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A Omar

Publications and source records attributed to A Omar.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

TPSAS-NF1676L-23428-DND

Recent theoretical advances now enable accurate characterization of both the single scattering and multiple scattering contributions to the lidar backscatter signals obtained from opaque water clouds (Hu et al., 2006). As a consequence, lidar measurements of opaque water clouds have increasingly broad applications, especially for space-based polarization-sensitive lidars such as CALIOP. Among the most prominent and useful of these are (1) calibration and assessments of calibration accuracy (e.g., O'Connor et al., 2004; Hu et al., 2006); (2) accurate estimates of extrinsic (e.g., optical depths) and intrinsic (e.g., extinction-to-backscatter ratios) optical properties of clouds and aerosol layers lying above opaque water clouds (Hu et al., 2007; Liu et al., 2015); and (3) retrievals of water cloud microphysical properties such as cloud droplet number concentrations (Hu et al., 2007; Li et al., 2011; Zeng et al., 2014). In the first part of this presentation we give an overview of the recent advances in this subject area. The second part introduces several new studies of water clouds using the multi-wavelength depolarization measurement capabilities of NASA's airborne high spectral resolution lidars (HSRL). We use these measurements to assess existing theory, validate the measurement concept and explore several new application concepts. The third part discusses changes in Arctic water clouds using CALIOP measurements. The HSRL water cloud study is supported by NASA's atmospheric composition program.

Y Hu↗
NASA NTRS2010

TPSAS-NF1676L-11609-DND

China and much of East Asia experienced a drought in the spring of 2010 that is said to be the worst in the past century. Strong winds (wind speed > 7 m/s) occurred in spring this year ~40% more than average in recent years. MODIS imagery indicates numerous major dust storms occurring in the Taklimakan and Gobi deserts. Intense, persistent dust was subsequently observed over North America by space-based, airborne and ground-based lidars during April 2010. Using CALIPSO lidar (CALIOP) measurements and air parcel back trajectories, we track the dust measured over North America back to East Asian source regions (mainly in the Tarim Basin). We also interpret the CALIOP and other A-Train observations using results from a 3D chemical transport model (GEOS-Chem) and investigate the meteorological context that gave rise to these dust storms.

Z Liu↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31882-DND

Lidar ratios are required to retrieve the extinction profiles in CALIOP algorithm. Unconstrained retrievals: A default lidar ratio is used depending upon the aerosol subtype. Constrained retrievals: The lidar ratio (S) is retrieved from the measured two way transmittance (T2), when clear air is found both above and below the layer for at least 2.5 km (T^2 = / ; S = (1-T2)/2γ’), where and are the mean attenuated scattering ratios below and above the layer respectively, and γ’ is the layer integrated attenuated backscatter. A multiple scattering factor of 1 is assumed. In general, constrained lidar ratios are expected to be more representative (but also noisier) than the unconstrained cases, hence accuracy of the unconstrained lidar ratios can be assessed using the constrained ones.

J Kar↗
NASA NTRSDate not supplied

TPSAS-NF1676L-20108-DND

This presentation describes several enhancements planned for the version 4 aerosol subtyping and lidar ratio selection algorithms of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP subtyping algorithm determines the most likely aerosol type from CALIOP measurements (attenuated backscatter, estimated particulate depolarization ratios de, layer altitude), and surface type. The aerosol type, so determined, is associated with a lidar ratio (LR) from a discrete set of values. In the version 3 algorithms, there are 6 pairs of 532 and 1064 nm lidar ratios. Some of these lidar ratios will be updated in the version 4 algorithms. In particular, the dust and polluted dust will be adjusted to reflect the latest measurements and model studies of these types. The algorithms are being updated to eliminate the occasional confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions that are used to determine the presence of smoke aerosols over the ocean. In the subtyping algorithms pure dust is determined by high estimated particulate depolarization ratios [de > 0.20]. Mixtures of dust and other aerosol types are determined by intermediate values of the estimated depolarization ratio [0.075< de <0.2]. The version 3 algorithms are limited to mixtures of dust and smoke, the so-called polluted dust aerosol type. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the revised classification algorithms, polluted dust will still defined as dust + smoke/pollution but in the marine boundary layer instances of moderate depolarization will be typed as dusty marine aerosols with a lower lidar ratio [LR = 35 sr] than polluted dust [currently LR = 55 sr]. The dusty marine type introduced in version 4 is modeled as a mixture of dust + marine aerosol. In the v3 algorithms the frequency of dust and polluted dust aerosols at daytime is higher than at nighttime. We present possible reasons for this and present the v4 distributions resulting from both improved background slope corrections of the daytime depolarization ratios and changes to the daytime thresholds for the polluted dust and dusty marine types of version 4. To gauge the impact of the enhancements, we contrast the following between versions 3 and 4: aerosol type, parameter distributions of each type, layer heights of maximum frequency, and distributions of smoke in biomass burning regions. To illustrate specific impacts the presentation shows case studies of version 3 and version 4 vertical feature masks of the aerosol subtypes, where appropriate, for the above enhancements.

A Omar↗
NASA NTRSDate not supplied

Long-term Engagement in Authentic Research with NASA (LEARN): Innovative Practices Suggested by a New Model for Teacher Research Experiences

NASA's LEARN Project is an innovative program that provides long-term immersion in the practice of atmospheric science for middle and high school in-service teachers. Working alongside NASA scientists and using authentic NASA Science Mission Directorate research data, teachers develop individual research topics of interest during two weeks in the summer while on-site at NASA Langley. With continued, intensive mentoring by NASA scientists, the teachers further develop their research throughout the academic year through virtual group meetings and data team meetings mirroring scientific collaborations. At the end of the first year, LEARN teachers present scientific posters. The LEARN experience has had such an impact that multiple teachers from the first two cohorts have elected to continue their research. The LEARN project evaluation has provided insights into particularly effective elements of this new approach. Findings indicate that teachers? perceptions of the scientific enterprise have changed, and that LEARN provided substantial resources to help them take real-world research to their students. This presentation will focus on key factors from LEARN?s implementation that inform best practices for the incorporation of authentic scientific research into teacher professional development experiences. We suggest that these factors should be considered in the development of other such experiences, including: (1) The involvement of a single scientist as both the project leader/manager and the project scientist, to ensure that the project can meet teachers? needs. (2) An emphasis on framing and approaching scientific research questions, so that teachers can learn to evaluate the feasibility of studies based on scope, scale, and availability of data. (3) Long term, ongoing relationships where teachers and scientists work as collaborators, beyond the workshop ?mold.? (4) A focus on meeting the needs of individual teachers, whether their needs relate to elements of research and analysis, or to their tight professional schedules. (5) Above all, flexibility and patience. LEARN builds relationships with teachers slowly, over a long period of time. In the middle, life often intervenes. LEARN has emphasized that teachers? success is more important than deadlines or following a rigid protocol.

M Pippin↗