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Results for “spatial variability”

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At least 1,621 records · Page 90

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NASA NTRS2000

Discriminability measures for predicting readability of text on textured backgrounds

Several discriminability measures were examined for their ability to predict reading search times for three levels of text contrast and a range of backgrounds (plain, a periodic texture, and four spatial-frequency-filtered textures created from the periodic texture). Search times indicate that these background variations only affect readability when the text contrast is low, and that spatial frequency content of the background affects readability. These results were not well predicted by the single variables of text contrast (Spearman rank correlation = -0.64) and background RMS contrast (0.08), but a global masking index and a spatial-frequency-selective masking index led to better predictions (-0.84 and -0.81, respectively). c2000 Optical Society of America.

NASA Discipline Space Human Factors↗

DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗

NASA NTRS1989

The annual and interannual variabilities of precipitable water, surface wind speed, and sea surface temperature over the tropical Pacific

The Nimbus-7 Scanning Multichannel Microwave Radiometer (SSMR) provided simultaneous measurements of three geophysical parameters, each of which describing a certain aspect of the evolution of the 1982-1983 ENSO: the sea-surface temperature (T), precipitable water (W), and surface-wind speed (U). In this paper, values derived from the SSMR were compared with in situ measurements from ships, research buoys, and operational island stations in the tropical Pacific between January 1980 and October 1983, demonstrating the temporal and spatial coherence of the SSMR measurements. The results show that the variabilities of the surface convergence, sea surface temperature, and precipitable water are related. It was found that W anomalies were not always colocated with T anomalies, and that W anomalies were often associated with negative U anomalies, interpreted as surface convergence.

Liu, W. Timothy↗

DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments

Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Parameterizing Convective Organization Effects With a Moisture-PDF Approach in Climate Models: Concept and a Regional Case Simulation

We propose a parameterization scheme of convective organization effects based on a moisture-distribution approach, which can reflect aggregation of convective cells within a model grid as well as the interaction between convection and spatial heterogeneity in free-troposphere moisture. With this concept, convective cells in an aggregated state are surrounded by air that is moister than the grid-mean condition, which provides a “shielding” effect favorable for deeper convective updrafts. Such effects are represented quantitatively via utilizing a schematic diagram about the geometric interpretation of convective cluster, dry area, and their contact area, in which both the convective cluster size and dry area fraction increase during the development of convection. Our sensitivity analyses indicate that the new scheme performs well in capturing the mean precipitation features. Excluding the convective organization effects in the model leads to a considerable reduction in the simulated precipitation magnitude. Notably, there exists an inverse relationship between background mean moisture condition and sub-grid moisture variability for a given rain rate, suggesting that with a high spatial heterogeneity in free-troposphere moisture, convection can still develop and maintain its strength under relatively dry background conditions, consistent with the convection-permitting model simulation. Overall, our new parameterization of convective organization effects can successfully reproduce the relationship between precipitation and sub-grid moisture variability, which is a missing element in traditional convection parameterization schemes but important for the simulations of precipitation variability at various scales in climate models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

Procedures for analysis of spatial relationships among ship survey data and sea surface temperature

The establishment of a limited spatial data base for the U.S. eastern seaboard vicinity is discussed along with the demonstration of computer assisted analysis techniques for investigating spatial patterns and relationships among ship survey data and remotely sensed sea surface temperature. Ship survey variables included concentrations of two zooplankton, two icthyoplankton, and two fish species, in addition to physical data of depth to bottom and surface and bottom water temperatures. Continuous spatial distributions of these data were created by both weighted nearest neighbor and iterative smoothing interpolation techniques. Maps of surface water temperature were created by digitizing GOES satellite images. All mapped data were spatially registered by conversion of latitude and longitude coordinates to rotated Lambert conic conformal rectilinear coordinates and stored in grid format of approximately one hundred square kilometers per cell. The analysis of these data include the generation of statistical summaries and maps describing the joint occurrence among variables.

