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NASA NTRS1994

The 1994 International Geoscience and Remote Sensing Symposium (IGARSS 1994)

The papers presented at the symposium focus on remote sensing, particularly on global monitoring of the earth with emphasis on the solution of environmental problems. Topics discussed include remote sensing of clouds and earth troposphere, sea ice remote sensing, optical remote sensing, land monitoring and thermal sensing, atmospheric sounding and monitoring, atmospheric correction, and satellite imaging data. Other subject areas are ecosystems and vegetation monitoring; ocean winds and surface scattering; ocean waves, currents and bathymetry; satellite oceanography; SAR for remote sensing; neural nets application to remote sensing; geographical information systems; and electromagnetic wave propagation. Also discussed environmental monitoring using ERS-1; Topex/Poseidon results; spaceborne instruments; image processing and classification algorithms; and future space missions.

Source record↗

NASA NTRS2004

Perspectives on African Ozone from Sondes, Dobson and Aircraft Measurements

We have been studying variability in ozone over Africa using data from ozonesondes (vertical profiles from surface to stratosphere), aircraft (the MOZAIC dataset with cruise altitude and landing/takeoff profiles) and the ground (Dobson spectrophotometer total ozone column measurement). The following may give context for ozone investigations during AMMA: 1. Total ozone measurements since 1989 show considerable variability in mean value among the African stations in Algeria, Kenya, Egypt, South Africa, as well as in seasonal cycles and year-to-year. Trends are not evident. 2. The impacts of convection, stratospheric injection, biomass burning and lightning appear in ozone sounding profile data. Time-series analysis and case studies point to periodic influences of long-range interactions with the Atlantic ("ozone paradox," wave-one") and Indian Oceans. 3. Tropospheric ozone variations, observed in tropospheric profiles and integrated column amount, follow general seasonal patterns but short- term variability is so strong that simple averages are inadequate for describing "climatology" and statistical classification approaches may be required.

Thompson, A. M.↗

DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗

NASA NTRS1993

Beamforming in an acoustic shadow

The sound field deep within an acoustic shadow region is less well understood than that outside the shadow region. Signal levels are substantially lower within the shadow, but beamforming difficulties arise for other reasons such as loss of spatial coherence. Based on analysis of JAPE-91 data, and other data, three types of characteristic signals within acoustic shadow regions are identified. These signal types may correspond to different, intermittent signal propagation conditions. Detection and classification algorithms might take advantage of the signal characteristics. Frequency coherence is also discussed. The extent of coherence across frequencies is shown to be limited, causing difficulties for source classification based on harmonic amplitude relationships. Discussions emphasize short-term characteristics on the order of one second. A video presentation on frequency coherence shows the similarity, in the presence of atmospheric turbulence, between the received signal from a stable set of harmonics generated by a loudspeaker and that received from a helicopter hovering behind a hill.

Havelock, David↗

NASA NTRS1985

Applications of Some Artificial Intelligence Methods to Satellite Soundings

Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

Munteanu, M. J.↗

NASA NTRS2010

Peak Wind Tool for General Forecasting

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

Barrett, Joe H., III↗

NASA NTRS1990

Segmentation, modeling and classification of the compact objects in a pile

The problem of interpreting dense range images obtained from the scene of a heap of man-made objects is discussed. A range image interpretation system consisting of segmentation, modeling, verification, and classification procedures is described. First, the range image is segmented into regions and reasoning is done about the physical support of these regions. Second, for each region several possible three-dimensional interpretations are made based on various scenarios of the objects physical support. Finally each interpretation is tested against the data for its consistency. The superquadric model is selected as the three-dimensional shape descriptor, plus tapering deformations along the major axis. Experimental results obtained from some complex range images of mail pieces are reported to demonstrate the soundness and the robustness of our approach.

Gupta, Alok↗

NASA NTRSDate not supplied

How Well Satellite Remote Sensing Can Inform Surface Level Ozone Production Sensitivity to Precursor Trace Gas Emissions?

