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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Cloud Macroscopic Organization: Order Emerging from Randomness

Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds, and that it follows a power-law distribution with exponent gamma close to 2. gamma is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate this self-organizing behavior of clouds emerges naturally from a complex network model with simple, physical organizing principles: random clumping and merging. We also demonstrate symmetry between clear and cloudy skies in terms of macroscopic organization because of similar fundamental underlying organizing principles. The order in the apparently complex cloud-clear field thus has its root in random local interactions. Studying cloud organization with complex network models is an attractive new approach that has wide applications in climate science. We also propose a concept of cloud statistic mechanics approach. This approach is fully complementary to deterministic models, and the two approaches provide a powerful framework to meet the challenge of representing clouds in our climate models when working in tandem.

Yuan, Tianle↗
NASA NTRS2013

EO-1 Hyperion Reflectance Time Series at Calibration and Validation Sites: Stability and Sensitivity to Seasonal Dynamics

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5-5 percent throughout most of the spectral range spanning the 12-plus year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers.

Calibration↗
NASA NTRS2016

Precipitation and Carbon-Water Coupling Jointly Control the Interannual Variability of Global Land Gross Primary Production

Carbon uptake by terrestrial ecosystems is increasing along with the rising of atmospheric CO2 concentration. Embedded in this trend, recent studies suggested that the interannual variability (IAV) of global carbon fluxes may be dominated by semi-arid ecosystems, but the underlying mechanisms of this high variability in these specific regions are not well known. Here we derive an ensemble of gross primary production (GPP) estimates using the average of three data-driven models and eleven process-based models. These models are weighted by their spatial representativeness of the satellite-based solar-induced chlorophyll fluorescence (SIF). We then use this weighted GPP ensemble to investigate the GPP variability for different aridity regimes. We show that semi-arid regions contribute to 57% of the detrended IAV of global GPP. Moreover, in regions with higher GPP variability, GPP fluctuations are mostly controlled by precipitation and strongly coupled with evapotranspiration (ET). This higher GPP IAV in semi-arid regions is co-limited by supply (precipitation)-induced ET variability and GPP-ET coupling strength. Our results demonstrate the importance of semi-arid regions to the global terrestrial carbon cycle and posit that there will be larger GPP and ET variations in the future with changes in precipitation patterns and dryland expansion.

chlorophyll fluorescence↗
NASA NTRS2016

Unique Spectroscopy and Imaging of Mars with the James Webb Space Telescope

In this paper, we summarize the main capabilities of the James Webb Space Telescope (JWST) for performing observations of Mars. The distinctive vantage point of JWST at the Sun-Earth Lagrange point (L2) will allow sampling the full observable disk, permitting the study of short-term phenomena, diurnal processes (across the east-west axis), and latitudinal processes between the hemispheres (including seasonal effects) with excellent spatial resolutions (0.''07 at 2 micron). Spectroscopic observations will be achievable in the 0.7-5 micron spectral region with NIRSpec at a maximum resolving power of 2700 and with 8000 in the 1-1.25 micron range. Imaging will be attainable with the Near-Infrared Camera at 4.3 micrometers and with two narrow filters near 2 micron, while the nightside will be accessible with several filters in 0.5 to 2 micron. Such a powerful suite of instruments will be a major asset for the exploration and characterization of Mars. Some science cases include the mapping of the water D/H ratio, investigations of the Martian mesosphere via the characterization of the non-local thermodynamic equilibrium CO2 emission at 4.3 micron, studies of chemical transport via observations of the O2 nightglow at 1.27 micron, high-cadence mapping of the variability dust and water-ice clouds, and sensitive searches for trace species and hydrated features on the Martian surface. In-flight characterization of the instruments may allow for additional science opportunities.

Villanueva, Geronimo L.↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS2017

The Climatology of Carbon Monoxide and Water Vapor on Mars as Observed by CRISM and Modeled by the GEM-Mars General Circulation Model

Radiative transfer modeling of near-infrared spectra taken by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument onboard Mars Reconnaissance Orbiter (MRO) enables the column-integrated abundance of carbon monoxide (CO) and water vapor (H 2 O) to be retrieved. These results provide a detailed global description of the seasonal and spatial distribution of CO in the Mars atmosphere and new information about the interannual variability of H2O. The CRISM retrievals show the seasonally and globally averaged carbon monoxide mixing ratio to be near 800 parts per million, but with strong seasonal variations, especially at high latitudes. At low latitudes, the carbon monoxide mixing ratio varies in response to the mean seasonal cycle of surface pressure and shows little variation with topography. At high latitudes, carbon monoxide is depleted in the summer hemisphere by a factor of two or more, while in the winter hemisphere there is relatively higher mixing ratio in regions with low-lying topography. Water vapor shows only modest interannual variations, with the largest observed difference being unusually dry conditions in the wake of the Mars Year 28 global dust storm. Modeling results from the GEM (Global Environmental Multiscale) - Mars general circulation model generally reproduce the observed seasonal and spatial trends and provide insight into the underlying physical processes.

