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Digital image-processing system assembles maplike mosaic images of large areas of Earth from overlapping images of smaller areas generated by spaceborne synthetic-aperture radar (SAR). Automatically registers and blends smaller images with each other. Eliminates need for time-consuming, tedious manual processing. Modifiable to accept images from spaceborne optical sensors.
We examine the requirements for on-board aircraft sensor systems that would allow pilots to 'see through' poor weather, especially fog, and land and rollout aircraft under conditions that currently cause flight cancellations and airport closures. Three visual aspects of landing and rollout are distinguished: guidance, hazard detection and hazard recognition. The visual features which support the tasks are discussed. Three broad categories of sensor technology are examined: passive millimeter wave (PMMW), imaging radar, and passive infrared (IR). PMMW and imaging radar exhibit good weather penetration, but poor spatial and temporal resolution. Imaging radar exhibits good weather penetration, but typically relies on a flat-earth assumption which can lead to interpretive errors. PMMW systems have a narrow field of view. IR has poorer weather penetration but good spatial resolution. We recommend using both millimeter-wave and infrared sensor systems, blending the images using multiresolution digital-image pyramid-processing technology, and fusing the resulting real-time images with stored database imagery of the same scene.
We describe progress in the development of adjustable grazing incidence X-ray optics for 0.5 arcsec resolution cosmic X-ray imaging. To date, no optics technology is available to blend high resolution imaging like the Chandra X-ray Observatory, with square meter collecting area. Our approach to achieve these goals simultaneously is to directly deposit thin film piezoelectric actuators on the back surface of thin, lightweight Wolter-I or Wolter- Schwarschild mirror segments. The actuators are used to correct mirror figure errors due to fabrication, mounting and alignment, using calibration and a one-time figure adjustment on the ground. If necessary, it will also be possible to correct for residual gravity release and thermal effects on-orbit. In this paper we discuss our most recent results measuring influence functions of the piezoelectric actuators using a Shack-Hartmann wavefront sensor. We describe accelerated and real-time lifetime testing of the piezoelectric material, and we also discuss changes to, and recent results of, our simulations of mirror correction.
This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA [1,2] software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.
This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.
The Charon/Pluto mass ratio is a fundamental but poorly known parameter of the two-body system. Previous values for the mass ratio have ranged from 0.0837 plus or minus 0.0147 (Null et al., 1993, Astron. J. 105, 2319-2335) to 0.1566 plus or minus 0.0035 (Young et al., 1994, Icarus 108,186-199). We report here a new determination of the Charon/Pluto mass ratio, using five sets of groundbased images taken at four sites in support of Pluto occultation predictions. Unlike the Null et al. and Young et A determinations, where the centers of light for Pluto and Charon could be determined separately, this technique examines the motion of the center of light of the blended Pluto-Charon image. We compute the offsets of the observed center-of-light position of Pluto-Charon from the ephemeris position of the system and fit these offsets to a model of the Pluto-Charon system. The least-squares fits to the five data sets agree within their errors, and the weighted mean mass ratio is 0.117 plus or minus 0.006. The effects of errors in the Charon light fraction, semimajor axis, and ephemeris have been examined and are equal to only a small fraction of the formal error from the fit. This result is intermediate between those of Null et al., and Young et al. and matches a new value of 0.124 plus or minus 0.008 by Null and Owen (1996, Astron. J. 111, 1368-1381). The mass ratio and resulting individual masses and densities of Pluto and Charon are consistent with a collisional origin for the Pluto-Charon system.
Phase-diverse speckle (PDS) is a novel imaging technique intended to overcome the degrading effects of atmospheric turbulence on fine-resolution imaging. As its name suggests, PDS is a blend of phase-diversity and speckle-imaging concepts. PDS reconstructions on solar data were validated by simulation, by demonstrating internal consistency of PDS estimates, and by comparing PDS reconstructions with those produced from well accepted speckle-imaging processing. Several sources of error in data collected with the Swedish Vacuum Solar Telescope (SVST) were simulated: CCD noise, quantization error, image misalignment, and defocus error, as well as atmospheric turbulence model error. The simulations demonstrate that fine-resolution information can be reliably recovered out to at least 70% of the diffraction limit without significant introduction of image artifacts. Additional confidence in the SVST restoration is obtained by comparing its spatial power spectrum with previously-published power spectra derived from both space-based images and earth-based images corrected with traditional speckle-imaging techniques; the shape of the spectrum is found to match well the previous measurements. In addition, the imagery is found to be consistent with, but slightly sharper than, imagery reconstructed with accepted speckle-imaging techniques.
