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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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204 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) was viewing a combination of atmospheric column inclusive of clouds, aerosols and a variety of gases and land/ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass burning aerosols overlying marine boundary layer clouds in the Southeastern Atlantic Ocean. The algorithm’s ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies n an area where current clear sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules was used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30%, optical thickness of the underlying cloud layer was greater than 4 and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within +/-20 degrees) the agreement with source data further improved (0.977, 0.051 and 0.096 respectively). Algorithm closure was examined using a single case out of the five 38 used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with offset -0.007 and RMSE of 0.097 at pixel uncertainty level of less than 40%, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

MODIS↗
NASA NTRS2005

Light-emitting diodes as an illumination source for plants: a review of research at Kennedy Space Center

The provision of sufficient light is a fundamental requirement to support long-term plant growth in space. Several types of electric lamps have been tested to provide radiant energy for plants in this regard, including fluorescent, high-pressure sodium, and metal halide lamps. These lamps vary in terms of spectral quality, which can result in differences in plant growth and morphology. Current lighting research for space-based plant culture is focused on innovative lighting technologies that demonstrate high electrical efficiency and reduced mass and volume. Among the lighting technologies considered for space are light-emitting diodes (LEDs). The combination of red and blue LEDs has proven to be an effective lighting source for several crops, yet the appearance of plants under red and blue lighting is purplish gray, making visual assessment of plant health difficult. Additional green light would make the plant leaves appear green and normal, similar to a natural setting under white light, and may also offer psychological benefits for the crew. The addition of 24% green light (500-600 nm) to red and blue LEDs enhanced the growth of lettuce plants compared with plants grown under cool white fluorescent lamps. Coincidentally, these plants grown under additional green light would have the additional aesthetic appeal of a green appearance.

NASA Discipline Life Support Systems↗
NASA NTRS2003

Seed-to-seed growth of Arabidopsis thaliana on the International Space Station

The assembly of the International Space Station (ISS) as a permanent experimental outpost has provided the opportunity for quality plant research in space. To take advantage of this orbital laboratory, engineers and scientists at the Wisconsin Center for Space Automation and Robotics (WCSAR), University of Wisconsin-Madison, developed a plant growth facility capable of supporting plant growth in the microgravity environment. Utilizing this Advanced Astroculture (ADVASC) plant growth facility, an experiment was conducted with the objective to grow Arabidopsis thaliana plants from seed-to-seed on the ISS. Dry Arabidopsis seeds were anchored in the root tray of the ADVASC growth chamber. These seeds were successfully germinated from May 10 until the end of June 2001. Arabidopsis plants grew and completed a full life cycle in microgravity. This experiment demonstrated that ADVASC is capable of providing environment conditions suitable for plant growth and development in microgravity. The normal progression through the life cycle, as well as the postflight morphometric analyses, demonstrate that Arabidopsis thaliana does not require the presence of gravity for growth and development. c2003 COSPAR. Published by Elsevier Ltd. All rights reserved.

ISS Project↗
NASA NTRS2013

Physical and Optical Characteristics of the October 2010 Haze Event Over Singapore: A Photometric and Lidar Analysis

Trans-boundary biomass burning smoke episodes have increased dramatically during the past 20-30 years and have become an annual phenomenon in the South‐East‐Asia region. On 15th October 2010, elevated levels of fire activity were detected by remote sensing satellites (e.g. MODIS). On the same date, measurements of fine particulate matter (PM2.5) at Singapore and Malaysia found high levels of fine mode particles in the local environment. All these observations were indicative of the initial onset of a smoke episode that lasted for several days. In this work, we investigate the temporal evolution of this smoke episode by analyzing the physical and optical properties of smoke particles with the aid of an AERONET Sun photometer, an MPLNet micropulse lidar, and surface PM2.5 measurements. Elevated levels of fire activity coupled with high aerosol optical depth and PM2.5 were observed over a period of nine days. Increased variability of parameters such as aerosol optical depth, Angstrom exponent number and its fine mode equivalents all indicated high levels of fine particulate presence in the atmosphere. Smoke particle growth due to aging, coagulation and condensation mechanisms was detected during the afternoons and over several days. Retrieved lidar ratios were compatible with the presence of fine particulate within the boundary/aerosol layer. Moreover, retrieved particle size distribution as well as single scattering albedo indicated the prevalence of the fine mode particulate regime as well as particles showing enhanced levels of absorption respectively.

Air quality↗
NASA NTRSDate not supplied

Cropland and Land-Clearing Emission Factors From Firex-AQ

The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign is a NOAA/NASA interagency intensive study of North American fires that took place from July to September 2019. Agricultural fires were sampled on 7 flights from August 21st to September 3rd, 2019. We present emission factors for gas-phase and aerosol-phase species for the different fuel types identified during the campaign. We discuss the dependence of these emission factors on both fuel type and burning characteristics such as modified combustion efficiency (MCE). We analyze 73 fires encompassing four different agricultural fuels (corn, rice, soybean, wheat) and four different types of land-clearing fuels (grass, slash, pile, shrub). Approximately half of all sampled fires were burning corn agricultural residue. We report emission factors for 118 VOCs, where two thirds of emissions are made up of species with lifetimes against oxidation by OH of less than 12 hours (near-field impacts) and the remainder have longer-lifetimes (far-field impacts). We also include an additional 9 aerosol species, 12 halogenated species, 5 sulfur-containing species, and 22 nitrogen-containing species, including NH3 and NOx. There is a statistically significant difference in MCE between agricultural and land-clearing fires that accounts for some of the differences in emission factors across these fuels. VOCs generally have a strong negative correlation with MCE but with different slopes across fuel types. There is a significant positive relationship between MCE and three gas-phase (NO, NO2, SO2) and two aerosol-phase (pCl, pNH4) species. Many emission factors show additional dependence factors beyond MCE that could be related to fuel composition, fire temperature, soil moisture, or other possible differences in burning conditions. Therefore, important differences in emission factors may result when averaging together different types of agricultural and land-clearing fuels in compilations for use in modeling efforts.

biomass burning↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