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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Providing Long-term Solar and Meteorological Resource Information from NASA Research through the GIS-Enabled POWER Web Services Portal

Solar and other renewable energy systems are optimized using geophysical parameters describing information about the solar resource and meteorological environments and how those environments may be changing in time. The Prediction of Worldwide Renewable Energy Resource (POWER) team at the NASA LaRC Research Center deployed its first version of the revitalized POWER geophysical parameter website that employs Esri Geographic Information System (GIS) tools. This web application provides access to both time series and climatological data sets spanning from a few days behind real-time back to the early 1980’s with a spatial resolution of 0.5 x 0.5 degree, thus extending over 30 years. The GIS tools enable, generate and store climatological averages using spatial queries and calculations in a spatial database resulting in greater accessibility to government agencies, industry and individuals. There is an API that provides data through a URL coding and can also invoked inside user software packages. Output data formats now include ASCII, CSV, geoTiff, JSON and .netCDF. Since May 2018, over 10.4 TB of data have been delivered to fulfill over 110 million data requests from 240,000 unique user IPs (https://power.larc.nasa.gov). This presentation provides an overview of this project and the current version of the new POWER web capabilities through to the end usage. Surface solar parameters are adapted from both the NASA/GEWEX (Global and Energy Water Cycle Exchange) SRB (Surface Radiation Budget) project and the CERES (Clouds and Earth’s Radiant Energy System) data products. The meteorological parameters are adapted from NASA’s MERRA-2 (Modern Era Retrospective-analysis for Research and Applications). We review uncertainty of various basic parameters using surface measurements. After the introduction, we specifically discuss various clusters of parameters for equator pointing tilted surfaces that provide for the estimation of optimal tilt angle by month and year. The discussion will emphasize a few examples of how solar and building engineers are using the data products. We will then preview the capabilities of Version 2 featuring examples of hourly solar and meteorological data products, customized user reports and expanded web mapping services from CERES data products. The web services provide a unique, expandable resource for renewable energy systems engineers to design, evaluate and optimize to environments worldwide.

Solar Resource↗
NASA NTRS2011

Rotation Rate of Saturn's Magnetosphere using CAPS Plasma Measurements

We present the present status of an investigation of the rotation rate of Saturn's magnetosphere using a 3D velocity moment technique being developed at Goddard which is similar to the 2D version used by Sittler et al. for SOI and similar to that used by Thomsen et al.. This technique allows one to nearly cover the full energy range of the Cassini Plasma Spectrometer (CAPS) IMS from 1 V . E/Q < 50 kV. Since our technique maps the observations into a local inertial frame, it does work during roll maneuvers. We make comparisons with the bi-Maxwellian fitting technique developed by Wilson et al. and the similar velocity moment technique by Thomsen et al. . We concentrate our analysis when ion composition data is available, which is used to weight the non-compositional data, referred to as singles data, to separate H+, H2+ and water group ions (W+) from each other. The chosen periods have high enough telemetry rates (4 kbps or higher) so that coincidence ion data, similar to that used by Sittler et al. for SOI is available. The ion data set is especially valuable for measuring flow velocities for protons, which are more difficult to derive using singles data within the inner magnetosphere, where the signal is dominated by heavy ions (i.e., proton peak merges with W+ peak as low energy shoulder). Our technique uses a flux function, which is zero in the proper plasma flow frame, to estimate fluid parameter uncertainties. The comparisons investigate the experimental errors and potential for systematic errors in the analyses, including ours. The rolls provide the best data set when it comes to getting 4PI coverage of the plasma but are more susceptible to time aliasing effects. In the future we will then make comparisons with magnetic field observations, Saturn ionosphere conductivities as presently known and the field aligned currents necessary for the planet to enforce corotation of the rotating plasma.

Sittler, E.↗
DOE OSTI2022

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗
DOE OSTI2018

Estimating canopy fuel characteristics for predicting crown fire potential in common forest types of the Atlantic Coastal Plain, USA

We computed four stand-level canopy stratum variables important for crown fire modelling – canopy cover, stand height, canopy base height and canopy bulk density – from forest inventory data. We modelled the relationship between the canopy variables and a set of common inventory parameters – site index, stem density, basal area, stand age or stand height – and number of prescribed burns. We used a logistic model to estimate canopy cover, a linear model to estimate the other canopy variables, and the information theoretic approach for model selection. Coefficients of determination across five forest groups were 0.72–0.91 for stand height, 0.36–0.83 for canopy base height, 0.39–0.80 for canopy cover, and 0.63–0.78 for canopy bulk density. We assessed crown fire potential (1) for several sets of environmental conditions in all seasons, and (2) with increasing age, density and number of prescribed burns using our modelled canopy bulk density and canopy base height variables and local weather data to populate the Crown Fire Initiation and Spread model. Results indicated that passive crown fire is possible in any season in Atlantic coastal plain pine stands with heavy surface fuel loads and active crown fire is most probable in infrequently burned, dense stands at low fuel moistures.

