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At least 37 records · Page 2
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NASA NTRSDate not supplied

Ice-Over-Water Cloud Identification in an Artificial Neural Network Approach

An artificial neural network (ANN) algorithm, employing several Aqua MODIS channels, the retrieved cloud phase and total cloud visible optical depth, and temperature and humidity vertical profiles is trained to detect multilayer (ML) ice-over-water cloud systems identified by matched 2008 CloudSat and CALIPSO (CC) data. The trained MLANN was applied to 2009 MODIS data resulting in combined ML and single layer detection accuracies of 87% (89%) and 86% (89%) for snow-free (snow-covered) regions during the day and night, respectively. When corrected for the viewing-zenith-angle dependence of each parameter, the ML fraction detected is relatively invariant across the swath. Compared to the CC ML variability, the MLANN is robust seasonally and interannually, and produces similar distribution patterns over the globe, except in the polar regions. Additional research is needed to conclusively evaluate the VZA dependence and further improve the MLANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors.

MODIS↗
NASA NTRS2001

A Model for the Breakup of Comet Linear (C/1999 S4)

We propose a mechanism based on the rubble-pile hypothesis of the cometary nucleus (Weissman 1986) to explain the catastrophic breakup of comet LINEAR (C/1999 S4) observed during July-August 2000. We suggest that a solid nucleus made up of 10-100 m "cometesimals" (Weidenschilling 1997) contains a network of inter-connected voids in the inter-cometesimal regions. The production of super-volatile (i.e., species more volatile than water) gases into these voids occurs due to the thermal wave propagating through the nucleus and associated phase transitions of water ice. The network of voids provides an efficient pathway for rapid propagation of these gases within the nucleus resulting in gas pressure caused stresses over a wide regime of the nucleus. This provides a mechanism for catastrophic breakups of small cometary nuclei such as comet LINEAR (C/1999 S4) as well as for some observed cometary outbursts including those that occur at large heliocentric distances (e.g., West et al. 1991). We emphasize the importance of techniques such as radar reflection tomography and radiowave transmission tomography (e.g., Kofman et al. 1998) aboard cometary missions to determine the three dimensional structure of the nucleus in particular the extent of large scale voids.

Samarasinha, Nalin H.↗
NASA NTRS2019

A Joint Analysis of GPS Displacement and GRACE Geopotential Data for Simultaneous Estimation of Geocenter Motion and Gravitational Field

Gravitational potential data from GRACE are being used to study mass redistribution within and between the atmosphere, hydrosphere, cryosphere, and solid Earth. The GRACE data are made available in a reference frame with its origin at the center of mass of the Earth system (geocenter) while many other geophysical models and data sets refer to a reference frame attached to the Earth's surface. Changes in the offset between these reference frames (geocenter motion) must be accounted for when GRACE data are used to quantify surface mass changes. In this study, we developed a technique for co‐estimation of geocenter motion and gravitational potential field seamlessly from degree 1 to 90 by simultaneously inverting a set of globally‐distributed GPS displacement time series and the temporally‐varying GRACE gravity data. We found that the effect of geocenter motion was evident particularly in the GPS time series of horizontal displacements. Our estimates of geocenter motion are most consistent with the Satellite Laser Ranging (SLR) results within 1 mm in X and Z components and a submillimeter in Y component, when compared to monthly variability averaged over the period of 2003–2016. The overall magnitude of the degree‐1 (l = 1) surface mass load is estimated to be ~3 cm in equivalent water height annually migrating south‐westward from Europe (December–January) to the South Pacific (June–July). Our results also show that dense GPS network data improve water storage recovery in major river basins in the United States and Europe by contributing significantly to the recovery of higher‐degree (l ≥ ~20) geopotential coefficients.

Razeghi, Mahdiyeh↗
NASA NTRS2006

Projecting Future Urbanization with Prescott College's Spatial Growth Model to Promote Environmental Sustainability and Smart Growth, A Case Study in Atlanta, Georgia

Planning is an integral element of good management and necessary to anticipate events not merely respond to them. Projecting the quantity and spatial distribution of urban growth is essential to effectively plan for the delivery of city services and to evaluate potential environmental impacts. The major drivers of growth in large urban areas are increasing population, employment opportunities, and quality of life attractors such as a favorable climate and recreation opportunities. The spatial distribution of urban growth is dictated by the amount and location of developable land, topography, energy and water resources, transportation network, climate change, and the existing land use configuration. The Atlanta region is growing very rapidly both in population and the consumption of forestland or low-density residential development. Air pollution and water availability are significant ongoing environmental issues. The Prescott Spatial Growth Model (SGM) was used to make growth projections for the metropolitan Atlanta region to 2010,2020 and 2030 and results used for environmental assessment in both business as usual and smart growth scenarios. The Prescott SGM is a tool that uses an ESRI ArcView extension and can be applied at the parcel level or more coarse spatial scales and can accommodate a wide range of user inputs to develop any number of growth rules each of which can be weighted depending on growth assumptions. These projections were used in conjunction with meteorological and air quality models to evaluate future environmental impacts. This presentation will focus on the application of the SGM to the 13-County Atlanta Regional Commission planning jurisdiction as a case study. The SGM will be described, including how rule sets are developed and the decision process for allocation of future development to available land use categories. Data inputs required to effectively run the model will be discussed. Spatial growth projections for ten, twenty, and thirty year planning horizons will be presented and results discussed, including regional climate and air quality impacts.