The Advanced Cockpit Evaluation System (ACES) includes communication, computing, and display subsystems, mounted in a van, that synthesize out-the-window views to approximate the views of the outside world as it would be seen from the cockpit of a crewed spacecraft, aircraft, or remote control of a ground vehicle or UAV (unmanned aerial vehicle). The system includes five flat-panel display units arranged approximately in a semicircle around an operator, like cockpit windows. The scene displayed on each panel represents the view through the corresponding cockpit window. Each display unit is driven by a personal computer equipped with a video-capture card that accepts live input from any of a variety of sensors (typically, visible and/or infrared video cameras). Software running in the computers blends the live video images with synthetic images that could be generated, for example, from heads-up-display outputs, waypoints, corridors, or from satellite photographs of the same geographic region. Data from a Global Positioning System receiver and an inertial navigation system aboard the remote vehicle are used by the ACES software to keep the synthetic and live views in registration. If the live image were to fail, the synthetic scenes could still be displayed to maintain situational awareness.
We map the average line-of-sight velocities of the X-ray-emitting mass in the supernova remnant Cassiopeia A using measurements of the centroid of the Si-Healpha blend with the Advanced CCD Imaging Spectrometer on the Chandra X-Ray Observatory. This 4 inch scale map confirms the broad trends noted in previous studies, namely, that the line-of-sight velocity scale is roughly 2000-3000 kilometers per second with relatively more blueshifted material in the southeastern region of the remnant than in the northwest; new details are that the northwestern region consists of two arcs, with the southernmost one representing the most redshifted X-ray-emitting material in Cas A. These results are consistent with contemporaneous results from XMM-Newton. The X-ray patterns resemble the complex velocity patterns measured at optical wavelengths for much denser ejecta, and they support the growing body of evidence that the explosion and subsequent evolution of Cas A were highly asymmetrical.
A computer program automatically builds large, full-resolution mosaics of multispectral images of Earth landmasses from images acquired by Landsat 7, complete with matching of colors and blending between adjacent scenes. While the code has been used extensively for Landsat, it could also be used for other data sources. A single mosaic of as many as 8,000 scenes, represented by more than 5 terabytes of data and the largest set produced in this work, demonstrated what the code could do to provide global coverage. The program first statistically analyzes input images to determine areas of coverage and data-value distributions. It then transforms the input images from their original universal transverse Mercator coordinates to other geographical coordinates, with scaling. It applies a first-order polynomial brightness correction to each band in each scene. It uses a data-mask image for selecting data and blending of input scenes. Under control by a user, the program can be made to operate on small parts of the output image space, with check-point and restart capabilities. The program runs on SGI IRIX computers. It is capable of parallel processing using shared-memory code, large memories, and tens of central processing units. It can retrieve input data and store output data at locations remote from the processors on which it is executed.
The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its National Weather Service (NWS) partners that can be used to initialize local model runs within the Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). These real-time datasets consist of surface-based information updated at least once per day, and produced in a composite or gridded product that is easily incorporated into the WRF EMS. The primary goal for making these NASA datasets available to the WRF EMS community is to provide timely and high-quality information at a spatial resolution comparable to that used in the local model configurations (i.e., convection-allowing scales). The current suite of SPoRT products supported in the WRF EMS include a Sea Surface Temperature (SST) composite, a Great Lakes sea-ice extent, a Greenness Vegetation Fraction (GVF) composite, and Land Information System (LIS) gridded output. The SPoRT SST composite is a blend of primarily the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared and Advanced Microwave Scanning Radiometer for Earth Observing System data for non-precipitation coverage over the oceans at 2-km resolution. The composite includes a special lake surface temperature analysis over the Great Lakes using contributions from the Remote Sensing Systems temperature data. The Great Lakes Environmental Research Laboratory Ice Percentage product is used to create a sea-ice mask in the SPoRT SST composite. The sea-ice mask is produced daily (in-season) at 1.8-km resolution and identifies ice percentage from 0 100% in 10% increments, with values above 90% flagged as ice.
Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.
The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).
A joint U.S. Air Force/NASA blended, global snow product that utilizes Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and QuikSCAT (Quick Scatterometer) (QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by employing a newly-developed Air Force Weather Agency (AFWA)/National Aeronautics and Space Administration (NASA) Snow Algorithm (ANSA). This initial blended-snow product uses minimal modeling to expeditiously yield improved snow products, which include snow cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt, and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the U.S., from Colorado during the Cold Lands Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or Diurnal Amplitude Variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.
