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NASA NTRSDate not supplied

International Satellite Cloud Climatology Project

The International Satellite Cloud Climatology Project (ISCCP) was established as the first project of the World Climate Research Programme (WCP-2) to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. The operational phase of ISCCP began in July 1983 and is currently planned to continue through June 2010. [Mission Objectives] To produce a global, reduced resolution, infrared and visible, calibrated and normalized radiance data set containing basic information on the radiative properties of the atmosphere from which cloud parameters can be derived. To stimulate and coordinate basic research on techniques for inferring the physical properties of clouds from the condensed radiance data set and to apply the resulting algorithms to derive and validate a global cloud climatology for improving the parameterization of clouds in climate models. To promote research using ISCCP data and contributing to improved understanding of the Earth's radiation budget (top of the atmosphere and surface) and hydrological cycle. [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

Rossow, William B.↗

NASA NTRS2017

New Data-Driven Estimation of Terrestrial CO2 Fluxes in Asia Using a Standardized Database of Eddy Covariance Measurements, Remote Sensing Data, and Support Vector Regression

The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial carbon dioxide fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem carbon dioxide exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated carbon dioxide fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r (exp 2) =0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r (exp 2)=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land carbon dioxide fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land carbon dioxide fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land carbon dioxide fluxes. These data-driven estimates can provide a new opportunity to assess carbon dioxide fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

chlorophyll fluorescence↗

DOE OSTI2020

Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration

Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

42 ENGINEERING↗

DOE OSTI2023

Optimal reference genes for RNA tissue analysis in small animal models of hemorrhagic fever viruses

Abstract Reverse-transcription quantitative polymerase chain reaction assays are frequently used to evaluate gene expression in animal model studies. Data analyses depend on normalization using a suitable reference gene (RG) to minimize effects of variation due to sample collection, sample processing, or experimental set-up. Here, we investigated the suitability of nine potential RGs in laboratory animals commonly used to study viral hemorrhagic fever infection. Using tissues (liver, spleen, gonad [ovary or testis], kidney, heart, lung, eye, brain, and blood) collected from naïve animals and those infected with Crimean–Congo hemorrhagic fever (mice), Nipah (hamsters), or Lassa (guinea pigs) viruses, optimal species-specific RGs were identified based on five web-based algorithms to assess RG stability. Notably, the Ppia RG demonstrated stability across all rodent tissues tested. Optimal RG pairs that include Ppia were determined for each rodent species ( Ppia and Gusb for mice; Ppia and Hrpt for hamsters; and Ppia and Gapdh for guinea pigs). These RG pair assays were multiplexed with viral targets to improve assay turnaround time and economize sample usage. Finally, a pan-rodent Ppia assay capable of detecting Ppia across multiple rodent species was developed and successfully used in ecological investigations of field-caught rodents, further supporting its pan-species utility.

Davies, Katherine A.↗

DOE OSTI2022

Quantum Machine Learning with SQUID

In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

Analysis of lightning field changes during active Florida thunderstorms

A computer algorithm for deriving accurate values of lightning-caused changes in cloud electric fields under active storm conditions was developed and applied to data obtained during two thunderstorms from a network of ground-based electric field mills at the NASA Kennedy Space Center and the Cape Canaveral Air Force Station. The resulting field changes were analyzed using a least-squares optimization procedure and point-charge (Q) and point-dipole (P) models. The values and the time variations of the Q-model parameters under active storm conditions were found to be similar to those reported by Maier and Krider (1986) for small storms, when the computations were carried out with the same analysis criteria and comparable biases. The parameters of the P solutions were found to vary with time within the storm interval and from storm to storm.

Koshak, William J.↗

NASA NTRS1977

An implementable digital adaptive flight controller designed using stabilized single-stage algorithms

An explicit adaptive controller, which makes direct use of on-line parameter identification, has been developed and applied to both the linearized and nonlinear equations of motion for the F-8 aircraft. This controller is composed of an on-line weighted least squares parameter identifier, a Kalman state filter, and a real model following control law designed using single-stage performance indices. The corresponding control gains are readily adjustable in accordance with parameter changes to ensure asymptotic stability if the conditions of perfect model following are satisfied, and stability in the sense of boundedness otherwise. Simulation experiments with realistic measurement noise indicate that the controller was effective in compensating for parameter variations and capable of rapid recovery from a set of erroneous initial parameter estimates which defined a set of destabilizing gains.

Alag, G.↗

NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗

NASA NTRS1979

Uniform color space analysis of LACIE image products

The author has identified the following significant results. Analysis and comparison of image products generated by different algorithms show that the scaling and biasing of data channels for control of PFC primaries lead to loss of information (in a probability-of misclassification sense) by two major processes. In order of importance they are: neglecting the input of one channel of data in any one image, and failing to provide sufficient color resolution of the data. The scaling and biasing approach tends to distort distance relationships in data space and provides less than desirable resolution when the data variation is typical of a developed, nonhazy agricultural scene.

