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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.

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NASA NTRS2002

Upper-Stratospheric Ozone Trends 1979-1998

Extensive analyses of ozone observations between 1978 and 1998 measured by Dobson Umkehr, Stratospheric Aerosol and Gas Experiment (SAGE) I and II, and Solar Backscattered Ultraviolet (SBUV) and (SBUV)/2 indicate continued significant ozone decline throughout the extratropical upper stratosphere from 30-45 km altitude. The maximum annual linear decline of -0.8 +/- 0.2 %/yr(2sigma) occurs at 40 km and is well described in terms of a linear decline modulated by the 11-year solar variation. The minimum decline of -0.110.1% yr-1(2o) occurs at 25 km in midlatitudes, with remarkable symmetry between the Northern and Southern Hemispheres at 40 km altitude. Midlatitude upper-stratospheric zonal trends exhibit significant seasonal variation (+/- 30% in the Northern Hemisphere, +/- 40% in the Southern Hemisphere) with the most negative trends of -1.2%/yr occurring in the winter. Significant seasonal trends of -0.7 to -0.9%/yr occur at 40 km in the tropics between April and September. Subjecting the statistical models used to calculate the ozone trends to intercomparison tests on a variety of common data sets yields results that indicate the standard deviation between trends estimated by 10 different statistical models is less than 0.1%/yr in the annual-mean trend for SAGE data and less than 0.2%/yr in the most demanding conditions (seasons with irregular, sparse data) [World Meteorological Organization (WMO), 1998]. These consistent trend results between statistical models together with extensive consistency between the independent measurement-system trend observations by Dobson Umkehr, SAGE I and II, and SBUV and SBUV/2 provide a high degree of confidence in the accuracy of the declining ozone amounts reported here. Additional details of ozone trend results from 1978 to 1996 (2 years shorter than reported here) along with lower-stratospheric and tropospheric ozone trends, extensive intercomparisons to assess relative instrument drifts, and retrieval algorithm details are given by WMO [1998].

Newchurch, M. J.↗
NASA NTRS1997

Bidirectional Reflectance Functions for Application to Earth Radiation Budget Studies

Reflected solar radiative fluxes emerging for the Earth's top of the atmosphere are inferred from satellite broadband radiance measurements by applying bidirectional reflectance functions (BDRFs) to account for the anisotropy of the radiation field. BDRF's are dependent upon the viewing geometry (i.e. solar zenith angle, view zenith angle, and relative azimuth angle), the amount and type of cloud cover, the condition of the intervening atmosphere, and the reflectance characteristics of the underlying surface. A set of operational Earth Radiation Budget Experiment (ERBE) BDRFs is available which was developed from the Nimbus 7 ERB (Earth Radiation Budget) scanner data for a three-angle grid system, An improved set of bidirectional reflectance is required for mission planning and data analysis of future earth radiation budget instruments, such as the Clouds and Earth's Radiant Energy System (CERES), and for the enhancement of existing radiation budget data products. This study presents an analytic expression for BDRFs formulated by applying a fit to the ERBE operational model tabulations. A set of model coefficients applicable to any viewing condition is computed for an overcast and a clear sky scene over four geographical surface types: ocean, land, snow, and desert, and partly cloudy scenes over ocean and land. The models are smooth in terms of the directional angles and adhere to the principle of reciprocity, i.e., they are invariant with respect to the interchange of the incoming and outgoing directional angles. The analytic BDRFs and the radiance standard deviations are compared with the operational ERBE models and validated with ERBE data. The clear ocean model is validated with Dlhopolsky's clear ocean model. Dlhopolsky developed a BDRF of higher angular resolution for clear sky ocean from ERBE radiances. Additionally, the effectiveness of the models accounting for anisotropy for various viewing directions is tested with the ERBE along tract data. An area viewed from nadir and from the side give two different radiance measurements but should yield the same flux when converted by the BDRF. The analytic BDRFs are in very good qualitative agreement with the ERBE models. The overcast scenes exhibit constant retrieved albedo over viewing zenith angles for solar zenith angles less than 60 degrees. The clear ocean model does not produce constant retrieved albedo over viewing zenith angles but gives an improvement over the ERBE operational clear sky ocean BDRF.

