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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

An Efficient Objective Analysis System for Parallel Computers

A new atmospheric objective analysis system designed for parallel computers will be described. The system can produce a global analysis (on a 1 X 1 lat-lon grid with 18 levels of heights and winds and 10 levels of moisture) using 120,000 observations in 17 minutes on 32 CPUs (SGI Origin 2000). No special parallel code is needed (e.g. MPI or multitasking) and the 32 CPUs do not have to be on the same platform. The system is totally portable and can run on several different architectures at once. In addition, the system can easily scale up to 100 or more CPUS. This will allow for much higher resolution and significant increases in input data. The system scales linearly as the number of observations and the number of grid points. The cost overhead in going from 1 to 32 CPUs is 18%. In addition, the analysis results are identical regardless of the number of processors used. This system has all the characteristics of optimal interpolation, combining detailed instrument and first guess error statistics to produce the best estimate of the atmospheric state. Static tests with a 2 X 2.5 resolution version of this system showed it's analysis increments are comparable to the latest NASA operational system including maintenance of mass-wind balance. Results from several months of cycling test in the Goddard EOS Data Assimilation System (GEOS DAS) show this new analysis retains the same level of agreement between the first guess and observations (O-F statistics) as the current operational system.

Stobie, J.↗
NASA NTRS2011

Evaluating the Performance of Single and Double Moment Microphysics Schemes During a Synoptic-Scale Snowfall Event

Increases in computing resources have allowed for the utilization of high-resolution weather forecast models capable of resolving cloud microphysical and precipitation processes among varying numbers of hydrometeor categories. Several microphysics schemes are currently available within the Weather Research and Forecasting (WRF) model, ranging from single-moment predictions of precipitation content to double-moment predictions that include a prediction of particle number concentrations. Each scheme incorporates several assumptions related to the size distribution, shape, and fall speed relationships of ice crystals in order to simulate cold-cloud processes and resulting precipitation. Field campaign data offer a means of evaluating the assumptions present within each scheme. The Canadian CloudSat/CALIPSO Validation Project (C3VP) represented collaboration among the CloudSat, CALIPSO, and NASA Global Precipitation Measurement mission communities, to observe cold season precipitation processes relevant to forecast model evaluation and the eventual development of satellite retrievals of cloud properties and precipitation rates. During the C3VP campaign, widespread snowfall occurred on 22 January 2007, sampled by aircraft and surface instrumentation that provided particle size distributions, ice water content, and fall speed estimations along with traditional surface measurements of temperature and precipitation. In this study, four single-moment and two double-moment microphysics schemes were utilized to generate hypothetical WRF forecasts of the event, with C3VP data used in evaluation of their varying assumptions. Schemes that incorporate flexibility in size distribution parameters and density assumptions are shown to be preferable to fixed constants, and that a double-moment representation of the snow category may be beneficial when representing the effects of aggregation. These results may guide forecast centers in optimal configurations of their forecast models for winter weather and identify best practices present within these various schemes.

Molthan, Andrew L.↗
NASA NTRS1990

A reduced-dynamic technique for precise orbit determination

Observations of the Global Positioning System (GPS) will enable a reduced-dynamic technique for achieving subdecimeter orbit determination of earth-orbiting satellites. With this technique, information on the transition between satellite states at different observing times is furnished by both a formal dynamic model and observed satellite positional change (which is inferred kinematically from continuous GPS carrier-phase data). The relative weighting of dynamic and kinematic information can be freely varied. Covariance studies show that in situations where observing geometry is poor and the dynamic model is good, the model dominates determination of the state transition; where the dynamic model is poor and the geometry strong, carrier phase governs the determination of the transition. When neither kinematic nor dynamic information is clearly superior, the reduced-dynamic combination of the two can substantially improve the orbit-determination solution. Guidelines are given here for selecting a near-optimal weighting for the reduced-dynamic solution, and sensitivity of solution accuracy to this weighting is examined.

