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The present volume of abstracts of conference papers discusses topics associated with the role of meteorite impacts on the Earth, the moon, and Titan. Particular attention is given to the description of the impact damage and the description of the actual craters. Attention is also given to the Sudbury structure, and the Chicxulub crater. Mineralogical, geophysical, petrographic, seismic and image data are described and discussed.
This presentation provides a brief overview of the scientific activities of the International GPS Service (IGS). This was an approved activity of the International Association of Geodesy (IAG) with official start of service on 1 Jan 1994. The mission of the IGS is "To provide a service to support geodetic and geophysical research activities, through GPS data and data products." The presentation explains the concept of the IGS working group, and pilot projects, and reviews the current working groups and pilot projects.
Overview of project: we combined and jointly analysed geophysical, remote sensing and glaciological data for investigating the temporal changes in ice flow and the role of geologic control on glacial drainage. The project included two different studies, the investigation of recent changes of the Kangerlussuaq glacier and the study of geologic control of ice flow in NW Greenland, around the Humboldt, Petermann and Ryder glaciers.
AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.
The separation of cloud and clear scenes is usually one of the first steps in satellite data analysis. Before deriving a geophysical product, almost every satellite mission requires a cloud mask to label a scene as either clear or cloudy through a cloud detection procedure. For clear scenes, products such as surface properties may be retrieved; for cloudy scenes, scientist can focus on studying the cloud properties. Hence the quality of cloud detection directly affects the quality of most satellite operational and research products. This is certainly true for the Ice, Cloud, and land Elevation Satellite-2 (lCESat-2), which is the successor to the ICESat-l. As a top priority mission, ICESat-2 will continue to provide measurements of ice sheets and sea ice elevation on a global scale. Studies have shown that clouds can significantly affect the accuracy of the retrieved results. For example, some of the photons (a photon is a basic unit of light) in the laser beam will be scattered by cloud particles on its way. So instead of traveling in a straight line, these photons are scattered sideways and have traveled a longer path. This will result in biases in ice sheet elevation measurements. Hence cloud screening must be done and be done accurately before the retrievals.
The Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has the objective of frequent, global mapping of near-surface soil moisture and its freeze-thaw state. The SMAP measurement system utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operate on a spacecraft in a 685 km polar orbit with 6am/6pm nodal crossings, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments will yield global maps of soil moisture and freeze/thaw state at 10 km and 3 km resolutions, respectively, every two to three days. The 10-km soil moisture product will be generated using a combined radar and radiometer retrieval algorithm. SMAP will also provide a radiometer-only soil moisture product at 40-km spatial resolution and a radar-only soil moisture product at 3-km resolution. The relative accuracies of these products will vary regionally and will depend on surface characteristics such as vegetation water content, vegetation type, surface roughness, and landscape heterogeneity. The SMAP soil moisture and freeze/thaw measurements will enable significantly improved estimates of the fluxes of water, energy and carbon between the land and atmosphere. Soil moisture and freeze/thaw controls of these fluxes are key factors in the performance of models used for weather and climate predictions and for quantifYing the global carbon balance. Soil moisture measurements are also of importance in modeling and predicting extreme events such as floods and droughts. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. In the Testbed algorithms are developed and evaluated using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including data from the SMOS and Aquarius missions. We report here on the development status of the SMAP data products. The Testbed simulations are designed to capture various sources of errors in the products including environment effects, instrument effects (nonideal aspects of the measurement system), and retrieval algorithm errors. The SMAP project has developed a Calibration and Validation (Cal/Val) Plan that is designed to support algorithm development (pre-launch) and data product validation (post-launch). A key component of the Cal/Val Plan is the identification, characterization, and instrumentation of sites that can be used to calibrate and validate the sensor data (Level l) and derived geophysical products (Level 2 and higher).
The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) dataset released by the Precipitation Processing System (PPS) will be updated to a final version within the next year. These updates are based on increased knowledge in recent years of radiometer calibration and sensor performance issues. In particular, the Global Precipitation Measurement (GPM) Microwave Imager (GMI) is used as a model for many of the TMI version updates. This paper discusses four aspects of the TMI data product that will be improved: spacecraft attitude, calibration and quality control, along-scan bias corrections, and sensor pointing accuracy. These updates will be incorporated into the final TMI data version, improving the quality of the data product and ensuring accurate geophysical parameters can be derived from TMI.