Berry, J. K.↗

NASA NTRS1995

Spectroscopic measurements of element abundances in the solar corona: Variations on the FIP theme

Solar wind and solar energetic particle (SEP) data yield systematic differences between elemental abundances in the corona and in the photosphere related to the first ionization potential (FIP) of the elements: low-FIP elements are preferentially enhanced relative to high-FIP elements by about a factor of four. Spectroscopic studies of the inner corona show that such a pattern may apply on average but not in detail for coronal loops: substantial abundance differences occur between different types of coronal structures, and variations have been found from flare to flare, from one active region to another, and over time in the same region; further, in some flares, anomalies such as enhanced Ne:O ratios, distinctly at odds with the FIP pattern, show that a competing element selection mechanism sometimes operates. Details of the observed abundance variability -- such as the magnitude of the variations, the relevant temporal and spatial scales, and correlations with other properties of the given coronal structure -- may give important clues to the processes which supply and heat the corona, or they may reflect the changing physical conditions or locations where those processes take place. However, many such details remain to be established definitively. At present, abundance variability is primarily a major complication to data analysis and interpretation. However, once it is better understood, it may provide a new diagnostic tool for probing the lower layers of the solar atmosphere.

Saba, J. L. R.↗

DOE OSTI2026

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗

NASA NTRS2011

Interannual Variation in Phytoplankton Concentration and Community in the Pacific Ocean

Climate events such as El Nino have been shown to have an effect on the biology of our ocean. Because of the lack of data, we still have very little knowledge about the spatial and temporal effect these climate events may have on biological marine systems. In this study, we used the NASA Ocean Biogeochemical Model (NOBM) to assess the interannual variability in phytoplankton community in the Pacific Ocean between 1998 and 2005. In the North Central and Equatorial Pacific Ocean, changes in the Multivariate El Nino Index were associated with changes in phytoplankton composition. The model identified an increase in diatoms of approx.33 % in the equatorial Pacific in 1999 during a La Nina event. This increase in diatoms coincided with a decrease of approx.11 % in cyanobacteria concentration. The inverse relationship between cyanobacteria and diatoms concentration was significant (p<0.05) throughout the period of study. The use of a numerical model allows us to assess the impact climate variability has on key phytoplankton groups known to lead to contrasting food chain at a spatial and temporal resolution unachievable when relying solely on in-situ observations.

Rousseaux, C. S.↗

DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗

NASA NTRS2008

Analysis of Terrestrial Water Storage Changes from GRACE and GLDAS

Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided first estimates of land water storage variations by monitoring the time-variable component of Earth's gravity field. Here we characterize spatial-temporal variations in terrestrial water storage changes (TWSC) from GRACE and compare them to those simulated with the Global Land Data Assimilation System (GLDAS). Additionally, we use GLDAS simulations to infer how TWSC is partitioned into snow, canopy water and soil water components, and to understand how variations in the hydrologic fluxes act to enhance or dissipate the stores. Results quantify the range of GRACE-derived storage changes during the studied period and place them in the context of seasonal variations in global climate and hydrologic extremes including drought and flood, by impacting land memory processes. The role of the largest continental river basins as major locations for freshwater redistribution is highlighted. GRACE-based storage changes are in good agreement with those obtained from GLDAS simulations. Analysis of GLDAS-simulated TWSC illustrates several key characteristics of spatial and temporal land water storage variations. Global averages of TWSC were partitioned nearly equally between soil moisture and snow water equivalent, while zonal averages of TWSC revealed the importance of soil moisture storage at low latitudes and snow storage at high latitudes. Evapotranspiration plays a key role in dissipating globally averaged terrestrial water storage. Latitudinal averages showed how precipitation dominates TWSC variations in the tropics, evapotranspiration is most effective in the midlatitudes, and snowmelt runoff is a key dissipating flux at high latitudes. Results have implications for monitoring water storage response to climate variability and change, and for constraining land model hydrology simulations.

