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NASA NTRS2020

Attribution of Chemistry-Climate Model Initiative (CCMI) Ozone Radiative Flux Bias from Satellites

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6-μm ozone band is a fundamental quantity for understanding chemistry-climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6-μm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Inter-comparison Project (ACCMIP) simulations. The principal culprits are tropical mid and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mWm-2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mWm-2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mWm-2). We found that AM3 and CMAM have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to difference processes. Overall, the multi-model ensemble mean bias is –133±98 mWm-2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation.

Aura Tropospheric Emission Spectrometer (TES) meas↗
NASA NTRS2022

A Novel Machine Learning-Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data By Combining Downscaling and Regression

Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.

Soni Yatheendradas↗
NASA NTRS1982

Empirical source noise prediction method with application to subsonic coaxial jet mixing noise

A general empirical method, developed for source noise predictions, uses tensor splines to represent the dependence of the acoustic field on frequency and direction and Taylor's series to represent the dependence on source state parameters. The method is applied to prediction of mixing noise from subsonic circular and coaxial jets. A noise data base of 1/3-octave-band sound pressure levels (SPL's) from 540 tests was gathered from three countries: United States, United Kingdom, and France. The SPL's depend on seven variables: frequency, polar direction angle, and five source state parameters: inner and outer nozzle pressure ratios, inner and outer stream total temperatures, and nozzle area ratio. A least-squares seven-dimensional curve fit defines a table of constants which is used for the prediction method. The resulting prediction has a mean error of 0 dB and a standard deviation of 1.2 dB. The prediction method is used to search for a coaxial jet which has the greatest coaxial noise benefit as compared with an equivalent single jet. It is found that benefits of about 6 dB are possible.

Zorumski, W. E.↗
NASA NTRS2010

A Modeling and Verification Study of Summer Precipitation Systems Using NASA Surface Initialization Datasets

One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse-type convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within parameterization schemes, model resolution limitations, and uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture, soil temperature, and sea surface temperature (SST) are necessary to better simulate the interactions between the surface and atmosphere, and ultimately improve predictions of summertime pulse convection. This paper describes a sensitivity experiment using the Weather Research and Forecasting (WRF) model. Interpolated land and ocean surface fields from a large-scale model are replaced with high-resolution datasets provided by unique NASA assets in an experimental simulation: the Land Information System (LIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) SSTs. The LIS is run in an offline mode for several years at the same grid resolution as the WRF model to provide compatible land surface initial conditions in an equilibrium state. The MODIS SSTs provide detailed analyses of SSTs over the oceans and large lakes compared to current operational products. The WRF model runs initialized with the LIS+MODIS datasets result in a reduction in the overprediction of rainfall areas; however, the skill is almost equally as low in both experiments using traditional verification methodologies. Output from object-based verification within NCAR s Meteorological Evaluation Tools reveals that the WRF runs initialized with LIS+MODIS data consistently generated precipitation objects that better matched observed precipitation objects, especially at higher precipitation intensities. The LIS+MODIS runs produced on average a 4% increase in matched precipitation areas and a simultaneous 4% decrease in unmatched areas during three months of daily simulations.

Jonathan L. Case↗
NASA NTRS2022

Improved Prediction of Losses with Large Eddy Simulation in a Low-Pressure Turbine

There is a need to improve predictions of losses resulting from large eddy simulations (LES) of low-pressure turbines (LPT) in gas turbines. This may be done by assessing the accuracy of predictions against validation data and understanding the source of any inaccuracies. LES is a promising approach for capturing the laminar/turbulent transition process in a LPT. In previous studies, the authors utilized LES to model the flow field over a Variable Speed Power Turbine (VSPT) blade and successfully captured characteristic features of separation/reattachment and transition on the suction side at both the cruise (positive incidence) and take-off conditions (negative incidence) and as well, simulated the effect of free-stream turbulence (FST) on those phenomena. The predicted pressure loading profiles agreed well with the experimental data for both a high and a low FST case at a Reynolds number of Re(ex)=220,000. In this paper, we present wake profiles resulting from computations for a range of FST values. Although the predicted wake profiles for the lowest FST case (Tu=0.5%) matched the experimental data, at higher FST (Tu=10-15%,) the wake was wider than the experimentally measured wake and for both cases were displaced laterally when compared to the experimental measurements. In our investigation of the causes of the said discrepancies we have identified important effects which could strongly influence the predicted wake profile. Predicted losses were improved by assuring the validity of the flow solution. This was done by utilizing spectral analysis to scrutinize the dynamic behavior of the wake and determine solution accuracy resulting from low mesh density and low accuracy of convective modeling.

