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NASA NTRS1992

Cloud-property retrieval using merged HIRS and AVHRR data

A technique is developed that uses a multispectral, multiresolution method to improve the overall retrieval of mid- to high-level cloud properties by combining HIRS sounding channel data with higher spatial resolution AVHRR radiometric data collocated with the HIRS footprint. Cirrus cloud radiative and physical properties are determined using satellite data, surface-based measurements provided by rawinsondes and lidar, and aircraft-based lidar data collected during the First International Satellite Cloud Climatology Program Regional Experiment in Wisconsin during the months of October and November 1986. HIRS cloud-height retrievals are compared to ground-based lidar and aircraft lidar when possible. Retrieved cloud heights are found to have close agreement with lidar for thin cloud, but are higher than lidar for optically thick cloud. The results of the reflectance-emittance relationships derived are compared to theoretical scattering model results for both water-droplet spheres and randomly oriented hexagonal ice crystals. It is found that the assumption of 10-micron water droplets is inadequate to describe the reflectance-emittance relationship for the ice clouds seen here. Use of this assumption would lead to lower cloud heights using the ISCCP approach. The theoretical results show that use of hexagonal ice crystal phase functions could lead to much improved results for cloud retrieval algorithms using a bispectral approach.

Baum, Bryan A.↗

NASA NTRS1991

Active member vibration control experiment in a KC-135 reduced gravity environment

An active member vibration control experiment in a KC-135 reduced gravity environment was carried out by the Air Force Flight Dynamics Laboratory and the Jet Propulsion Laboratory. Two active members, consisting of piezoelectric actuators, displacement sensors, and load cells, were incorporated into a 12-meter, 104 kg box-type test structure. The active member control design involved the use of bridge (compound) feedback concept, in which the collocated force and velocity signals are feedback locally. An impact-type test was designed to accommodate the extremely short duration of the reduced gravity testing window in each parabolic flight. The moving block analysis technique was used to estimate the modal frequencies and dampings from the free-decay responses. A broadband damping performance was demonstrated up to the ninth mode of 40 Hz. The best damping performance achieved in the flight test was about 5 percent in the fourth mode of the test structure.

Lawrence, C. R.↗

NASA NTRS2018

MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds

Global wind observations are fundamental for studying weather and climate dynamics and for operational forecasting. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques for their height assignments, which are subject to large uncertainties in the presence of weak or reversed vertical temperature gradients near the planetary boundary layer (PBL)and tropopause folds. Stereo imaging can overcome the height assignment problem using geometric parallax for feature height determination. In this study we develop a stereo 3D-Wind algorithm to simultaneously retrieve AMV and height from geostationary (GEO) and low Earth orbit (LEO) satellite imagery and apply it to collocated Geostationary Operational Environmental Satellite (GOES)and Multi-angle Imaging SpectroRadiometer (MISR) imagery. The new algorithm improves AMV and height relative to products from GOES or MISR alone, with an estimated accuracy of <0.5 m/s in AMV and <200 m in height with 2.2 km sampling. The algorithm can be generalized to other LEO-GEO or LEO-LEO combinations for greater spatiotemporal coverage. The technique demonstrated with MISR and GOES has important implications for future high-quality AMV observations, for which a low-cost constellation of CubeSats can play a vital role.

MISR↗

DOE OSTI2013

Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions

We propose a multi-element stochastic collocation method that can be applied in high-dimensional parameter space for functions with discontinuities lying along manifolds of general geometries. The key feature of the method is that the parameter space is decomposed into multiple elements defined by the discontinuities and thus only the minimal number of elements are utilized. On each of the resulting elements the function is smooth and can be approximated using high-order methods with fast convergence properties. The decomposition strategy is in direct contrast to the traditional multi-element approaches which define the sub-domains by repeated splitting of the axes in the parameter space. Such methods are more prone to the curse-of-dimensionality because of the fast growth of the number of elements caused by the axis based splitting. The present method is a two-step approach. Firstly a discontinuity detector is used to partition parameter space into disjoint elements in each of which the function is smooth. The detector uses an efficient combination of the high-order polynomial annihilation technique along with adaptive sparse grids, and this allows resolution of general discontinuities with a smaller number of points when the discontinuity manifold is low-dimensional. After partitioning, an adaptive technique based on the least orthogonal interpolant is used to construct a generalized Polynomial Chaos surrogate on each element. The adaptive technique reuses all information from the partitioning and is variance-suppressing. We present numerous numerical examples that illustrate the accuracy, efficiency, and generality of the method. When compared against standard locally-adaptive sparse grid methods, the present method uses many fewer number of collocation samples and is more accurate.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A New Decade in Seismoacoustics (2010–2022)

