Results for “spatial prediction” — Page 25

NASA NTRS2019

NASA Disasters Program: A Partner for Coastal Disaster Response, Resiliency, and Risk Reduction

Summary: The NASA Applied Sciences: Disasters Program has kicked off a pilot project as “Mid-Atlantic Resiliency Demonstration Study, Communities at Intensive Risk” in FY17 and FY18; Efforts focus on a holistic, interdisciplinary approach to integrate a full suite of capabilities from numerical weather prediction, tide and surge modeling at high spatial resolution and urban/neighborhood scales, and remote sensing capabilities for water detection and depth estimation; Future efforts - Partner with Federal Agencies, academia professional societies to complement and augment ongoing work using Earth observations unique to NASA - Translate hazards to risk in order to understand local and regional impacts and minimize vulnerability.

Bell, Jordan↗

NASA NTRS2019

The Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Cesana, Gregory V.↗

DOE OSTI2024

Simulations of divertor designs that spatially separate power and particle exhaust using mid-leg divertor particle pumping

Predictive design modeling of a Dissipation-Focused Divertor for future operation in DIII-D reveals that increasing the poloidal distance of the pump duct entrance from the target surface along the low-field side divertor baffle increases neutral compression and modifies the spatial distribution of power dissipation. With a divertor pump located mid-leg between the target and the X-point, SOLPS-ITER boundary plasma simulations without drifts predict the formation of a dense neutral cloud near the target with > 30x higher neutral compression in detachment, a more stable detachment front located further from the target, and ~25% lower outer midplane separatrix density required for detachment onset, compared to a pump located in the scrape-off layer at the target surface. Up to 19 MW of power flowing into the divertors is modeled using the following two numerical implementations for particle pumping: a specified fraction of particles incident on variable wall sections of the plasma grid is removed from the computational domain (so-called albedo pumping), and a pump duct is modeled which includes dynamics of kinetic neutrals in the duct. The simulations show that the detachment front is located between the divertor target and the X-point and is relatively stable near the pump entrance, without a strong dependence on gas puff rate or injected power. The mid-leg pump design spatially separates the two primary functions of a divertor (power handling and particle exhaust), with the majority of power dissipation occurring near the target plate and particle exhaust taking place further upstream. The benefit of enhanced dissipation using mid-leg pumping comes at the cost of a higher outer midplane separatrix density for a given amount of particle injection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1999

Synthetic Scene Generation of the Stennis V and V Target Range for the Calibration of Remote Sensing Systems

The verification and validation (V&V) target range developed at Stennis Space Center is a useful test site for the calibration of remote sensing systems. In this paper, we present a simple algorithm for generating synthetic radiance scenes or digital models of this target range. The radiation propagation for the target in the solar reflective and thermal infrared spectral regions is modeled using the atmospheric radiative transfer code MODTRAN 4. The at-sensor, in-band radiance and spectral radiance for a given sensor at a given altitude is predicted. Software is developed to generate scenes with different spatial and spectral resolutions using the simulated at-sensor radiance values. The radiometric accuracy of the simulation is evaluated by comparing simulated with AVIRIS acquired radiance values. The results show that in general there is a good match between AVIRIS sensor measured and MODTRAN predicted radiance values for the target despite the fact that some anomalies exist. Synthetic scenes provide a cost-effective way for in-flight validation of the spatial and radiometric accuracy of the data. Other applications include mission planning, sensor simulation, and trade-off analysis in sensor design.