Syed, Tajdarul H.↗

DOE OSTI2024

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵ m ≃ 7 x 10 -4 g 2 kg −2 s −1 for mixing ratio and ϵ θ ≃ 1.6 x 10 -3 K 2 s −1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃ 10 -2 m 2 s −3 . We also demonstrate that the vertical wind ACF coefficient k w ∝ $\overline{w'^{2}}$ and the TKE dissipation ϵ ∝ ($\overline{w'^{2}}$) 3/2 . For the molecular destruction rates, we show that ϵ m ∝ $\overline{m'^{2}}$ ($\overline{w'^{2}}$) 1/2 and ϵ θ ∝ $\overline{θ'^{2}}$ ($\overline{w'^{2}}$) 1/2 . These equations can be used for parameterizations of ϵ, ϵ m , and ϵ θ . All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

dissipation rate↗

NASA NTRS1996

Temperature, Pressure, and Infrared Image Survey of an Axisymmetric Heated Exhaust Plume

The focus of this research is to numerically predict an infrared image of a jet engine exhaust plume, given field variables such as temperature, pressure, and exhaust plume constituents as a function of spatial position within the plume, and to compare this predicted image directly with measured data. This work is motivated by the need to validate computational fluid dynamic (CFD) codes through infrared imaging. The technique of reducing the three-dimensional field variable domain to a two-dimensional infrared image invokes the use of an inverse Monte Carlo ray trace algorithm and an infrared band model for exhaust gases. This report describes an experiment in which the above-mentioned field variables were carefully measured. Results from this experiment, namely tables of measured temperature and pressure data, as well as measured infrared images, are given. The inverse Monte Carlo ray trace technique is described. Finally, experimentally obtained infrared images are directly compared to infrared images predicted from the measured field variables.

Nelson, Edward L.↗

NASA NTRS2021

IceNode: A Buoyant Vehicle for Acquiring Well-Distributed, Long-Duration Melt Rate Measurements Under Ice Shelves

Antarctic ice shelves buttress the Antarctic Ice Sheet from sliding into the ocean and significantly raising global sea level. However, the accelerating dynamics of ice shelf melt in a warming environment are poorly understood, and the collapse of Antarctic ice shelves remains one of the largest sources of uncertainty in global sea level rise projections. The cavities below Antarctic ice shelves are notoriously difficult to access, making model-based hypotheses about the relationship between ocean warming and greater ice shelf melting difficult to verify because of a lack of in-situ data to constrain model parameters and examine key assumptions. We present early progress on IceNode, a novel vehicle under development at the NASA Jet Propulsion Laboratory designed to acquire well-distributed, concurrent, long-duration melt rate measurements under ice shelves. IceNodes are deployed as an array from a ship at the shelf edge, and use variable buoyancy to ride melt-driven exchange currents far into the cavity. Once underneath their target, they release a ballast weight to become highly positively buoyant and attach to the underside of the ice shelf, where they acquire in-situ measurements of basal melt rate directly at the ice-ocean interface for a year or more. Finally, IceNodes detach from their landing structure and use variable buoyancy to ride melt-driven exchange currents back to open water, where they surface and transmit their mission data home. IceNodes are designed to be relatively low-cost, expendable, and have simple logistics, enabling scientists to deploy scalable arrays that simultaneously measure co-varying ice shelf melt and ocean conditions over large spatial areas, thereby providing an unprecedented view of ice shelf melt rate variability and its drivers.

Zapien, Xavier↗

NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗

DOE OSTI2023

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

It is widely hoped that artificial intelligence will boost data-driven surrogate models in science and engineering. However, fundamental spatial aspects of AI surrogate models remain under-studied. We investigate the ability of neural-network surrogate models to predict solutions to PDEs under variable boundary values. We do not wish to retrain the model when the boundary values change but to make them inputs to the model and infer the solution of the PDE under those boundary conditions. Such a capability is essential to making AI-based surrogate models practically useful. While simple feedforward networks are used for one-dimensional (1D) Poisson equation, an encoder-decoder architecture with a tensor-product layer is developed for the two-dimensional Poisson equation posed on a rectangular domain. We show that it is indeed possible to infer solutions to PDEs from variable boundary data using neural networks in this relatively simple setting, and point to future directions.

Kashi, Aditya↗