We report the analysis of additional multiband photometry and spectroscopy and new adaptive optics (AO) imaging of the nearby planetary microlensing event TCP J05074264+2447555 (Kojima-1), which was discovered toward the Galactic anticenter in 2017 (Nucita et al.). We confirm the planetary nature of the light-curve anomaly around the peak while finding no additional planetary feature in this event. We also confirm the presence of apparent blending flux and the absence of significant parallax signal reported in the literature. The AO image reveals no contaminating sources, making it most likely that the blending flux comes from the lens star. The measured multiband lens flux, combined with a constraint from the microlensing model, allows us to narrow down the previously unresolved mass and distance of the lens system. We find that the primary lens is a dwarf on the K/M boundary (0.581 ± 0.033M(ʘ)) located at 505±47 pc, and the companion (Kojima-1Lb) is a Neptune-mass planet (20.0 ± 2.0M(⊕)) with a semimajor axis of 1.08(+0.62, -0.18) au. This orbit is a few times smaller than those of typical microlensing planets and is comparable to the snow-line location at young ages. We calculate that the a priori detection probability of Kojima-1Lb is only ∼35%, which may imply that Neptunes are common around the snow line, as recently suggested by the transit and radial velocity techniques. The host star is the brightest among the microlensing planetary systems (K(s) = 13.7), offering a great opportunity to spectroscopically characterize this system, even with current facilities.
We develop a technique. for the analysis of He I 1083 nanometer spectra which addresses several difficulties through determination of a continuum background by comparison with a well calibrated standard and through removal of nearby solar and telluric blends by differential comparison to an average spectrum. The method is compared with earlier analysis of imaging spectroscopy obtained at the National Solar Observatory/Kitt Peak Vacuum Telescope (NSO/KPVT) with the NASA/NSO Spectromagnetograph (SPM). We examine distributions of Doppler velocity and line width as a function of central intensity for an active region, filament, quiet Sun, and coronal hole. For our example, we find that line widths and central intensity are oppositely correlated in a coronal hole and quiet Sun. Line widths are comparable to the quiet sun in the active region, are systematically lower in the filament, and extend to higher values in the coronal hole. Outward velocities of approximately equal to 2 to 4 kilometers per second are typically observed in the coronal hole. The sensitivity of these results to analysis technique is discussed.
Sea surface temperature (SST) studies are often focused on improving accuracy, or understanding and quantifying uncertainties in the measurement, as SST is a leading indicator of climate change and represents the longest time series of any ocean variable observed from space. Over the past several decades SST has been studied with the use of satellite data. This allows a larger area to be studied with much more frequent measurements being taken than direct measurements collected aboard ship or buoys. The Group for High Resolution Sea Surface Temperature (GHRSST) is an international project that distributes satellite derived sea surface temperatures (SST) data from multiple platforms and sensors. The goal of the project is to distribute these SSTs for operational uses such as ocean model assimilation and decision support applications, as well as support fundamental SST research and climate studies. Examples of near real time applications include hurricane and fisheries studies and numerical weather forecasting. The JPL group has produced a new 1 km daily global Level 4 SST product, the Multiscale Ultrahigh Resolution (MUR), that blends SST data from 3 distinct NASA radiometers: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR), and the Advanced Microwave Scanning Radiometer ? Earth Observing System(AMSRE). This new product requires further validation and accuracy assessment, especially in coastal regions.We examined the accuracy of the new MUR SST product by comparing the high resolution version and a lower resolution version that has been smoothed to 19 km (but still gridded to 1 km). Both versions were compared to the same data set of in situ buoy temperature measurements with a focus on study regions of the oceans surrounding North and Central America as well as two smaller regions around the Gulf Stream and California coast. Ocean fronts exhibit high temperature gradients (Roden, 1976), and thus satellite data of SST can be used in the detection of these fronts. In this case, accuracy is less of a concern because the primary focus is on the spatial derivative of SST. We calculated the gradients for both versions of the MUR data set and did statistical comparisons focusing on the same regions.
The high-magnification microlensing event MACHO-97-BLG-28 was previously determined to be a binary system composed of either two M dwarfs or an M dwarf and a brown dwarf. We present a revised light-curve model using additional data from the Mt. Stromlo 74″telescope, model estimates of stellar limb darkening, and fitting of the blend separately for each telescope and passband. We find a lensing system with a larger mass ratio, q=0.28±0.01, and smaller projected separation, s=0.61±0.01, than those presented in the original study. We revise the estimate of the lens–source relative proper motion to μ(rel)=2.8±0.5 mas/yr, which indicates that 16.07 yr after the event maximum the lens and source should have separated by 46±8 mas. We revise the radius of the source star using more recent reddening maps and angular diameter–color relations to R(*)=(10.3±1.9) R(ʘ). K- and J-band adaptive optics images of the field taken at this epoch using the NIRC2 imager on the Keck telescope show that the source and lens are still blended, consistent with our light-curve model. With no statistically significant excess flux detection we constrain the mass, M(L)=0.24 (+0.28, -0.12) M(ʘ), and distance, D(L)=7.0±1.0 kpc, of the lensing system. This supports the interpretation of this event as a stellar binary in the Galactic bulge. This lens mass gives a companion mass of M= 0.07(+0.08,-0.04) M(ʘ), close to the boundary between being a star and a brown dwarf.