Cao, Chang-Yong↗

DOE OSTI2022

Dynamics of deuterium retention and desorption from plasma-facing materials in fusion reactor-relevant conditions

Hydrogen isotopes retention and desorption during and after discharges in fusion devices are still not well understood due to the complex device conditions and limitations of in-situ diagnostics and measurements. Here, we simulated well-diagnosed recent experiments at the DIII-D facility to benchmark our ITMC-DYN integrated package of modeling deuterium diffusion, retention, and desorption during and after D discharge irradiation. Modeling results were compared with detail experimental data of D desorption fluxes for various irradiation conditions. We predicted the temporal evolution of free and trapped D distribution in tungsten (W) plasma-facing material (PFM). Effects of key parameters namely diffusion coefficient, recombination rate, trapping energies against different defect types, were examined in these simulations. Existing experimental data of these parameters in literature varies significantly which makes it harder to identify key mechanisms and physics responsible for hydrogen isotope retention and desorption. The purpose of this work is to accurately simulate recent well-diagnosed reactor experiments given the uncertainties in such parameters and identify mechanisms responsible for the retention and desorption. We implemented the best identified diffusion, recombination, and trapping parameters in ITMC-DYN package that integrate both various collisional and thermal processes. We predicted, for example, that sample cooling between discharges in DIII-D operations can significantly affect the spatial distribution of trapped D in W under reactor irradiation conditions. Correct prediction of desorption spectra from samples irradiated during 10 DIII-D discharges showed that up to 35% of D can be retained in high binding energy defects such as vacancy clusters or voids.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗

NASA NTRS1995

Combining Speed Information Across Space

We used speed discrimination tasks to measure the ability of observers to combine speed information from multiple stimuli distributed across space. We compared speed discrimination thresholds in a classical discrimination paradigm to those in an uncertainty/search paradigm. Thresholds were measured using a temporal two-interval forced-choice design. In the discrimination paradigm, the n gratings in each interval all moved at the same speed and observers were asked to choose the interval with the faster gratings. Discrimination thresholds for this paradigm decreased as the number of gratings increased. This decrease was not due to increasing the effective stimulus area as a control experiment that increased the area of a single grating did not show a similar improvement in thresholds. Adding independent speed noise to each of the n gratings caused thresholds to decrease at a rate similar to the original no-noise case, consistent with observers combining an independent sample of speed from each grating in both the added- and no-noise cases. In the search paradigm, observers were asked to choose the interval in which one of the n gratings moved faster. Thresholds in this case increased with the number of gratings, behavior traditionally attributed to an input bottleneck. However, results from the discrimination paradigm showed that the increase was not due to observers' inability to process these gratings. We have also shown that the opposite trends of the data in the two paradigms can be predicted by a decision theory model that combines independent samples of speed information across space. This demonstrates that models typically used in classical detection and discrimination paradigms are also applicable to search paradigms. As our model does not distinguish between samples in space and time, it predicts that discrimination performance should be the same regardless of whether the gratings are presented in two spatial intervals or two temporal intervals. Our last experiment largely confirmed this prediction.

Verghese, Preeti↗

NASA NTRS1995

Combining Speed Information Across Space

We used speed discrimination tasks to measure the ability of observers to combine speed information from multiple stimuli distributed across space. We compared speed discrimination thresholds in a classical discrimination paradigm to those in an uncertainty/search paradigm. Thresholds were measured using a temporal two-interval forced-choice design. In the discrimination paradigm, the n gratings in each interval all moved at the same speed and observers were asked to choose the interval with the faster gratings. Discrimination thresholds for this paradigm decreased as the number of gratings increased. This decrease was not due to increasing the effective stimulus area as a control experiment that increased the area of a single grating did not show a similar improvement in thresholds. Adding independent speed noise to each of the n gratings caused thresholds to decrease at a rate similar to the original no-noise case, consistent with observers combining an independent sample of speed from each grating in both the added- and no-noise cases. In the search paradigm, observers were asked to choose the interval in which one of the n gratings moved faster. Thresholds in this case increased with the number of gratings, behavior traditionally attributed to an input bottleneck. However, results from the discrimination paradigm showed that the increase was not due to observers' inability to process these gratings. We have also shown that the opposite trends of the data in the two paradigms can be predicted by a decision theory model that combines independent samples of speed information across space. This demonstrates that models typically used in classical detection and discrimination paradigms are also applicable to search paradigms. As our model does not distinguish between samples in space and time, it predicts that discrimination performance should be the same regardless of whether the gratings are presented in two spatial intervals or two temporal intervals. Our last experiment largely confirmed this prediction.

