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At least 595 records · Page 33

Influence of 3D Radiative Effects on Satellite Retrievals of Cloud Properties

When cloud properties are retrieved from satellite observations, the calculations apply 1D theory to the 3D world: they only consider vertical structures and ignore horizontal cloud variability. This presentation discusses how big the resulting errors can be in the operational retrievals of cloud optical thickness. A new technique was developed to estimate the magnitude of potential errors by analyzing the spatial patterns of visible and infrared images. The proposed technique was used to set error bars for optical depths retrieved from new MODIS measurements. Initial results indicate that the 1 km resolution retrievals are subject to abundant uncertainties. Averaging over 50 by 50 km areas reduces the errors, but does not remove them completely; even in the relatively simple case of high sun (30 degree zenith angle), about a fifth of the examined areas had biases larger than ten percent. As expected, errors increase substantially for more oblique illumination.

Varnai, Tamas↗

Aerosol Retrieval and Atmospheric Correction Algorithms for EPIC

EPIC is a multi-spectral imager onboard planned Deep Space Climate ObserVatoRy (DSCOVR) designed for observations of the full illuminated disk of the Earth with high temporal and coarse spatial resolution (10 km) from Lagrangian L1 point. During the course of the day, EPIC will view the same Earth surface area in the full range of solar and view zenith angles at equator with fixed scattering angle near the backscattering direction. This talk will describe a new aerosol retrieval/atmospheric correction algorithm developed for EPIC and tested with EPIC Simulator data. This algorithm uses the time series approach and consists of two stages: the first stage is designed to periodically re-initialize the surface spectral bidirectional reflectance (BRF) on stable low AOD days. Such days can be selected based on the same measured reflectance between the morning and afternoon reciprocal view geometries of EPIC. On the second stage, the algorithm will monitor the diurnal cycle of aerosol optical depth and fine mode fraction based on the known spectral surface BRF. Testing of the developed algorithm with simulated EPIC data over continental USA showed a good accuracy of AOD retrievals (10-20%) except over very bright surfaces.

Wang, Yujie↗

Evaluation of Satellite Remote Sensing Albedo Retrievals over the Ablation Area of the Southwestern Greenland Ice Sheet

MODerate resolution Imaging Spectroradiometer (MODIS) albedo products have been validated over spatially uniform, snow-covered areas of the Greenland ice sheet (GrIS) using the so-called single 'point-to-pixel' method. This study expands on this methodology by applying a 'multiple-point-to-pixel' method and examination of spatial autocorrelation (here using semivariogram analysis) by using in situ observations, high-resolution World- View-2 (WV-2) surface reflectances, and MODIS Collection V006 daily blue-sky albedo over a spatially heterogeneous surfaces in the lower ablation zone in southwest Greenland. Our results using 232 ground-based samples within two MODIS pixels, one being more spatial heterogeneous than the other, show little difference in accuracy among narrow and broad band albedos (except for Band 2). Within the more homogenous pixel area, in situ and MODIS albedos were very close (error varied from −4% to +7%) and within the range of ASD standard errors. The semivariogram analysis revealed that the minimum observational footprint needed for a spatially representative sample is 30 m. In contrast, over the more spatially heterogeneous surface pixel, a minimum footprint size was not quantifiable due to spatial autocorrelation, and far exceeds the effective resolution of the MODIS retrievals. Over the high spatial heterogeneity surface pixel, MODIS is lower than ground measurements by 4-7%, partly due to a known in situ undersampling of darker surfaces that often are impassable by foot (e.g., meltwater features and shadowing effects over crevasses). Despite the sampling issue, our analysis errors are very close to the stated general accuracy of the MODIS product of 5%. Thus, our study suggests that the MODIS albedo product performs well in a very heterogeneous, low-albedo, area of the ice sheet ablation zone. Furthermore, we demonstrate that single 'point-to-pixel' methods alone are insufficient in characterizing and validating the variation of surface albedo displayed in the lower ablation area. This is true because the distribution of in situ data deviations from MODIS albedo show a substantial range, with the average values for the 10th and 90th percentiles being −0.30 and 0.43 across all bands. Thus, if only single point is taken for ground validation, and is randomly selected from either distribution tails, the error would appear to be considerable. Given the need for multiple in-situ points, concurrent albedo measurements derived from existing AWSs, (low-flying vehicles (airborne or unmanned) and high-resolution imagery (WV-2)) are needed to resolve high sub-pixel variability in the ablation zone, and thus, further improve our characterization of Greenland's surface albedo.