Smith, Michael D.↗
NASA NTRS2018

NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

NASA↗
NASA NTRS2021

The Climatology of Carbon Monoxide on Mars as Observed by NOMAD Nadir-geometry Observations

More than a full Martian year of observations have now been made by the Nadir Occultation for MArs Discovery (NOMAD) instrument suite on-board the ExoMars Trace Gas Orbiter. Radiative transfer modeling of NOMAD observations taken in the nadir geometry enable the seasonal and global-scale variations of carbon monoxide gas in the Martian atmosphere to be characterized. These retrievals show the column-averaged volume mixing ratio of carbon monoxide to be about 800 ppmv, with significant variations at high latitudes caused by the condensation and sublimation of the background CO2 gas. Near summer solstice in each hemisphere, the CO volume mixing ratio falls to 400 ppmv in the south and 600 ppmv in the north. At low latitudes, carbon monoxide volume mixing ratio inversely follows the annual cycle of surface pressure. Comparison of our retrieved CO volume mixing ratio against that computed by the GEM-Mars general circulation model reveals a good match in their respective seasonal and spatial trends, and can provide insight into the physical processes that control the distribution of CO gas in the current Martian atmosphere.

Mars↗
NASA NTRSDate not supplied

Optical Design of Multi-field Integral Field Unit Spectrograph for the ORCAS Keck Instrument Development II (ORKID II) Instrument

We have designed an Integral Field Unit Spectrometer for the ORCAS Keck Instrument Development II (ORKID II) Instrument. Building on the success of the ORKID instrument (which achieved 15.1 masec FWHM visible spectrum imaging), ORKID II will add Integral Field Spectroscopy to analyze Active Galactic Nuclei (AGN), Supernovae redshift and brightness, and other science observations. Several design options have been explored based on image slicers manufactured by the Canon Corporation’s machining process. Field layouts can include up to three disparate spatial sampling, with a lower limit of 6.7 masec. Spectral resolutions are considered from R 100 to R 10,000. The modular design of the concept allows for configuring the instrument for various observing campaigns.

Instrumentation and Photography↗
NASA NTRSDate not supplied

Optical Design of Multi-field Integral Field Unit Spectrograph for the ORCAS Keck Instrument Development II (ORKID II) Instrument

We have designed an Integral Field Unit for the ORCAS Keck Instrument Development II (ORKID II) Instrument. Building on the success of the ORKID camera which achieved 15.2 masec PSF FWHM visible light imaging, ORKID II will add Integral Field Spectroscopy to analyze Active Galactic Nuclei (AGN), supernovae redshift and brightness, and other observations. Several design options have been explored based on image slicers manufactured by the Canon Corporation’s machining process. Field layouts can include up to three disparate spatial sampling, with a lower limit of 6.7 masec. Spectral resolutions are considered from R 100 to R 10,000.

Space Instrumentation↗
NASA NTRS1993

Moist wind relationships

Equations describing the temporal and spatial behavior of the kinematic moisture and heat flux are introduced. In these nonlinear equations, the contribution by diabatic processes to the large-scale flux is composed of two parts. One part is associated with a Rayleigh damping term, while the other arises from temporal and spatial changes in the pressure gradient term. The influence of diabatic processes on the large-scale moisture fluxes depends greatly on the degree of balance between forcing and damping terms in the governing equations. The existence of a near balance requires a reduction in the large-scale horizontal geostrophic wind speed. From a scale analysis of the moisture flux equations it is argued that reductions in the large-scale horizontal wind speed, observed within major cumulus cloud systems, help conserve large-scale moisture fluxes. The deviation of the wind from geostrophic conditions is easily estimated. This wind modification induces secondary vertical circulations that contribute to the convergence, creating or supporting long-lived mesoscale flows. In the tropics the wind modification has an antitriptic relationship. These diagnostic findings suggest possible modifications to the wind field in the application of cumulus parameterization, and may be important in diabatic initialization of numerical weather prediction models.

Raymond, William H.↗
NASA NTRS1992

Desirable features of an infrared imaging system for aerodynamic research

Advantage of non-intrusiveness, capability for field measurement, and increased availability of IR imaging systems have resulted in their wider use for aerodynamic research. However, certain difficulties persist while using currently available systems for such applications. A critical evaluation of the IR imaging systems is presented on the basis of the state-of-the-art of IR imaging technology and experiences in wind tunnel and flight testing at NASA's Langley Research Center. The requirements for using IR thermography as a measurement tool in aerodynamic research are examined in terms of range, sensitivity, and accuracy of temperature measurement, temporal and spatial resolution, and features of target. Deficiencies of present IR imaging systems are identified, and user precautions to avoid such problems by proper selection and operation of these units are suggested. Different aspects of imager performance such as imager optics, video capabilities, and environmental tolerance are discussed. Electronic data recording and image processing hardware and software requirements are evaluated. Slit response tests and spatial resolution are discussed with the objective of obtaining reliable, accurate, and meaningful information from IR thermography measurements for aerodynamic studies.