Wu, S. C.↗
NASA NTRS2004

Clouds Versus Carbon: Predicting Vegetation Roughness by Maximizing Productivity

Surface roughness is one of the dominant vegetation properties that affects land surface exchange of energy, water, carbon, and momentum with the overlying atmosphere. We hypothesize that the canopy structure of terrestrial vegetation adapts optimally to climate by maximizing productivity, leading to an optimum surface roughness. An optimum should exist because increasing values of surface roughness cause increased surface exchange, leading to increased supply of carbon dioxide for photosynthesis. At the same time, increased roughness enhances evapotranspiration and cloud cover, thereby reducing the supply of photosynthetically active radiation. We demonstrate the optimum through sensitivity simulations using a coupled dynamic vegetation-climate model for present day conditions, in which we vary the value of surface roughness for vegetated surfaces. We find that the maximum in productivity occurs at a roughness length of 2 meters, a value commonly used to describe the roughness of today's forested surfaces. The sensitivity simulations also illustrate the strong climatic impacts of vegetation roughness on the energy and water balances over land: with increasing vegetation roughness, solar radiation is reduced by up to 20 W/sq m in the global land mean, causing shifts in the energy partitioning and leading to general cooling of the surface by 1.5 K. We conclude that the roughness of vegetated surfaces can be understood as a reflection of optimum adaptation, and it is associated with substantial changes in the surface energy and water balances over land. The role of the cloud feedback in shaping the optimum underlines the importance of an integrated perspective that views vegetation and its adaptive nature as an integrated component of the Earth system.

Olsen, Lola M.↗
NASA NTRS2009

Hybrid NN/SVM Computational System for Optimizing Designs

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Rai, Man Mohan↗
NASA NTRS2009

Large Area Scene Selection Interface (LASSI). Methodology of Selecting Landsat Imagery for the Global Land Survey 2005

The Global Land Survey (GLS) 2005 is a cloud-free, orthorectified collection of Landsat imagery acquired during the 2004-2007 epoch intended to support global land-cover and ecological monitoring. Due to the numerous complexities in selecting imagery for the GLS2005, NASA and the U.S. Geological Survey (USGS) sponsored the development of an automated scene selection tool, the Large Area Scene Selection Interface (LASSI), to aid in the selection of imagery for this data set. This innovative approach to scene selection applied a user-defined weighting system to various scene parameters: image cloud cover, image vegetation greenness, choice of sensor, and the ability of the Landsat 7 Scan Line Corrector (SLC)-off pair to completely fill image gaps, among others. The parameters considered in scene selection were weighted according to their relative importance to the data set, along with the algorithm's sensitivity to that weight. This paper describes the methodology and analysis that established the parameter weighting strategy, as well as the post-screening processes used in selecting the optimal data set for GLS2005.

Franks, Shannon↗
NASA NTRS2014

NASA's Space Launch System: A Cornerstone Capability for Exploration

Under construction today, the National Aeronautics and Space Administration's (NASA) Space Launch System (SLS), managed at the Marshall Space Flight Center, will provide a robust new capability for human and robotic exploration beyond Earth orbit. The vehicle's initial configuration, scheduled for first launch in 2017, will enable human missions into lunar space and beyond, as well as provide game-changing benefits for space science missions, including offering substantially reduced transit times for conventionally designed spacecraft. From there, the vehicle will undergo a series of block upgrades via an evolutionary development process designed to expedite mission capture as capability increases. The Space Launch System offers multiple benefits for a variety of utilization areas. From a mass-lift perspective, the initial configuration of the vehicle, capable of delivering 70 metric tons (t) to low Earth orbit (LEO), will be the world's most powerful launch vehicle. Optimized for missions beyond Earth orbit, it will also be the world's only exploration-class launch vehicle capable of delivering 25 t to lunar orbit. The evolved configuration, with a capability of 130 t to LEO, will be the most powerful launch vehicle ever flown. From a volume perspective, SLS will be compatible with the payload envelopes of contemporary launch vehicles, but will also offer options for larger fairings with unprecedented volume-lift capability. The vehicle's mass-lift capability also means that it offers extremely high characteristic energy for missions into deep space. This paper will discuss the impacts that these factors - mass-lift, volume, and characteristic energy - have on a variety of mission classes, particularly human exploration and space science. It will address the vehicle's capability to enable existing architectures for deep-space exploration, such as those documented in the Global Exploration Roadmap, a capabilities-driven outline for future deep-space voyages created by the International Space Exploration Coordination Group, which represents 12 of the world's space agencies. In addition, this paper will detail this new rocket's capability to support missions beyond the human exploration roadmap, including robotic precursor missions to other worlds or uniquely high-mass space operation facilities in Earth orbit. As this paper will explain, the SLS Program is currently building a global infrastructure asset that will provide robust space launch capability to deliver sustainable solutions for exploration.

