Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model transferability”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Radiative Heating Profiles over Ascension Island during the 2016 and 2017 Biomass Burning Seasons

Marine boundary layer clouds, including the transition from stratocumulus to cumulus, are poorly represented in numerical weather prediction and general circulation models. In many cases, the complex physical relationships between cloud morphology and the environmental conditions in which marine boundary layer clouds exist are not well understood. Such uncertainties arise in the presence of biomass burning carbonaceous aerosol, as is the case over the southeast Atlantic Ocean, where it is likely that the absorbing and heating properties of these aerosols modify the microphysical composition and macrophysical arrangement of marine stratocumulus and trade cumulus. The deployment of the Atmospheric Radiation Measurement Mobile Facility #1 (AMF1) in support of LASIC (Layered Atlantic Smoke Interactions with Clouds) provided a unique opportunity to observe thermodynamic, cloud and aerosol properties during two consecutive biomass burning seasons from July through October of 2016 and 2017 over Ascension Island (7.96 S, 14.35 W). These observations in conjunction with radiation transfer modeling were used to assess the impact of biomass burning carbonaceous aerosol plumes as they passed over the site.Thermodynamic profiles were generated using a combination of radiosonde data and thermodynamic profilers to provide high temporal resolution profiles of quantities that are important for cloud development, such as CAPE and CIN. Coincident Ka-band radar and lidar profiles were used to characterize the cloud and sub-cloud structure. The resulting thermodynamic and cloud profiles are used as input forcing for the Rapid Radiative Transfer Model (RRTM) to compute radiative heating profiles over the observation site. Idealized experiments using RRTM, with and without aerosols present, are used to assess the impacts of the absorbing aerosol on the heating rate profiles.

Cumulus↗
NASA NTRS2006

Impact of Surface Roughness on AMSR-E Sea Ice Products

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper mere assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors.

Stroeve, Julienne C.↗
NASA NTRS2000

Fate of Earth Microbes on Mars -- UV Radiation Effects

A radiative transfer model is used to quantitatively investigate aspects of the martian ultraviolet radiation environment. Biological action spectra for DNA inactivation are used to estimate biologically effective irradiances for the martian surface under cloudless skies. Although the present-day martian UV flux is similar to early earth and thus may not be a limitation to life in the evolutionary context, it is a constraint to an unadapted biota and will rapidly kill spacecraft-borne microbes not covered by a martian dust layer. Here calculations for loss of microbial viability on the Pathfinder and Polar lander spacecraft are presented and the effects of martian dust on loss of viability are discussed. Details of the radiative transfer model are presented.

Cockell, Charles↗
NASA NTRS2000

Fate of Earth Microbes on Mars: UV Radiation Effects

A radiative transfer model is used to quantitatively investigate aspects of the martian ultraviolet radiation environment. Biological action spectra for DNA inactivation are used to estimate biologically effective irradiances for the martian surface under cloudless skies. Although the present-day martian UV flux is similar to early earth and thus may not be a limitation to life in the evolutionary context, it is a constraint to an unadapted biota and will rapidly kill spacecraft-borne microbes not covered by a martian dust layer. Here calculations for loss of microbial viability on the Pathfinder and Polar lander spacecraft are presented and the effects of martian dust on loss of viability are discussed. Details of the radiative transfer model are presented.

Cockell, Charles↗
NASA NTRS1993

Improvement and further development of SSM/I overland parameter algorithms using the WetNet workstation