Ablation zone↗

Near-Real Time Cloud Retrievals from Operational and Research Meteorological Satellites

A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES- 10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms. Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have potential for use in many other applications.

Minnis, Patrick↗

Global analyses of water vapor, cloud and precipitation derived from a diagnostic assimilation of SSM/I geophysical retrievals

An analytical approach is described for diagnostically assimilating moisture data from Special Sensor Microwave Imager (SSM/I) into a global analysis of water vapor, cloud content, and precipitation. In this method, 3D fields of wind and temperature values taken from ECMWF gridded analysis are used to drive moisture conservation equations with parameterized microphysical treatment of vapor, liquid, and ice; the evolving field of water vapor is periodically updated or constrained by SSM/I retrievals of precipitable water. Initial results indicate that this diagnostic model can produce realistic large-scale fields of cloud and precipitation. The resulting water vapor analyses agree well with SSM/I and have an additional advantage of being synoptic.

Robertson, Franklin R.↗

Validation of Land Surface Temperature Products Derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) Using Ground-based and Heritage Satellite Measurements

Thermal infrared satellite observations of the Earth's surface are widely used to retrieve Land Surface Temperature (LST) and monitor LST changes around the world. Since January 2012, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) has provided daily observations of LST with a spatial resolution of 750 m at nadir. Comparison of the standard VIIRS LST product with the equivalent daily standard product from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and with ground-based measurements over vegetated and inland water surfaces showed good agreement. Analysis indicated the accuracy and precision of the VIIRS product over these cover types was 0.2 K and 2.0 K respectively provided the analyses included appropriate compensation for any spatial heterogeneity in LST within the validation site. However, comparisons between in situ LST and the VIIRS and MODIS LST.

Land surface temperature↗

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗

Chapter 10: Development of a Precipitation Retrieval Scheme for Cross-track Passive Microwave Sounding Instruments

The measurement of precipitation (snowfall and rainfall) across the globe is of great importance to both science and society. Despite the importance, measurements from surface-based instruments are limited in extent and are generally absent from sparsely populated or inhospitable regions, including over the world’s oceans. Satellite-based observations have the potential to provide the necessary information to generate estimates of precipitation across the whole of the Earth’s surface. However, adequate temporal and spatial sampling is required to capture vagaries of precipitation and it is necessary to exploit all possible satellite (and, where appropriate surface) information. While much research has been carried out into the retrieval of precipitation from observations gathered by conically-scanning passive microwave imagers in previous decades, relatively little has been done to fully utilise the observations from the cross-track scanning passive microwave sounding instruments. This paper will review a number of cross-track precipitation retrieval schemes, together an evaluation of their performance, and future directions.

Precipitation retrievals↗

Phase-weighted slant stacking for surface wave dispersion measurement

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

58 GEOSCIENCES↗

Deep learning methods for obtaining photometric redshift estimations from images

ABSTRACT Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it is impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with ‘traditional’ machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model that allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filtre (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE) =0.009, which was a significant improvement ($30{{\ \rm per\ cent}}$) over the traditional random forest (RF), and the model performed even better at lower redshifts achieving a MSE = 0.0007 (a $50{{\ \rm per\ cent}}$ improvement over the RF) in the range of z < 0.3. This method could be hugely beneficial to upcoming surveys, such as Euclid and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.

79 ASTRONOMY AND ASTROPHYSICS↗

Inversion of coherent surface scattering images via deep learning network

Coherent surface scattering imaging is a powerful tool for imaging a surface/interface of a thin nanostructure deposited on an opaque substrate. A mathematical conversion of an object image from a scattering pattern is essential for coherent surface scattering imaging to visualize structures of specimens. It has been achieved by using phase retrieval algorithms requiring oversampling in scattering patterns and employing alternating projection approaches. It is a computationally challenging and time-consuming process. In this paper, we demonstrate CSSI-NN, which is a deep learning neural network model to predict images of objects from scattering intensities in coherent surface scattering imaging. This model allowing for the instant outcome from scattering patterns would be tremendously beneficial not only for effective experiments but also for data analysis of phase retrieval.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Imaging and speciation of intracellular metallic implant debris using synchrotron-based X-ray fluorescence micro-spectroscopy: a study of two cases