Several sources of interest often generate both low‐frequency acoustic and seismic signals due to energy propagation through the atmosphere and the solid Earth. Seismic and acoustic observations are associated with a wide range of sources, including earthquakes, volcanoes, bolides, chemical and nuclear explosions, ocean noise, and others. The fusion of seismic and acoustic observations contributes to a better understanding of the source, both in terms of constraining source location and physics, as well as the seismic to acoustic coupling of energy. Here in this review, we summarize progress in seismoacoustic data processing, including recent developments in open‐source data availability, low‐cost seismic and acoustic sensors, and large‐scale deployments of collocated sensors from 2010 to 2022. Similarly, we outline the recent advancements in modeling efforts for both source characteristics and propagation dynamics. Finally, we highlight the advantages of fusing multiphenomenological signals, focusing on current and future techniques to improve source detection, localization, and characterization efforts. This review aims to serve as a reference for seismologists, acousticians, and others within the growing field of seismoacoustics and multiphenomenology research.

58 GEOSCIENCES↗

NASA NTRS2014

Improvement of Aerosol Optical Depth Retrieval over Hong Kong from a Geostationary Meteorological Satellite Using Critical Reflectance with Background Optical Depth Correction

Despite continuous efforts to retrieve aerosol optical depth (AOD) using a conventional 5-channelmeteorological imager in geostationary orbit, the accuracy in urban areas has been poorer than other areas primarily due to complex urban surface properties and mixed aerosol types from different emission sources. The two largest error sources in aerosol retrieval have been aerosol type selection and surface reflectance. In selecting the aerosol type from a single visible channel, the season-dependent aerosol optical properties were adopted from longterm measurements of Aerosol Robotic Network (AERONET) sun-photometers. With the aerosol optical properties obtained fromthe AERONET inversion data, look-up tableswere calculated by using a radiative transfer code: the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Surface reflectance was estimated using the clear sky composite method, awidely used technique for geostationary retrievals. Over East Asia, the AOD retrieved from the Meteorological Imager showed good agreement, although the values were affected by cloud contamination errors. However, the conventional retrieval of the AOD over Hong Kong was largely underestimated due to the lack of information on the aerosol type and surface properties. To detect spatial and temporal variation of aerosol type over the area, the critical reflectance method, a technique to retrieve single scattering albedo (SSA), was applied. Additionally, the background aerosol effect was corrected to improve the accuracy of the surface reflectance over Hong Kong. The AOD retrieved froma modified algorithmwas compared to the collocated data measured by AERONET in Hong Kong. The comparison showed that the new aerosol type selection using the critical reflectance and the corrected surface reflectance significantly improved the accuracy of AODs in Hong Kong areas,with a correlation coefficient increase from0.65 to 0.76 and a regression line change from τMI [basic algorithm] = 0.41τAERONET + 0.16 to τMI [new algorithm] = 0.70τAERONET + 0.01.

aerosols↗

NASA NTRS2016

The New Sun-Sky-Lunar Cimel CE318-T Multiband Photometer - A Comprehensive Performance Evaluation

