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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Space and time variability of the surface color field in the northern Adriatic Sea

A time series of coastal zone color scanner images for the years 1979 and 1980 was used to observe the spatial and temporal variability of bio-optical processes and circulation patterns of the northern Adriatic Sea on monthly, seasonal, and interannual scales. The chlorophyll-like pigment concentrations derived from satellite data exhibited a high correlation with sea truth measurements performed during seven surveys in the summer of both years. Comparison of the mean pigment fields indicates a general increase in concentration values and larger scales of coastal features from 1979 to 1980. This variability may be linked to the different patterns of nutrient influx due to coastal runoff in the 2 years. The distribution of surface features is consistent with the general cyclonic circulation pattern. The pigment heterogeneity appears to be governed by fluctuations of freshwater discharge, while the dominant wind fields do not appear to have important direct effects. The Po River presents a plume spreading predominantly in a southeastern direction, with scales positively correlated with its outflow. The spatial scales of the western coastal layer, in contrast, are negatively correlated with this outflow and the plume scales. Both results are consistent with, and may be rationalized by, recent theoretical and experimental results involving a dynamical balance between nonlinear advection and bottom friction, with alternate predominance of one of the two effects.

Barale, Vittorio↗
NASA NTRS2022

Development and Evaluation of Ensemble Consensus Precipitation Estimates over High Mountain Asia

Precipitation estimates are highly uncertain in complex regions such as High-Mountain Asia (HMA), where ground measurements are very difficult to obtain, and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets (the Integrated Multi-satellitE Retrieals for Global Precipitation Measurement IMERG, the Climate Hazards group Infrared Precipitation with Stations CHIRPS, and the ECMWF Reanalysis ERA5) with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatio-temporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

Fadji Z Maina↗
NASA NTRS1998

Pulmonary blood flow redistribution by increased gravitational force

This study was undertaken to assess the influence of gravity on the distribution of pulmonary blood flow (PBF) using increased inertial force as a perturbation. PBF was studied in unanesthetized swine exposed to -Gx (dorsal-to-ventral direction, prone position), where G is the magnitude of the force of gravity at the surface of the Earth, on the Armstrong Laboratory Centrifuge at Brooks Air Force Base. PBF was measured using 15-micron fluorescent microspheres, a method with markedly enhanced spatial resolution. Each animal was exposed randomly to -1, -2, and -3 Gx. Pulmonary vascular pressures, cardiac output, heart rate, arterial blood gases, and PBF distribution were measured at each G level. Heterogeneity of PBF distribution as measured by the coefficient of variation of PBF distribution increased from 0.38 +/- 0.05 to 0.55 +/- 0.11 to 0.72 +/- 0.16 at -1, -2, and -3 Gx, respectively. At -1 Gx, PBF was greatest in the ventral and cranial and lowest in the dorsal and caudal regions of the lung. With increased -Gx, this gradient was augmented in both directions. Extrapolation of these values to 0 G predicts a slight dorsal (nondependent) region dominance of PBF and a coefficient of variation of 0.22 in microgravity. Analysis of variance revealed that a fixed component (vascular structure) accounted for 81% and nonstructure components (including gravity) accounted for the remaining 19% of the PBF variance across the entire experiment (all 3 gravitational levels). The results are inconsistent with the predictions of the zone model.

NASA Discipline Cardiopulmonary↗
NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 13

Contents include the following: A Fast, Non-Destructive Method for Classifying Ordinary Chondrite Falls Using Density and Magnetic Susceptibility. An Update on Results from the Magnetic Properties Experiments on the Mars Exploration Rovers, Spirit and Opportunity. Measurement Protocols for In Situ Analysis of Organic Compounds at Mars and Comets. Piping Structures on Earth and Possibly Mars: Astrobiological Implications. Uranium and Lead in the Early Planetary Core Formation: New Insights Given by High Pressure and Temperature Experiments. The Mast Cameras and Mars Descent Imager (MARDI) for the 2009 Mars Science Laboratory. MGS MOC: First Views of Mars at Sub-Meter Resolution from Orbit. Analysis of Candor Chasma Interior Layered Deposits from OMEGA/MEX Spectra. Analysis of Valley Networks on Valles Marineris Plateau Using HRSC/MEX Data. Solar Abundance of Elements from Neutron-Capture Cross Sections. Preliminary Evaluation of the Secondary Ion/Accelerator Mass Spectrometer, MegaSIMS. Equilibrium Landforms in the Dry Valleys of Antarctica: Implications for Landscape Evolution and Climate Change on Mars. Continued Study of Ba Isotopic Compositions of Presolar Silicon Carbide Grains from Supernovae. Paleoenviromental Evolution of the Holden-Uzboi Area. Stability of Magnesium Sulfate Minerals in Martian Environments. Tungsten Isotopic Constraints on the Formation and Evolution of Iron Meteorite Parent Bodies. Migration of Dust Particles and Volatiles Delivery to the Inner Planets. On the Sitting of Trapped Noble Gases in Insoluble Organic Matter of Primitive Meteorites. Trapping of Xenon Upon Evaporation-Condensation of Organic Matter Under UV Irradiation: Isotopic Fractionation and Electron Paramagnetic Resonance Analysis. Stability of Water on Mars. A Didactic Activity. Analysis of Coronae in the Parga Chasma Region, Venus. Photometric and Compositional Surface Properties of the Gusev Crater Region, Mars, as Derived from Multi-Angle, Multi-Spectral Investigation of Mars Express HRSC Data. Mapping Compositional Diversity on Mars: Spatial Distribution and Geological Implications. A New Simulation Chamber for Studying Planetary Environments. Folded Structure in Terra Sirenum. Mars. Nitrogen-Noble Gas Static Mass Spectrometry of Genesis Collector Materials. Neon Isotope Heterogeneity in the Terrestrial Mantle: Implication for the Acquisition of Volatile Elements in Terrestrial Planets. The Cosmic Clock, the Cycle of Terrestrial Mass Extinctions.