Creech, Stephen D.↗
NASA NTRS2014

NASA Space Launch System: A Cornerstone Capability for Exploration

Under construction today, the National Aeronautics and Space Administration's (NASA) Space Launch System (SLS), managed at the Marshall Space Flight Center, will provide a robust new capability for human and robotic exploration beyond Earth orbit. The vehicle's initial configuration, sched will enable human missions into lunar space and beyond, as well as provide game-changing benefits for space science missions, including offering substantially reduced transit times for conventionally designed spacecraft. From there, the vehicle will undergo a series of block upgrades via an evolutionary development process designed to expedite mission capture as capability increases. The Space Launch System offers multiple benefits for a variety of utilization areas. From a mass-lift perspective, the initial configuration of the vehicle, capable of delivering 70 metric tons (t) to low Earth orbit (LEO), will be the world's most powerful launch vehicle. Optimized for missions beyond Earth orbit, it will also be the world's only exploration-class launch vehicle capable of delivering 25 t to lunar orbit. The evolved configuration, with a capability of 130 t to LEO, will be the most powerful launch vehicle ever flown. From a volume perspective, SLS will be compatible with the payload envelopes of contemporary launch vehicles, but will also offer options for larger fairings with unprecedented volume-lift capability. The vehicle's mass-lift capability also means that it offers extremely high characteristic energy for missions into deep space. This paper will discuss the impacts that these factors - mass-lift, volume, and characteristic energy - have on a variety of mission classes, particularly human exploration and space science. It will address the vehicle's capability to enable existing architectures for deep-space exploration, such as those documented in the Global Exploration Roadmap, a capabilities-driven outline for future deep-space voyages created by the International Space Exploration Coordination Group, which represents 14 of the world's space agencies. In addition, this paper will detail this new rocket's capability to support missions beyond the human exploration roadmap, including robotic precursor missions to other worlds or uniquely high-mass space operation facilities in Earth orbit. As this paper will explain, the SLS Program is currently building a global infrastructure asset that will provide robust space launch capability to deliver sustainable solutions for exploration.

Creech, Stephen D.↗
NASA NTRS2015

Simulated Impacts of Climate Change on Water Use and Yield of Irrigated Sugarcane in South Africa

Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current (1980-2010) and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm(A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model. Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated. Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimize productivity. Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

Climate change↗
NASA NTRS1993

GLRS-R 2-colour retroreflector target design and predicted performance

This paper reports on the retroreflector ground-target design for the GLRS-R spaceborne dual-wavelength laser ranging system. The described passive design flows down from the requirements of high station autonomy, high global FOV (up to 60 degrees zenith angle), little or no multiple pulse returns, and adequate optical cross section for most ranging geometries. The proposed solution makes use of 5 hollow cube-corner retroreflectors of which one points to the zenith and the remaining four are inclined from the vertical at uniform azimuthal spacings. The need for fairly large (is approximately 10 cm) retroreflectors is expected (within turbulence limitations) to generate quite narrow diffraction lobes, thus placing non-trivial requirements on the vectorial accuracy of velocity aberration corrections. A good compromise solution is found by appropriately spoiling just one of the retroreflector dihedral angles from 90 degrees, thus generating two symmetrically oriented diffraction lobes in the return beam. The required spoil angles are found to have little dependence on ground target latitude. Various link budget analyses are presented, showing the influence of such factors as point-ahead optimization, turbulence, ranging angle, atmospheric visibility and ground target thermal deformations.

Lund, Glenn↗
NASA NTRS1999

An Efficient Objective Analysis System for Parallel Computers

A new objective analysis system designed for parallel computers will be described. The system can produce a global analysis (on a 2 x 2.5 lat-lon grid with 20 levels of heights and winds and 10 levels of moisture) using 120,000 observations in less than 3 minutes on 32 CPUs (SGI Origin 2000). No special parallel code is needed (e.g. MPI or multitasking) and the 32 CPUs do not have to be on the same platform. The system Ls totally portable and can run on -several different architectures at once. In addition, the system can easily scale up to 100 or more CPUS. This will allow for much higher resolution and significant increases in input data. The system scales linearly as the number of observations and the number of grid points. The cost overhead in going from I to 32 CPus is 18%. in addition, the analysis results are identical regardless of the number of processors used. T'his system has all the characteristics of optimal interpolation, combining detailed instrument and first guess error statistics to produce the best estimate of the atmospheric state. It also includes a new quality control (buddy check) system. Static tests with the system showed it's analysis increments are comparable to the latest NASA operational system including maintenance of mass-wind balance. Results from a 2-month cycling test in the Goddard EOS Data Assimilation System (GEOS DAS) show this new analysis retains the same level of agreement between the first guess and observations (0-F statistics) throughout the entire two months.