Surface-level ozone (O3) pollution mitigation strategies rely on a clear understanding of the atmospheric chemical processes involving O3, NOx (nitric oxide (NO) + nitrogen dioxide (NO2)) and volatile organic compounds (VOCs). A robust spatiotemporal classification of O3 photochemical regimes with relative chemical sensitivity of local O3 formation to emission reductions of NOx (NOx-limited regime) versus VOCs (radical-limited regime) is required to design anthropogenic emission reduction policies in order to improve surface air quality. Numerous studies found that the ratio of satellite-retrieved Vertical Column Densities (VCDs) of formaldehyde (HCHO) to NO2 can diagnose O3 production sensitivity. However, uncertainty still remains about the accuracy of such estimates using satellite-based retrievals. This study conducts an investigation to identify how well satellite data can inform surface-level O3 sensitivity by validating satellite-based estimates against aircraft-based retrievals taken during the Long Island Sound Tropospheric Ozone Study (LISTOS-2018) and Ozone Water-Land Environmental Transition Study (OWLETS-2) field campaigns. For this study, we use HCHO and NO2 retrievals from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite, and high spatiotemporal resolution TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite. The Community Multiscale Air Quality (CMAQ) modelling system is used to provide trace gas vertical profiles to recalculate air mass factor for deriving consistent satellite-based VCDs. To demonstrate the ability of satellites to diagnose O3 production regimes, we validate satellite-derived O3 sensitivity regimes, along with the OMI and TROPOMI products applied, using HCHO and NO2 retrievals from the aircraft-based instruments: Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) spectrometer and Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instruments deployed during the LISTOS-2018 and OWLETS-2 campaigns. The results of this work will advance our fundamental knowledge of the spatiotemporal evolution of the complex O3-NOx-VOC chemistry, and will advance our understanding about the quality of remote-sensing, suborbital, and satellite data for accurately observing surface-level O3 production sensitivity.

Satellite↗

DOE OSTI2021

An Introduction to Word Embeddings and Language Models

Language models have advanced at a phenomenal pace over the past decade. This document provides a short introduction to terminology, word embeddings (aka low-dimensional representations), and popular large-scale language models (LMs). Word embeddings are used to represent words as numerical vectors and are context-independent, meaning a word can only have a single representation (e.g., club can only be club sandwich, not golf club ). Language models can determine the probability of a given sequence of words occurring in a sentence and can provide context to distinguish between words and phrases that sound similar. LMs are context-dependent (e.g., club can be club sandwich or golf club ) and largely fall in two main classes – autoregressive and autoencoding models. Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the previous ones. Those models can be fine-tuned and achieve great results on many tasks, the most natural application is text generation. A typical example of such models is GPT, but others include GPT-2, GPT-3, CTLR, TRANSFORMER-XL, REFORMER, XLNET. Autoencoding models are pretrained by corrupting the input tokens in some way and trying to reconstruct the original sentence. They can be fine-tuned and achieve great results on many tasks such as text generation, but their most natural application is sentence classification or token classification. A typical example of such models is BERT, but others include ROBERTA, ALBERT, XML, XML-ROBERTA, FLAUBERT AND LONGFORMER.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

Detection and Retrieval of Multi-Layered Cloud Properties Using Satellite Data

Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO2-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the Aqua satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earth's Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes.

Minnis, Patrick↗

NASA NTRS1981

Far-ultraviolet spectra and flux distributions of some Orion stars

Far-ultraviolet (950-1800 A) spectra with about 2 A resolution were obtained of a number of stars in Orion during a sounding-rocket flight 1975 December 6. These spectra have been reduced to absolute flux distributions with the aid of preflight calibrations. The derived fluxes are in good agreement with model-atmosphere predictions and previous observations down to about 1200 A. In the 1200-1080 A range, the present results are in good agreement with model predictions but fall above the rocket measurements of Brune, Mount and Feldman. Below 1080 A, our measurements fall below the model predictions, reaching a deviation of a factor of 2 near 1010 A and a factor of 4 near 950 A. The present results are compared with those of Brune et al. via Copernicus U2 observations in this spectral range, and possible sources of discrepancies between the various observations and model-atmosphere predictions are discussed. Other aspects of the spectra, particularly with regard to spectral classification, are briefly discussed.

