High temperature geothermal distributed high-fidelity measurements in multiphase systems
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The differential absorption lidar (DIAL) technique was first applied to the remote measurement of atmospheric water vapor profiles from airborne platforms in 1981. The successful interpretation of the lidar profiles relies strongly on an accurate knowledge of specific water vapor absorption line parameters: line strength, pressure broadening coefficient, pressure-induced shift coefficient and the respective temperature-dependence factors. NASA Langley Research Center has developed and is currently testing an autonomous airborne water vapor lidar system: LASE (Lidar Atmospheric Sensing Experiment). This DIAL system uses a Nd:YAG-pumped Ti:Sapphire laser seeded by a diode laser as a lidar transmitter. The tunable diode has been selected to operate in the 813-818 nm wavelength region. This 5-nm spectral interval offers a large distribution of strengths for temperature-insensitive water vapor absorption lines. In support of the LASE project, a series of spectroscopic measurements were conducted for the 16 absorption lines that have been identified for use in the LASE measurements. Prior to this work, the experimental data for this water vapor absorption band were limited - to our knowledge - to the line strengths and to the line positions.
At a wavelength of about 0.9 cm, microwave attenuation is demonstrated to be linearly related to rainfall rate and independent of drop size distribution and temperature. In addition, practical methods for measuring path- and area-averaged rainfall rate are reviewed. A compromise between maximum path-averaged rainfall rate sensitivity and minimum sensing errors may be achieved by the use of one-way methods between the transmitter and the receiver, with a wavelength of 1.5 to 2.0 cm. Corrections for nonspherical drops and for multiple scattering are also discussed.
Unitary array of sensors measures temperatures at points distributed over designated area on surface. Useful in measuring surface temperatures of aerodynamic models and thermally controlled objects. Made of combination of integrated-circuit microchips and film circuitry. Temperature-sensing chips scanned at speeds approaching 10 kHz. Operating range minus 40 degrees C to 120 degrees C. Flexibility of array conforms to curved surfaces. Multiplexer eliminates numerous monitoring cables. Control of acquisition and recording of data effected by connecting array to microcomputers via suitable interface circuitry.
Seismology is a powerful tool for investigating the inner structure and dynamic processes of a planetary body. The interior structure information derived from seismic measurements is complementary to other methods of probing the subsurface (such as gravity and electromagnetics), both in terms of spatial and depth resolution and the relevant types of material properties being sensed. The propagation of seismic waves is sensitive to composition (via density and elastic parameters), temperature (via attenuation) and physical state (solid vs. liquid). In addition, the seismicity (level and distribution in space and time of seismic activity) provides information on the impact flux and tectonic forces currently active within the body. The major satellites of the outer solar system provide obvious targets for seismic investigations. In addition, small bodies, such as asteroids and comets, can also benefit from seismic measurements. We have developed an extremely small, lightweight, low-power seismometer for planetary applications which is ideally suited for use in the outer solar system. This instrument has previously been proposed and selected for use on a comet (on the Rosetta Lander, subsequently deselected for programmatic reasons) and Mars (on the NetLander mission). Additional information is contained in the original extended abstract.
Several types of intestinal nematodes, that can infect humans and specially school-age children living in poverty, develop part of their life cycle in soil. Presence and survival of these parasites in the soil depend on given environmental characteristics like temperature and moisture that can be inferred with remote sensing (RS) technology. Prevalence of diseases caused by these parasitic worms can be controlled and even eradicated with anthelmintic drug treatments and sanitation improvement. Reliable and updated identification of geographic areas at risk is required to implement effective public health programs; to calculate amount of drug required and to distribute funding for sanitation projects. RS technology and geographical information systems (GIS) will be used to analyze for associations between in situ prevalence and remotely sensed data in order to establish RS proxies of environmental parameters that indicate the presence of these parasits. In situ data on helminthisasis will be overlaid over an ecological map derived from RS data using ARC Map 9.3 (ESRI). Temperature, vegetation, and distance to bodies of water will be inferred using data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat TM and ETM+. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections are determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). This study will demonstrate the importance of an integrated GIS/RS approach to define clusters and areas at risk. Such information will help to the implementation of time and cost efficient control programs and sanitation efforts.
Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.
Bimetallic nanoparticles (BNPs) consist of two different types of metals or alloys that are bonded together. Unique properties such as optical, electronic, thermal, and catalytic effects differ for each type of BNP. Important BNPs range from Au-Pd, Ag- Pt, Au-Pt, and Ag-Ni. Pd bimetallic nanoparticles are of interest due to their many applications such as catalysis and sensing. Bimetallic catalysts have increase reaction rates and have improved catalyst stability through the geometry and ligand distribution. Pd nanoparticles are considered to be a strong catalyst due to their high activity at low temperatures and high tolerance to moisture. The catalytic properties of bimetallic nanoparticles depend on the structural properties such as size and shape. Core-shell, hollow structure, and multi-shell alloy are three possible structures nanoparticles can form as bimetallic catalysts. BNPs can be synthesized through different methods to control the size, shape, and structure. To obtain different morphologies, a variety of methods can be performed. Different methods can range from the usage of the glancing angle deposition (GLAD) to the galvanic replacement reaction, but the methods all depend on the properties of the metals. The galvanic displacement reaction was the method used to obtain Pd-based nanoparticles. This reaction is best know for obtaining hollow shaped NPs. To determine what redox process was preformed, the activity series of metals was used. From the activity series of metals, silver (Ag) was selected to preform Pd-based nanoparticles. Objectives: Synthesize Ag nanoparticles and Ag-Pd nanoparticles to understand the morphology. Characterize the synthesized nanoparticles using scanning electron microscopy (SEM), phase analysis light scattering (PALS), dynamic light scattering (DLS), energy dispersive X-ray spectroscopy (EDS), and UV-Vis spectroscopy. Results: In the UV-Vis spectrum, the Ag-Pd bimetallic NP's plasmon band decreased as the volume of palladium increased. The surface charge increases as the concentration of palladium increases. The Pd{sup 2+} ions interact with the sodium citrate surface, and decrease the negative charge. Conclusion: Ag-Pd nanoparticles were successfully created and stabilized with sodium citrate. The addition of Pd decreased the prominent plasmon band of the Ag nanoparticles. The SEM analysis showed that Ag nanoparticles had a well-defined structure, while the Ag-Pd nanoparticles showed hollow and rough structure. The EDX analysis confirmed the presence of silver and palladium. This material can be used in many industrial and research fields such as organic synthesis, fuel cells, and environmental sensing and remediation.
Over the ocean satellite infrared spectral measurements in the 18 micrometer water vapor band and the 11 micrometer window region were used to derive precipitable water vapor, w, in the atmosphere and the sea surface temperature, SST. Seasonal maps of w on the oceans derived from these data reveal the dynamical influence of the large scale atmospheric circulation. With the help of a model for the vertical distribution of water vapor, the configuration of the atmospheric boundary layer over the oceans can be inferred from w when the information of SST is combined. The gross seasonal mean structure of the boundary layer inferred in this fashion reveals the broad areas of the trade wind inversion and the convectively active areas such as the intertropical convergence zones.
Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.