Stobie, James G.↗
NASA NTRS1992

Radar sensitivity and antenna scan pattern study for a satellite-based Radar Wind Sounder (RAWS)

Modeling global atmospheric circulations and forecasting the weather would improve greatly if worldwide information on winds aloft were available. Recognition of this led to the inclusion of the LAser Wind Sounder (LAWS) system to measure Doppler shifts from aerosols in the planned for Earth Observation System (EOS). However, gaps will exist in LAWS coverage where heavy clouds are present. The RAdar Wind Sensor (RAWS) is an instrument that could fill these gaps by measuring Doppler shifts from clouds and rain. Previous studies conducted at the University of Kansas show RAWS as a feasible instrument. This thesis pertains to the signal-to-noise ratio (SNR) sensitivity, transmit waveform, and limitations to the antenna scan pattern of the RAWS system. A dop-size distribution model is selected and applied to the radar range equation for the sensitivity analysis. Six frequencies are used in computing the SNR for several cloud types to determine the optimal transmit frequency. the results show the use of two frequencies, one higher (94 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) for better penetration in rain, provide ample SNR. The waveform design supports covariance estimation processing. This estimator eliminates the Doppler ambiguities compounded by the selection of such high transmit frequencies, while providing an estimate of the mean frequency. the unambiguous range and velocity computation shows them to be within acceptable limits. The design goal for the RAWS system is to limit the wind-speed error to less than 1 ms(exp -1). Due to linear dependence between vectors for a three-vector scan pattern, a reasonable wind-speed error is unattainable. Only the two-vector scan pattern falls within the wind-error limits for azimuth angles between 16 deg to 70 deg. However, this scan only allows two components of the wind to be determined. As a result, a technique is then shown, based on the Z-R-V relationships, that permit the vertical component (i.e., rain) to be computed. Thus the horizontal wind components may be obtained form the covariance estimator and the vertical component from the reflectivity factor. Finally, a new candidate system is introduced which summarizes the parameters taken from previous RAWS studies, or those modified in this thesis.

Stuart, Michael A.↗
NASA NTRS2015

Implementing Marine Organic Aerosols Into the GEOS-Chem Model

Marine-sourced organic aerosols (MOA) have been shown to play an important role in tropospheric chemistry by impacting surface mass, cloud condensation nuclei, and ice nuclei concentrations over remote marine and coastal regions. In this work, an online marine primary organic aerosol emission parameterization, designed to be used for both global and regional models, was implemented into the GEOS-Chem model. The implemented emission scheme improved the large under-prediction of organic aerosol concentrations in clean marine regions (normalized mean bias decreases from -79% when using the default settings to -12% when marine organic aerosols are added). Model predictions were also in good agreement (correlation coefficient of 0.62 and normalized mean bias of -36%) with hourly surface concentrations of MOA observed during the summertime at an inland site near Paris, France. Our study shows that MOA have weaker coastal-to-inland concentration gradients than sea-salt aerosols, leading to several inland European cities having > 10% of their surface submicron organic aerosol mass concentration with a marine source. The addition of MOA tracers to GEOS-Chem enabled us to identify the regions with large contributions of freshly-emitted or aged aerosol having distinct physicochemical properties, potentially indicating optimal locations for future field studies.

Aerosols↗
NASA NTRS2014

The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part 1: African Wave Circulation

The performance of the NCAR Weather Research and Forecasting Model (WRF) as a West African regional-atmospheric model is evaluated. The study tests the sensitivity of WRF-simulated vorticity maxima associated with African easterly waves to 64 combinations of alternative parameterizations in a series of simulations in September. In all, 104 simulations of 12-day duration during 11 consecutive years are examined. The 64 combinations combine WRF parameterizations of cumulus convection, radiation transfer, surface hydrology, and PBL physics. Simulated daily and mean circulation results are validated against NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP/Department of Energy Global Reanalysis 2. Precipitation is considered in a second part of this two-part paper. A wide range of 700-hPa vorticity validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve correlations against reanalysis of 0.40-0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year while a parallel-benchmark simulation by the NASA Regional Model-3 (RM3) achieves higher correlations, but less realistic spatiotemporal variability. The largest favorable impact on WRF-vorticity validation is achieved by selecting the Grell-Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis than simulations using the Kain-Fritch convection. Other parameterizations have less-obvious impact, although WRF configurations incorporating one surface model and PBL scheme consistently performed poorly. A comparison of reanalysis circulation against two NASA radiosonde stations confirms that both reanalyses represent observations well enough to validate the WRF results. Validation statistics for optimized WRF configurations simulating the parallel period during 10 additional years are less favorable than for 2006.