Carruthers, G. R.↗

DOE OSTI2019

Identifying paleowater in California drinking water wells

Identification of paleowater in aquifers tapped for the public water supply is important both for sound water management, because paleowater is likely to be withdrawn unsustainably in high demand regions in the absence of large scale artificial recharge operations, and for contamination susceptibility assessment, because paleowater is typically isolated from contamination sources. In this study, paleowater, which herein includes pre-Holocene groundwater and also water that recharged well before the onset of significant human alteration of the hydrologic system in California, is identified using three key isotopic indicators of groundwater residence time: tritium ( 3 H), radiocarbon ( 14 C), and radiogenic helium-4 ( 4 He rad ). In this work, we compared results of these three tracers from a unique data set of more than 2000 wells that are predominantly long-screened drinking water production wells. Although considerable uncertainty is associated with calculated apparent ages for individual samples, non-parametric statistical tests indicate that the composite set of isotopic indicators support classification of samples into categories that allow identification of wells most likely to produce paleowater. Approximately 7% of the wells included in the study show strong evidence for producing paleowater, with screens extending greater than 146 m below ground surface, in which 3 H activity is less than 1 pCi/L, 14C activity is less than 40.9 pmC, and 4Herad concentration exceeds 7.4 × 10 –8 cm 3 STP/g water . An additional 22% of wells produce mixed-age water with a component of paleowater, with screens extending greater than 95 m below ground surface, in which 3 H is less than 5 pCi/L, and 14C activity is less than 95.91 pmC. Wells in desert basins of southeastern California and wells in the southwestern quadrant of the Central Valley are most likely to produce paleowater that is pre-Holocene in age. Very few wells in the northwestern portion of the state, the foothills and Sierra Nevada regions, and coastal basins with intensive artificial recharge activities are categorized as producing paleowater. Climate is the primary control on paleowater occurrence, with arid portions of the state that were wetter during the Pleistocene having the largest number of wells categorized as producing paleowater. Secondarily, paleowater is found at the end of very long flow paths in confined aquifers, e.g., in the center of the Northern Sacramento Valley. In contrast, paleowater may be masked in areas where unconfined or semi-confined conditions allow substantial mixing between modern recharge and paleowater. Modern, artificially recharged water has replaced very old groundwater on a large scale in urban coastal basins.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Challenges of SLOWPOKE-2 HEU Irradiated Fuel Transportation from Canada to USA - 19123(2)

The SLOWPOKE-2 (Safe Low-Power Kritical Experiment) was designed by AECL (Atomic Energy of Canada Limited) in the late 1960's. The core consists of HEU (highly enriched uranium) fuel with an enrichment of 93%. The fuel cage typically has approximately 300 fuel pins assembled in a triangular pitch between the two plates. The fuel pins are made from uranium-aluminum alloy cladded with aluminum. The SLOWPOKE-2 reactor is rated at a nominal 20 kW. with varying neutron flux, maximum being 1 E12 n/cm{sup 2}/s. The mass of the fuel cage is 5.06 kg. The life of the core can be extended by adding layers of beryllium reflector. The transportation flask for the irradiated fuel is a Type B(U)F. The shipment classification is UN3328 Class 7. To date, 4 SLOWPOKE-2 reactors have been decommissioned. The fuel from all these reactors was transported to the U.S. The CNSC (Canadian Nuclear Safety Commission) joint Canada-United States guide for approval of Type B(U) and fissile material transportation packages (RD-364) satisfies the requirements stipulated in International Atomic energy Agency (IAEA) TS-R-1 1996 edition (revised) and is acceptable to the DOT (Department of Transportation) and NRC (Nuclear Regulatory Commission) for complying with U.S. code of federal regulations 10 CFR Part 71. The export license is issued by the CNSC. The transportation licence is issued by CNSC after approval from TC (Transport Canada) and also by DOT after approval from NRC. The final home of the HEU SLOWPOKE-2 irradiated fuel is in Savannah River Site of DOE (Department of Energy). The transportation licence requires that the Type B(U)F flask is structurally sound in case of a crash. The US DOT provides a CAC (Competent Authority Certificate) which describes the irradiated fuel specifications that can be transported in the designated flask. The U.S. DOT also requires a leak test certification in accordance with ANSI N14.5-2014 to verify a leakage rate not to exceed 1 E-7 std-cm{sup 3}/s. As most SLOWPOKE-2 facilities are not located on the existing approved routes for nuclear fuel transportation, a route survey needs to be performed and submitted to the NRC. The route survey report is assessed by the NRC and issues a route approval for the transportation of the fissile material. This technical paper will discuss the detailed processes required to obtain approvals in order to transport the HEU SLOWPOKE-2 irradiated fuel from Canada to U.S. based upon recent experiences of decommissioning SLOWPOKE-2 reactors in Canada. It will provide details on the requirements of various regulators in Canada and the U.S. that govern and regulate this shipment including transportation, safety and security. It will also present lessons learned from previous experiences that can be utilized for best practice on other similar projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRSDate not supplied