This paper presents the new photometer CE318-T, able to perform daytime and night-time photometric measurements using the sun and the moon as light source. Therefore,this new device permits a complete cycle of diurnal aerosol and water vapour measurements valuable to enhance atmospheric monitoring to be extracted. In this study wehave found significantly higher precision of triplets when comparing the CE318-T master instrument and the Cimel AErosol RObotic NET work (AERONET) master (CE318-AERONET) triplets as a result of the new CE318-T tracking system. Regarding the instrument calibration, two new methodologies to transfer the calibration from a reference instrument using only daytime measurements (Sun Ratio and Sun-Moon gain factor techniques) are presented and discussed. These methods allow the reduction of the previous complexities inherent to nocturnal calibration. A quantitative estimation of CE318-T AOD uncertainty by means of error propagation theory during daytime revealed AOD uncertainties (u(sup D)(sub AOD)) for Langley-calibrated instruments similar to the expected values for other reference instruments (0.002-0.009). We have also found u(sup D)(sub AOD) values similar to the values reported in sun photometry for field instruments (approximately 0.015). In the case of the night-time period, the CE318-T-estimated standard combined uncertainty (u(sup N)(sub AOD)) is dependent not only on the calibration technique but also on illumination conditions and the instrumental noise. These values range from 0.011-0.018 for Lunar Langley-calibrated instruments to 0.012-0.021 for instruments calibrated using the Sun Ratio technique. In the case of moon-calibrated instruments using the Sun-Moon gain factor method and sun calibrated using the Langley technique, we found u(sup N)(sub AOD) ranging from 0.016 to 0.017 (up to 0.019 in 440 nm channel), not dependent on any lunar irradiance model. A subsequent performance evaluation including CE318-T and collocated measurements from independent reference instruments has served to assess the CE318-T performance as well as to confirm its estimated uncertainty. Daytime AOD evaluation, performed at Izana station from March to June 2014, encompassed measurements from a reference CE318-T, a CE318-AERONET master instrument, a Precision Filter Radiometer (PFR) and a Precision Spectroradiometer (PSR) prototype, reporting low AOD discrepancies between the four instruments (up to 0.006). The nocturnal AOD evaluation was performed using CE318-T- and starphotometer-collocated measurements and also by means of a day/night coherence transition test using the CE318-T master instrument and the CE318 daytime data from the CE318-AERONET master instrument. Results showed low discrepancies with the star photometer at 870 and 500 nm channels(less tna or equal to 0.013) and differences with AERONET daytime data (1 h after and before sunset and sunrise) in agreement with the estimated u(sup N)(sub AOD) values at all illumination conditions in the case of channels within the visible spectral range, and only for high moon's illumination conditions in the case of near infrared channels. Perceptible water vapour (PWV) validation showed a good agreement between CE318-T and Global Navigation Satellite System (GNSS) PWV values for all illumination conditions, within the expected precision for sun photometry. Finally, two case studies have been included to highlight the ability of the new CE318-T to capture the diurnal cycle of aerosols and water vapour as well as short-term atmospheric variations, critical for climate studies.

Barreto, Africa↗

NASA NTRS2006

Combined Active and Passive Remote Sensing of Hurricane Ocean Winds

The synergism of active and passive microwave techniques for hurricane ocean wind remote sensing is explored. We performed the analysis of Windsat data for Atlantic hurricanes in 2003-2005. The polarimetric third Stokes parameter observations from the Windsat 10, 18 and 37 GHz channels were collocated with the ocean surface winds from the Holland wind model, the QuikSCAT wind vectors and the Global Data Assimilation System (GDAS) operated by the National Center for Environmental Prediction (NCEP). The collocated data were binned as a function of wind speed and wind direction, and were expanded by sinusoidal series of the relative azimuth angles between wind and observation directions. The coefficients of the sinusoidal series, corrected for atmospheric attenuation, have been used to develop an empirical geophysical model function (GMF). The Windsat GMF for extreme high wind compares very well with the aircraft radiometer and radar measurements.

sea surface winds↗

NASA NTRS2018

New Neural Network Cloud Mask Algorithm Based on Radiative Transfer Simulations

Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Comparedto threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.

cloud mask algorithms↗

NASA NTRS1984

A spectral collocation method for the Navier-Stokes equations

A Fourier-Chebyshev spectral method for the incompressible Navier-Stokes equations is described. It is applicable to a variety of problems including some with fluid properties which vary strongly both in the normal direction and in time. In this fully spectral algorithm, a preconditioned iterative technique is used for solving the implicit equations arising from semi-implicit treatment of pressure, mean advection and vertical diffusion terms. The algorithm is tested by applying it to hydrodynamic stability problems in channel flow and in external boundary layers with both constant and variable viscosity.

