Results for “sparsity estimation”

NASA NTRS2002

Consistency of ARESE II Cloud Absorption Estimates and Sampling Issues

Data from three cloudy days (March 3, 21, 29, 2000) of the ARM Enhanced Shortwave Experiment II (ARESE II) were analyzed. Grand averages of broadband absorptance among three sets of instruments were compared. Fractional solar absorptances were approx. 0.21-0.22 with the exception of March 3 when two sets of instruments gave values smaller by approx. 0.03-0.04. The robustness of these values was investigated by looking into possible sampling problems with the aid of 500 nm spectral fluxes. Grand averages of 500 nm apparent absorptance cover a wide range of values for these three days, namely from a large positive (approx. 0.011) average for March 3, to a small negative (approximately -0.03) for March 21, to near zero (approx. 0.01) for March 29. We present evidence suggesting that a large part of the discrepancies among the three days is due to the different nature of clouds and their non-uniform sampling. Hence, corrections to the grand average broadband absorptance values may be necessary. However, application of the known correction techniques may be precarious due to the sparsity of collocated flux measurements above and below the clouds. Our analysis leads to the conclusion that only March 29 fulfills all requirements for reliable estimates of cloud absorption, that is, the presence of thick, overcast, homogeneous clouds.

Oreopoulos, L.↗

NASA NTRS1993

A general rough-surface inversion algorithm: Theory and application to SAR data

Rough-surface inversion has significant applications in interpretation of SAR data obtained over bare soil surfaces and agricultural lands. Due to the sparsity of data and the large pixel size in SAR applications, it is not feasible to carry out inversions based on numerical scattering models. The alternative is to use parameter estimation techniques based on approximate analytical or empirical models. Hence, there are two issues to be addressed, namely, what model to choose and what estimation algorithm to apply. Here, a small perturbation model (SPM) is used to express the backscattering coefficients of the rough surface in terms of three surface parameters. The algorithm used to estimate these parameters is based on a nonlinear least-squares criterion. The least-squares optimization methods are widely used in estimation theory, but the distinguishing factor for SAR applications is incorporating the stochastic nature of both the unknown parameters and the data into formulation, which will be discussed in detail. The algorithm is tested with synthetic data, and several Newton-type least-squares minimization methods are discussed to compare their convergence characteristics. Finally, the algorithm is applied to multifrequency polarimetric SAR data obtained over some bare soil and agricultural fields. Results will be shown and compared to ground-truth measurements obtained from these areas. The strength of this general approach to inversion of SAR data is that it can be easily modified for use with any scattering model without changing any of the inversion steps. Note also that, for the same reason it is not limited to inversion of rough surfaces, and can be applied to any parameterized scattering process.

Moghaddam, M.↗

NASA NTRS2021

Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

Satellite altimeter↗

NASA NTRS2016

Evaluation of the Effect of Surface Finish on High-Cycle Fatigue for SLM-IN718

A high-cycle fatigue (HCF) knockdown factor was estimated for Inconel 718, manufactured with the selective laser melt (SLM) process. This factor is the reduction at a common fatigue life from the maximum stress in fatigue for low-stress ground (LSG) specimens to the maximum stress of those left with the original surface condition. Various vendors provided specimens. To reduce the number of degrees-of-freedom, only one heat treat condition was evaluated. Testing temperatures included room temperature, 800F, 1000F, and 1200F. The two surface conditions were compared at constant lives, where data was available. The recommended knockdown factor of the as-built surface condition (average roughness of approximately 245 micro-inches/inch) versus low-stress ground condition (roughness <= 4 micro-inches/inch) is approximately 1/3 or 33%. This is to say that for the as-built surface condition, a maximum stress of 2/3 of the stress for LSG can be expected to produce the same life in the as built surface condition. As an alternative method, the surface finish was incorporated into a new parameter with the maximum stress. The new parameter was formulated to be similar to the fracture mechanics stress intensity factor, and it was named the pseudo stress intensity factor, Kp. Using Kp, the variance seemed acceptable across all sources, and the knockdown factor was estimated over the range of data identified by Kp where data occurred. A plot of the results suggests that the knockdown factor is a function of temperature, and that for low lives the knockdown is greater than the knockdown observed above about one million cycles, where it stabilizes. One data point at room temperature was clearly different, and the sparsity of data in the higher life region reduces the value of these results. The method does appear to provide useful results, and further characterization of the method is suggested.