Nalepka, R. F.↗

NASA NTRS1988

Development of an integrated BEM approach for hot fluid structure interaction

Significant progress was made toward the goal of developing a general purpose boundary element method for hot fluid-structure interaction. For the solid phase, a boundary-only formulation was developed and implemented for uncoupled transient thermoelasticity in two dimensions. The elimination of volume discretization not only drastically reduces required modeling effort, but also permits unconstrained variation of the through-the-thickness temperature distribution. Meanwhile, for the fluids, fundamental solutions were derived for transient incompressible and compressible flow in the absence of the convective terms. Boundary element formulations were developed and described. For the incompressible case, the necessary kernal functions, under transient and steady-state conditions, were derived and fully implemented into a general purpose, multi-region boundary element code. Several examples were examined to study the suitability and convergence characteristics of the various algorithms.

Dargush, Gary F.↗

NASA NTRS1992

Reliability analysis of laminated CMC components through shell subelement techniques

An updated version of the integrated design program Composite Ceramics Analysis and Reliability Evaluation of Structures (C/CARES) was developed for the reliability evaluation of ceramic matrix composites (CMC) laminated shell components. The algorithm is now split into two modules: a finite-element data interface program and a reliability evaluation algorithm. More flexibility is achieved, allowing for easy implementation with various finite-element programs. The interface program creates a neutral data base which is then read by the reliability module. This neutral data base concept allows easy data transfer between different computer systems. The new interface program from the finite-element code Matrix Automated Reduction and Coupling (MARC) also includes the option of using hybrid laminates (a combination of plies of different materials or different layups) and allows for variations in temperature fields throughout the component. In the current version of C/CARES, a subelement technique was implemented, enabling stress gradients within an element to be taken into account. The noninteractive reliability function is now evaluated at each Gaussian integration point instead of using averaging techniques. As a result of the increased number of stress evaluation points, considerable improvements in the accuracy of reliability analyses were realized.

Starlinger, Alois↗

NASA NTRS1998

Resiliency of the Multiscale Retinex Image Enhancement Algorithm

The multiscale retinex with color restoration (MSRCR) continues to prove itself in extensive testing to be very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition, However, issues remain with regard to the resiliency of the MSRCR to different image sources and arbitrary image manipulations which may have been applied prior to retinex processing. In this paper we define these areas of concern, provide experimental results, and, examine the effects of commonly occurring image manipulation on retinex performance. In virtually all cases the MSRCR is highly resilient to the effects of both the image source variations and commonly encountered prior image-processing. Significant artifacts are primarily observed for the case of selective color channel clipping in large dark zones in a image. These issues are of concerning the processing of digital image archives and other applications where there is neither control over the image acquisition process, nor knowledge about any processing done on th data beforehand.

Rahman, Zia-Ur↗

NASA NTRS2006

Mechanisms for Diurnal Variability of Global Tropical Rainfall Observed from TRMM

The behavior and various controls of diurnal variability in tropical-subtropical rainfall are investigated using Tropical Rainfall Measuring Mission (TRMM) precipitation measurements retrieved from the three level-2 TRMM standard profile algorithms for the 1998 annual cycle. Results show that diurnal variability characteristics of precipitation are consistent for all three algorithms, providing assurance that TRMM retrievals are producing consistent estimates of rainfall variability. As anticipated, most ocean areas exhibit more rainfall at night, while over most land areas, rainfall peaks during daytime; however, important exceptions are noted. The dominant feature of the oceanic diurnal cycle is a rainfall maximum in late-evening-early-morning (LE-EM) hours, while over land the dominant maximum occurs in the mid- to late afternoon (MLA). In conjunction with these maxima are pronounced seasonal variations of the diurnal amplitudes. Amplitude analysis shows that the diurnal pattern and its seasonal evolution are closely related to the rainfall accumulation pattern and its seasonal evolution. In addition, the horizontal distribution of diurnal variability indicates that for oceanic rainfall, there is a secondary MLA maximum coexisting with the LE-EM maximum at latitudes dominated by large-scale convergence and deep convection. Analogously, there is a preponderancy for an LE EM maximum over land coexisting with the stronger MLA maximum, although it is not evident that this secondary continental feature is closely associated with the large-scale circulation. Neither of the secondary maxima exhibit phase behavior that can be considered semidiurnal in nature. Diurnal rainfall variability over the ocean associated with large-scale convection is clearly an integral component of the general circulation. Phase analysis reveals differences in regional and seasonal features of the diurnal cycle, indicating that underlying forcing mechanisms differ from place to place. This is underscored by the appearance of secondary ocean maxima in the presence of large-scale convection, along with other important features. Among these, there are clear-cut differences between the diurnal variability of seasonal rainfall over the mid-Pacific and Indian Ocean Basins. The mid-Pacific exhibits double maxima in spring and winter but only LE-EM maxima in summer and autumn, while the Indian Ocean exhibits double maxima in spring and summer and only an LE-EM maximum in autumn and winter. There are also evident daytime maxima within the major large-scale marine stratocumulus regions off the west coasts of continents. The study concludes with a discussion concerning how the observational evidence either supports or repudiates possible forcing mechanisms that have been suggested to explain diurnal rainfall variability.