Estes, Maurice G., Jr.↗
NASA NTRSDate not supplied

Neural Network Atmospheric Correction of Remote Sensing Imagery Over Water Using a Synthetic Dataset

Remote sensing atmospheric correction methods have primarily focused on imagery over land. However, accurate correction over water is important for monitoring and research of aquatic environments. More research in this area is ongoing, though one of the biggest challenges is enough quality data to develop and validate correction methods. This is especially true for neural network (NN) -based models which have shown promise in this area given enough quality data. To address this deficiency of data, we are leveraging a synthetic dataset produced by a model called SWIPE that uses radiative transfer modeling to simulate the atmospheric effects on water-leaving (WL) reflectance to estimate top-of-atmosphere (TOA) reflectance. This allows us to produce almost unlimited pairs of WL reflectance and corresponding TOA reflectance for model training across a variety of atmospheric conditions. We use two approaches for our atmospheric correction model. One uses a conditional variational autoencoder (VAE) to estimate a single WL reflectance value from a single TOA reflectance value. The second is based on a UNET architecture and estimates an array of WL reflectance values from an array of TOA reflectance values. The goal of the second method is to capture atmospheric effects that occur spatially between values within the array as compared to the first method.

deep learning↗
NASA NTRS1999

Retrieval Assimilation and Modeling of Atmospheric Water Vapor from Ground- and Space-Based GPS Networks: Investigation of the Global and Regional Hydrological Cycles

Uncertainty over the response of the atmospheric hydrological cycle (particularly the distribution of water vapor and cloudiness) to anthropogenic forcing is a primary source of doubt in current estimates of global climate sensitivity, which raises severe difficulties in evaluating its likely societal impact. Fortunately, a variety of advanced techniques and sensors are beginning to shed new light on the atmospheric hydrological cycle. One of the most promising makes use of the sensitivity of the Global Positioning System (GPS) to the thermodynamic state, and in particular the water vapor content, of the atmosphere through which the radio signals propagate. Our strategy to derive the maximum benefit for hydrological studies from the rapidly increasing GPS data stream will proceed in three stages: (1) systematically analyze and archive quality-controlled retrievals using state-of-the-art techniques; (2) employ both currently available and innovative assimilation procedures to incorporate these determinations into advanced regional and global atmospheric models and assess their effects; and (3) apply the results to investigate selected scientific issues of relevance to regional and global hydrological studies. An archive of GPS-based estimation of total zenith delay (TZD) data and water vapor where applicable has been established with expanded automated quality control. The accuracy of the GPS estimates is being monitored; the investigation of systematic errors is ongoing using comparisons with water vapor radiometers. Meteorological packages have been implemented. The accuracy and utilization of the TZD estimates has been improved by implementing a troposphere gradient model. GPS-based gradients have been validated as real atmospheric moisture gradients, establishing a link between the estimated gradients and the passage of weather fronts. We have developed a generalized ray tracing inversion scheme that can be used to analyze occultation data acquired from space- or land-based receivers. The National Center for Atmospheric Research mesoscale model (version MM5) has been adapted for Southern California, and assimilation studies are underway. Additional information is contained in the original.

Dickey, Jean O.↗
NASA NTRS2005

Capillary-Driven Flow in Liquid Filaments Connecting Orthogonal Channels

Capillary phenomena plays an important role in the management of product water in PEM fuel cells because of the length scales associated with the porous layers and the gas flow channels. The distribution of liquid water within the network of gas flow channels can be dramatically altered by capillary flow. We experimentally demonstrate the rapid movement of significant volumes of liquid via capillarity through thin liquid films which connect orthogonal channels. The microfluidic experiments discussed provide a good benchmark against which the proper modeling of capillarity by computational models may be tested. The effect of surface wettability, as expressed through the contact angle, on capillary flow will also be discussed.