LES-LDKM↗
NASA NTRS2010

An Assessment of the Potential Predictability of Drought Over the United States Based on Climate Model Simulations with Specified SST

The USCLIV AR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. The runs were done with several global atmospheric models including NASA/NSIPP-l, NCEP/GFS, GFDLlAM2, and NCAR CCM3 and CAM3.5. Specific questions that the runs are designed to address include: What are mechanisms that maintain drought across the seasonal cycle and from one year to the next. To what extent can droughts develop independently of ocean variability due to year-to-year memory that may be inherent to the land. What is the role of the different ocean basins? Here we focus on the potential predictability of drought conditions over the United States. Specific issues addressed include the seasonality and regionality of the signal-to-noise ratios associated with Pacific and Atlantic SST forcing, and the sensitivity of the results to the climatological stationary waves simulated by the different AGCMs.

Schubert, Siegfried↗
NASA NTRS2002

Single-base-pair discrimination of terminal mismatches by using oligonucleotide microarrays and neural network analyses

The effects of single-base-pair near-terminal and terminal mismatches on the dissociation temperature (T(d)) and signal intensity of short DNA duplexes were determined by using oligonucleotide microarrays and neural network (NN) analyses. Two perfect-match probes and 29 probes having a single-base-pair mismatch at positions 1 to 5 from the 5' terminus of the probe were designed to target one of two short sequences representing 16S rRNA. Nonequilibrium dissociation rates (i.e., melting profiles) of all probe-target duplexes were determined simultaneously. Analysis of variance revealed that position of the mismatch, type of mismatch, and formamide concentration significantly affected the T(d) and signal intensity. Increasing the concentration of formamide in the washing buffer decreased the T(d) and signal intensity, and it decreased the variability of the signal. Although T(d)s of probe-target duplexes with mismatches in the first or second position were not significantly different from one another, duplexes with mismatches in the third to fifth positions had significantly lower T(d)s than those with mismatches in the first or second position. The trained NNs predicted the T(d) with high accuracies (R(2) = 0.93). However, the NNs predicted the signal intensity only moderately accurately (R(2) = 0.67), presumably due to increased noise in the signal intensity at low formamide concentrations. Sensitivity analysis revealed that the concentration of formamide explained most (75%) of the variability in T(d)s, followed by position of the mismatch (19%) and type of mismatch (6%). The results suggest that position of the mismatch at or near the 5' terminus plays a greater role in determining the T(d) and signal intensity of duplexes than the type of mismatch.

Non-NASA Center↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRS2000

Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction

BACKGROUND: Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. METHODS AND RESULTS: Time and frequency domain heart rate (HR) variability measures, along with short- and long-term correlation (fractal) properties of R-R intervals (exponents alpha(1) and alpha(2)) and power-law scaling of the power spectra (exponent beta), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction </=35%). During a mean+/-SD follow-up period of 685+/-360 days, 114 patients died (25.6%), with 75 deaths classified as arrhythmic (17.0%) and 28 as nonarrhythmic (6.3%) cardiac deaths. Several traditional and fractal measures of R-R interval variability were significant univariate predictors of all-cause mortality. Reduced short-term scaling exponent alpha(1) was the most powerful R-R interval variability measure as a predictor of all-cause mortality (alpha(1) <0.75, relative risk 3.0, 95% confidence interval 2.5 to 4.2, P<0.001). It remained an independent predictor of death (P<0.001) after adjustment for other postinfarction risk markers, such as age, ejection fraction, NYHA class, and medication. Reduced alpha(1) predicted both arrhythmic death (P<0.001) and nonarrhythmic cardiac death (P<0.001). CONCLUSIONS: Analysis of the fractal characteristics of short-term R-R interval dynamics yields more powerful prognostic information than the traditional measures of HR variability among patients with depressed left ventricular function after an acute myocardial infarction.

Non-NASA Center↗
NASA NTRS1975

NECAP: NASA's Energy-Cost Analysis Program. Part 1: User's manual

The NECAP is a sophisticated building design and energy analysis tool which has embodied within it all of the latest ASHRAE state-of-the-art techniques for performing thermal load calculation and energy usage predictions. It is a set of six individual computer programs which include: response factor program, data verification program, thermal load analysis program, variable temperature program, system and equipment simulation program, and owning and operating cost program. Each segment of NECAP is described, and instructions are set forth for preparing the required input data and for interpreting the resulting reports.

Henninger, R. H.↗
NASA NTRS1978

Future directions in experimental gamma ray astronomy

Better vehicles and instruments are needed if gamma ray spectroscopy in astrophysics and cosmology is to advance. A gamma ray observatory will (1) permit long-term observations of selected sources to measure their variability and to achieve high sensitivity; (2) measure periods in days or weeks; and (3) assess the entire sky to observe various predicted sources, to measure the energy spectrum, and to map the isotropy of the cosmic ray background over larger collecting areas (of the order of a square meter). Conventional and unconventional instruments must cover the energy range from 0.1 MeV to 20 MeV with improved sensitivity. Angular resolution must be improved one degree or more to study discrete X-ray sources in the galactic center. Actively collimated detectors, improved double Compton instruments, and gamma ray correlators to actively synthesize the absolute energy spectrum of the sky protons are discussed as well as the need for scientific balloons.