Debris generated from total hip arthroplasty (THA) components made from metal alloys can cause, in some cases, inflammatory cell (e.g., macrophages) responses that lead to adverse local tissue reactions (ALTR) and implant failure. The lack of information on intracellular chemical alterations of metal debris has hindered the understanding of the pathogenesis of ALTR. The goal of this study was to characterize intracellular debris within macrophages using Synchrotron imaging and spectroscopy. We studied periprosthetic tissues of two retrieved THAs with (1) a metal-on-metal (MoM) articulation and (2) a metal-on-polyethylene (MoP) articulation exhibiting corrosion of the metal femoral head. The MoM-THA exhibited different valence states of chromium- and cobalt-containing debris, suggesting three different moieties: Cr 2 O 3 , CrPO 4 , and an alloy-oxide mixture. The findings further suggest that Cr 2 O 3 formed in the tribological interfaces of the implant, while CrPO 4 is a by-product of the phagocytosis process of cobalt alloy-containing debris. Titanium debris appeared to occur in a mixed crystalline/amorphous oxide state. It remains unclear if this chemical state results from the tribochemical processes at the implant surface or intracellular alterations. The MoP-THA specimen exhibited no intracellular particulate debris associated with macrohpages, indicating that the ALTR may be entirely triggered by metal ionic species in this case. A better understanding of in vivo chemical alteration of implant debris will aid in assessing the risk for ALTR during implant design and material choice. However, various techniques are needed to accurately determine the interaction between metal particles and the inta- and extra-cellular environment.

60 APPLIED LIFE SCIENCES↗

Integrated remote sensing of the earth from low earth orbit in the 1990's

Trends in the areas of science, data systems, instruments and space technologies and their related infrastructures that will be experienced in earth remote sensing activities from LEO in the 1990s are discussed. Interdisciplinary studies will examine the flow of energy through the earth system, encompassing surveys of atmospheric and ocean circulations, the hydrologic cycle, and biogeochemical cycles. Mainframe computer assimilation of large data inputs will be associated with distributed microcomputer processing and analyses. Single satellite antennas will scan several microwave frequencies simultaneously, e.g., taking data on vegetation, water, and ice. Broadening the range of simultaneous observations will require multiinstrumented free-flying platforms, placed in orbit, repaired and/or retrieved by the Shuttle. Future instrumentation will include visible/IR imagers and the multifrequency microwave sounders with capabilities mainly limited by the availability of sufficient data-handling facilities.

Butler, D. M.↗

Model-based vision for space applications

This paper describes a method for tracking moving image features by combining spatial and temporal edge information with model based feature information. The algorithm updates the two-dimensional position of object features by correlating predicted model features with current image data. The results of the correlation process are used to compute an updated model. The algorithm makes use of a high temporal sampling rate with respect to spatial changes of the image features and operates in a real-time multiprocessing environment. Preliminary results demonstrate successful tracking for image feature velocities between 1.1 and 4.5 pixels every image frame. This work has applications for docking, assembly, retrieval of floating objects and a host of other space-related tasks.

Chaconas, Karen↗

A Modified Linear-Mixing Method for Calculating Atmospheric Path Radiances of Aerosol Mixtures

The top-of-atmosphere (TOA) path radiance generated by an aerosol mixture can be synthesized by linearly adding the contributions of the individual aerosol components, weighted by their fractional optical depths. The method, known as linear mixing, is exact in the single-scattering limit. When multiple scattering is significant, the method reproduces the atmospheric path radiance of the mixture with less than 3% errors for weakly absorbing aerosols up to optical thickness of 0.5. However, when strongly absorbing aerosols are included in the mixture, the errors are much larger. This is due to neglecting the effect of multiple interactions between the aerosol components, especially when the values of the single-scattering albedos of these components are so different that the parameter epsilon = (Sigma)f(sub i) absolute value of bar omega(sub i) - bar omega(sub mix)/bar omega(sub i), is larger than approx. 0.1, where bar omega(sub i) and f(sub i) are the single-scattering albedo and the fractional abundance of the i th component, and bar omega(sub i) is the effective single-scattering albedo of the mixture. We describe an empirical, modified linear-mixing method which effectively accounts for the multiple interactions between aerosol components. The modified and standard methods are identical when epsilon = 0.0 and give similar results when epsilon less than or equal to 0.05. For optical depths larger than approx. 0.5, or when epsilon greater than 0.05, only the modified method can reproduce the radiances within 5% error for common aerosol types up to optical thickness of 2.0. Because this method facilitates efficient and accurate atmospheric path radiance calculations for mixtures of a wide variety of aerosol types, it will be used as part of the aerosol retrieval methodology for the Earth Observing System (EOS) multiangle imaging spectroradiometer (MISR), scheduled for launch into polar orbit in 1998.