Output from numerical weather models is often used to simulate observations from remote sensing instruments, for purposes ranging from data assimilation, synthetic retrievals of geophysical quantities, and optimization of observing systems. However, when hydrometeors are present, the level of detail provided by the weather model is generally insufficient to fully constrain the input to the radiative transfer model (RTM), and further assumptions must be made by the RTM user in order to produce synthetic observations. Using a hierarchy of models including cloud-resolving, double-moment, bin microphysical, and ice-habit predicting models, along with scattering properties from the OpenSSP, Atmospheric Radiative Transfer Simulator (ARTS) databases, as well as relatively simple geometries (e.g., cylindrical plates and columns), we demonstrate the spread in synthetic observation output and the extent to which it is reduced when microphysics is more strongly constrained by the model. As an intermediate step, an error budget for the RTM simulations was derived and from that we developed and will describe best practices for observation simulation (e.g., optimal number of hydrometeor size bins, truncation of the particle size distribution, angular resolution of scattering phase function). Some statistical comparisons with observations will also be presented.
Space-time Data Fusion (STDF) is a methodology for combing heterogeneous remote sensing data to optimally estimate the true values of a geophysical field of interest, and obtain uncertainties for those estimates. The input data sets may have different observing characteristics including different footprints, spatial resolutions and fields of view, orbit cycles, biases, and noise characteristics. Despite these differences all observed data can be linked to the underlying field, and therefore the each other, by a statistical model. Differences in footprints and other geometric characteristics are accounted for by parameterizing pixel-level remote sensing observations as spatial integrals of true field values lying within pixel boundaries, plus measurement error. Both spatial and temporal correlations in the true field and in the observations are estimated and incorporated through the use of a space-time random effects (STRE) model. Once the models parameters are estimated, we use it to derive expressions for optimal (minimum mean squared error and unbiased) estimates of the true field at any arbitrary location of interest, computed from the observations. Standard errors of these estimates are also produced, allowing confidence intervals to be constructed. The procedure is carried out on a fine spatial grid to approximate a continuous field. We demonstrate STDF by applying it to the problem of estimating CO2 concentration in the lower-atmosphere using data from the Atmospheric Infrared Sounder (AIRS) and the Japanese Greenhouse Gasses Observing Satellite (GOSAT) over one year for the continental US.
Hyperspectral observations from satellite-based sensors provide high information content for the Earth’s atmospheric temperature, water vapor and trace gas vertical profiles. We have developed a radiometrically consistent spectral fingerprinting method to derive climate change signals from Aqua AIRS/AMSU and S-NPP CrIS/ATMS data. The climate variables include temperature and water vapor profiles, cloud, trace gases, and surface skin temperature. The radiative kernels obtained via a single field of view physical retrieval algorithm under all-sky conditions. A key component to this work is a Principal Component-based Radiative Transfer Model (PCRTM). It is 4 orders of magnitude faster than a line-by-line radiative transfer model while keeping a similar accuracy (0.03 K RMS errors with close to zero bias). The PCRTM includes multiple scattering of clouds and non-thermodynamics equilibrium of CO2 in the RT calculations. Instead of quantifying the radiometric differences between AIRS/AMSU and CrIS/ATMS measurements directly using Simultaneous Nadir Overpass (SNO) or Double Difference Technique (DDT), we use the radiometric consistent fingerprinting scheme to derive two sets of space-time averaged anomalies from the Level 1 data of AIRS/AMSU and CrIS/ATMS. The derived anomalies in geophysical space will form a long-term, stable, and continuous climate data record. We can further infer the causes of any offset or drift by studying the differences between two overlapping data sets. For example, the offset in surface skin temperature anomaly time series will most likely caused by the Blackbody temperature calibration errors of the sounder instruments.
This study has demonstrated that high-resolution scatterometer measurements in tropical cyclones and other high-marine surface wind regimes may be retrieved accurately for wind speeds up to about 35 mls (1-hour average at 10 m) when the scatterometer data are processed through a revised geophysical model function, and a spatial adaptive algorithm is applied which utilizes the fact that wind direction is so tightly constrained in tile inner core of severe marine storms that wind direction may be prescribed from conventional data. This potential is demonstrated through case studies with NSCAT data in a severe West Pacific Typhoon (Violet, 1996) and an intense North Atlantic hurricane (Lili, 1996). However, operational scatterometer winds from NSCAT and QuickScat in hurricanes and severe winter storms are biased low in winds above 25 m/s. We have developed an inverse model to specify the entire surface wind field about a tropical cyclone from operational QuickScat scatterometer measurements within 150 nm of a storm center with the restriction that only wind speeds up to 20 m/s are used until improved model function are introduced. The inverse model is used to specify the wind field over the entire life-cycle of Hurricane Floyd (1999) for use to drive an ocean wave model. The wind field compares very favorably with wind fields developed from the copious aircraft flight level winds obtained in this storm.
Application of the Backus-Gilbert theory for geophysical inverse problems to the seismic body wave travel-time problem is described. In particular, it is shown how to generate earth models that fit travel-time data to within one standard error and having generated such models how to describe their degree of uniqueness. An example is given to illustrate the process.