Since the launch of the DMSP Special Sensor Microwave/Imager (SSM/I), several algorithms have been developed to retrieve overland parameters. These include the present operational algorithms resulting from the Navy calibration/validation effort such as land surface type (Neale et al. 1990), land surface temperature (McFarland et al. 1990), surface moisture (McFarland and Neale, 1991) and snow parameters (McFarland and Neale, 1991). In addition, other work has been done including the classification of snow cover and precipitation using the SSM/I (Grody, 1991). Due to the empirical nature of most of the above mentioned algorithms, further research is warranted and improvements can probably be obtained through a combination of radiative transfer modelling to study the physical processes governing the microwave emissions at the SSM/I frequencies, and the incorporation of additional ground truth data and special cases into the regression data sets. We have proposed specifically to improve the retrieval of surface moisture and snow parameters using the WetNet SSM/I data sets along with ground truth information namely climatic variables from the NOAA cooperative network of weather stations as well as imagery from other satellite sensors such as the AVHRR and Thematic Mapper. In the case of surface moisture retrievals the characterization of vegetation density is of primary concern. The higher spatial resolution satellite imagery collected at concurrent periods will be used to characterize vegetation types and amounts which, along with radiative transfer modelling should lead to more physically based retrievals. Snow parameter retrieval algorithm improvement will initially concentrate on the classification of snowpacks (dry snow, wet snow, refrozen snow) and later on specific products such as snow water equivalent. Significant accomplishments in the past year are presented.

Neale, Christopher M. U.↗
NASA NTRS1994

Temperature-moisture biases in ECMWF analyses based on clear sky longwave simulations constrained by SSMI and MSU measurements and comparisons to ERBE estimates

Clear sky longwave radiation fluxes for the summer of 1988 and winter of 1989 have been simulated with a radiative transfer model that includes detailed treatment of atmospheric gas absorption. The input data to the model are humidity and temperature profiles from European Center for Medium-Range Weather Forecasts (ECMWF) analyses and surface temperature measurements from the International Satellite Cloud Climatology Project. To reduce the inherent uncertainties in humidity profiles, the ECMWF moisture fields have been adjusted, based on the climatological relationship between the moisture profile and total precipitable water (PW), so that the ECMWF PW is equal to that derived from Special Sensor Microwave Imager (SSMI) data. SSMI and ECMWF PW patterns are generally similar, but significant differences in magnitude are found in the low latitudes, particularly over the subtropical oceans in the Southern Hemisphere. At the same time the tropospheric brightness temperature (T(sub B)) at 53.74 GHz has been computed using a microwave transfer model with ECMWF temperature and humidity fields as inputs. The comparison of computed T(sub B) with the Microwave Sounding Unit (MSU) channel 2 T(sub B) revealed differences larger than 1.5 K over most of the oceans, suggesting that the ECMWF model atmosphere has a cold bias. Maximum biases greater than 3 K are found in low latitudes. After removing the bias from the ECMWF temperature field, a new set of temperature profiles yielding brightness temperature equal to MSU channel 2 T(sub B) has been obtained. Simulation results clearly demonstrate that inclusion of satellite estimates of PW and T(sub B) enhances the accuracy of the clear sky LW flux simulation, substantially reducing the differences from Earth Radiation Budget Experiment (ERBE) estimates. Global averages indicate that the model-derived top-of-atmosphere (TOA) clear sky fluxes from the adjusted moisture and temperature profiles are in very good agreement with satellite-measured ERBE values, differing by only 1.4 W/sq m. By contrast, the calculation using only ECMWF analyses without any adjustments gives a significant underestimation of TOA clear sky fluxes up to 10.7 W/sq m compared to the satellite measurements, suggesting that the constraint method can significantly improve the accuracy in the radiation budget simulation. The results also indicate that the ECMWF-based simulation errors are, at least for the two seasons studied, mainly attributed to the error in the temperature field rather than to errors in the moisture field.