Forestry↗
DOE OSTI2019

From Escherichia coli mutant 13 C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13 C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13 C-Metabolic Flux Analysis ( 13 C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13 C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Autonomous Modal Identification of the Space Shuttle Tail Rudder

Autonomous modal identification automates the calculation of natural vibration frequencies, damping, and mode shapes of a structure from experimental data. This technology complements damage detection techniques that use continuous or periodic monitoring of vibration characteristics. The approach shown in the paper incorporates the Eigensystem Realization Algorithm (ERA) as a data analysis engine and an autonomous supervisor to condense multiple estimates of modal parameters using ERA's Consistent-Mode Indicator and correlation of mode shapes. The procedure was applied to free-decay responses of a Space Shuttle tail rudder and successfully identified the seven modes of the structure below 250 Hz. The final modal parameters are a condensed set of results for 87 individual ERA cases requiring approximately five minutes of CPU time on a DEC Alpha computer.

Pappa, Richard S.↗
DOE OSTI2017

Advanced, Energy-Efficient Hybrid Membrane System for Industrial Water Reuse

In the U.S. manufacturing sector, current industrial water use practices are energy-intensive and utilize and discharge high volumes of waters, rendering them not sustainable especially in light of the growing scarcity of suitable water supplies. To help address this problem, the goal of this project was to develop an advanced, cost-effective, hybrid membrane-based water treatment system that can improve the energy efficiency of industrial wastewater treatment while allowing at least 50% water reuse efficiency. This hybrid process would combine emerging Forward Osmosis (FO) and Membrane Distillation (MD) technology components into an integrated FO-MD system that can beneficially utilize low-grade waste heat (i.e., T < 450 °F) in industrial facilities to produce distilled-quality product water for reuse. In this project, laboratory-, bench-, and pilot-scale experiments on the hybrid FO-MD system were conducted for industrial wastewater treatment. It was demonstrated at laboratory, bench, and pilot scales that FO-MD membrane technology can concentrate brine to very high total dissolved solids (TDS) levels (>200,000 ppm) that are at least 2.5 times higher than the TDS level to which RO can achieve. In laboratory testing, currently available FO and MD membranes were tested to select for high-performing membranes with high salt rejection and high water flux. Multiple FO membrane/draw-salt solution combinations that gave high water flux with higher than 98% salt rejection were also identified. Reverse draw-salt fluxes were observed to be much lower for divalent salts than for monovalent salts. MD membranes were identified that had 99.9+% salt rejection and water flux as high as 50-90 L/(m 2 ·h) for flat-sheet membranes and >20 L/(m 2 ·h) for hollow fibers. In bench-scale testing, a single unit of commercially available FO and MD membrane modules were evaluated for continuous, integrated operation. Using the laboratory- and bench-scale test data, numerical modeling was performed on the FO and MD processes to estimate engineering parameters for a larger-scale pilot unit. Based on the experimental studies and modeling results, a pilot-scale, integrated FO-MD prototype unit was designed and built for trailer-mounted operation. This prototype system was fed real industrial wastewater, which could not be further treated by conventional technologies, from an oil production facility and was successfully operated for over 15 weeks without major stoppage. About 90% water recovery was possible, while concentrating the TDS from 12,000 ppm up to 190,500 ppm. The FO-MD prototype rejected most wastewater contaminants while producing water with <300 ppm TDS, even when the feed TDS was higher than 150,000 ppm. No chemical cleaning was necessary during the pilot testing period. Flushing the system with dechlorinated tap water was sufficient to reset the membranes for the next set of test conditions. Pilot performance and membrane autopsy showed that, even though the feed was concentrated more than 10 times, membrane fouling was unnoticeable and no defects were detected on the FO and MD membrane surfaces. This project demonstrated the technical feasibility of the hybrid FO-MD process by taking water already treated to the limit with the highest level of current technologies and further concentrating it 10-fold by using mostly low-cost materials. Because no membranes suitable for full-scale plant applications are available at present, economical feasibility of the hybrid technology is still uncertain, but it is expected that broader industry participation can further reduce FO-MD process costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