Verghese, Preeti↗

NASA NTRS2010

Vibration Response Predictions for Heavy Panel Mounted Components from Panel Acreage Environment Specifications

The development of new launch vehicles in the Aerospace industry often relies on response measurements taken from previously developed vehicles during various stages of liftoff and ascent, and from wind tunnel models. These measurements include sound pressure levels, dynamic pressures in turbulent boundary layers and accelerations. Rigorous statistical scaling methods are applied to the data to derive new environments and estimate the performance of new skin panel structures. Scaling methods have proven to be reliable, particularly for designs similar to the vehicles used as the basis for scaling, and especially in regions of smooth acreage without exterior protuberances or heavy components mounted to the panel. To account for response attenuation of a panel-mounted component due to its apparent mass at higher frequencies, the vibroacoustics engineer often reduces the acreage vibration according to a weight ratio first suggested by Barrett. The accuracy of the reduction is reduced with increased weight of the panel-mounted component, and does not account for low-frequency amplification of the component/panel response as a system. A method is proposed that combines acreage vibration from scaling methods with finite element analysis to account for the frequency-dependent dynamics of heavy panel-mounted components. Since the acreage and mass-loaded skins respond to the same dynamic input pressure, such pressure may be eliminated in favor of a frequency-dependent scaling function applied to the acreage vibration to predict the mass-loaded panel response. The scaling function replaces the Barrett weight ratio, and contains all of the dynamic character of the loaded and unloaded skin panels. The solution simplifies for spatially uncorrelated and fully correlated input pressures. Since the prediction uses finite element models of the loaded and unloaded skins, a rich suite of response data are available to the design engineer, including interface forces, stress and strain, as well as acceleration and displacement. An extension of the method is also developed to incorporate the effect of a local protuberance near a heavy component. Acreage environments from traditional scaling methods with and without protuberance effects serve as the basis for the extension. Authors:

Harrison, Phillip↗

DOE OSTI2021

Distributed Solar Adoption in Orlando: A Household-Level Model for Distribution Resource Planning

Potential for rooftop solar in Florida is massive (47% of retail sales, 3rd overall nationally), yet adoption lags (12th nationally). A 2018 Florida Public Service Commission ruling authorizing solar third-party ownership (leasing) has substantially increased attention on distributed solar in the state. The city of Orlando has committed to a 100% clean-energy target by 2050 and deployment of solar and storage are expected to contribute significantly to reaching the goal. Deployment of customer-adopted solar, unlike utility-procured solar, is uncertain, but known to be spatially correlated with demographic factors and existing adoption. We develop a new method to adapt NREL's dGen model in order to represent building-level agents in adoption forecasts for the Orlando Utility Commission (OUC) service territory. Using the agent-based model we develop projections of solar adoption, subject to scenarios varying future solar costs and valuation, and aggregate adoption predictions by OUC distribution feeder. We find substantial spatial heterogeneity in the projected level of adoption by OUC distribution feeder. For instance, 25% of all projected adoption through 2050 would be concentrated on just 5% of feeders and 88% of projected adoption on 50% of feeders. Because of the uncertainty in adoption, bottoms-up solar adoption forecasting methods at the household-level are integral to long-term resource planning by anticipating system needs as customers increasingly adopt distributed solar, storage, electric vehicles, and other distributed energy resources.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