Allen, Jeffrey S.↗
NASA NTRS2010

Improving NOAA's NWLON Through Enhanced Data Inputs from NASA's Ocean Surface Topography

This report assesses the benefit of incorporating NASA's OSTM (Ocean Surface Topography Mission) altimeter data (C- and Ku-band) into NOAA's (National Oceanic and Atmospheric Administration) NWLON (National Water Level Observation Network) DSS (Decision Support System). This data will enhance the NWLON DSS by providing additional inforrnation because not all stations collect all meteorological parameters (sea-surface height, ocean tides, wave height, and wind speed over waves). OSTM will also provide data where NWLON stations are not present. OSTM will provide data on seasurface heights for determining sea-level rise and ocean circulation. Researchers and operational users currently use satellite altimeter data products with the GSFCOO NASA data model to obtain sea-surface height and ocean circulation inforrnation. Accurate and tirnely inforrnation concerning sea-level height, tide, and ocean currents is needed to irnprove coastal tidal predictions, tsunarni and storm surge warnings, and wetland restoration.

Guest, DeNeice C.↗
NASA NTRS2012

High-Thermal-Conductivity Fabrics

Heat management with common textiles such as nylon and spandex is hindered by the poor thermal conductivity from the skin surface to cooling surfaces. This innovation showed marked improvement in thermal conductivity of the individual fibers and tubing, as well as components assembled from them. The problem is centered on improving the heat removal of the liquid-cooled ventilation garments (LCVGs) used by astronauts. The current design uses an extensive network of water-cooling tubes that introduces bulkiness and discomfort, and increases fatigue. Range of motion and ease of movement are affected as well. The current technology is the same as developed during the Apollo program of the 1960s. Tubing material is hand-threaded through a spandex/nylon mesh layer, in a series of loops throughout the torso and limbs such that there is close, form-fitting contact with the user. Usually, there is a nylon liner layer to improve comfort. Circulating water is chilled by an external heat exchanger (sublimator). The purpose of this innovation is to produce new LCVG components with improved thermal conductivity. This was addressed using nanocomposite engineering incorporating high-thermalconductivity nanoscale fillers in the fabric and tubing components. Specifically, carbon nanotubes were added using normal processing methods such as thermoplastic melt mixing (compounding twin screw extruder) and downstream processing (fiber spinning, tubing extrusion). Fibers were produced as yarns and woven into fabric cloths. The application of isotropic nanofillers can be modeled using a modified Nielsen Model for conductive fillers in a matrix based on Einstein s viscosity model. This is a drop-in technology with no additional equipment needed. The loading is limited by the ability to maintain adequate dispersion. Undispersed materials will plug filtering screens in processing equipment. Generally, the viscosity increases were acceptable, and allowed the filled polymers to still be processed.The novel feature is that fabrics do not inherently possess good thermal conductivity. In fact, fabrics are used for thermal insulation, not heat removal. The technology represents the first material that is a wearable fabric, based on company textiles and materials that will significantly conduct heat.

Chibante, L. P. Felipe↗
NASA NTRS2019

Terrestrial Water Storage

Terrestrial water storage can be defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow,ice, water stored in the vegetation, river and lake water. Terrestrial water storage (TWS) changes have been observed by the Gravity Recovery and Climate Experiment (GRACE) mission since 2002. GRACE has provided an unprecedented view of the terrestrial water storage variations at large scales. Extremes in water storage often are associated with droughts and flooding events because they are driven by the surplus or deficit of water. Few hydrologic observing networks yield sufficient data for comprehensive monitoring of changes in the total amount of water stored in a region. GRACE observations have helped to fill this gap. This book chapter is divided into 4 sections.Section 1 provides an overview of the GRACE mission. Section 2 reviews the terrestrial water storage solutions that are available. Section 3 describes regional to global applications of GRACE for monitoring extremes in the terrestrial water storage, including droughts and flooding events. Conclusions and future directions are reported in Section 4.

Terrestrial Water Storage↗
NASA NTRS2000

A New Neural Network Approach Including First-Guess for Retrieval of Atmospheric Water Vapor, Cloud Liquid Water Path, Surface Temperature and Emissivities Over Land From Satellite Microwave Observations

The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of efficient retrieval methods for non-linear cases, however, first-guess estimates, which are used in variational methods to avoid problems of solution non-uniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first-guess. Conceptual bridges are established between the neural network and variational methods. The new neural method retrieves the surface skin temperature, the integrated water vapor content, the cloud liquid water path and the microwave surface emissivities between 19 and 85 GHz over land from SSM/I observations. The retrieval, in parallel, of all these quantities improves the results for consistency reasons. A data base to train the neural network is calculated with a radiative transfer model and a a global collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data and from microwave emissivity atlases previously calculated. The results of the neural network inversion are very encouraging. The r.m.s. error of the surface temperature retrieval over the globe is 1.3 K in clear sky conditions and 1.6 K in cloudy scenes. Water vapor is retrieved with a r.m.s. error of 3.8 kg/sq m in clear conditions and 4.9 kg/sq m in cloudy situations. The r.m.s. error in cloud liquid water path is 0.08 kg/sq m . The surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.