Wright, Robert E., Jr.↗
NASA NTRS1999

Mass Transport in Global Geophysical Fluids

Mass transports occurring in the atmosphere-hydrosphere-solid Earth-core system (the "global geophysical fluids") are important geophysical phenomena. They occur on all temporal and spatial scales. Examples include air mass and ocean circulations, tides, hydrological water redistribution, mantle processes such as post-glacial rebound, earthquakes and tectonic motions, and core geodynamo activities. With only a few exceptions on the Earth surface, the temporal history and spatial pattern of such mass transport are often not amenable to direct observations. Space geodesy techniques, however, have the capability of monitoring certain direct consequences of the mass transport, including Earth's rotation variations, gravitational field variations, and the geocenter motion. These techniques include the very-long-baseline interferometry, satellite laser ranging and Doppler tracking, and the Global Positioning System, all entail global observational networks. While considerable advances have been made in observing and understanding of the dynamics of Earth's rotation, only the lowest-degree gravitational variations have been observed and limited knowledge of geocenter motion obtained. New space missions, projects and initiatives promise to further improve the measurements and hence our knowledge about the global mass transports. The latter contributes to our understanding and modeling capability of the geophysical processes that produce and regulate the mass transports, as well as the solid Earth's response to such changes in constraining the modeling of Earth's mechanical properties.

Chao, B. F.↗
NASA NTRS1992

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the West African Sahel

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land-cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.

Marsh, S. E.↗
NASA NTRS2003

Adaptation of Mesoscale Weather Models to Local Forecasting

Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.

Manobianco, John T.↗
NASA NTRS2006

The Spatial Standard Observer

The spatial standard observer is a computational model that provides a measure of the visibility of a target in a uniform background image or of the visual discriminability of two images. Standard observers have long been used in science and industry to quantify the discriminability of colors. Color standard observers address the spectral characteristics of visual stimuli, while the spatial standard observer (SSO), as its name indicates, addresses spatial characteristics. The SSO is based on a model of human vision. The SSO was developed in a process that included evaluation of a number of earlier mathematical models that address optical, physiological, and psychophysical aspects of spatial characteristics of human visual perception. Elements of the prior models are incorporated into the SSO, which is formulated as a compromise between accuracy and simplicity. The SSO operates on a digitized monochrome still image or on a pair of such images. The SSO consists of three submodels that operate sequentially on the input image(s): 1. A contrast model, which converts an input monochrome image to a luminance contrast image, wherein luminance values are expressed as excursions from, and normalized to, a mean; 2. A contrast-sensitivity-filter model that includes an oblique-effect filter (which accounts for the decline in contrast sensitivity at oblique viewing angles); and 3. A spatial summation model, in which responses are spatially pooled by raising each pixel to the power beta, adding the results, and raising the sum to the 1/b power. In this model, b=2.9 was found to be a suitable value. The net effect of the SSO is to compute a numerical measure of the perceptual strength of the single image, or of the visible difference (denoted the perceptual distance) between two images. The unit of a measure used in the SSO is the just noticeable difference (JND), which is a standard measure of perceptual discriminability. A target that is just visible has a measure of 1 JND.

Watson, Andrew B.↗
NASA NTRS2003

Fractals and Spatial Methods for Mining Remote Sensing Imagery

The rapid increase in digital remote sensing and GIS data raises a critical problem -- how can such an enormous amount of data be handled and analyzed so that useful information can be derived quickly? Efficient handling and analysis of large spatial data sets is central to environmental research, particularly in global change studies that employ time series. Advances in large-scale environmental monitoring and modeling require not only high-quality data, but also reliable tools to analyze the various types of data. A major difficulty facing geographers and environmental scientists in environmental assessment and monitoring is that spatial analytical tools are not easily accessible. Although many spatial techniques have been described recently in the literature, they are typically presented in an analytical form and are difficult to transform to a numerical algorithm. Moreover, these spatial techniques are not necessarily designed for remote sensing and GIS applications, and research must be conducted to examine their applicability and effectiveness in different types of environmental applications. This poses a chicken-and-egg problem: on one hand we need more research to examine the usability of the newer techniques and tools, yet on the other hand, this type of research is difficult to conduct if the tools to be explored are not accessible. Another problem that is fundamental to environmental research are issues related to spatial scale. The scale issue is especially acute in the context of global change studies because of the need to integrate remote-sensing and other spatial data that are collected at different scales and resolutions. Extrapolation of results across broad spatial scales remains the most difficult problem in global environmental research. There is a need for basic characterization of the effects of scale on image data, and the techniques used to measure these effects must be developed and implemented to allow for a multiple scale assessment of the data before any useful process-oriented modeling involving scale-dependent data can be conducted. Through the support of research grants from NASA, we have developed a software module called ICAMS (Image Characterization And Modeling System) to address the need to develop innovative spatial techniques and make them available to the broader scientific communities. ICAMS provides new spatial techniques, such as fractal analysis, geostatistical functions, and multiscale analysis that are not easily available in commercial GIS/image processing software. By bundling newer spatial methods in a user-friendly software module, researchers can begin to test and experiment with the new spatial analysis methods and they can gauge scale effects using a variety of remote sensing imagery. In the following, we describe briefly the development of ICAMS and present application examples.

Lam, Nina↗