A review of the theory for the electromagnetic functional used to date to determine the lunar conductivity profile from spectral analyses of lunar magnetometer data. The 'hard' boundary condition used by Sonett et al. (1971) and others appears to be a good approximation for the sunlit lunar hemisphere. The use of the spectral data in conjunction with the functional to find a least squares conductivity profile is examined from the point of view of the nonuniqueness of nonlinear estimation. An unsuccessful search was carried out to find models that were both geophysically reasonable and consistent with the data. It is asserted that the failure to find these models is due to the inadequacy of the functional. Of the models generated, those that best fit the data all have a conductivity peak at a depth of 240 km similar to that of the model discussed by Sonett et al. However, all of these models are quite distinct elsewhere in the profile. It is shown how such models are dependent on both the nature of the functional as well as the initial guess in the least squares procedure. A correlation analysis shows that the only resolvable features from this type of model are a low conductivity crust, a high conductivity peak of limited radial extent, and a low conductivity zone beneath the peak.
Very long baseline interferometry presents an opportunity to measure at the centimeter level such geodetic parameters as baseline length and instantaneous pole position. In order to achieve such precision, the geophysical and astronomical models used in data analysis must be as accurate as possible. The Mark-3 interactive data analysis system includes a number of refinements beyond conventional practice in modeling precession, nutation, diurnal polar motion, UT1, solid Earth tides, relativistic light deflection, and reduction to solar system barycentric coordinates. The algorithms and their effects on the recovered geodetic, geophysical, and astrometric parameters are discussed.
Passive microwave measurements performed from Earth orbit can be used to provide global data on a wide range of geophysical and meteorological phenomena. A Scanning Multichannel Microwave Radiometer (SMMR) is being flown on the Nimbus-G satellite. The SMMR Simulator duplicates the frequency bands utilized in the spacecraft instruments through an amalgamate of radiometer systems. The algorithm developed utilizes data from the fall 1978 NASA CV-990 Nimbus-G underflight test series and subsequent laboratory testing.
Within the 200 East Area of the Central Plateau and southeastward toward the Columbia River, a high-hydraulic conductivity zone (HCZ) has been interpreted to extend through the 200-PO-1 and 200-BP-5 operable units on the Hanford Site. The HCZ is a controlling hydraulic feature that impacts groundwater flow out of the 200 East Area and the fate of eastwardly migrating plumes from the 200 West Area. The lateral extent of the HCZ is highly uncertain, and despite strong evidence for the existence of the HCZ based on water-level data and contaminant plume tracking, there is still a limited understanding of how to define its boundaries. To provide additional information on the nature and extent of the HCZ, three surface geophysical methods – electrical resistivity tomography (ERT), time-domain electromagnetics (TEM), and seismic methods – were used to collect data south of 200 East. In addition, existing data from 200 East, consisting of surface seismic data, a borehole check shot survey in 699-37-47A, and borehole stratigraphic interpretations, were used to aid interpretations of newly collected seismic data south of 200 East. This work presumed that the contrast in subsurface geophysical properties would be a first-order aid identifying a transmissive zone(s) within the HCZ analysis area by imaging contrasts and/or anomalies in geophysical properties. While seismic, ERT, and TEM methods have sensitivity to overlapping physical properties (porosity, moisture content, lithology), the resolution and physics used to acquire each of these datasets are different, and therefore the information can also be different. Figure S.1 shows the locations of the geophysical data considered in this report.
The Earth Observing System (EOS), the major NASA contribution to the U.S. Global Change Research Program, consists of a series of satellites with sensors designed to measure the crucial variables to monitor processes on the land, ocean, and atmosphere, a Data and Information System (EOSDIS) to analyze geophysical and biological products and distribute them to investigators, and a scientific research program. EOSDIS began in 1991 with organization of existing data sets, with the goal of a useful, accessible system in 1994. The launch of the first medium-size EOS platform will occur in 1998, with launches every 18 to 24 months and replacements every 5 years to provide a 15-year series of reliable scientific products. The selection of the instruments that comprise the payloads is designed to address the highest priority science/policy questions, as identified by the inter-agency Committee on Earth and Environmental Sciences (CEES) and the Intergovernmental Panel on Climate Change (IPCC).
The author has identified the following significant results: (1) An original map of lineaments of the Armorican Massif and the Vendean platform was prepared. (2) Validity of spatial information through comparison with maps of various kinds, such as geological, geophysical, morphological, etc., was verified. (3) It was confirmed that LANDSAT images, in many cases, reflect data on deep phenomena which were only accessible geophysically and by means of borings. Tectonic domains were outlined, and known lineaments were extended.