Using Earth Observations to Map Bull Kelp in the Puget Sound, Washington, to Support Conservation and Restoration

Bull kelp (Nereocystis luetkeana) is a critical component of nearshore ecosystems in the Puget Sound region of the Salish Sea. The Port of Seattle and Washington State Department of Natural Resources (DNR) have identified possible declines in bull kelp extent and changes in its distribution throughout the Central Puget Sound near Seattle, Washington. Bull kelp losses threaten critical ecological services and marine habitat, as well as important cultural resources. However, these changes are not well tracked or understood due to the expensive and time-intensive nature of traditional kelp canopy monitoring methods. The Port of Seattle and Washington DNR partnered with the NASA DEVELOP team to explore the feasibility of using Earth observations between 2016 and 2021 obtained from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) as a potential tool to monitor and map bull kelp. Our team identified a variety of challenges that need to be addressed before this approach can be utilized as an effective means for identifying or mapping nearshore urban kelp beds. We found that neither Sentinel-2 nor Landsat 8 significantly differentiates between kelp and no-known kelp using the Normalized Difference Vegetation Index (NDVI) or Normalized Difference Red-Edge Blue (NDREB). While the tidal and current filtering methods discussed here may be beneficial for identifying promising single image dates for kelp classification, the filters we used reduced the number of images each year to the point that modeling or mapping yearly kelp extent or creating time series of kelp did not appear to be feasible.

Mike Hitchner↗

NASA NTRS2022

Puget Sound Water Resources: Using Earth Observations to Map Bull Kelp in the Puget Sound, Washington, to Support Conservation and Restoration

