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Limb sounders measure atmospheric radiation that is dependent on atmospheric temperature and constituents that have a radial and angular distribution in Earth-centered coordinates.
A method has been developed to determine the oceanic daily mean mixed layer depth from satellite observations and a mixed layer thermal inertia (MLTI) model. The algorithms were developed to use remotely-sensed values of sea surface temperature, albedo, and surface wind speeds to calculate the thermal inertia and to predict changes in subsurface diurnal mixed layer depth. The MLTI model, based on a mixed layer model of the upper ocean, has been used to simulate the diurnal mixing process and thermal inertia distribution in the Sargasso Sea around 34 deg N, 70 deg W. Sea surface temperature and albedo have been obtained from the NOAA7-AVHRR images. Surface wind speeds have been derived from the Scanning Multichannel Microwave Radiometer (SMMR) aboard Nimbus 7. Image processing was performed for images gathered between June and July 1982. The daily mean mixed layer depths predicted by the MLTI model agree well with data gathered at the LOTUS mooring located in the Sargasso Sea. This suggests that vertical mixing is the dominant physical process that controls the thermal inertia distribution in the midocean, far from major current systems, and that remote sensing is a promising tool to study such upper ocean processes.
The establishment of a limited spatial data base for the U.S. eastern seaboard vicinity is discussed along with the demonstration of computer assisted analysis techniques for investigating spatial patterns and relationships among ship survey data and remotely sensed sea surface temperature. Ship survey variables included concentrations of two zooplankton, two icthyoplankton, and two fish species, in addition to physical data of depth to bottom and surface and bottom water temperatures. Continuous spatial distributions of these data were created by both weighted nearest neighbor and iterative smoothing interpolation techniques. Maps of surface water temperature were created by digitizing GOES satellite images. All mapped data were spatially registered by conversion of latitude and longitude coordinates to rotated Lambert conic conformal rectilinear coordinates and stored in grid format of approximately one hundred square kilometers per cell. The analysis of these data include the generation of statistical summaries and maps describing the joint occurrence among variables.
Observations and theoretical models of planetary atmospheres published during the period 1983-1986 are reviewed, including Vega and Voyager data and results from ground-based remote sensing. Consideration is given to water-vapor and sulfur-compound distributions, electrical phenomena, and dynamics in the Venus atmosphere; dust storms, water cycles, and water and ice erosion on Mars; the compositions, temperature profiles, and dynamics of the Jovian and Saturnian atmospheres; chemical processes and zonal winds on Titan; and the radiation budgets and chemical compositions of the outer planets.
The U.S. Geological Survey (USGS) and the U.S. Agency for International Development (USAID) are providing technical assistance to the AGRHYMET program in West Africa. AGRHYMET staff use remote sensing technology to produce satellite image maps of the Sahel region of West Africa. These image maps may show vegetation greenness, sea surface temperatures, or processed weather satellite imagery. The image maps must be distributed from the AGRHYMET Regional Center in Niger to national AGRHYMET centers in the member countries of Burkina Faso, Cape Verde, Chad, Gambia, Guinea-Bissau, Mali, Mauritania, Niger, and Senegal. After consideration of a number of land- and space-based solutions for image map distribution, the best solution was determined to be use of International Maritime Satellite Organization (INMARSAT) land-based terminals. In April 1992, a field test and proof-of-concept demonstration using land-mobile terminals produced favorable results. The USGS and USAID are setting up a wide area network using INMARSAT terminals to link the AGRHYMET sites for image data transfer. The system is in the procurement and installation phase and initial opening capability may be operational for the 1993 growing season, starting in May 1993.
Severe weather phenomena represent the extreme upper end of the spectrum of convection and precipitation and tend to be highly localized and relatively rare compared to the rest of the distribution, but they can cause damage and loss disproportionate to their scale and frequency. Fortunately, severe convection exhibits distinct signatures in spaceborne remote-sensing datasets (e.g. overshooting cloud tops in visible/IR, or brightness temperature depressions in passive-microwave imagery). Leveraging these signatures individually has become a long-established practice to detect, analyze and establish climatologies of severe thunderstorms, especially in instances where traditional ground-based data may be unavailable. Spaceborne visible/IR and passive-microwave approaches are not without their pitfalls, however: passive-microwave channels have large footprints and exhibit non-uniform beam filling. Visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. To address this, we investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together and assess the extent to which these datasets can be combined to improve severe thunderstorm detection. We pair AMSR-E and MODIS signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis in six different geographical regimes throughout the Aqua domain. We present a census of potentially severe convective storms and their environments as seen by multiple instruments simultaneously, investigating how MODIS and AMSR-E signatures may be used together to diagnose storm properties and processes, and how the interrelationships between the signatures varies seasonally and geographically. Using statistical machine learning analysis, we aim to quantify the optimal MODIS and AMSR-E parameter sets for discriminating severe from non-severe storm cells and assess what improvement (if any) in detection results from combining the IR, visible, and microwave datasets.