Yang, Song↗

NASA NTRS2008

High Vertically Resolved Atmospheric and Surface/Cloud Parameters Retrieved with Infrared Atmospheric Sounding Interferometer (IASI)

The Joint Airborne IASI Validation Experiment (JAIVEx) was conducted during April 2007 mainly for validation of the IASI on the MetOp satellite. IASI possesses an ultra-spectral resolution of 0.25/cm and a spectral coverage from 645 to 2760/cm. Ultra-spectral resolution infrared spectral radiance obtained from near nadir observations provide atmospheric, surface, and cloud property information. An advanced retrieval algorithm with a fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. This physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the cloud-free and/or clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multi-variable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. It is shown that relatively accurate temperature and moisture retrievals are achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with relatively high accuracy (i.e., error < 1 km). Preliminary retrievals of atmospheric soundings, surface properties, and cloud optical/microphysical properties with the IASI observations are obtained and presented. These retrievals will be further inter-compared with those obtained from airborne FTS system, such as the NPOESS Airborne Sounder Testbed - Interferometer (NAST-I), dedicated dropsondes, radiosondes, and ground based Raman Lidar. The capabilities of satellite ultra-spectral sounder such as the IASI are investigated indicating a high vertical structure of atmosphere is retrieved.

Zhou, Daniel K.↗

NASA NTRS2023

Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network

The regional and global scale of aerosols in the atmosphere can be quantified using the aerosol optical depth (AOD) retrieved from satellite observations. To obtain reliable satellite AODs, conducting consistent validations and refining retrieval algorithms are crucial. AODs and Ångström exponents (AEs) measured with the aerosol robotic network (AERONET) are considered as the ground truth for satellite validations. AERONET AEs are used to collocate the wavelength of the AERONET AODs to those of the satellite AODs when there is a discordancy in their wavelengths. However, numerous validation studies have proposed different strategies by applying the AERONET AODs and AEs, and spatiotemporal collocation criteria. This study examined the impact of the wavelength and spatial collocation radius variations by comparing AODs at 550 nm derived from the geostationary ocean color imager (GOCI) with those obtained from the AERONET for the year 2016. The estimated AERONET AODs at 550 nm varied from 5.18% to 11.73% depending on the selection of AOD and AE, and the spatial collocation radii from 0 to 40 km, respectively. The longer the collocation radius and the higher the AODs, the greater the variability observed in the validation results. Overall, the selection of the spatial collocation radius had a stronger impact on the variability in the validation results obtained compared to the selection of the wavelength. The variability was also found in seasonal analysis. Therefore, it is recommended to carefully select the data wavelength and spatial collocation radius, consider seasonal effects, and provide this information when validating satellite AODs using AERONET.

GOCI↗

DOE OSTI2021

Quantum simulation of $\phi^4$ theories in qudit systems

We discuss the implementation of quantum algorithms for lattice $\Phi^4$ theory on circuit quantum electrodynamics (cQED) system. The field is represented on qudits in a discretized field amplitude basis. The main advantage of qudit systems is that its multi-level characteristic allows the field interaction to be implemented only with diagonal single-qudit gates. Considering the set of universal gates formed by the single-qudit phase gate and the displacement gate, we address initial state preparation and single-qudit gate synthesis with variational methods.

Kurkcuoglu, Doga Murat↗

DOE OSTI2021

Monte Carlo MP2-F12 for Noncovalent Interactions: The C 60 Dimer

A scalable stochastic algorithm is presented that can evaluate explicitly correlated (F12) second-order many-body perturbation (MP2) energies of weak, noncovalent, intermolecular interactions. It first transforms the formulas of the MP2 and F12 energy differences into a short sum of high-dimensional integrals of Green’s functions in real space and imaginary time. Furthermore, these integrals are then evaluated by the Monte Carlo method augmented by parallel execution, redundant-walker convergence acceleration, direct-sampling autocorrelation elimination, and control-variate error reduction. By sharing electron-pair walkers across the supermolecule and its subsystems spanned by the joint basis set, the statistical uncertainty is reduced by one to 2 orders of magnitude in the MP2 binding energy corrected for the basis-set incompleteness and superposition errors. The method predicts the MP2-F12/aug-cc-pVDZ binding energy of 19.1 ± 4.0 kcal mol –1 for the C 60 dimer at the center distance of 9.748 Å.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