Opportunities, challenges and pitfalls in characterizing plant water-use strategies
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Environmental Sciences & Ecology↗
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Results for “Temporal modeling”
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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Theoretical models of gas dynamics and star formation in interacting ring galaxies
A series of one and two dimensional hydrodynamic simulations of a ring wave in interstellar gas disks was completed. These calculations included nonlinear source terms to model the effects of interstellar interactions and star formation, as well as the spatial-temporal gas flow. Toomre's kinematical model was merged with the Arnold, Shandarin, and Zeldovich 'pancake' theory of caustics in galaxy formation. The resulting theory can describe almost all the structure in restricted three-body simulations of single-pass collisions, even with multi-component potentials. Off-center galactic collisions were studied to understand the dynamics involved. Multi-color optical and near-infrared observations of faint tidal features were performed in about two dozen interacting galaxies selected from the Arp atlas. This sample provided evidence for ongoing star formation in tidal structures, and even enhancements of star formation in some cases. The task of assembling the data for gas-rich, late-type galaxies, was undertaken to see if a more coherent picture of the gas distribution would emerge from the more complete data. Analytic solutions of the equations with subsonic flows to balance gas consumption for expulsion form a galactic fountain were also derived.
Struck-Marcell, Curtis↗
Large-eddy simulation of a plane wake
Previously the theoretical development leading to the dynamic localization model (DLM) for large-eddy simulation (LES) was presented. The method has been successfully applied to isotropic turbulence, channel flow, and the flow over a backward-facing step. Here we apply the model to the computation of the temporally developing place wake. The two main objectives of this project are: (1) Use the model to perform an LES of a time developing plane wake and compare the results with direction numerical simulation (DNS) data to see if important statistical measures can be readily predicted, and to provide a relative evaluation of the several versions of the model in terms of predictive capability and cost; and (2) If the tests in (1) show that the model generates reliable predictions, then use the LES to study various aspects of the physics of turbulent wakes and mixing layers.
Ghosal, Sandip↗
Using Model Checking to Validate AI Planner Domain Models
This report describes an investigation into using model checking to assist validation of domain models for the HSTS planner. The planner models are specified using a qualitative temporal interval logic with quantitative duration constraints. We conducted several experiments to translate the domain modeling language into the SMV, Spin and Murphi model checkers. This allowed a direct comparison of how the different systems would support specific types of validation tasks. The preliminary results indicate that model checking is useful for finding faults in models that may not be easily identified by generating test plans.
Penix, John↗
Atmospheric Variability and Measurement Uncertainty: Pitfalls in Averaging in situ Data
Satellite measurements and atmospheric models, two essential components of the integrated global observing system, provide crucial tools for monitoring and predicting regional and global foci, spanning numerous Earth Science fields. Unfortunately, models can lack the spatial and temporal resolution needed to resolve finer scale structure. Satellite measurements also have similar tempo-spatial restriction issues, but additionally can only measure certain species and can have biases that must be evaluated. Ground measurement networks are critical components of this system, by providing both independent inputs of species needed by models (including those that satellites do not provide) and assisting with investigation of biases in satellite products. Similarly, aircraft measurements play a vital role in closing the gaps between satellite measurements, model products, and ground monitoring networks by providing high accuracy, high-resolution data on local to regional spatial scales. Therefore, both ground-based and airborne observations are widely used to assess model predictions and satellite observations. One of the great challenges in using both ground and aircraft data in this fashion is matching the data temporally and spatially to the model/satellite data. A meaningful comparison with model or satellite requires a solid assessment of the variability of the in-situ measurements, which include both the instrument uncertainty and the statistical uncertainty due to atmospheric variability. While instrument uncertainty is generally more straightforwardly characterized, it can be challenging to accurately capture this variability uncertainty as it often presents in a non-Gaussian manner (e.g. emission plumes, frontal passages). We will present results examining spatial and temporal variability over a selection of scales relevant to satellite measurements and models of several in situ measurement species spanning both airborne and ground measurements. The extent of non-Gaussian variability will be quantified, and we will discuss additional statistical parameters that help assess the fitness of gaussian variability assumption when temporally or spatially averaging.