Source record↗
NASA NTRS2006

Remote Sensing and Spatial Growth Modeling Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗
NASA NTRS2004

Magnetohydrodynamic Convection in the Outer Core and its Geodynamic Consequences

The Earth's fluid outer core is in vigorous convection through much of the Earth's history. In addition to generating and maintaining Earth s time-varying magnetic field (geodynamo), the core convection also generates mass redistribution in the core and a dynamical pressure field on the core-mantle boundary (CMB). All these shall result in various core-mantle interactions, and contribute to surface geodynamic observables. For example, electromagnetic core-mantle coupling arises from finite electrically conducting lower mantle; gravitational interaction occurs between the cores and the heterogeneous mantle; mechanical coupling may also occur when the CMB topography is aspherical. Besides changing the mantle rotation via the coupling torques, the mass-redistribution in the core shall produce a spatial-temporal gravity anomaly. Numerical modeling of the core dynamical processes contributes in several geophysical disciplines. It helps explain the physical causes of surface geodynamic observables via space geodetic techniques and other means, e.g. Earth's rotation variation on decadal time scales, and secular time-variable gravity. Conversely, identification of the sources of the observables can provide additional insights on the dynamics of the fluid core, leading to better constraints on the physics in the numerical modeling. In the past few years, our core dynamics modeling efforts, with respect to our MoSST model, have made significant progress in understanding individual geophysical consequences. However, integrated studies are desirable, not only because of more mature numerical core dynamics models, but also because of inter-correlation among the geophysical phenomena, e.g. mass redistribution in the outer core produces not only time-variable gravity, but also gravitational core-mantle coupling and thus the Earth's rotation variation. They are expected to further facilitate multidisciplinary studies of core dynamics and interactions of the core with other components of the Earth.

Kuang, Weijia↗
NASA NTRS2021

The sensitivity of runoff generation to spatial snowpack uniformity in an alpine watershed: Green Lakes Valley, Niwot Ridge Long-Term Ecological Research station

Seasonal water storage in high-elevation alpine catchments are critical sources of water for mountainous regions like the western U.S. The spatial distribution of snow in these topographically complex catchments is primarily governed by orography, solar radiation, and wind redistribution. While the effect of solar shading is relatively consistent from year-to-year, the redistribution of snow due to wind is more variable–capable of producing snowpacks that have varying degrees of uniformity across these hydrologically-important catchments. A reasonable hypothesis is that a warmer climate will cause snowfall to become more dense (i.e. wetter and heavier), possibly leading to less wind redistribution and thus produce a more uniformly distributed snowpack across the landscape. In this study, we investigate the role of increasingly uniform spatial snowpack distributions on streamflow generation in the Green Lakes Valley Niwot Ridge Long Term Ecological Research station, within the headwaters of the Boulder Creek watershed in Colorado. A set of idealized hydrologic simulation experiments driven by reconstructed snowpacks spanning 2001–2014 show that more a more uniform spatial snowpack distribution leads to an earlier melt-out of31 days on average and tends to produce less total streamflow, with maximum decreases as large as 7.5%. Isolating the role of snowpack heterogeneity from melt-season precipitation, we find that snowpack uniformity reduces total streamflow by as much as 13.2%. Reductions in streamflow are largely explained by greater expo-sure to solar radiation in the uniformly distributed case relative to a more heterogeneous snowpack, with this exposure driving shifts towards earlier snowmelt and changes in soil water storage. Overall, we find that the runoff efficiency from shallower snowpacks is more sensitive to the effects of uniformity than deeper snowpacks, which has potential implications for a warming climate where shallower snowpacks and enhanced sensitivities may be present.

