Progress of research on water vapor lidar

Research is summarized on applications of stimulated Raman scattering (SRS) of laser light into near infrared wavelengths suitable for atmospheric monitoring. Issues addressed are conversion efficiency, spectral purity, optimization of operating conditions, and amplification techniques. A Raman cell was developed and built for the laboratory program, and is now available to NASA-Langley, either as a design or as a completed cell for laboratory or flight applications. The Raman cell has been approved for flight in NASA's DC-8 aircraft. The self-seeding SRS technique developed here is suggested as an essential improvement for tunable near-IR DIAL applications at wavelengths of order 1 micrometer or greater

Development and applications of tunable, narrow band lasers and stimulated Raman scattering devices for atmospheric lidar

The main thrust of the program was the study of stimulated Raman processes for application to atmospheric lidar measurements. This has involved the development of tunable lasers, the detailed study of stimulated Raman scattering, and the use of the Raman-shifted light for new measurements of molecular line strengths and line widths. The principal spectral region explored in this work was the visible and near-IR wavelengths between 500 nm and 1.5 microns. Recent alexandrite ring laser experiments are reported. The experiments involved diode injection-locking, Raman shifting, and frequency-doubling. The experiments succeeded in producing tunable light at 577 and 937 nm with line widths in the range 80-160 MHz

Kinematic analysis of conically scanned environmental properties

A method for determining the velocity of features such as wind. The method preferably includes producing sensor signals and projecting the sensor signals sequentially along lines lying on the surface of a cone. The sensor signals may be in the form of lidar, radar or sonar for example. As the sensor signals are transmitted, the signals contact objects and are backscattered. The backscattered sensor signals are received to determine the location of objects as they pass through the transmission path. The speed and direction the object is moving may be calculated using the backscattered data. The data may be plotted in a two dimensional array with a scan angle on one axis and a scan time on the other axis. The prominent curves that appear in the plot may be analyzed to determine the speed and direction the object is traveling

C2 and CN Emission in the Shock Tube

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70038/2/JCPSA6-27-6-1415-1.pd

Comparison of Two Lidar Methods of Wind Measurement by Cloud Tracking

We measured the horizontal wind speed vector with two separate lidar cloud tracking techniques. Data were taken during two measurement campaigns: HOLO-1, at Utah State University (USU), Utah, and HOLO-2 at St. Anselm College, New Hampshire. Army Research Office Lidar (AROL-2), Holographic Airborne Rotating Lidar Instrument (HARLIE), and a wide-angle camera were used during HOLO-1. Prototype Holographic Atmospheric Scanner for Environmental Remote Sensing (PHASERS) also participated in HOLO-2. Two measurement methods are described, and selected results from the two HOLO campaigns are shown

Development of Learning Objectives to Guide Enhancement of Chronic Disease Prevention and Management Curricula in Undergraduate Medical Education

Phenomenon: Chronic disease is a leading cause of death and disability in the United States. With an increase in the demand for healthcare and rising costs related to chronic care, physicians need to be better trained to address chronic disease at various stages of illness in a collaborative and cost-effective manner. Specific and measurable learning objectives are key to the design and evaluation of effective training, but there has been no consensus on chronic disease learning objectives appropriate to medical student education. Approach: Wagner’sChronic Care Model (CCM) was selected as a theoretical framework to guide development of an enhanced chronic dis-ease prevention and management (CDPM) curriculum. Findings of a literature review of CDPM competencies, objectives, and topical statements were mapped to each of the six domains of the CCM to understand the breadth of existing learning topics within each domain. At an in-person meeting, medical educators prepared a survey for the modified Delphi approach. Attendees iden-tified 51 possible learning objectives from the literature review mapping, rephrased the CCM domains as competencies, constructed possible CDPM learning objectives for each competency with the goal of reaching multi-institutional consensus on a limited number of CDPM learning objectives that would be feasible for institutions to use to guide enhancement of medical student curricula related to CDPM. After the meeting, the group developed a survey which included 39 learning objectives. In the study phase of the modified Delphi approach, 32 physician CDPM experts and educators completed an online survey to prioritize the top 20 objectives. The next step occurred at a CDPM interest group in-person meeting with the goal of identifying the top 10 objectives. Findings: The CCM domains were reframed as the following competencies for medical student education: patient self-care management, decision support, clinical information systems, community resources, delivery systems and teams, and health system practice and improvement. Eleven CDPM learning objectives were identified within the six competencies that were most important in developing curriculum for medical students. Insights: These learning objectives cut across education on the prevention and management of individual chronic diseases and frame chronic disease care as requiring the health system science competencies identified in the CCM. They are intended to be used in combination with traditional disease-specific pathophysiology and treatment objectives. Additional efforts are needed to identify specific curricular strategies and assessment tools for each learning objective

Lidar Based Emissions Measurement at the Whole Facility Scale: Method and Error Analysis

Particulate emissions from agricultural sources vary from dust created by operations and animal movement to the fine secondary particulates generated from ammonia and other emitted gases. The development of reliable facility emission data using point sampling methods designed to characterize regional, well-mixed aerosols are challenged by changing wind directions, disrupted flow fields caused by structures, varied surface temperatures, and the episodic nature of the sources found at these facilities. We describe a three-wavelength lidar-based method, which, when added to a standard point sampler array, provides unambiguous measurement and characterization of the particulate emissions from agricultural production operations in near real time. Point-sampled data are used to provide the aerosol characterization needed for the particle concentration and size fraction calibration, while the lidar provides 3D mapping of particulate concentrations entering, around, and leaving the facility. Differences between downwind and upwind measurements provide an integrated aerosol concentration profile, which, when multiplied by the wind speed profile, produces the facility source flux. This approach assumes only conservation of mass, eliminating reliance on boundary layer theory. We describe the method, examine measurement error, and demonstrate the approach using data collected over a range of agricultural operations, including a swine grow-finish operation, an almond harvest, and a cotton gin emission study

Retrieval of Physical Properties of Particulate Emission from Animal Feeding Operations Using Three-Wavelength Elastic Lidar Measurements

Agricultural operations produce a variety of particulates and gases that influence ambient air quality. Lidar (LIght Detection And Ranging) technology provides a means to derive quantitative information of particulate spatial distribution and optical/physical properties over remote distances. A three-wavelength scanning lidar system built at the Space Dynamic Laboratory (SDL) is used to extract optical parameters of particulate matter and to convert these optical properties to physical parameters of particles. This particulate emission includes background aerosols, emissions from the agricultural feeding operations, and fugitive dust from the road. Aerosol optical parameters are retrieved using the widely accepted solution proposed by Klett. The inversion algorithm takes advantage of measurements taken simultaneously at three lidar wavelengths (355, 532, and 1064 nm) and allows us to estimate the particle size distribution. A bimodal lognormal particle size distribution is assumed and mode radius, width of the distribution, and total number density are estimated, minimizing the difference between calculated and measured extinction coefficients at the three lidar wavelengths. The results of these retrievals are then compared with simultaneous point measurements at the feeding operation site, taken with standard equipment including optical particle counters, portable PM10 and PM2.5 ambient air samplers, multistage impactors, and an aerosol mass spectrometer