variability↗
NASA NTRSDate not supplied

Cloud-Based Demodulation and Data Distribution of a Satellite Downlink

Ground station networks connected to the cloud allow space missions to have global communications coverage without operating their own infrastructure. In this work, we describe the communications architecture for the TechEdSat-13 mission, which performed the first in-space characterization of a neuromorphic processor. The mission utilizes a commercial provider for S-band downlinks. A suite of cloud services and open-source software such as GNU Radio are leveraged to demodulate signals received by an AWS ground station during passes with TechEdSat-13 and store recovered data. Once a pass is scheduled, the entire process takes place without human intervention. On-orbit results the past year of operations are presented, demonstrating the advantages of this approach over traditional operator-owned ground stations. Use of software-defined radio makes possible custom signal processing. The homogeneity of apertures and their interfaces to the cloud simplifies scaling across many sites. This abundance of candidate links lays the groundwork for intelligent scheduling agents to optimize pass selection across several factors, automatically recover from failed contacts, and gather metrics to learn from past performance.

cloud demodulation↗
NASA NTRS2010

Life at the Common Denominator: Mechanistic and Quantitative Biology for the Earth and Space Sciences

The remarkable challenges and possibilities of the coming few decades will compel the biogeochemical and astrobiological sciences to characterize the interactions between biology and its environment in a fundamental, mechanistic, and quantitative fashion. The clear need for integrative and scalable biology-environment models is exemplified in the Earth sciences by the challenge of effectively addressing anthropogenic global change, and in the space sciences by the challenge of mounting a well-constrained yet sufficiently adaptive and inclusive search for life beyond Earth. Our understanding of the life-planet interaction is still, however, largely empirical. A variety of approaches seek to move from empirical to mechanistic descriptions. One approach focuses on the relationship between biology and energy, which is at once universal (all life requires energy), unique (life manages energy flow in a fashion not seen in abiotic systems), and amenable to characterization and quantification in thermodynamic terms. Simultaneously, a focus on energy flow addresses a critical point of interface between life and its geological, chemical, and physical environment. Characterizing and quantifying this relationship for life on Earth will support the development of integrative and predictive models for biology-environment dynamics. Understanding this relationship at its most fundamental level holds potential for developing concepts of habitability and biosignatures that can optimize astrobiological exploration strategies and are extensible to all life.

Hoehler, Tori M.↗
NASA NTRS2014

The Effect of Growth Environment and Salinity on Lipid Production and Composition of Salicornia virginica

Finding a viable and sustainable source of renewable energy is a global task. Biofuels as a renewable energy source can potentially be a viable option for sustaining long-term energy needs. Biodiesel from halophytes shows great promise due to their ability to serve not only as a fuel source, but a food source as well. Halophytes are one of the few biomass plant species that can tolerate a wide range of saline conditions. We investigate the feasibility of using the halophyte, Salicornia virginica as a biofuel source by conducting a series of experiments utilizing various growth and salinity conditions. The goal is to determine if the saline content of Salicornia virginica in our indoor growth vs outdoor growth conditions has an influence on lipid recovery and total biomass composition. We focused on using standard lipid extraction protocols and characterization methods to evaluate twelve Salicornia virginica samples under six saline values ranging from freshwater to seawater and two growth conditions. The overall goal is to develop an optimal lipid extraction protocol for Salicornia virginica and potentially apply this protocol to halophytes in general.

Bomani, Bilal Mark McDowell↗
NASA NTRS2008

Data Assimilation Cycling for Weather Analysis

This software package runs the atmospheric model MM5 in data assimilation cycling mode to produce an optimized weather analysis, including the ability to insert or adjust a hurricane vortex. The program runs MM5 through a cycle of short forecasts every three hours where the vortex is adjusted to match the observed hurricane location and storm intensity. This technique adjusts the surrounding environment so that the proper steering current and environmental shear are achieved. MM5cycle uses a Cressman analysis to blend observation into model fields to get a more accurate weather analysis. Quality control of observations is also done in every cycle to remove bad data that may contaminate the analysis. This technique can assimilate and propagate data in time from intermittent and infrequent observations while maintaining the atmospheric field in a dynamically balanced state. The software consists of a C-shell script (MM5cycle.driver) and three FORTRAN programs (splitMM5files.F, comRegrid.F, and insert_vortex.F), and are contained in the pre-processor component of MM5 called "Regridder." The model is first initialized with data from a global model such as the Global Forecast System (GFS), which also provides lateral boundary conditions. These data are separated into single-time files using splitMM5.F. The hurricane vortex is then bogussed in the correct location and with the correct wind field using insert_vortex.F. The modified initial and boundary conditions are then recombined into the model fields using comRegrid.F. The model then makes a three-hour forecast. The three-hour forecast data from MM5 now become the analysis for the next short forecast run, where the vortex will again be adjusted. The process repeats itself until the desired time of analysis is achieved. This code can also assimilate observations if desired.

Tran, Nam↗