ALBERT

Description: Afforestation is a viable and widely practiced method of sequestering carbon dioxide from the atmosphere. However, because of a change in surface albedo, placement of less reflective forests can cause an increase in net-absorbed radiation and localized surface warming. This effect is enhanced in northern high latitudes where the presence of snow cover exacerbates the albedo difference. Regions where afforestation could provide a climate benefit are determined by comparing net ecosystem production and net radiation differences from afforestation in midlatitude and high latitude of North America. Using the dynamic vegetation model Integrated Biosphere Simulator, agricultural version (Agro-IBIS), we find a boundary through North America where afforestation results in a positive equivalent carbon balance (cooling) to the south, and a negative equivalent carbon balance (warming) to the north. Including the effects of stand age and fraction cover affect whether a site contributes to mitigating global warming. ©2017. American Geophysical Union. All Rights Reserved.

Description: Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems, and human communities, worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "Big Data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically-based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

Description: Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean–land–atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

Description: Previously cited in issue 5, p. 642, Accession no. A83-16486

Description: Laser Doppler anemometers (LDAs) that are arranged to measure nonorthogonal velocity components (from which orthogonal components are computed through transformation equations) are more susceptible to calibration and sampling errors than are systems with uncoupled channels. In this paper uncertainty methods and estimation theory are used to evaluate, respectively, the systematic and statistical errors that are present when such devices are applied to the measurement of mean velocities in turbulent flows. Statistical errors are estimated for two-channel LDA data that are either correlated or uncorrelated. For uncorrelated data the directional uncertainty of the measured velocity vector is considered for applications where mean streamline patterns are desired.

Description: A method and apparatus for calibrating laser Doppler velocimeters having one or more intersecting beam pairs are described. These velocimeters measure fluid velocity by observing the light scattered by particles in the fluid stream. Moving fluid particulates are simulated by fine taut wires that are radially mounted on a disk that is rotated at a known velocity. The laser beam intersection locus is first aimed at the very center of the disk and then the disk is translated so that the locus is swept by the rotating wires. The radial distance traversed is precisely measured so that the velocity of the wires (pseudo particles) may be calculated.

Description: The determination of mean streamline patterns by moving the test point in the direction of the measured velocity is shown to produce cumulative errors that are unacceptable. A two-dimensional algorithm that minimizes these errors is presented and is analytically validated using simple potential flows. The algorithm is extended to three-dimensional flows and is again validated analytically. Finally, as an example of a typical application of the algorithm, mean streamlines are measured in a complex, turbulent flow with a three-dimensional laser anemometer.

Description: A unique laser velocimeter has been developed that is capable of sensing two orthogonal velocity components from a variable remote distance of 2.6 to 10 m for use in the Ames 40- by 80-Foot and 80- by 120-Foot wind tunnels, as well as in the Ames Outdoor Aerodynamic Research Facility. The system hardware, positioning instrumentation, and data acquisition equipment are described in detail. System capabilities and limitations are discussed, and expressions for systematic and statistical accuracy are developed. Direct and coupled laboratory measurements of a turbulent swirling flow are compared with measurements taken with a laser velocimeter of higher spatial resolution. Sample measurements taken in the open-circuit exhaust flow of the 80- by 120-Foot Wind Tunnel 1/50-scale model are presented.

Description: The historical development of laser velocimetry and its application to low-speed (less than 100 m/sec) aerodynamic flows in the subsonic wind tunnels at Ames Research Center is reviewed. A fully three dimensional velocimeter for the Ames 7- by 10-Foot Wind Tunnel is described, and its capabilities are presented through sample data from a recent experiment. Finally, a long-range (2.6 to 10 m) velocimeter that is designed to be installed within the test section of the Ames 40- by 80-Foot Wind Tunnel is described and sample data are presented.