Malik, M. R.↗

NASA NTRS1985

A spectral collocation method for the Navier-Stokes equations

Malik, M. R.↗

NASA NTRS1988

A spectral collocation solution to the compressible stability eigenvalue problem

A newly developed spectral compressible linear stability code (SPECLS) (staggered pressure mesh) is presented for analysis of shear flow stability, and applied to high speed boundary layers and free shear flows. The formulation utilizes the first application of a staggered mesh for a compressible flow analysis by a spectral technique. An order of magnitude less number of points is needed for equivalent accuracy of growth rates compared to those calculated by a finite difference formulation. Supersonic disturbances which are found to have oscillatory structures were resolved by a spectral multi-domain discretization, which requires a factor of three fewer points than the single domain spectral stability code. It is indicated, as expected, that stability of mixing layers is enhanced by viscosity and increasing Mach number. The mean flow involves a jet being injected into a quiescent gas. Higher temperatures of the injected gas is also found to enhance stability characteristics of the free shear layer.

Macaraeg, Michele G.↗

NASA NTRS2014

How Well Do Satellite AOD Observations Represent the Spatial and Temporal Variability of PM2.5 Concentration for the United States?

Due to their extensive spatial coverage, satellite Aerosol Optical Depth (AOD) observations have been widely used to estimate and predict surface PM2.5 concentrations. While most previous studies have focused on establishing relationships between collocated, hourly or daily AOD and PM2.5 measurements, in this study, we instead focus on the comparison of the large-scale spatial and temporal variability between satellite AOD and PM2.5 using monthly mean measurements. A newly developed spectral analysis technique e Combined Maximum Covariance Analysis (CMCA) is applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Ozone Monitoring Instrument (OMI) AOD datasets and Environmental Protection Agency (EPA) PM2.5 data, in order to extract and compare the dominant modes of variability. Results indicate that AOD and PM2.5 agree well in terms of interannual variability. An overall decrease is found in both AOD and PM2.5 across the United States, with the strongest signal over the eastern US. With respect to seasonality, good agreement is found only for Eastern US, while for Central and Western US, AOD and PM2.5 seasonal cycles are largely different or even reversed. These results are verified using Aerosol Robotic Network (AERONET) AOD observations and differences between satellite and AERONET are also examined. MODIS and MISR appear to have the best agreement with AERONET. In order to explain the disagreement between AOD and PM2.5 seasonality, we further use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) extinction profile data to investigate the effect of two possible contributing factors, namely aerosol vertical distribution and cloud-free sampling. We find that seasonal changes in aerosol vertical distribution, due to the seasonally varying mixing height, is the primary cause for the AOD and PM2.5 seasonal discrepancy, in particular, the low AOD but high PM2.5 observed during the winter season for Central and Western US. In addition, cloud-free sampling by passive sensors also induces some bias in AOD seasonality, especially for the Western US, where the largest seasonal change in cloud fraction is found. The seasonal agreement between low level (below 500 m AGL), all sky CALIOP AOD and PM2.5 is significantly better than column AOD from MODIS, MISR, SeaWiFS and OMI. In particular, the correlation between low level, all sky AOD and PM2.5 seasonal cycles increases to above 0.7 for Central and Western US, as opposed to near zero or negative correlation for column, clear sky AOD. This result highlights the importance of accounting for the seasonally varying aerosol profiles and cloud-free sampling bias when using column AOD measurements to infer surface PM2.5 concentrations.

MODIS (radiometry)↗

DOE OSTI2018

Distributed caching for processing raw arrays

As applications continue to generate multi-dimensional data at exponentially increasing rates, fast analytics to extract meaningful results is becoming extremely important. The database community has developed array databases that alleviate this problem through a series of techniques. In-situ mechanisms provide direct access to raw data in the original format---without loading and partitioning. Parallel processing scales to the largest datasets. In-memory caching reduces latency when the same data are accessed across a workload of queries. However, we are not aware of any work on distributed caching of multi-dimensional raw arrays. In this paper, we introduce a distributed framework for cost-based caching of multi-dimensional arrays in native format. Given a set of files that contain portions of an array and an online query workload, the framework computes an effective caching plan in two stages. First, the plan identifies the cells to be cached locally from each of the input files by continuously refining an evolving R-tree index. In the second stage, an optimal assignment of cells to nodes that collocates dependent cells in order to minimize the overall data transfer is determined. We design cache eviction and placement heuristic algorithms that consider the historical query workload. A thorough experimental evaluation over two real datasets in three file formats confirms the superiority -- by as much as two orders of magnitude -- of the proposed framework over existing techniques in terms of cache overhead and workload execution time.