Sohn, Byung-Ju↗
NASA NTRS2022

VLTI-MATISSE L- and N-Band Aperture-Synthesis Imaging of the Unclassified B[e] star FS Canis Majoris

Context. FS Canis Majoris (FS CMa, HD 45677) is an unclassified B[e] star surrounded by an inclined dust disk. The evolutionary stage of FS CMa is still debated. Perpendicular to the circumstellar disk, a bipolar outflow was detected. Infrared aperture-synthesis imaging provides us with a unique opportunity to study the disk structure. Aims. Our aim is to study the intensity distribution of the disk of FS CMa in the mid-infrared L and N bands. Methods. We performed aperture-synthesis imaging of FS CMa with the MATISSE instrument (Multi AperTure mid-Infrared SpectroScopic Experiment) in the low spectral resolution mode to obtain images in the L and N bands. We computed radiative transfer models that reproduce the L- and N-band intensity distributions of the resolved disks. Results. We present L- and N-band aperture-synthesis images of FS CMa reconstructed in the wavelength bands of 3.4–3.8 and 8.6–9.0 μm. In the L-band image, the inner rim region of an inclined circumstellar disk and the central object can be seen with a spatial resolution of 2.7 milliarcsec (mas). An inner disk cavity with an angular diameter of ~6 × 12 mas is resolved. The L-band disk consists of a bright northwestern (NW) disk region and a much fainter southeastern (SE) region. The images suggest that we are looking at the bright inner wall of the NW disk rim, which is on the far side of the disk. In the N band, only the bright NW disk region is seen. In addition to deriving the inclination and the inner disk radius, fitting the reconstructed brightness distributions via radiative transfer modelling allows one to constrain the innermost disk structure, in particular the shape of the inner disk rim.

techniques↗
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗
DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗
DOE OSTI2025

Modeling Charge Transfer Excitation with Variation After Response Quantum Monte Carlo (Final Technical Report)

The electronically excited states of molecules play critical roles both in spectroscopy and in technological application, ranging from UV/Vis absorption experiments to the production of solar fuels. However, certain types of excitations, such as charge transfer excitations, remain difficult to treat with existing methods such as time dependent density functional theory or linear response coupled cluster. Under this Early Career Research Award, we developed a number of different theoretical methods to address this issue, which began with Variational Monte Carlo methods and branched into multiple types of excited-state-specific quantum chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1976

An investigation of condensation heat transfer in a closed tube containing a soluble noncondensable gas

A more exact one-dimensional condensation heat transfer model for insoluble gases was developed and compared with experimental data. Modifications to this model to accommodate soluble gas behavior were also accomplished, and the effects on gas front behavior demonstrated. Analytical models for condensation heat transfer are documented, and an optical method used for measuring gas concentration profiles is outlined. Experimental data is then presented and interpreted.

Saaski, E. W.↗
NASA NTRS1976

An investigation of condensation heat transfer in a closed tube containing a soluble noncondensable gas

An exact one-dimensional condensation heat transfer model for insoluble gases has been developed and compared with experimental data. Modifications to this model to accommodate soluble gas behavior have also been accomplished, and the effects on gas front behavior demonstrated. Analytical models for condensation heat transfer are documented, and a novel optical method used for measuring gas concentration profiles is outlined.

Saaski, E. W.↗
NASA NTRS1980

SMMR Simulator radiative transfer calibration model. 2: Algorithm development

Passive microwave measurements performed from Earth orbit can be used to provide global data on a wide range of geophysical and meteorological phenomena. A Scanning Multichannel Microwave Radiometer (SMMR) is being flown on the Nimbus-G satellite. The SMMR Simulator duplicates the frequency bands utilized in the spacecraft instruments through an amalgamate of radiometer systems. The algorithm developed utilizes data from the fall 1978 NASA CV-990 Nimbus-G underflight test series and subsequent laboratory testing.

Link, S.↗
NASA NTRS1985

Forward and reverse transfer function model synthesis

A process for synthesizing a mathematical model for a linear mechanical system using the forward and reverse Fourier transform functions is described. The differential equation for a system model is given. The Bode conversion of the differential equation, and the frequency and time-domain optimization matching of the model to the forward and reverse transform functions using the geometric simplex method of Nelder and Mead (1965) are examined. The effect of the window function on the linear mechanical system is analyzed. The model is applied to two examples; in one the signal damps down before the end of the time window and in the second the signal has significant energy at the end of the time window.

Houghton, J. R.↗