satellite validation↗
Skill of Mesoscale Models in Forecasting Springtime Macrophysical Cloud Properties at the Savannah River Site in the Southeastern US
Predicting boundary layer clouds is important for the accurate modeling of pollutant dispersion. Higher resolution mesoscale models would be expected to produce better forecasts of cloud properties that affect dispersion. Using ceilometer observations, we assess the skill of two operational mesoscale models (RAMS and WRF) to forecast cloud base altitude and cloud fraction at the Savannah River Site in the southeastern US during the springtime. Verifications were performed at small spatial and temporal scales necessary for dispersion modeling. Both models were unreliable with a 50% (RAMS) and a 46% (WRF) rate of predicting clouds observed by the ceilometer which led to low cloud fraction predictions. Results indicated that WRF better predicted daytime cloud bases from convection that occurred frequently later in the period and RAMS better predicted nighttime cloud bases. Using root mean squared error (RMSE) to score the forecast periods also highlighted this diurnal dichotomy, with WRF scores better during the day and RAMS scores better at night. Analysis of forecast errors revealed divergent model cloud base biases—WRF low and RAMS high. A hybrid solution which weighs more heavily the RAMS nighttime forecasts and WRF daytime forecasts will likely provide the best prediction of cloud properties for dispersion.
54 ENVIRONMENTAL SCIENCES↗
Estimating Traveler Populations at Airport and Cruise Terminals for Population Distribution and Dynamics
In recent years, uses of high-resolution population distribution databases are increasing steadily for environmental, socioeconomic, public health, and disaster-related research and operations. With the development of daytime population distribution, temporal resolution of such databases has been improved. However, the lack of incorporation of transitional population, namely business and leisure travelers, leaves a significant population unaccounted for within the critical infrastructure networks, such as at transportation hubs. This paper presents two general methodologies for estimating passenger populations in airport and cruise port terminals at a high temporal resolution which can be incorporated into existing population distribution models. The methodologies are geographically scalable and are based on, and demonstrate how, two different transportation hubs with disparate temporal population dynamics can be modeled utilizing publicly available databases including novel data sources of flight activity from the Internet which are updated in near-real time. The airport population estimation model shows great potential for rapid implementation for a large collection of airports on a national scale, and the results suggest reasonable accuracy in the estimated passenger traffic. By incorporating population dynamics at high temporal resolutions into population distribution models, we hope to improve the estimates of populations exposed to or at risk to disasters, thereby improving emergency planning and response, and leading to more informed policy decisions.
airport↗
Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate
Urban buildings account for up to 75% of total energy use in the United States (U.S.). Understanding urban building energy use is important for developing feasible options to mitigate energy use and greenhouse gas emissions. In this study, an improved bottom-up building energy use model, named City Building Energy Use Model (City-BEUM), was developed to estimate building energy use for all buildings in Polk County, Iowa. First, 28 commercial and 6 residential building prototypes were designed by combing Assessor's parcel data and building footprint data. Then, the EnergyPlus in the CityBEUM was calibrated for all building prototypes using the U.S. Energy Information Administration's survey data, monthly utility meter data, and actual weather data. Finally, spatial and temporal variations of building energy use in the study area were estimated using the CityBEUM. Results indicate that the spatial variation of building energy use in the study area can be captured using the CityBEUM. With the monthly-calibrated model, the temporal pattern of urban building energy use can be well represented. The comparison of building energy use using the Typical Meteorological Year and actual weather data demonstrates the importance of using actual weather data in building energy modeling for an improved temporal representation.
CityBEUM, Urban building energy use, High spatiote↗
Target & Propagation Models for the FINDER Radar
Finding persons still alive in piles of rubble following an earthquake, a severe storm, or other disaster is a difficult problem. JPL is currently developing a victim detection radar called FINDER (Finding Individuals in Emergency and Response). The subject of this paper is directed toward development of propagation & target models needed for simulation & testing of such a system. These models are both physical (real rubble piles) and numerical. Early results from the numerical modeling phase show spatial and temporal spreading characteristics when signals are passed through a randomly mixed rubble pile.
propagation↗
Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis
Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Most assessments of model performance occur at individual temporal scales, but ecosystems respond to drivers at multiple time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of twenty-one ecosystem models at nine eddy-covariance towers as part of the North American Carbon Program's site-level inter-comparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model failure are consistent across a diverse range of models and sites.