Alpine catchment↗
NASA NTRS2023

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers, and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Carl Malings↗
NASA NTRSDate not supplied

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗
NASA NTRS1989

Atmospheric correction of high resolution land surface images

Algorithms to correct for atmospheric-scattering effects in high-spatial resolution land-surface images require the ability to perform rapid and accurate computations of the top-of-atmosphere diffuse radiance field for arbitrarily general surface reflectance distributions (which may be both heterogeneous and non-Lambertian) and atmospheric models. Using three-dimensional radiative transfer (3DRT) theory algorithms are being developed. The methodology used to perform the 3DRT calculations is described. It is shown how these calculations are used to perform atmospheric corrections, and the sensitivity of the retrieved surface reflectances to atmospheric structural parameters is illustrated.

Diner, D. J.↗
NASA NTRS2011

Space-Time Data Fusion

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.

Greenhouse Gases Observing Satellite (GOSAT)↗
NASA NTRS1995

Estimation of polar stratospheric cloud infrared extinction climatology using visible satellite observations

Polar stratospheric clouds (PSC's) provide surfaces for heterogeneous processes which can dramatically alter the normal partitioning of odd nitrogen and chlorine families in the winter polar stratospheres, setting up conditions for significant ozone depletion as manifested in the springtime Antarctic ozone hole. The spatial and temporal distribution of PSC's is important for parameterizing PSC occurrence in multidimensional photochemical models whose use is essential for fully understanding observed Antarctic ozone losses as well as for accessing the possibility of a similar phemonenon occurring in the future in the Arctic. The Stratospheric Aerosol Measurement (SAM) 2 sensor, a single-channel (1mu m) photometer launched into a Sun-synchronous orbit aboard the Nimbus 7 satellite in October 1978, provided a unique database to establish the climatology of PSC's. Poole and Pitts (1994) used the record of high-latitude aerosol extinction obtained by SAM II from 1979-1989 to establish the climatology of PSC occurrences in the Arctic and Antarctic. Unfortunately, little information about PSC composition or type was detectable from the single-wavelength SAM II data.

Pitts, Michael C.↗
NASA NTRS2012

Coordinated Oxygen Isotopic and Petrologic Studies of CAIS Record Varying Composition of Protosolar

Ca-, Al-rich inclusions (CAIs) record the O-isotope composition of Solar nebular gas from which they grew [1]. High spatial resolution O-isotope measurements afforded by ion microprobe analysis across the rims and margin of CAIs reveal systematic variations in (Delta)O-17 and suggest formation from a diversity of nebular environments [2-4]. This heterogeneity has been explained by isotopic mixing between the O-16-rich Solar reservoir [6] and a second O-16-poor reservoir (probably nebular gas) with a "planetary-like" isotopic composition [e.g., 1, 6-7], but the mechanism and location(s) where these events occur within the protoplanetary disk remain uncertain. The orientation of large and systematic variations in (Delta)O-17 reported by [3] for a compact Type A CAI from the Efremovka reduced CV3 chondrite differs dramatically from reports by [4] of a similar CAI, A37 from the Allende oxidized CV3 chondrite. Both studies conclude that CAIs were exposed to distinct, nebular O-isotope reservoirs, implying the transfer of CAIs among different settings within the protoplanetary disk [4]. To test this hypothesis further and the extent of intra-CAI O-isotopic variation, a pristine compact Type A CAI, Ef-1 from Efremovka, and a Type B2 CAI, TS4 from Allende were studied. Our new results are equally intriguing because, collectively, O-isotopic zoning patterns in the CAIs indicate a progressive and cyclic record. The results imply that CAIs were commonly exposed to multiple environments of distinct gas during their formation. Numerical models help constrain conditions and duration of these events.