Aires, F.↗
NASA NTRS1987

Generation of liquid water on Mars through the melting of a dusty snowpack

An optical/thermal model for dusty snowpacks at temperate Martian latitudes is used to investigate the possibility of valley network formation by liquid water that was provided by snowmelts, assuming insolation absorption under clear-sky conditions. The mean-annual surface temperatures for snow and the atmospheric exchange terms of the surface energy balance are constrained by global climate model results. Under favorable conditions, liquid water is generated at atmospheric pressures as low as 30-100 mbar, provided that the substrate is composed of regolith; this condition is in keeping with the cratered terrain expected in an ancient Martian surface.

Clow, Gary D.↗
NASA NTRS1974

Retransmission of water resources data using the ERTS-1 data collection system

The Water Survey of Canada operates a network of approximately 2400 gauging stations at which water level data are collected. Nine DCPs were installed in isolated areas of northern and western Canada. It was felt that DCPs in these locations would be exposed to climatic conditions severe enough to provide a check on system reliability. Water level data are transmitted from all sites and, also, some of the DCPs are used to transmit ice break-up, water velocity, precipitation, air temperature, water stage recorder clock operation or DCP battery voltage. Consideration is being given to transmitting other parameters that would be of value in flood or flow forecasting. Results of the project have been excellent. There has been no data loss that can be attributed to failure of a DCP although data were lost because of sensor malfunctions. The quality of data collected compares favourably with that of the hard record obtained at the remote sites. Costs of using the ERTS Data Collection System are reasonable.

Halliday, R. A.↗
NASA NTRS2021

Advances in the Ocean Color Component of the Aerosol Robotic Network (AERONET-OC)

The Ocean Color Component of the Aerosol Robotic Network(AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type.

In situ oceanic observations↗
NASA NTRS1993

Fluvial valleys in the heavily cratered terrains of Mars: Evidence for paleoclimatic change?

Whether the formation of the Martian valley networks provides unequivocal evidence for drastically different climatic conditions remains debatable. Recent theoretical climate modeling precludes the existence of a temperate climate early in Mars' geological history. An alternative hypothesis suggests that Mars had a globally higher heat flow early in its geological history, bringing water tables to within 350 m of the surface. While a globally higher heat flow would initiate ground water circulation at depth, the valley networks probably required water tables to be even closer to the surface. Additionally, it was previously reported that the clustered distribution of the valley networks within terrain types, particularly in the heavily cratered highlands, suggests regional hydrological processes were important. The case for localized hydrothermal systems is summarized and estimates of both erosion volumes and of the implied water volumes for several Martian valley systems are presented.

Gulick, V. C.↗
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
NASA NTRS2009

Microfluidic Extraction of Biomarkers using Water as Solvent

A proposed device, denoted a miniature microfluidic biomarker extractor (mu-EX), would extract trace amounts of chemicals of interest from samples, such as soils and rocks. Traditionally, such extractions are performed on a large scale with hazardous organic solvents; each solvent capable of dissolving only those molecules lying within narrow ranges of specific chemical and physical characteristics that notably include volatility, electric charge, and polarity. In contrast, in the mu-EX, extractions could be performed by use of small amounts (typically between 0.1 and 100 L) of water as a universal solvent. As a rule of thumb, in order to enable solvation and extraction of molecules, it is necessary to use solvents that have polarity sufficiently close to the polarity of the target molecules. The mu-EX would make selection of specific organic solvents unnecessary, because mu-EX would exploit a unique property of liquid water: the possibility of tuning its polarity to match the polarity of organic solvents appropriate for extraction of molecules of interest. The change of the permittivity of water would be achieved by exploiting interactions between the translational states of water molecules and an imposed electromagnetic field in the frequency range of 300 to 600 GHz. On a molecular level, these interactions would result in disruption of the three-dimensional hydrogen-bonding network among liquid-water molecules and subsequent solvation and hydrolysis of target molecules. The mu-EX is expected to be an efficient means of hydrolyzing chemical bonds in complex macromolecules as well and, thus, enabling analysis of the building blocks of these complex chemical systems. The mu-EX device would include a microfluidic channel, part of which would lie within a waveguide coupled to an electronically tuned source of broad-band electromagnetic radiation in the frequency range from 300 to 600 GHz (see figure). The part of the microfluidic channel lying in the waveguide would constitute an interaction volume. The dimensions of the interaction volume would be chosen in accordance with the anticipated amount of solid sample material needed to ensure extraction of sufficient amount of target molecules for detection and analysis. By means that were not specified at the time of reporting the information for this article, the solid sample material would be placed in the interaction volume. Then the electromagnetic field would be imposed within the waveguide and water would be pumped through the interaction volume to effect the extraction.

Amashukeli, Xenia↗