Mike Hitchner↗

NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 19

The topics include: 1) The abundances of Iron-60 in Pyroxene Chondrules from Unequilibrated Ordinary Chondrites; 2) LL-Ordinary Chondrite Impact on the Moon: Results from the 3.9 Ga Impact Melt at the Landing Site of Appolo 17; 3) Evaluation of Chemical Methods for Projectile Identification in Terrestrial and Lunar Impactites; 4) Impact Cratering Experiments in Microgravity Environment; 5) New Achondrites with High-Calcium Pyroxene and Its implication for Igneous Differentiation of Asteroids; 6) Climate History of the Polar Regions of Mars Deduced form Geologic Mapping Results; 7) The crater Production Function for Mars: A-2 Cumulative Power-Law Slope for Pristine Craters Greater than 5 km in Diameter Based on Crater Distribution for Northern Plains Materials; 8) High Resolution Al-26 Chronology: Resolved Time Interval Between Rim and Interior of a Highly Fractionated Compact Type a CAI from Efremovka; 9) Assessing Aqueous Alteration on Mars Using Global Distributions of K and Th; 10) FeNi Metal Grains in LaPaz Mare Basalt Meteorites and Appolo 12 Basalts; 11) Unique Properties of Lunar Soil for In Situ Resource Utilization on the Moon; 12) U-Pb Systematics of Phosphates in Nakhlites; 13) Measurements of Sound Speed in Granular Materials Simulated Regolith; 14) The Effects of Oxygen, Sulphur and Silicon on the Dihedral Angles Between Fe-rich Liquid Metal and Olivine, Ringwoodite and Silicate Perovskite: Implications for Planetary Core Formation; 15) Seismic Shaking Removal of Craters 0.2-0.5 km in Diameter on Asteroid 433 Eros; 16) Focused Ion Beam Microscoopy of ALH84001 Carbonate Disks; 17) Simulating Micro-Gravity in the Laboratory; 18) Mars Atmospheric Sample Return Instrument Development; 19) Combined Remote LIBS and Raman Spectroscopy Measurements; 20) Unusual Radar Backscatter Properties Along the Northern Rim of Imbrium Basin; 21) The Mars Express/NASAS Project at JPL; 22) The Geology of the Viking 2 Lander Site Revisited; 23) An Impact Genesis for Loki Patera? 24) Mars Polar Cap Edges Tracked over 3 Full Mars Years; 25) Elemental Abundance in Presolar SiC: Comparing Grains Separated by Acid Residue and Gently Separation Procedures; 26) First Results from the Descent Imager/Spectral Radiometer (DISR) Experiment on the Huygens Entry Probe of Titan; 27) Minor Element Behavior of Pallasite Olivine: Understanding Pallasite Thermal History and Chronology; 28) Canonical Anorthite in a Grosnaja Forsterite-bearing CAI; 29) Experimental Evidence for Condensation of 'Astrophysical' Carbonate; 30) Distribution and Classification of Multiple Coronae on Venus; 31) Recognition of Rayed Craters on Mars in THEMIS Thermal Infrared Imagery: Implications for Martian Meteorite Source Regions; 32) Geochemical Modeling of Evaporites on Mars: Insight from Meridiani Planum; 33) Hadean Crustal Processes Revealed from Oxygen Isotopes and U-Th-Pb Depth Profiling of Pre-4.0 Ga Detrital Zircons from Western Australia; 34) On Modeling the Seepage of Water into the Martian Subsurface; 35) Martial Gullies and Groundwater: A Series of Unfortunate Exceptions; 36) Olivine and Carbonate Globules in ALH84001: A Terrestrial Analog, and Implications for Water on Mars; 37) A Reevaluation of Mass Movements Within the Valles Marineris Region of Mars Using MOLA and MOC Data; 38) Evidence of Hydrated 109P/Swift-Tuttle Meteoroids from Meteor Spectroscopy; 39) Cr-54 Anomalies in the Solar System: Their Extent and Origin; 40) Reevaluation of the Mn-53-Cr-53 Systematic in the Basaltic Achondrites; 41) Effective Liquid Metal-Silicate Mixing Upon Shock by Power-Law Droplet Size Scaling in Richtmyer-Meshkov Like Perturbations; 42) Post-Impact Deformation of Impact Craters: Towards a Better Understanding Through the Study of Mjolnir Crater; 43) Cutting Silica Aerogel for Particle Extraction; 44) Liquid Hydrocarbons on Titan's Surface? How Cassini ISS Observations Fit into the Story (So Far); and 45) Mesoscale Simulations of Polar Circulations: Late Spring to Late Summe

Source record↗

NASA NTRS2004

Variability in Tropical Tropospheric Ozone as Observed by SHADOZ

The SHADOZ (Southern Hemisphere Additional Ozonesondes) ozone sounding network was initiated in 1998 to improve the coverage of tropical in-situ ozone measurements for satellite validation, algorithm development and related process studies. Over 2000 soundings have been archived at the central website, , for 12 stations: Ascension Island; Nairobi and Malindi, Kenya; Irene, South Africa; Reunion Island; Watukosek, Java; Fiji; Tahiti; American Samoa; San Cristobal, Galapagos; Natal, Brazil; Paramaribo, Surinam. Some results to date indicate reliability of the measurement and highly variable interactions between ozone and tropical meteorology. For example: 1. By using ECC sondes with similar procedures, 5-10% accuracy and precision (1-sigma) of the sonde total ozone measurement was achieved [Thompson et al., 2003al; 2. Week-to-week variability in tropospheric ozone is so great that statistics are frequently not Gaussian and most stations vary up to a factor of 3 in column amount over the course of a year [Thompson et al., 2002b]. 3. Longitudinal variability in tropospheric ozone profiles is a consistent feature, with a 10- 15 DU column-integrated difference between Atlantic and Pacific sites; this is the cause of the zonal wave-one feature in total ozone [Shiotani, 1992]. The ozone record from Paramaribo, Surinam (6N, 55W) is a marked contrast to southern tropical ozone because Surinam is often north of the Intertropical Convergence Zone. Interpretations of SHADOZ time-series and approaches to classification suggested by SHADOZ data over Africa and the Indian Ocean will be described.

Thompson, Anne M.↗