Manalo-Smith, N.↗
NASA NTRS2021

A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.

dust↗
NASA NTRS2019

Evaluation of Global Simulations of Aerosol Particle and Cloud Condensation Nuclei Number, with Implications for Cloud Droplet Formation

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN(0.2)) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer.

cloud droplets↗
NASA NTRS2016

Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.

SMAP L4_SM↗
NASA NTRS2000

Radiance and Jacobian Intercomparison of Radiative Transfer Models Applied to HIRS and AMSU Channels

The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: 7 HIRS (High-resolution Infrared Sounder) and 4 AMSU (Advanced Microwave Sounding Unit) channels. Interest in this topic was evidenced by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances, but also Jacobians (of temperature, water vapor and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others. The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging, In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However significant differences were noted among LBL models, Extending the intercomparison to the Jacobians proved very useful in detecting subtle and more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences. Recommendations for future intercomparisons are suggested.

Garand, L.↗
NASA NTRS2000

Radiance and Jacobian Intercomparison of Radiative Transfer Models Applied to HIRS and AMSU Channels

The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: seven HIRS (High-resolution Infrared Sounder) and four AMSU (Advanced Microwave Sounding Unit) channels. Interest in this topic was evidenced by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances, but also Jacobians (of temperature, water vapor, and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others. The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging. In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However significant differences were noted among LBL models. Extending the intercomparison to the Jacobians proved very useful in detecting subtle and more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences. Recommendations for future intercomparisons are suggested.

Garand, L.↗
NASA NTRS1986

Geodesy by radio interferometry - Evidence for contemporary plate motion

Analysis of 211 very long baseline interferometry observing sessions carried out between November 1979 and August 1984 has yielded estimates of the distances between various radio telescopes located in North America and Europe. The average rate of change of the distances between four radio telescopes in North America (Haystack Observatory, Massachusetts; Westford Radio Telescope, Massachusetts; George R. Agassiz Station, Texas; and Owens Valley Radio Observatory, California) and one in Europe (Onsala Space Observatory, Sweden) obtained from the analysis of these data is 19 + or 10 mm/yr, where the (68 percent confidence interval) standard deviation is for the estimate of the rate of change of the Haystack-Onsala baseline length, the one determined most accurately from these data. This estimate of the standard deviation is dominated by the effects of correlated systematic errors due mostly to errors in the model used for the atmospheric delay which introduces errors in each baseline length estimate of 40 mm standard deviation and 60 days correlation time. (By contrast the statistical standard deviation is only 2 mm/yr). The estimated geologic rates of change of these baseline lengths, averaged over 10 to the 6th years, are 15 to 17 + or - 3 mm/yr for the various North American sites to Ondala.

Herring, T. A.↗
NASA NTRS2005

Atmospheric Hydroperoxides in West Antarctica: Links to Stratospheric Ozone and Atmospheric Oxidation Capacity

The troposphere above the West Antarctic Ice Sheet (WAIS) was sampled for hydroperoxides at 21 locations during 2-month-long summer traverses from 2000 to 2002, as part of US ITASE (International Transantarctic Scientific Expedition). First time quantitative measurements using an HPLC method showed that methylhydroperoxide (MHP) is the only important organic hydroperoxide occurring in the Antarctic troposphere, and that it is found at levels ten times those previously predicted by photochemical models. During three field seasons, means and standard deviations for hydrogen peroxide (H2O2) were 321+/-158 pptv, 650+/-176 pptv and 330+/-147 pptv. While MHP was detected, but not quantified in December 2000, levels in summer 2001 and 2002 were 317+128 pptv and 304+/-172.2 pptv. Results from firn air experiments and diurnal variability of the two species showed that atmospheric H2O2 is significantly impacted by a physical snow pack source between 76 and 90degS, whereas MHP is not. We show strong evidence of a positive feedback between stratospheric ozone and H2O2 at the surface. Between November-27 and December-12 in 2001, when ozone column densities dropped below 220 DU (means in 2000 and 2001 were 318 DU and 334 DU, respectively), H2O2 was 1.7 times that observed in the same period in 2000 and 2002, while MHP was only 80% of the levels encountered in 2002. Photochemical box model runs suggest that NO and OH levels on WAIS are closer to coastal values, while Antarctic Plateau levels are higher, confirming that region to be a highly oxidizing environment. The modeled sensitivity of H2O2 and particularly MHP to NO offers the potential to use atmospheric hydroperoxides to constrain the NO background and thus estimate the past oxidation capacity of the remote atmosphere. Index Terms: 0365 Atmospheric Composition and Structure: Troposphere: composition and chemistry; 0322 Atmospheric Composition and Structure: Constituent sources and sinks; 1610 Global Change: Atmosphere (03 15,0325); 0736 Cryosphere: Snow (1827, 1863); 0724 Cryosphere: Ice Cores (4932) Keywords: hydrogen peroxide, methylhydroperoxide, Antarctica, air-snow exchange, stratospheric ozone, atmospheric oxidation capacity