58 GEOSCIENCES↗
Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes
While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Additionally, various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water and carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.
58 GEOSCIENCES↗
Modeling Streamflow and Sediment Transport Under Current and Future Climates at the West Valley Site - 20231
Performance Assessment models often require inputs representing specific hydrologic processes such as streamflow and sediment flux. This paper details the development of a process-level hydrologic model and its use in a Probabilistic Performance Assessment (PPA) system-level model for the West Valley Site in western New York. For this PPA model, a watershed model was developed using SWAT (Soil and Water Assessment Tool) and used to evaluate water and sediment fluxes in nearby streams draining the Site. A particular challenge of probabilistic modeling over long periods of time is scaling parameters appropriately so that outcomes are not grossly over- or underestimated. This challenge is especially important when the spatial and temporal scales of process and system level models differ. In this instance, the SWAT watershed model and GoldSim PPA model have similar spatial scales of stream reaches and sub-catchments within a single watershed (approximately hundreds to tens of thousands of square meters, but the temporal scales differ considerably. The SWAT model calculates streamflow and sediment transport rates at the daily scale, while the PPA model requires long-term average values that are applied over hundreds to thousands of year-long periods. As such, distributions of daily climatic parameters such as average precipitation rate, temperature, wind speed, and relative humidity were developed to force a transient SWAT model that captures the dynamic behavior of the watershed in response to daily changes in the weather. Output from the model, which includes stream flow (m{sup 3}/y) and sediment transport rate (Mg/y) is then averaged over a 100-year period as an approximation for the long-term average values which can be applied in GoldSim. A single SWAT model was run 5000 times with varying climatic inputs and deterministic soil, land use, and elevation data, and the range of outputs were compiled into a distribution which can be implemented stochastically into the PPA Model. Additionally, this paper includes a high-level discussion of how changes in future climate affect weather input distributions, and a summary of how those changes in weather affect the outcomes from the SWAT model and the inputs into the PPA model. In this analysis, future climate distributions were developed based on climate research documenting the expected changes to precipitation and temperature in western New York. The 5,000 SWAT simulations were then repeated with these future climate distributions, and the resulting output was implemented into the PPA Model as hydrologic conditions under a future climate. In the current version of the PPA Model, hydrologic parameters change linearly from the 'current climate' conditions to the 'future climate' conditions over a period of 100 years, after which they remain constant. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States
Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Finally, data from models and observations should be put on the same spatial and temporal scales before comparison.
54 ENVIRONMENTAL SCIENCES↗
DELAYED ONSET OF HIGH-ENERGY EMISSIONS IN LEPTONIC AND HADRONIC MODELS OF GAMMA-RAY BURSTS
The temporal-spectral evolution of the prompt emission of gamma-ray bursts is simulated numerically for both leptonic and hadronic models. For weak enough magnetic fields, leptonic models can reproduce the few seconds delay of the onset of GeV photon emission observed by Fermi-LAT, due to the slow growth of the target photon field for inverse Compton scattering. For stronger magnetic fields, the GeV delay can be explained with hadronic models, due to the long acceleration timescale of protons and the continuous photopion production after the end of the particle injection. While the FWHMs of the MeV and GeV light curves are almost the same in one-zone leptonic models, the FWHMs of the 1-30 GeV light curves in hadronic models are significantly wider than those of the 0.1-1 MeV light curves. The amount of the GeV delay depends on the importance of the Klein-Nishina effect in both the leptonic and hadronic models. In our examples of hadronic models the energies of the escaped neutrons are comparable to the gamma-ray energy, although their contribution to the ultra high-energy cosmic rays is still subdominant. The resulting neutrino spectra are hard enough to avoid the flux limit constraint from IceCube. The delay of the neutrino emission onset is up to several times longer than the corresponding delay of the GeV photon emission onset. The quantitative differences in the light curves for various models may be further tested with future atmospheric Cerenkov telescopes whose effective area is larger than that of Fermi-LAT, such as CTA.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Data-Augmented Modeling of Transition to Turbulence in Rayleigh-Taylor Mixing Layers