Simon, Justin I.↗
NASA NTRS2019

Bounding Methods for Heterogeneous Lidar-derived Navigational Geofences

Safe Unmanned Aerial Vehicle (UAV) operations near the ground require navigation methods that avoid fixed obstacles such as buildings, power lines and trees. Aerial lidar surveys of ground structures are available with the precision and accuracy to geolocate obstacles, but the high volume of raw survey data can exceed the compute power of onboard processors and the rendering ability of ground-based flight planning maps. Representing ground structures with bounding polyhedra instead of point clouds greatly reduces the data size and can enable effective obstacle avoidance, as long as the bounding geometry envelopes the structures with high spatial fidelity. This report describes in detail four methods to compute bounding geometries of ground obstacles from lidar point clouds. The four methods are: 1) 2.5D Maximum Elevation Box, 2) 2.5D Ground Map Extrusion, 3) 3D Bounding Cylinder, and 4) 3D Bounding Box. The methods are applied to five point cloud datasets from lidar surveys of UAV flight research sites in Georgia and Virginia with an average point spacing that ranges from 0.1m to 0.6m. The methods are assessed using survey areas with geometrically heterogeneous ground structures: buildings, vegetation, power lines, and sub-meter structures such as road signs and guy wires. The 2.5D Maximum Elevation Box method is useful for simple structures. The 2.5D Ground Map Extrusion method efficiently encloses vegetation, but requires handdrawn ground footprints. The 3D Bounding Cylinder method excels at enclosing linear structures such as power lines and fences. The 3D Bounding Box method excels at enclosing planar structures such as buildings. The methods are compared on the basis of data compression and boundary fidelity on selected areas. The 2.5D methods yield the highest data compression but the polyhedra produced by them enclose significant amounts of empty space. Boundary fidelity is superior for the 3D methods, though this fidelity comes at the cost of a roughly thirtyfold lower data compression ratio than the 2.5D Maximum Elevation Box method. A mix of these output geometries is proposed for autonomous UAV navigation with limited on-board computing. Both the accuracy and spatial detail of emerging satellite-based survey technology lower than that of aerial lidar scanning survey technology. Sub-meter structures and thin linear structures are not reliably mapped at present by satellite-based surveys.

Moore, Andrew J.↗
NASA NTRS2012

The NASA Soil Moisture Active Passive (SMAP) Mission - Science and Data Product Development Status

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).

Nloku, E.↗
NASA NTRS1984

Electromagnetic induction by finite wavenumber source fields in 2-D lateral heterogeneities - The transverse electric mode

Electromagnetic induction in a laterally homogeneous earth is analyzed in terms of a source field with finite dimensions. Attention is focused on a time-varying two-dimensional current source directed parallel to the strike of a two-dimensional anomalous structure within the earth, i.e., the E-parallel mode. The spatially harmonic source field is expressed as discontinuities in the magnetic (or electric) field of the current in the source. The model is applied to describing the magnetic gradients across megatectonic features, and may be used to predict the magnetic fields encountered by a satellite orbiting above the ionosphere.

Hermance, J. F.↗
NASA NTRSDate not supplied

Illustrating the Spatiotemporal Complexity of No2 Columns Using A Multi-Perspective Observing System: Moving Toward Geostationary Product Validation and Applications

As a precursor to secondary pollutants like ozone and PM2.5, nitrogen dioxide (NO2) is crucial to understand when addressing air quality issues. However, due to NO2’s short lifetime during the daytime and complexity of emission sources in urbanized regions, interpreting datasets from ground or satellite perspectives alone are challenged by variance in spatial and temporal resolutions. High resolution airborne mapping (< 1 km) of NO2 column densities across morning, midday, and afternoon add a unique perspective toward interpreting satellite data with respect to ground-measurements. This presentation focuses on the interpretation of spatiotemporal complexity of NO2 columns from the Synergistic TEMPO Air Quality Science Study (STAQS). The mission’s goal is to integrate geostationary observations from Tropospheric Emissions: Monitoring of Pollution (TEMPO) with traditional and enhanced air quality monitoring to improve the understanding of air quality science for increased societal benefit. We will demonstrate the interweaved perspective of NO2 columns from ground-based Pandora spectrometers and satellite-based observations (e.g., TROPOMI) as compared to high spatial resolution airborne observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS). This includes the evaluation of each dataset through comparison to each other to identify potential biases in data products and the impact of heterogeneity on these comparisons. Airborne data will also be used as a proxy for geostationary observations with morning, midday, and afternoon raster maps collected over four cities (Los Angeles, Chicago, Toronto, and New York City). Finally, recent research outcomes will be presented to demonstrate how airborne and geostationary observations can be used to evaluate emission inventories and air quality models.

Laura Judd↗
NASA NTRS2011

An Extended View of Ozone and Chemistry in the Atmosphere of Mars

We present an ongoing effort to characterize chemistry in Mars' atmosphere in multiple seasons on timescales longer than spaceflight missions through coordinated efforts by GSFC's HIPWAC spectrometer and Mars Express SPICAM, archival measurements, and tests/application of photochemical models. The trace species ozone (03) is an effective probe of Mars' atmospheric chemistry because it is destroyed by odd-hydrogen species (HOx, from water vapor photolysis). Observed ozone is a critical test for specific predictions by 3-D photochemical models (spatial, diurnal, seasonal). Coordinated measurements by HIPWAC and SPICAM quantitatively linked mission data to the 23-year GSFC ozone data record and also revealed unanticipated inter-decadal variability of same-season ozone abundances, a possible indicator of changing cloud activity (heterogeneous sink for HOx). A detailed study of long-term conditions is critical to characterizing the predictability of Mars' seasonal chemical behavior, particularly in light of the implications of and the lack of explanation for reported methane behavior.

Smith, Ramsey L.↗