LAMOST Time-Domain survey: first results of four K2 plates

From Oct. 2019 to Apr. 2020, LAMOST performed a time-domain (TD) spectroscopic survey of four K2 plates with both low- and medium-resolution observations. The low-resolution spectroscopic survey acquired 282 exposures ( ≈ 46.6 h) over 25 nights, yielding a total of about 767 000 spectra, and the medium-resolution survey took 177 exposures ( ≈ 49.1 h) over 27 nights, collecting about 478 000 spectra. More than 70%/50% of low-resolution/medium-resolution spectra have signal-to-noise ratio higher than 10. We determine stellar parameters (e.g., T eff , log g, [Fe/H]) and radial velocity (RV) with different methods, including LASP, DD-Payne and SLAM. In general, these parameter estimations from different methods show good agreement, and the stellar parameter values are consistent with those of APOGEE. We use the Gaia DR2 RV values to calculate a median RV zero point (RVZP) for each spectrograph exposure by exposure, and the RVZP-corrected RVs agree well with the APOGEE data. The stellar evolutionary and spectroscopic masses are estimated based on the stellar parameters, multi-band magnitudes, distances and extinction values. Finally, we construct a binary catalog including about 2700 candidates by analyzing their light curves, fitting the RV data, calculating the binarity parameters from medium-resolution spectra and cross-matching the spatially resolved binary catalog from Gaia EDR3. In conclusion, the LAMOST TD survey is expected to represent a breakthrough in various scientific topics, such as binary systems, stellar activity, stellar pulsation, etc.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

Regression Analysis of Top of Descent Location for Idle-thrust Descents

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

trajectory prediction↗
DOE OSTI2017

Estimating a Service-Life Distribution Based on Production Counts and a Failure Database

A manufacturer wanted to compare the service-life distributions of two similar products. These concern product lifetimes after installation (not manufacture). For each product, there were available production counts and an imperfect database providing information on failing units. In the real case, these units were expensive repairable units warrantied against repairs. Failure (of interest here) was relatively rare and driven by a different mode/mechanism than ordinary repair events (not of interest here). Approach: Data models for the service life based on a standard parametric lifetime distribution and a related limited failure population were developed. These models were used to develop expressions for the likelihood of the available data that properly accounts for information missing in the failure database. Results: A Bayesian approach was employed to obtain estimates of model parameters (with associated uncertainty) in order to investigate characteristics of the service-life distribution. Custom software was developed and is included as Supplemental Material to this case study. One part of a responsible approach to the original case was a simulation experiment used to validate the correctness of the software and the behavior of the statistical methodology before using its results in the application, and an example of such an experiment is included here. Because of confidentiality issues that prevent use of the original data, simulated data with characteristics like the manufacturer’s proprietary data are used to illustrate some aspects of our real analyses. Lastly, we also note that, although this case focuses on rare and complete product failure, the statistical methodology provided is directly applicable to more standard warranty data problems involving typically much larger warranty databases where entries are warranty claims (often for repairs) rather than reports of complete failures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

This paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and PMU measurements are calculated separately through unscented transformation and a vector autoregression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case of synchronization issues between SCADA and PMU measurements, either forecasted PMU measurements or prior SCADA measurements from the last estimation run are leveraged to restore system observability. After that, robust generalized maximum-likelihood (GM)-estimator is developed to integrate full measurement correlations and handle bad data in SCADA and PMU measurements. Thanks to the robustness and flexibility of the proposed robust estimator, bad data as well as imperfect measurement synchronization are mitigated, yielding high statistical efficiency. Comprehensive comparison results with other alternatives under various conditions demonstrate the benefits of the proposed framework.

Zhao, Junbo (ORCID:0000000284989666)↗
DOE OSTI2022

Scaled Vecchia Approximation for Fast Computer-Model Emulation

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia’s ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Quantifying Model Form Uncertainty of Epidemic Forecasting Models from Incidence Data

Objective We present a mathematical framework for non-parametric estimation of the force of infection, together with statistical upper and lower confidence bands. The resulting estimates allow to assess how well simpler models, such as SEIR, fit the observed time series of incidence data.IntroductionUncertainty Quantification (UQ), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. While quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. For the latter, one needs a "universal model" to which more restrictive parametric models are compared too.MethodsThis talk presents a useful modeling framework for time series of incidence data from contagious diseases that enables one to identify and quantify the impact of model form uncertainty. Specifically, we propose to focus on estimating the timedependent force of infection. The latter is a universal parameters for all contagious disease model. Using a machine learning technique for estimating monotone functions, i.e., isotonic regression and its variants, one can estimate the force ofinfection without addtional assumptions. We note that most contagious disease model do satisfy this monotonicity assumption, due to a combination of factors: depletion of susceptibles, implementation of mitigation strategies, behavior change, etc. Comparing the resulting "non-parametric" estimate with parametric estimates, obtained by fitting an SEIR for example, can reveal model deficiencies and help quantify model form uncertainties.Finally, we discuss how ideas from "strict bound theory" can be used to develop upper and lower uncertainty bands for force of infection that acknowledge the intrinsic stochasticity in the data.ResultsWe demonstrate the application of the methodology to weekly Influenza Like Illness (ILI) incidence data from France andcompare the results to fitted SIR and SEIR models. This comparison can be seen as a nonparametric goodness of fit test, providing one with tools to do simple model selection.ConclusionsWe present a novel and flexible model to statistically describe the force of infection as a function of time. Comparing the fit to incidence data of that model with the fit of simpler parametric models enables the quantification of model form uncertainty and associated model selection.