Existing turbulence models based on the Reynolds-averaged Navier–Stokes (RANS) equations are ill-equipped to represent transitional phenomena that are characteristic of problems such as those involved in Rayleigh-Taylor mixing layers. In this work, large eddy simulation(LES) data is used to augment and improve the predictive capability of RANS models. In-verse models are used to extract temporally varying embedded discrepancy functions from a number of LES datasets. These discrepancies are generalized into functional forms using neural networks and embedded inside the RANS solver to augment predictions. Specifically, the data-augmented models are demonstrated to provide improved predictions in Rayleigh-Taylor mixing problems at low Atwood numbers.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Measurements and Modeling of Black-Carbon Aerosols in the Arctic
We have made the first continuous measurements of black carbon in Barrow, Alaska at the ARM aerosol-observing site at the NOAA Barrow Observatory using a Single-Particle Soot Photometer (SP2). These data demonstrate that BC particles are extremely small, and a majority of the particles (by number density) are smaller than 0.5 fg, the lower limit of reliability of the SP2. We developed the first numerical model capable of quantitatively reproducing the laser-induced incandescence (LII) and scattering signals produced by the SP2, the industry-standard BC instrument. Our model reproduces the SP2 signal temporally and spectrally and demonstrates that the current SP2 optical design allows substantial contamination of LII on the scattering signal. We ran CAM5-SE in nudged mode, i.e., by constraining the transport used in the model with meteorological data. The results demonstrate the problem observed previously of under-predicting BC at high latitudes. The cause of the discrepancy is currently unknown, but we suspect that it is associated with scavenging and rainout mechanisms.
54 ENVIRONMENTAL SCIENCES↗
A Composite View of Lower Stratospheric Ozone Developed Using a Chemistry Transport Model and Observations from Airborne Lidar and Satellite
An ozone simulation from the Goddard three-dimensional chemistry and transport model for the 1995-96 northern hemisphere winter is compared with ozone observations from airborne Differential Absorption Lidar (DIAL), from the Polar Ozone and Aerosol Measurement (POAM), from the Microwave Limb Sounder (MLS), and from the Halogen Occultation Experiment (HALOE). The 3D model uses winds from the Goddard Data Assimilation System. The 3D model reproduces the latitude dependence of the horizontal and vertical ozone gradients of the subtropical DIAL observations. Comparisons with subtropical satellite observations, which lack the spatial resolution of DIAL but provide near continuous coverage throughout the subtropics, show that the model also reproduces longitude and temporal dependence in the tropical-midlatitude boundary. At polar latitudes, observations from DIAL flights on December 9 and January 30, and POAM and MLS between late December and late January are compared with the 3D model. Data from the three platforms consistently show that the observed ozone has a negative trend relative to the modeled ozone, and that the trend is uniform in time between early and mid winter, with no obvious dependence on proximity to the vortex edge.
Douglass, A. R.↗
Comparison of Cirrus Cloud Models: A Project of the GEWEX Cloud System Study (GCSS) Working Group on Cirrus Cloud Systems
The GEWEX Cloud System Study (GCSS, GEWEX is the Global Energy and Water Cycle Experiment) is a community activity aiming to promote development of improved cloud parameterizations for application in the large-scale general circulation models (GCMs) used for climate research and for numerical weather prediction. The GCSS strategy is founded upon the use of cloud-system models (CSMs). These are "process" models with sufficient spatial and temporal resolution to represent individual cloud elements, but spanning a wide range of space and time scales to enable statistical analysis of simulated cloud systems. GCSS also employs single-column versions of the parametric cloud models (SCMs) used in GCMs. GCSS has working groups on boundary-layer clouds, cirrus clouds, extratropical layer cloud systems, precipitating deep convective cloud systems, and polar clouds.
Starr, David O'C.↗