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↗

DOE OSTI2021

Gamma Spectrometry Examination of the AGR-3/4 Irradiation

The results from gamma spectrometry examination of the different components from the combined third and fourth U.S. Advanced Gas Reactor (AGR) TRISO-coated particle fuel irradiation tests (AGR-3/4) have been analyzed. This experiment was designed to provide information about in-pile fission product migration. In each of the 12 capsules, a single stack of four compacts with designed-to-fail (DTF) particles surrounded by inner and outer graphite and/or graphitic matrix rings and a graphite sink ring were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). Gamma spectrometry has been used to evaluate the gamma-emitting fission product inventory of compacts from the irradiation and evaluate the burnup of these compacts based on the activity of the radioactive cesium isotopes (i.e., Cs-134 and Cs-137) in the compacts. Burnup from gamma spectrometry compares well with predicted burnup from simulations. The inner and outer rings were also examined by gamma spectrometry to evaluate the total fission product inventory of the rings, and gamma emission computed tomography (GECT) was used to investigate the spatial distribution of gamma-emitting fission products within the rings. This report focuses on results obtained via the Precision Gamma Scanner (PGS) for the compacts and inner and outer rings. These non-destructive gamma measurements are currently being compared to recent destructive measurements of spatial fission product distributions in the rings and to predictions made via mathematical modeling. A separate report discusses the entire AGR-3/4 fission product mass balance for all irradiation capsule components including the inner and outer rings, graphite sink rings, capsule spacers, capsule foils, through tubes, and felts. This report is similar to the conference proceedings: Harp, J.M., Demkowicz, P.A., and Stempien, J.D., “Initial gamma spectrometry examination of the AGR-3/4 irradiation,” International Topical Meeting High Temperature Reactor Technology (HTR 2016), Las Vegas, NV, USA, November 2016, Paper HTR2016-18593, but it has been updated to reflect all the data that has been collected on AGR-3/4 using PGS. The Appendices contain extensive data from the collected gamma spectra.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Gamma Spectrometry Examination of the AGR-3/4 Irradiation

The results from gamma spectrometry examination of the different components from the combined third and fourth U.S. Advanced Gas Reactor (AGR) TRISO-coated particle fuel irradiation tests (AGR-3/4) have been analyzed. This experiment was designed to provide information about in-pile fission product migration. In each of the 12 capsules, a single stack of four compacts with designed-to-fail (DTF) particles surrounded by inner and outer graphite and/or graphitic matrix rings and a graphite sink ring were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). Gamma spectrometry has been used to evaluate the gamma-emitting fission product inventory of compacts from the irradiation and evaluate the burnup of these compacts based on the activity of the radioactive cesium isotopes (i.e., Cs-134 and Cs-137) in the compacts. Burnup from gamma spectrometry compares well with predicted burnup from simulations. The inner and outer rings were also examined by gamma spectrometry to evaluate the total fission product inventory of the rings, and gamma emission computed tomography (GECT) was used to investigate the spatial distribution of gamma-emitting fission products within the rings. This report focuses on results obtained via the Precision Gamma Scanner (PGS) for the compacts and inner and outer rings. These non-destructive gamma measurements are currently being compared to recent destructive measurements of spatial fission product distributions in the rings and to predictions made via mathematical modeling. A separate report discusses the entire AGR-3/4 fission product mass balance for all irradiation capsule components including the inner and outer rings, graphite sink rings, capsule spacers, capsule foils, through tubes, and felts. This report is similar to the conference proceedings: Harp, J.M., Demkowicz, P.A., and Stempien, J.D., “Initial gamma spectrometry examination of the AGR-3/4 irradiation,” International Topical Meeting High Temperature Reactor Technology (HTR 2016), Las Vegas, NV, USA, November 2016, Paper HTR2016-18593, but it has been updated to reflect all the data that has been collected on AGR-3/4 using PGS. The Appendices contain extensive data from the collected gamma spectra.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2008