Lambert, Dennis M.↗

NASA NTRSDate not supplied

Adaptive Pressure Profile Method to Locate the Isolator Shock Train Leading Edge Given Limited Pressure Information

To maximize the performance of high-speed air-breathing engines, such as dual-mode scramjets, the streamwise location of the shock train leading edge (STLE) is ideally placed as far upstream in the isolator as possible while avoiding engine unstart. Thus, it is of interest to quantify and control the STLE location as the vehicle travels along its flight trajectory. The STLE location is typically quantified using wall static pressure measurements but there are often restrictions on the number and placement of transducers, thus reducing the accuracy and overall capability of STLE detection methods. In this work, the Adaptive Pressure Profile (APP) method is introduced to address such sparsity concerns. This method is data driven and does not heavily rely on prior information about the flow regime or engine model. Instead, the \mname method uses real-time pressure measurements from a small number of transducers to adaptively learn the isolator pressure profile. This adaptively-learned profile is fit to the pressure data at each time instance to estimate STLE location. The \mname method produces accurate estimates even when (1) the STLE location is not bounded by two or more transducers or (2) when the STLE location is between two transducers that are situated several duct heights apart. Data from two direct-connect isolator models are used to evaluate the accuracy of the \mname method and demonstrate its robustness for different back-pressure scenarios and transducer configurations.

Robin L Hunt↗

NASA NTRSDate not supplied

Adaptive Pressure Profile Method to Locate the Isolator Shock Train Leading Edge Given Limited Pressure Information

To maximize the performance of high-speed air-breathing engines, such as dual-mode scramjets, the streamwise location of the shock train leading edge (STLE) is ideally placed as far upstream in the isolator as possible while avoiding engine unstart. Thus, it is of interest to quantify and control the STLE location as the vehicle travels along its flight trajectory. The STLE location is typically quantified using wall static pressure measurements but there are often restrictions on the number and placement of transducers, thus reducing the accuracy and overall capability of STLE detection methods. In this work, the Adaptive Pressure Profile (APP) method is introduced to address such sparsity concerns. This method is data driven and does not heavily rely on prior information about the flow regime or engine model. Instead, the \mname method uses real-time pressure measurements from a small number of transducers to adaptively learn the isolator pressure profile. This adaptively-learned profile is fit to the pressure data at each time instance to estimate STLE location. The \mname method produces accurate estimates even when (1) the STLE location is not bounded by two or more transducers or (2) when the STLE location is between two transducers that are situated several duct heights apart. Data from two direct-connect isolator models are used to evaluate the accuracy of the \mname method and demonstrate its robustness for different back-pressure scenarios and transducer configurations.

Robin Hunt↗

NASA NTRS2016

Role of Updraft Velocity in Temporal Variability of Global Cloud Hydrometeor Number

Understanding how dynamical and aerosol inputs affect the temporal variability of hydrometeor formation in climate models will help to explain sources of model diversity in cloud forcing, to provide robust comparisons with data, and, ultimately, to reduce the uncertainty in estimates of the aerosol indirect effect. This variability attribution can be done at various spatial and temporal resolutions with metrics derived from online adjoint sensitivities of droplet and crystal number to relevant inputs. Such metrics are defined and calculated from simulations using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) and the National Center for Atmospheric Research Community Atmosphere Model Version 5.1 (CAM5.1). Input updraft velocity fluctuations can explain as much as 48% of temporal variability in output ice crystal number and 61% in droplet number in GEOS-5 and up to 89% of temporal variability in output ice crystal number in CAM5.1. In both models, this vertical velocity attribution depends strongly on altitude. Despite its importance for hydrometeor formation, simulated vertical velocity distributions are rarely evaluated against observations due to the sparsity of relevant data. Coordinated effort by the atmospheric community to develop more consistent, observationally based updraft treatments will help to close this knowledge gap.

cloud condensation nuclei↗