Frey, Markus M.↗
NASA NTRS2010

Comparison of Iterative and Non-Iterative Strain-Gage Balance Load Calculation Methods

The accuracy of iterative and non-iterative strain-gage balance load calculation methods was compared using data from the calibration of a force balance. Two iterative and one non-iterative method were investigated. In addition, transformations were applied to balance loads in order to process the calibration data in both direct read and force balance format. NASA's regression model optimization tool BALFIT was used to generate optimized regression models of the calibration data for each of the three load calculation methods. This approach made sure that the selected regression models met strict statistical quality requirements. The comparison of the standard deviation of the load residuals showed that the first iterative method may be applied to data in both the direct read and force balance format. The second iterative method, on the other hand, implicitly assumes that the primary gage sensitivities of all balance gages exist. Therefore, the second iterative method only works if the given balance data is processed in force balance format. The calibration data set was also processed using the non-iterative method. Standard deviations of the load residuals for the three load calculation methods were compared. Overall, the standard deviations show very good agreement. The load prediction accuracies of the three methods appear to be compatible as long as regression models used to analyze the calibration data meet strict statistical quality requirements. Recent improvements of the regression model optimization tool BALFIT are also discussed in the paper.

Ulbrich, N.↗
NASA NTRS2012

The Influence of Ozone Precursor Emissions from Four World Regions on Tropospheric Composition and Radiative Climate Forcing

Ozone (O3) precursor emissions influence regional and global climate and air quality through changes in tropospheric O3 and oxidants, which also influence methane (CH4) and sulfate aerosols (SO4 (sup 2-)). We examine changes in the tropospheric composition of O3, CH4, SO4 (sup 2-) and global net radiative forcing (RF) for 20% reductions in global CH4 burden and in anthropogenic O3 precursor emissions (NOx, NMVOC, and CO) from four regions (East Asia, Europe and Northern Africa, North America, and South Asia) using the Task Force on Hemispheric Transport of Air Pollution Source-Receptor global chemical transport model (CTM) simulations, assessing uncertainty (mean plus or minus 1 standard deviation) across multiple CTMs. We evaluate steady state O3 responses, including long-term feedbacks via CH4. With a radiative transfer model that includes greenhouse gases and the aerosol direct effect, we find that regional NOx reductions produce global, annually averaged positive net RFs (0.2 plus or minus 0.6 to 1.7 2 mWm(sup -2)/Tg N yr(sup -1), with some variation among models. Negative net RFs result from reductions in global CH4 (-162.6 plus or minus 2 mWm(sup -2) for a change from 1760 to 1408 ppbv CH4) and regional NMVOC (-0.4 plus or minus 0.2 to 0.7 plus or minus 0.2 mWm(sup -2)/Tg C yr(sup -1) and CO emissions (-0.13 plus or minus 0.02 to -0.15 plus or minus 0.02 mWm(sup-2)/Tg CO yr(sup-1). Including the effect of O3 on CO2 uptake by vegetation likely makes these net RFs more negative by -1.9 to- 5.2 mWm(sup -2)/Tg N yr(sup -1), -0.2 to -0.7 mWm(sup -2)/Tg C yr(sup -1), and -0.02 to -0.05 mWm(sup -2)/ Tg CO yr(sup -1). Net RF impacts reflect the distribution of concentration changes, where RF is affected locally by changes in SO4 (sup -2), regionally to hemispherically by O3, and globally by CH4. Global annual average SO4 2 responses to oxidant changes range from 0.4 plus or minus 2.6 to -1.9 plus or minus 1.3 Gg for NOx reductions, 0.1 plus or minus 1.2 to -0.9 plus or minus 0.8 Gg for NMVOC reductions, and -0.09 plus or minus 0.5 to -0.9 plus or minus 0.8 Gg for CO reductions, suggesting additional research is needed. The 100-year global warming potentials (GWP(sub 100)) are calculated for the global CH4 reduction (20.9 plus or minus 3.7 without stratospheric O3 or water vapor, 24.2 plus or minus 4.2 including those components), and for the regional NOx, NMVOC, and CO reductions (18.7 plus or minus 25.9 to 1.9 plus or minus 8.7 for NOx, 4.8 plus or minus 1.7 to 8.3 plus or minus 1.9 for NMVOC, and 1.5 plus or minus 0.4 to 1.7 plus or minus 0.5 for CO). Variation in GWP(sub 100) for NOx, NMVOC, and CO suggests that regionally specific GWPs may be necessary and could support the inclusion of O3 precursors in future policies that address air quality and climate change simultaneously. Both global net RF and GWP100 are more sensitive to NOx and NMVOC reductions from South Asia than the other three regions.