Abdou, W. A.↗

A Modified Linear-Mixing Method for Calculating Atmospheric Path Radiances of Aerosol Mixtures

The top-of-atmosphere (TOA) path radiance generated by an aerosol mixture can be synthesized by linearly adding the contributions of the individual aerosol components, weighted by their fractional optical depths. The method, known as linear mixing, is exact in the single-scattering limit. When multiple scattering is significant, the method reproduces the atmospheric path radiance of the mixture with less than 3% errors for weakly absorbing aerosols up to optical thickness of 0.5. However, when strongly absorbing aerosols are included in the mixture, the errors are much larger. This is due to neglecting the effect of multiple interactions between the aerosol components, especially when the values of the single-scattering albedos of these components are so different that the parameter e = the sum of f(sub i)[(bar)omega(sub i) - (bar)omega(sub mix)]/(bar)omega(sub i) is larger than approximately 0.1, where (bar)omega(sub i)and f(sub i) are the single-scattering albedo and the fractional abundance of the ith component, and (bar)omega(sub mix) is the effective single-scattering albedo of the Mixture. We describe an empirical, modified linear-mixing method which effectively accounts for the multiple interactions between aerosol components. The modified and standard methods are identical when epsilon = 0.0 and give similar results when epsilon is less than or equal to 0.05. For optical depths larger than approximately 0.5, or when epsilon is greater than 0.05, only the modified method can reproduce the radiances within 5% error for common aerosol types up to optical thickness of 2.0. Because this method facilitates efficient and accurate atmospheric path radiance calculations for mixtures of a wide variety of aerosol types, it will be used as part of the aerosol retrieval methodology for the Earth Observing System (EOS) multiangle imaging spectroradiometer (MISR), scheduled for launch into polar orbit in 1998.

Abdou, W. A.↗

SMART Ground-based Radiation Measurements during PRIDE

We deployed a suite of ground-based remote sensing instruments - SMART (Surface Measurements for Atmospheric Radiative Transfer), at the Roosevelt Road Naval Station in Puerto Rico during the Puerto Rico Dust Experiment (PRIDE). The instruments include several solar and infrared broadband radiometers, a sunphotometer, a shadow-band radiometer, a micro-pulse lidar, a total-sky imager, a microwave radiometer, and two solar spectrometers. These radiometers were set up on a mobile platform and a solar tracker. During 27 June - 23 July, about 25 days of data were acquired under partially cloudy sky conditions. The diurnal air temperature was fluctuating around 28.6 C to within a few degrees. Daytime average of solar irradiance reaching at the surface was ranged from about 400 W/sq m on a rainy day to about 640 W/sq m on a cloud-free day. The infrared irradiance at the surface during the measurement period was averaged about 408 W/sq m. The heights of boundary layer, dusts and clouds were captured by lidar images. Based on sunphotometer and shadow-band radiometer retrievals, the aerosol optical thickness varied from below 0.1 to over 0.6. Combining with radiative transfer modeling and other in-situ and remote sensing measurements, our ground-based measurements provide vital information on understanding the long-range transport of African dust into the Caribbean.

Tsay, Si-Chee↗

Detecting ocean surface wind using the TRMM precipitation radar

Although designed specifically for the measurement of precipitation in the atmosphere, the Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite alsomeasures the normalized radar cross-section at the Earth's surface. As such, this instrument providesan interesting opportunity to explore an alternative radar configuration in the satellite remote sensingnf ocean winds. In particular, it can measure the strength of the ocean winds in relatively higherspatial resolution as compared to the conventional scatterometer systems presently in use. The addition of wind sensing capability to precipitation radar also complements its rain profiling capabilitynicely and allows coincident wind and rain measurements. In this paper, we discuss the new ocean wind algorithm developed specifically for TRMM PR using maximum likelihood estimation. A uniquefeature of this wind algorithm is its capability to incorporate measurement sensitivity and noiseinformation consistently in both the along-track and cross-track directions. This PR wind algorithm hasbeen tested using data from the TRMM Microwave Imager (TMI) and NASA QuikSCAT scatterometer.Excellent agreement on the retrieved wind strengths is achieved among three sensors.

precipitation radar TRMM GPM CAMEX-4 ocean surface↗