Remote sensing of the surface of Europa with near-infrared instruments has suggested the presence of hydrated materials, including sulfate salts. Attention has been focused on these salts for the information they might yield regarding the evolution of a putative interior ocean, and the evaluation of its astrobiological potential. These materials exhibit distinct infrared absorption features due to bound water. The interactions of this water with the host molecules lead to fine structure that can be used to discriminate among these materials on the basis of their spectral behavior. This fine structure is even more pronounced at the low temperatures prevalent on icy satellites. Examination of hydrated sulfate salt spectra measured under cryogenic temperature conditions provides realistic constraints for future remote-sensing missions to Europa. In particular, it suggests that a spectrometer system capable of 2-5 nm spectral resolution or better, with a spatial resolution approaching 100 m, would be able to differentiate among proposed hydrated surface materials, if present, and constrain their distributions across the surface. Such information would provide valuable insights into the evolutionary history of Europa.
A real time thermal imaging system with temperature resolution better than + or - 1 C and spatial resolution of better than 0.5 mm was developed and applied to the analysis of melt surface thermal field distributions in both Czochralski and liquid encapsulated Czochralski (LEC) growth configurations. The melt is viewed in near normal incidence by a high resolution charge coupled device camera to which is attached a very narrow bandpass filter. The resulting image is digitized and processed using a pipelined pixel processor operating at an effective 40 million operations per second thus permitting real time high frequency spatial and temporal filtering of the high temperature scene. A multi-pixel averaging algorithm was developed which permits localized, low noise sensing of temperature variations at any location in the hot zone as a function of time. This signial is used to implement initial elements of a feedforward growth control scheme which is aimed at reducing disturbances to the melt caused by the batch nature of the growth process. The effect of magnetic melt stabilization on radial melt temperature distributions was measured using this technique. Problems associated with residual internal reflections and non-optimized path geometry are discussed.
In comparison to mid latitude cloud cover, knowledge of polar cirrus and other cloud cover is limited. The interpretations of satellite-based cloud imaging and retrievals in polar regions have major problems due to factors such as darkness and extreme low temperatures. Beginning in 2002 a NASA orbiting lidar instrument, GLAS, (Geoscience Laser Altimeter System) will unambiguously define cloud type and fraction with good coverage of polar regions. Active laser sensing gives the spatial and temporal distribution of clouds and diamond dust. In preparation for, and supplementing the GLAS measurements are ground based MP (micro pulse) lidar experiments providing continuous profiling. MP lidar installations have been operating at the South Pole since December 1999 and at the Atmospheric Radiation Measurement (ARM) program arctic site since 1996. Both at the ARM Barrow, Alaska site and at the South Pole station, Fourier-transform interferometers also observe clouds in the wavelength intervals between approximately 5 and 18 microns. Spectral instruments can yield cloud microphysical properties with additional information from lidar about the vertical extent of clouds being modeled. We examine the simultaneous lidar and spectral data from both Barrow and South Pole, to obtain cloud properties (optical depth, particle size) by the use of both instruments. The results have applications to interpretation of current satellite data, and GLAS measurements when available.
Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the ‘‘new Arctic’’). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.
The Woolsey Fire began on November 8, 2018, and lasted for almost two weeks, during which it burned almost 100,000 acres of valuable landscape and habitat, including a vast area of woodland. The persistence of key woodland species provides aesthetic, monetary, and ecological value to the landscape through carbon sequestration, air temperature moderation, and erosion mitigation, among other ecosystem services. This study investigated the impact of the Woolsey Fire on native woodland species distributions and identified areas suitable for restoration within the Santa Monica Mountains National Recreation Area. The team partnered with the Resource Conservation District of the Santa Monica Mountains; National Park Service, Santa Monica Mountains National Recreation Area; California Department of Parks and Recreation, Los Angeles County Division; County of Los Angeles Fire Department, Prevention Services Bureau, Forestry Division; County of Los Angeles Department of Regional Planning; and the University of Montana. The Earth observations used include data from Landsat 8 Operational Land Imager, NASA ER-2 Jet Airborne Visible InfraRed Imaging Spectrometer, Shuttle Radar Topography Mission, and RapidEye. The team produced maps of burn severity from the Woolsey Fire, its impact on plant species distributions, and habitat suitability projections for 2050 and 2099 to assist partners in prioritizing areas for restoration. A plant community classification was successfully created using Multiple Endmember Spectral Mixture Analysis (MESMA). Overall accuracy was assessed at 90.54% by comparing the classification to validation pixels derived from ground truth information provided by our partners.
Surface parameters such as emissivity and skin temperature have been retrieved from IASI measurements of last 16 years. Monthly and spatially gridded surface skin temperature is produced to show the phenomena of its natural variability, which is also reflected in the surface emissivity and/or soil moisture content derived from the same time series of measurements. The anomalies of surface skin temperature are used to estimate its trend. The IASI global surface skin temperature trend is comparable with the NASA GISTEMP5 global surface air temperature trend. The trend of IASI global surface skin temperature reports that an approximate 0.027 K/yr. increase has evolved during 2007–2023. Attention has been given to the arctic region where an average warming rate is near 3 times higher than the global average. In some arctic regions, the rapid warming rate has reached 0.2-0.3 K/yr. which is about 10 times higher than the global average. Geophysical distribution of the surface temperature trend in the arctic region is emphasized and reported.