Since the introduction of thermopile, pyranometers (solar, e.g., 0.3-3.0 micrometers) and pyrgeometers (terrestrial, e.g., 4-50 micrometers) have become instruments commonly used for measuring the broadband hemispherical irradiances at the surface in a long-term, monitoring mode for decades. These commercially available radiometers have been manufactured in several countries such as from the United States, Asia, and Europe, and are generally reliable and economical. These worldwide distributions of surface measurements become even more important in the era of Earth remote sensing in studying climate forcing. However, recent studies from field campaigns have pointed out that erroneous factors (e.g., temperature gradients between the filter dome and detector, emissivity of the thermopile) are responsible for the unacceptable level of uncertainty (e.g., 10-20 W m (exp -2)). Using a newly developed instrument of Quantum Well Infrared Photodetector (QWIP), we have characterized the brightness temperature fields of pyranometers and pyrgeometers under various sky conditions. The QWIP is based on the superlattice (GaAs/AlGaAs) technology and has a noise equivalent temperature (NE delta T) less than 0.1 K. The quality of pyranometer and pyrgeometer measurements can be improved largely by applying proper knowledge of the thermal parameters affecting the operation of the thermopile systems. For example, we show a method to determine the "dome factor" (the longwave emission divided by the longwave transmission of a pyrgeometer dome) from field measurements. The results show, and are verified independently by the QWIP, that our dome factors of 0.59 and 0.90 are much smaller than the value of 4.0 assumed by the WMO (World Meteorological Organization). Data correction procedure and algorithm will be presented and discussed.
Since the introduction of thermopile, pyranometers (solar, e.g., 0.3-3.0 microns) and pyrgeometers (terrestrial, e.g., 4-50 microns) have become instruments commonly used for measuring the broadband hemispherical irradiances at the surface in a long-term, monitoring mode for decades. These commercially available radiometers have been manufactured in several countries such as from the United States, Asia, and Europe, and are generally reliable and economical. These worldwide distributions of surface measurements become even more important in the era of Earth remote sensing in studying climate forcing. However, recent studies from field campaigns have pointed out that erroneous factors (e.g., temperature gradients between the filter dome and detector, emissivity of the thermopile) are responsible for the unacceptable level of uncertainty (e.g., 10-20 W/square Meter). Using a newly developed instrument of Quantum Well Infrared Photodetector (QWIP), we have characterized the brightness temperature fields of pyranometers and pyrgeometers under various sky conditions. The QWIP is based on the superlattice (GaAs/AlGaAs) technology and has a noise equivalent temperature (NE delta T) less than 0.1 K. The quality of pyranometer and pyrgeometer measurements can be improved largely by applying proper knowledge of the thermal parameters affecting the operation of the thermopile systems. For example, we show a method to determine the "dome factor" (the longwave emission divided by the longwave transmission of a pyrgeometer dome) from field measurements. The results show, and are verified independently by the QWIP, that our dome factors of 0.59 and 0.90 are much smaller than the value of 4.0 assumed by the WMO. Data correction procedure and algorithm will be presented and discussed.
Since the introduction of thermopile, pyranometers (solar, e.g., 0.3-3.0 micrometers) and pyrgeometers (terrestrial, e.g., 4-50 micrometers) have become instruments commonly used for measuring the broadband hemispherical irradiances at the surface in a long-term, monitoring mode for decades. These commercially available radiometers have been manufactured in several countries such as from the United States, Asia, and Europe, and are generally reliable and economical. These worldwide distributions of surface measurements become even more important in the era of Earth remote sensing in studying climate change. However, recent studies from field campaigns have pointed out that erroneous factors (e.g., temperature gradients between the filter dome and detector, emissivity of the thermopile) are responsible for the unacceptable level of uncertainty (e.g., 20 W m(exp -2)). Using a newly developed instrument of Quantum Well Infrared Photodetector (QWTP), we have characterized the brightness temperature fields of pyranometers and pyrgeometers under various sky conditions. The QWIP is based on the superlattice (GaAs/AlGaAs) technology and has a noise equivalent temperature (NEAT) less than 0.1 K. The quality of pyranometer and pyrgeometer measure- ments can be improved largely by applying proper knowledge of the thermal parameters affecting the operation of the thermopile systems. Data correction procedure and algorithm will be presented and discussed.