Zhao, Weijie↗

DOE OSTI2023

Use of Sobol’ Variance-Based Global Sensitivity Analysis and Multidimensional Legendre Polynomial Fitting for Reduced Order Modeling

Sandia National Laboratories (SNL) has developed a novel reduced order modeling approach. Prioritization of inputs is accomplished using Sobo' indices obtained through a more efficient variance-based global sensitivity analysis. To determine the Sobo' functions, simulated input values are aligned to collocation points to permit the use of Gauss-Lobatto integration, thereby reducing the number of simulation trials needed by more than an order of magnitude compared to standard Monte Carlo approaches. Furthermore, by leveraging the orthogonality of Legendre polynomials in conjunction with those same simulations at the collocation nodes, an efficient fitting method is developed to represent the Sobo' functions from which a reduced order model (ROM) is constructed. The developed method is both more efficient computationally, and the resulting ROM is more accurate. The efficacy of this technique is demonstrated on a nonlinear polynomial test function as well as the nonlinear Ishigami and Sobo' g functions.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2012

Use of In Situ and Airborne Multiangle Data to Assess MODIS- and Landsat-based Estimates of Surface Albedo

The quantification of uncertainty of global surface albedo data and products is a critical part of producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A current challenge in validating satellite retrievals of surface albedo is the ability to overcome the spatial scaling errors that can contribute on the order of 20% disagreement between satellite and field-measured values. Here, we present the results from an uncertain ty analysis of MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat albedo retrievals, based on collocated comparisons with tower and airborne multi-angular measurements collected at the Atmospheric Radiation Measurement Program s (ARM) Cloud and Radiation Testbed (CART) site during the 2007 Cloud and Land Surface Interaction Campaign (CLAS33 IC 07). Using standard error propagation techniques, airborne measurements obtained by NASA s Cloud Absorption Radiometer (CAR) were used to quantify the uncertainties associated with MODIS and Landsat albedos across a broad range of mixed vegetation and structural types. Initial focus was on evaluating inter-sensor consistency through assessments of temporal stability, as well as examining the overall performance of satellite-derived albedos obtained at all diurnal solar zenith angles. In general, the accuracy of the MODIS and Landsat albedos remained under a 10% margin of error in the SW(0.3 - 5.0 m) domain. However, results reveal a high degree of variability in the RMSE (root mean square error) and bias of albedos in both the visible (0.3 - 0.7 m) and near-infrared (0.3 - 5.0 m) broadband channels; where, in some cases, retrieval uncertainties were found to be in excess of 20%. For the period of CLASIC 07, the primary factors that contributed to uncertainties in the satellite-derived albedo values include: (1) the assumption of temporal stability in the retrieval of 500 m MODIS BRDF values over extended periods of cloud-contaminated observations; and (2) the assumption of spatial 45 and structural uniformity at the Landsat (30 m) pixel scale.

Roman, Miguel O.↗

NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗

DOE OSTI2023

Using optimal estimation to retrieve winds from velocity-azimuth display (VAD) scans by a Doppler lidar

Abstract. Low-powered commercially available coherent Doppler lidar (CDL) wind profilers provide continuous measurement of vertical profiles of wind in the lower troposphere, usually close to or up to the top of the planetary boundary layer. The vertical extent of these wind profiles is limited by the availability of scatterers and thus varies substantially throughout the day and from one day to the next. This makes it challenging to develop continuous products that rely on CDL-observed wind profiles. In order to overcome this problem, we have developed a new method for wind profile retrievals from CDL that combines the traditional velocity-azimuth display (VAD) technique with optimal estimation (OE) to provide continuous wind profiles up to 3 km. The new method exploits the level-to-level covariance present in the wind profile to fill in the gaps where the signal-to-noise ratio of the CDL return is too low to provide reliable results using the traditional VAD method. Another advantage of the new method is that it provides the full error covariance matrix of the solution and profiles of information content, which more easily facilitates the assimilation of the observed wind profiles into numerical weather prediction models. This method was tested using yearlong CDL measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility in 2019. Comparison with the ARM operational CDL wind profile product and collocated radiosonde wind measurements shows excellent agreement (R2>0.99) with no degradation in results where the traditional VAD provided a valid solution. In the region where traditional VAD does not provide results, the OE wind speed and wind vector have uncertainties of 3.44 and 4.33 m s−1, respectively. As a result, the new method provides additional information over the standard technique and increases the effective range of existing CDL systems without the need for additional hardware.

54 ENVIRONMENTAL SCIENCES↗