Haymes, R. C.↗
NASA NTRS1982

Dynamic stability of a buoyant quad-rotor aircraft

Stability characteristics of a buoyant quad-rotor aircraft (BQRA) in hover and forward flight are examined by considering linear, state-variable, and nonlinear flight simulation models of such a configuration. The effects of carrying a sling load on the vehicle dynamics is predicted by considering a coupled model of the two bodies. Inherent stability characteristics of the vehicle are analyzed and compared with those of a helicopter and an airship in free flight. Typical operational conditions that could lead to vehicle instability are described in the flight envelope of interest.

Nagabhushan, B. L.↗
NASA NTRS1983

Acoustic environmental accuracy requirements for response determination

A general purpose computer program was developed for the prediction of vehicle interior noise. This program, named VIN, has both modal and statistical energy analysis capabilities for structural/acoustic interaction analysis. The analytic models and their computer implementation were verified through simple test cases with well-defined experimental results. The model was also applied in a space shuttle payload bay launch acoustics prediction study. The computer program processes large and small problems with equal efficiency because all arrays are dynamically sized by program input variables at run time. A data base is built and easily accessed for design studies. The data base significantly reduces the computational costs of such studies by allowing the reuse of the still-valid calculated parameters of previous iterations.

Pettitt, M. R.↗
NASA NTRS1984

Calculation of unsteady fan rotor response caused by downstream flow distortions

The present model for fan rotor/support strut airfoil interaction uses a time-marching code for the rotor flow, coupled with a potential flow model for the stator-strut region. Study of the effect of strut design variables indicates that rotor flow disturbance is increased by the primary variables of larger strut thickness and circumferential spacing, while decreasing exponentially with increased rotor-strut separation. The time-marching code predicts local rotor pressure and flow perturbations in response to an unsteady downstream boundary condition.

Obrien, W. F.↗
NASA NTRS1986

Heat dissipation of high rate Li-SOCl sub 2 primary cells

The heat dissipation problem occurring in the lithium thionyl chloride cells discharged at relatively high rates under normal discharge conditions is examined. Four heat flow paths were identified, and the thermal resistances of the relating cell components along each flow path were accordingly calculated. From the thermal resistance network analysis, it was demonstrated that about 90 percent of the total heat produced within the cell should be dissipated along the radial direction in a spirally wound cell. In addition, the threshold value of the heat generation rate at which cell internal temperature could be maintained below 100 C, was calculated from total thermal resistance and found to be 2.9 W. However, these calculations were made only at the cell components' level, and the transient nature of the heat accumulation and dissipation was not considered. A simple transient model based on the lumped-heat-capacity concept was developed to predict the time-dependent cell temperature at different discharge rates. The overall objective was to examine the influence of cell design variable from the heat removal point of view under normal discharge conditions and to make recommendations to build more efficient lithium cells.

Cho, Y. I.↗
NASA NTRS1990

Generalized Transition Finite-Boundary Elements for high speed flight structures

A new class of 'Generalized Transition Finite-Boundary Elements' formulation is presented to predict temperature and/or stress distribution of flight structures at high speeds. A tweleve-noded three-dimensional transition element and a variable degree of freedom eight-noded element are formulated. These elements are incorporated into the formulation of the heat transfer and structural analysis problems by utilizing a newly introduced 'material approximation functions' concept. Results obtained from limited examples compared with the solutions from analytical and other finite element analysis solution. Numerical examples presented illustrate the effectiveness of these elements.

Sarigul-Klijn, Nesrin↗
NASA NTRS1993

NASA/MSFC FY92 Earth Science and Applications Program Research Review

A large amount of attention has recently been given to global issues such as the ozone hole, tropospheric temperature variability, etc. A scientific challenge is to better understand atmospheric processes on a variety of spatial and temporal scales in order to predict environmental changes. Measurement of geophysical parameters such as wind, temperature, and moisture are needed to validate theories, provide analyzed data sets, and initialize or constrain numerical models. One of NASA's initiatives is the Mission to Planet Earth Program comprised of an Earth Observation System (EOS) and the scientific strategy to analyze these data. This work describes these efforts in the context of satellite data analysis and fundamental studies of atmospheric dynamics which examine selected processes important to the global circulation.

Arnold, James E.↗
NASA NTRS1930

The prediction of airfoil characteristics

This report describes and develops methods by which the aerodynamic characteristics of an airfoil may be calculated with sufficient accuracy for use in airplane design. These methods for prediction are based on the present aerodynamic theory and on empirical formulas derived from data obtained in the N. A. C. A. variable density wind tunnel at a Reynolds number corresponding approximately to full scale. (author)

Higgins, George J↗