High-performance quantum memories are an essential component for regulating temporal events in quantum networks. As a component in quantum-repeaters, they have the potential to support the distribution of entanglement beyond the physical limitations of fiber loss. This will enable key applications such as quantum key distribution, network-enhanced quantum sensing, and distributed quantum computing. Here, we present a quantum memory engineered to meet real-world deployment and scaling challenges. The memory technology utilizes a warm rubidium vapor as the storage medium, and operates at room temperature, without the need for vacuum- and/or cryogenic- support. Here, we demonstrate performance specifications of high-fidelity retrieval (95%) and low operation error (10 –2 ) at a storage time of 160 µs for single-photon level quantum memory operations. We further show a substantially improved storage time (with classical-level light) of up to 1ms by suppressing atomic diffusions. The device is housed in an enclosure with a standard 2U rackmount form factor, and can robustly operate on a day scale in a noisy environment. This result marks an important step toward implementing quantum networks in the field.
Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.
The radiative characteristics of an anvil plume sensed by satellite radiometers are studied to discern the reason for an observed 'V' pattern of colder temperatures and a corresponding internal warm region. The measured black body temperature is affected by the ice water content and particle distributions in the cloud layer. Attention is given to 'V' anvil structure examined by Haynesfield et al. (1979). The GOES satellite IR data are treated with a plane parallel geometric radiative transfer model and a particle trajectory model is used to simulate ice particle concentrations downwind from a strongly divergent cloud top. Output from the two models indicate the 'V' pattern arises from divergence in the storm anvil. The particle concentration was elevated within the V-shaped region, causing lower temperatures in the V arms. The V is produced by strong upper level flow as well as strong mass and moisture divergence in the storm outflow.
Surface temperatures and reflectances of an upper elevation Sierran mixed conifer forest were monitored using the Thematic Mapper Simulator sensor during the summer of 1985 in order to explore the possibility of using remote sensing to determine the distribution of solar energy on forested watersheds. The results show that the method is capable of quantifying the relative energy allocation relationships between the two cover types defined in the study. It is noted that the method also has the potential to map forest latent heat flux densities.
The National Aeronautics and Space Administration (NASA) launched the Terra satellite in December 1999, as part of the Earth Science Enterprise promotion of interdisciplinary studies of the integrated Earth system. Aqua, the second satellite from the series of EOS constellation, was launched in May 2002. Both satellites carry the MODerate resolution Imaging Spectroradiometer (MODIS) instrument. MODIS data are processed at the Goddard Space Flight Center, Greenbelt, MD, and then archived and distributed by the Distributed Active Archive Centers (DAACs). Data products from the MODIS sensors present new challenges to remote sensing scientists due to specialized production level, data format, and map projection. MODIS data are distributed as calibrated radiances and as higher level products such as: surface reflectance, water-leaving radiances, ocean color and sea surface temperature, land surface kinetic temperature, vegetation indices, leaf area index, land cover, snow cover, sea ice extent, cloud mask, atmospheric profiles, aerosol properties, and many other geophysical parameters. MODIS data are stored in HDF- EOS format in both swath format and in several different map projections. This tutorial guides users through data set characteristics as well as search and order interfaces, data unpacking, data subsetting, and potential applications of the data. A CD-ROM with sample data sets, and software tools for working with the data will be provided to the course participants.
Composition and structure of neutral constituents in the lunar exosphere can be determined through measurements of phase space distributions of pickup ions borne from the exosphere [1]. An essential point made in an early study [ 1 ] and inferred by recent pickup ion measurements [2, 3] is that much lower neutral exosphere densities can be derived from ion mass spectrometer measurements of pickup ions than can be determined by conventional neutral mass spectrometers or remote sensing instruments. One approach for deriving properties of neutral exospheric source gasses is to first compare observed ion spectra with pickup ion model phase space distributions. Neutral exosphere properties are then inferred by adjusting exosphere model parameters to obtain the best fit between the resulting model pickup ion distributions and the observed ion spectra. Adopting this path, we obtain ion distributions from a new general pickup ion model, an extension of a simpler analytic description obtained from the Vlasov equation with an ion source [4]. In turn, the ion source is formed from a three-dimensional exospheric density distribution, which can range from the classical Chamberlain type distribution to one with variable exobase temperatures and nonthermal constituents as well as those empirically derived. The initial stage of this approach uses the Moon's known neutral He and Na exospheres to deriv e He+ and Na+ pickup ion exospheres, including their phase space distributions, densities and fluxes. The neutral exospheres used are those based on existing models and remote sensing studies. As mentioned, future ion measurements can be used to constrain the pickup ion model and subsequently improve the neutral exosphere descriptions. The pickup ion model is also used to estimate the exosphere sources of recently observed pickup ions on KAGUYA [3]. Future missions carrying ion spectrometers (e.g., ARTEMIS) will be able to study the lunar neutral exosphere with great sensitivity, yielding the necessary ion velocity spectra needed to further analysis of parent neutral exosphere properties.