Since the introduction of thermopile, pyranometers (solar, e.g., 0.3 - 3.0 microns) and pyrgeometers (terrestrial, e.g., 4 - 50 microns) have become instruments commonly used for measuring the broadband hemispherical irradiances at the surface in a long-term, monitoring mode for decades. These commercially available radiometers have been manufactured in several countries such as from the United States, Asia, and Europe, and are generally reliable and economical. These worldwide distributions of surface measurements become even more important in the era of Earth remote sensing in studying climate change. However, recent studies from field campaigns have pointed out that erroneous factors (e.g., temperature gradients between the filter dome and detector, emissivity of the thermopile) are responsible for the unacceptable level of uncertainty (e.g., 20 W/sq m). It is best to utilize an energy balance equation to describe the thermal dome effect of pyranometers and pyrgeometers. Therefore, quality of pyranometer and pyrgeometer measurements can be improved largely by applying proper knowledge of the thermal parameters affecting the operation of the thermopile systems. Data correction procedure and algorithm will be presented and discussed.
A simplied framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase hydrometeors. Properties considered included the shape parameter of a gamma size distribution andthe melted-equivalent mass median diameter D0, the particle density, dielectric mixing formula, and the choice of complex index of refraction for ice. We examine these properties at selected radiometer frequencies of 18.7, 36.5, 89.0, and 150.0 GHz; and radar frequencies at 2.8, 13.4, 35.6, and 94.0 GHz consistent with existing and planned remote sensing instruments. Passive and active microwave observables of ice particles arefound to be extremely sensitive to the melted-equivalent mass median diameter D0 ofthe size distribution. Similar large sensitivities are found for variations in the ice vol-ume fraction whenever the geometric mass median diameter exceeds approximately 1/8th of the wavelength. At 94 GHz the two-way path integrated attenuation is potentially large for dense compact particles. The distribution parameter mu has a relatively weak effect on any observable: less than 1-2 K in brightness temperature and up to 2.7 dB difference in the effective radar reflectivity. Reversal of the roles of ice and air in the MaxwellGarnett dielectric mixing formula leads to a signicant change in both microwave brightness temperature (10 K) and radar reflectivity (2 dB). The choice of Warren (1984) or Warren and Brandt (2008) for the complex index of refraction of ice can produce a 3%-4% change in the brightness temperature depression.
Day and night mapping of the global distributions of the horizontal cloud covers and the corresponding cloud-top pressure levels are derived from the same set of infrared radiance data used to retrieve clear-column temperature profiles. General formulation of the problem is presented with illustrations for the simple case of a single layer of non-reflecting clouds. Experimental verification is obtained using 15 micron data measured by the NOAA-VTPR infrared sounder. After correcting for water vapor emission, the results show that the effective cloud cover derived from 15 micron data is less than that obtained from visible data.
Antimonide-based type-II interband cascade lasers are promising in becoming efficient and compact mid-infrared light sources for many applications such as gas sensing and environmental monitoring. Significant progress toward such a goal has recently been made in terms of achieving low threshold current densities, high temperature operation, and continuous wave operation of single-mode distributed feedback lasers. We review the recent progress of Sb-based mid-IR IC lasers and address the requirements for further improvements in their performance.
Electronics for Distributed Turbine Engine Control and Space Exploration Missions are expected to encounter extreme temperatures and wide thermal swings. In particular, circuits deployed in a jet engine compartment are likely to be exposed to temperatures well exceeding 150 C. To meet this requirement, efforts exist at the NASA Glenn Research Center (GRC), in support of the Fundamental Aeronautics Program/Subsonic Fixed Wing Project, to develop temperature sensors geared for use in high temperature environments. The sensor and associated circuitry need to be located in the engine compartment under distributed control architecture to simplify system design, improve reliability, and ease signal multiplexing. Several circuits were designed using commercial-off-the-shelf as well as newly-developed components to perform temperature sensing at high temperatures. The temperature-sensing circuits will be described along with the results pertaining to their performance under extreme temperature.