ozone↗
NASA NTRS1978

An initial state perturbation experiment with the GISS model

Monthly mean global forecasts for January 1975 have been computed with the Goddard Institute for Space Studies model from four slightly different sets of initial conditions - a 'control' state and three random perturbations thereof - to simulate the effects of initial state uncertainty on forecast quality. Differences among the forecasts are examined in terms of energetics, synoptic patterns and forecast statistics. The 'noise level' of the model predictions is depicted on global maps of standard deviations of sea level pressures, 500 mb heights and 850 mb temperatures for the set of four forecasts. Initial small-scale random errors do not appear to result in any major degradation of the large-scale monthly mean forecast beyond that generated by the model itself, nor do they appear to represent the major source of large-scale forecast error.

Spar, J.↗
NASA NTRSDate not supplied

Development and Application of NASA SPoRT’s DustTracker-AI Model for Real-Time Identification and Tracking of Dust in Geostationary Satellite Imagery

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed the DustTracker-AI model for identifying and tracking dust in NASA/NOAA Geostationary Operational Environmental Satellite (GOES) imagery in a real-time framework. A training dataset consisting of day and night dust cases was gathered over the southwestern consisting of 115 distinct images and over a million dust pixels and 256 million no dust pixels. The dataset was separated into training (60%), testing (20%), and validation (20%). A simple random forest machine learning model was developed originally to overcome the problem of night-time dust detection and has been expanded to a comprehensive day/night model for dust identification and tracking. This physically-based machine-learning approach uses NASA/NOAA GOES-16 Advanced Baseline Imager infrared imagery as inputs to the model. The model probability of dust output achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels for all dust images in the validation data set. In conjunction with developing the machine-learning model, the NASA Short-term Prediction Research and Transition Center (SPoRT) partnered with NOAA National Weather Service forecast offices to evaluate the model for utility in weather forecasting operations during the 2021 and 2023 late winter-spring seasons. Preliminary evaluation has indicated the majority of forecasters described the DustTracker-AI probabilities as having added confidence to interpreting the Dust RGB and other satellite products to objectively assess the dust extent and trends and increased the amount of time the dust plume could be tracked into the night as compared to use of the Dust RGB. More recently, SPoRT tested small scale events associated with thunderstorm outflow and burn scars to determine the model’s ability to capture local events. This presentation highlights design of the model, validation/evaluation of model performance, and example cases collected during end user product assessments.