Hengartner, Nicholas↗
DOE OSTI2020

Fatigue behavior and modeling for additive manufactured 304L stainless steel: The effect of surface roughness

The fatigue strength of additively manufactured metallic parts in their as-built surface condition is mainly dominated by the surface roughness. Post-processing is often inevitable to reduce surface roughness effects even though post-processing diminishes the main advantage of additive manufacturing, which is net-shaped direct-to-service production. This work investigates the underlying mechanisms responsible for fatigue failure of additively manufactured 304L stainless steel parts in as-built and machined/polished surface conditions. Both strain- and force-controlled, fully reversed fatigue tests were conducted to gain a comprehensive understanding of surface roughness effects on fatigue behavior. The sensitivity to surface roughness is shown to be dependent on the control mode, with stress-based fatigue tests showing greater sensitivity than strain-based fatigue tests. Moreover, the fatigue life estimation for as-built specimens was performed based on surface roughness parameters as well as the fatigue properties of the specimens in machined/polished surface condition of the material without using any fatigue data of specimens in as-built surface condition. Accordingly, the effect of surface roughness on the fatigue behavior could be estimated reasonably well in both strain-life and stress-life approaches.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Wind field model-based estimation of Seasat scatterometer winds

A model-based approach to estimating near-surface wind fields over the ocean from Seasat scatterometer (SASS) measurements is presented. The approach is a direct assimilation technique in which wind field model parameters are estimated directly from the scatterometer measurements of the radar backscatter of the ocean's surface using maximum likelihood principles. The wind field estimate is then computed from the estimated model parameters. The wind field model used in this approach is based on geostrophic approximation and on simplistic assumptions about the wind field vorticity and divergence but includes ageostrophic winds. Nine days of SASS data were processed to obtain unique wind estimates. Comparisons in performance to the traditional two-step (point-wise wind retrieval followed by ambiguity removal) wind estimate method and the model-based method are provided using both simulated radar backscatter measurements and actual SASS measurements. In the latter case the results are compared to wind fields determined using subjective ambiguity removal. While the traditional approach results in missing measurements and reduced effective swath width due to fore/aft beam cell coregistration problems, the model-based approach uses all available measurements to increase the effective swath width and to reduce data gaps. The results reveal that the model-based wind estimates have accuracy comparable to traditionally estimated winds with less 'noise' in the directional estimates, particularly at low wind speeds.

Long, David G.↗
NASA NTRS1995

The second-moment climatology of the GATE rain rate data

The first part of this paper presents the description of the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) 1 rain-rate data and its two-dimensional spectral and correlation characteristics, which has made it possible to accomplish the following: to show the concentration of a significant power along the frequency axis in the spatiotemporal spectra; to detect a diurnal cycle (which has a range of variation of about 3.4-5.4 mm/n) as one of the sources of bias in the rain statistics of satellite data; to study the distinction between the north-south and east-west transport of spatial rain-rate field and character of its anisotropy; to evaluate the scales of the distinction between second-moment estimates associated with ground and satellite samples; and to determine the appropriate spatial and temporal scales of simple linear stochastic models fitted to averaged rain-rate fields. The second part of this paper is devoted to an analysis of the diffusion of the rain rate by establishing a relationship between the parameters of the multivariate autoregressive model and the coefficients of a diffusion equation. This analysis led to the use of rain data to estimate the rain advection velocity as well as other coefficients of the diffusion equation of the corresponding field. The results obtained can be used for comparison with corresponding estimates of other sources of data (satellite, Tropical Oceans Global Atmosphere Coupled Ocean - Atmosphere Response Experiment (TOGA, COARE) or simulated by physical models), for generating multiple samples of any size, for solving the inverse problems of some of the hydrodynamic equations, and in some other areas of rain data analysis and modeling.

Polyak, Ilya↗
NASA NTRSDate not supplied

CONUS and Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as wildlands. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have developed a carbon consumption database for the Continental United States (CONUS) and are developing a global carbon consumption database, both of which are directly link to distinct fuels within various fire danger categories. Fuel Characteristic Classification System (FCCS) fuels are used to parameterize biomass, and consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. Residual burned area is defined as burning in areas that have recently burned. We implemented the FCCS 30-meter CONUS fuelbed dataset during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets. The 300-meter global database builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized FCCS biomass parameters. These products can serve as valuable tools when used in conjunction with burned area data to rapidly and accurately estimate carbon consumed and released into the atmosphere.

FCCS↗