Grid Quality and Resolution Issues from the Drag Prediction Workshop Series

The drag prediction workshop series (DPW), held over the last six years, and sponsored by the AIAA Applied Aerodynamics Committee, has been extremely useful in providing an assessment of the state-of-the-art in computationally based aerodynamic drag prediction. An emerging consensus from the three workshop series has been the identification of spatial discretization errors as a dominant error source in absolute as well as incremental drag prediction. This paper provides an overview of the collective experience from the workshop series regarding the effect of grid-related issues on overall drag prediction accuracy. Examples based on workshop results are used to illustrate the effect of grid resolution and grid quality on drag prediction, and grid convergence behavior is examined in detail. For fully attached flows, various accurate and successful workshop results are demonstrated, while anomalous behavior is identified for a number of cases involving substantial regions of separated flow. Based on collective workshop experiences, recommendations for improvements in mesh generation technology which have the potential to impact the state-of-the-art of aerodynamic drag prediction are given.

Mavriplis, Dimitri J.↗

DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗

DOE OSTI2024

Stanford Thermal Earth Model for the Conterminous United States

Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below. A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..

15 GEOTHERMAL ENERGY↗

NASA NTRSDate not supplied

Characterizing Spatiotemporal Uncertainty in Interpolated Meteorological Data

Interpolated meteorological data invariably contain errors. These errors have structure in time and space, particularly autocorrelation, which can cause the effects of errors to compound when model outputs are aggregated temporally or spatially. One way to account for this uncertainty is with a probabilistic model from which samples can be drawn that are coherent with respect to underlying spatial and temporal covariance structure. This work describes a probabilistic method for spatial interpolation of point-wise meteorological time series. Observational data from weather stations are generally sparse in space and dense in time (but sometimes missing). The method works by projecting time series onto orthogonal basis vectors and spatially interpolating each resulting component independently. Under suitable assumptions, and data transformations to better satisfy those assumptions, Gaussian process regression provides a complete description of the joint predictive distribution over a Gaussian random field. Spatiotemporally coherent realizations are generated as the sum of conditional (spatial) simulations of each orthogonal (temporal) component. Data-derived and generic orthogonal bases are considered. In addition to spatial interpolation, imputation of missing observational data is examined. The method is applied using near-surface air temperature over the Western United States and validated by comparing theoretical versus actual coverage of predictive distributions and analyzing the degree to which spatial and temporal covariance structure is reproduced. Computational considerations, relating to conditional simulation of random fields, are also addressed.

Conor T Doherty↗

NASA NTRS1994

Surface gravity waves at equilibrium with a steady wind

Observations of wave fields' spatial evolution and of gravity wave spectra S(sub omega) are analyzed on the basis of the data reported by several research groups as well as on a 2-year data set of wind and wave measurements by stationary National Oceanic and Atmospheric Administration buoys near the Hawaiian Islands. We seek to clarify the role of the wave energy advection (with the wave group velocity) in the overall energy balance. This advective transfer appears to be no less important than the local (breaking wave induced) dissipation as a factor of wind-wave equilibrium. The advection is found to manifest itself in the shape of wave spectra by reducing the rate at which the spectra density of the wave energy, S(sub omega approximately omega (exp -p), falls off as the frequency increases away from the spectra peak. This and other conclusions are derived by comparing the field observations with theoretical predictions of the weak turbulence theory for a spatially inhomogeneous, statistically stationary, wave field. The observations also indicate that the typical wave age xi = C(sub 0)/U in the open ocean is much greater than the limiting value 1.2 attributed to the 'fully developed sea.' Although the observed spectra can be approximated by a power law with a single 'effective' exponent, this apparent exponent, p, is found to depend on the wave age. At high xi and at frequencies below the generation range, -p tends to -3 rather than the value of -11/3 predicted by the Zakharov-Zaslavskii theory. This deviation is interpreted as pointing to a nonconservative nature of the inverse cascade, the latter including a leakage of energy to low-wavenumber modes. Dependence of the overall effective exponent on xi is shown to be responsible for variation in the coefficients b, B, c, C appearing in empirical fetch laws, such as xi = C bar-x (exp c) and e = B bar-x (exp B), where bar-x and e are the dimensionless fetch and wave energy, respectively.

Glazman, Roman E.↗