Connor H Welch↗
NASA NTRS1977

Analysis of a first order phase locked loop in the presence of Gaussian noise

A first-order digital phase locked loop is analyzed by application of a Markov chain model. Steady state loop error probabilities, phase standard deviation, and mean loop transient times are determined for various input signal to noise ratios. Results for direct loop simulation are presented for comparison.

Blasche, P. R.↗
NASA NTRS2008

Two Empirical Models for Land-falling Hurricane Gust Factors

Gaussian and lognormal models for gust factors as a function of height and mean windspeed in land-falling hurricanes are presented. The models were empirically derived using data from 2004 hurricanes Frances and Jeanne and independently verified using data from 2005 hurricane Wilma. The data were collected from three wind towers at Kennedy Space Center and Cape Canaveral Air Force Station with instrumentation at multiple levels from 12 to 500 feet above ground level. An additional 200-foot tower was available for the verification. Mean wind speeds from 15 to 60 knots were included in the data. The models provide formulas for the mean and standard deviation of the gust factor given the mean windspeed and height above ground. These statistics may then be used to assess the probability of exceeding a specified peak wind threshold of operational significance given a specified mean wind speed.

Merceret, Franics J.↗
NASA NTRS1987

Probabilistic SSME blades structural response under random pulse loading

The purpose is to develop models of random impacts on a Space Shuttle Main Engine (SSME) turbopump blade and to predict the probabilistic structural response of the blade to these impacts. The random loading is caused by the impact of debris. The probabilistic structural response is characterized by distribution functions for stress and displacements as functions of the loading parameters which determine the random pulse model. These parameters include pulse arrival, amplitude, and location. The analysis can be extended to predict level crossing rates. This requires knowledge of the joint distribution of the response and its derivative. The model of random impacts chosen allows the pulse arrivals, pulse amplitudes, and pulse locations to be random. Specifically, the pulse arrivals are assumed to be governed by a Poisson process, which is characterized by a mean arrival rate. The pulse intensity is modelled as a normally distributed random variable with a zero mean chosen independently at each arrival. The standard deviation of the distribution is a measure of pulse intensity. Several different models were used for the pulse locations. For example, three points near the blade tip were chosen at which pulses were allowed to arrive with equal probability. Again, the locations were chosen independently at each arrival. The structural response was analyzed both by direct Monte Carlo simulation and by a semi-analytical method.

Shiao, Michael↗
NASA NTRS1993

The influence of thermal inertia on Mars' seasonal pressure variation and the effect of the weather component

Using a Leighton-Murray type diurnal and seasonal Mars thermal model, we found that it is possible to reproduce the seasonal variation in daily-averaged pressures (approximately 680-890 Pa) measured by Viking Lander 1 (VL1), during years without global dust storms, with a standard deviation of less than 5 Pa. In this simple model, surface CO2, frost condensation, and sublimation rates at each latitude are determined by the net effects of radiation, latent heat, and heat conduction in subsurface soil layers. An inherent assumption of our model is that the seasonal pressure variation is due entirely to the exchange of mass between the atmosphere and polar caps. However, the results of recent Mars GCM modeling have made it clear that there is a significant dynamical contribution to the seasonal pressure variation. This 'weather' component is primarily due to large-scale changes in atmospheric circulation, and its magnitude depends somewhat on the dust content of the atmosphere. The overall form of the theoretical weather component at the location of VL1, as calculated by the AMES GCM, remains the same over the typical range of Mars dust opacities.

Wood, S. E.↗
NASA NTRS1985

Proposed ozone reference models for the middle atmosphere

Since the publication of the last COSPAR International Reference Atmosphere (CIRA 72), large amounts of ozone data acquired from satellites have become available in addition to increasing quantities of rocketsonde, balloonsonde, Dobson, M83, and Umkehr measurements. From the available archived satellite data, models are developed for the new CIRA using 5 satellite experiments (Nimbus 7 SBUV and LIMS, AEM-2 SAGE, and SME IR and UVS) of the monthly latitudinal and altitudinal variations in the ozone mixing ratio in the middle atmosphere. Standard deviations and interannual variations are also quantified. The satellite models are shown to agree well with a previous reference model based on rocket and balloon measurements.

Keating, G. M.↗