDF) acquired over a 30-year period (1984-2014) by the National Aeronautics and Space Administration's (NASA's) Cloud Absorption Radiometer (CAR). Our BRDF database encompasses various natural surfaces that are representative of many land cover or ecosystem types found throughout the world. CAR's unique measurement geometry allows a comparison of measurements acquired from different satellite instruments with various geometrical configurations, none of which are capable of obtaining such a complete and nearly instantaneous BRDF. This database is therefore of great value in validating many satellite sensors and assessing corrections of reflectances for angular effects. These data can also be used to evaluate the ability of analytical models to reproduce the observed directional signatures, to develop BRDF models that are suitable for sub-kilometer-scale satellite observations over both homogeneous and heterogeneous landscape types, and to test future spaceborne sensors. All of these BRDF data are publicly available and accessible in hierarchical data format (http:car.gsfc.nasa.gov/).

albedo↗
NASA NTRS2019

Two Decades Observing Smoke Above Clouds in the South-Eastern Atlantic Ocean: Deep Blue Algorithm Updates and Validation with ORACLES Field Campaign Data

This study presents and evaluates an updated algorithmfor quantification of absorbing aerosols above clouds(AACs) from passive satellite measurements. The focus isbiomass burning in the south-eastern Atlantic Ocean duringthe 2016 and 2017 ObseRvations of Aerosols above CLoudsand their intEractionS (ORACLES) field campaign deployments.The algorithm retrieves the above-cloud aerosoloptical depth (AOD) and underlying liquid cloud opticaldepth and is applied to measurements from the Sea-viewingWide Field-of-view Sensor (SeaWiFS), Moderate ResolutionImaging Spectroradiometer (MODIS), and Visible InfraredImaging Radiometer Suite (VIIRS) from 1997 to 2017. AirborneNASA Ames Spectrometers for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) and NASA LangleyHigh Spectral Resolution Lidar 2 (HSRL2) data collectedduring ORACLES provide important validation for spectralAOD for MODIS and VIIRS; as the SeaWiFS missionended in 2010, it cannot be evaluated directly. The 4STARand HSRL2 comparisons are complementary and reveal performancegenerally in line with uncertainty estimates providedby the optimal estimation retrieval framework used. Atpresent the two MODIS-based data records seem the mostreliable, although there are differences between the deployments,which may indicate that the available data are not yetsufficient to provide a robust regional validation. Spatiotemporalpatterns in the data sets are similar, and the time seriesare very strongly correlated with each other (correlationcoefficients from 0.95 to 0.99). Offsets between the satellitedata sets are thought to be chiefly due to differences in absolutecalibration between the sensors. The available validationdata for this type of algorithm are limited to a small numberof field campaigns, and it is strongly recommended that suchairborne measurements continue to be made, both over thesouthern Atlantic Ocean and elsewhere.

Sayer, Andrew M.↗
NASA NTRS2020

Ensemble-Based Deep Learning for Estimating PM2.5 over California with Multisource Big Data Including Wildfire Smoke

Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g. wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.

air quality↗