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Advances to a Global Agroclimatology Solar Insolation and Meteorological Parameter Data Base: Improved Solar Irradiance up to Hourly Temporal Resolution
A primary objective of NASA’s Prediction of Worldwide Energy Resource (POWER) project is to facilitate the use of NASA Earth Science data holdings within the energy, agricultural, and architectural industries. To this end daily averaged solar data from several NASA projects and metrological data from a NASA assimilation model have long been reformatted and via a user friendly web based data portal (https://power.larc.nasa.gov) at the native resolution of each data products. Potential users can access solar and metrological data in a column formatted DSSAT ASCII format by entering single site specific coordinates or from an area by entering the appropriate area coordinates. Upgrades to the POWER data portal have been implemented that result in a complete upgrade of the base solar insolation data products. From the years 1984 through 2000, a new version of the NASA/GEWEX Surface Radiation Budget (SRB) and Clouds is utilized that reduces the RMS relative to surface measurements. Additionally, Clouds and the Earth’s Radiant Energy System (CERES) SYNoptic 1x1 Degree (SYN1Deg) data products are utilized starting from January 1, 2001 through 3 months of real-time. The CERES Fast Longwave and SHortwave radiative Fluxes (FLASHFlux) is still used to provided daily data spanning from the end of SYN1Deg to within 7 days of real-time. Meteorological parameters now are taken from the NASA Modern Era Retrospective-analysis for Research and Applications (MERRA-2) data set which provides higher resolution data products (hourly and 0.5 x 625 degree) covering the entire globe. Besides updating the solar and meteorological data products, this new version features new data products such as photosynthetically active radiation (PAR), more cloud information, estimates of soil temperatures and improved options for long-term climatological data requests. More than 36+ years daily time slices are included in the combined solar and meteorological data sets. However, perhaps the most important innovation of the POWER GIS-enabled Web Services is the provision of hourly solar and meteorological data products beginning in Jan 1, 2001. The hourly values will enable more detailed modeling and crop analysis that incorporates the diurnal variability of these parameters. The new capability is made possible by utilizing both the CERES SYN1Deg and MERRA-2 data products that include parameters at these resolutions. An assessment of both the meteorological and the solar irradiance data are based upon comparisons with globally distributed surface observations. The meteorological observations from the National Center for Environmental Information’s “Integrated Surface Database” as well as the “Global Summary of the Day” (GSOD) data files. The estimates of solar insolation are compared to the Baseline Solar Radiation Network (BSRN) and other high quality surface measurement networks. Results from the uncertainty assessments demonstrates that the NASA’s meteorological and solar irradiance data can represent a viable alternative to surface observations, particularly in data sparse regions of the world.
A High Spatio-temporal Resolution Land Surface Temperature (LST) Product for Urban Environments
No abstract provided
Enhancing the Spatial and Temporal Resolution of Air-Sea Flux Estimates From Space to Address Fine Mesoscale Variability: Challenges and Progress
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TEMPO High-Temporal-Resolution Aerosol & Gas Observations of Central American Volcanic Plumes
Poster presentation at the TEMPO Science Team Meeting, May 1-3, 2023.
The Use of Multi-Mission Datasets in NASA DEVELOP Projects: How the Harmonized Landsat and Sentinel-2 Product and Multi-Source Land Imaging Land Surface Phenology Product Provide Solutions to Temporal Resolution Constraints for Vegetation Monitoring
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Solid Phase Gene Extraction Isolates mRNA at High Spatial and Temporal Resolution
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Quantifying Dissolved Organic Carbon Dynamics Using a Three‐Dimensional Terrestrial Ecosystem Model at High Spatial‐Temporal Resolutions
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A Two-Axis Direct Fluid Shear Stress Sensor
This innovation is a miniature or micro sized semiconductor sensor design that provides two axis direct non-intrusive measurement of skin friction or wall shear stress in fluid flow. The sensor is fabricated by micro-electro-mechanical system (MEMS) technology, enabling small size and low cost reproductions. The sensors have been fabricated by utilizing MEMS fabrication processes to bond a sensing element wafer to a fluid coupling wafer. This layering technique provides for an out of plane dimension that is on the same order of length as the inplane dimensions. The sensor design has the following characteristics: a shear force collecting plate with dimensions that can be tailored to various application specific requirements such as spatial resolution, temporal resolution and shear force range and resolution. This plate is located coplanar to both the sensor body and flow boundary, and is connected to a dual axis gimbal structure by a connecting column or lever arm. The dual axis gimbal structure has torsional hinges with embedded piezoresistive torsional strain gauges which provide a voltage output that is correlated to the applied shear stress (and excitation current) on force collection plate that is located on the flow boundary surface (hence the transduction method). This combination of design elements create a force concentration and resolution structure that enables the generation of a large stress on the strain gauge from the small shear stress on the flow boundary wall. This design as well as the use of back side electrical contacts establishes a non-intrusive method to quantitatively measure the shear force vector on aerodynamic bodies.
Computation of UH-60A Airloads Using CFD/CSD Coupling on Unstructured Meshes
An unsteady Reynolds-averaged Navier-Stokes solver for unstructured grids is used to compute the rotor airloads on the UH-60A helicopter at high-speed and high thrust conditions. The flow solver is coupled to a rotorcraft comprehensive code in order to account for trim and aeroelastic deflections. Simulations are performed both with and without the fuselage, and the effects of grid resolution, temporal resolution and turbulence model are examined. Computed airloads are compared to flight data.
Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters
High-resolution space-based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise ratio. Different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.
An evaluation of observations from satellites for the study and prediction of mesoscale events and cyclone events
Quantitative observational guidelines have been developed for the study and forecasting of mesoscale events and cyclone events; many of these guidelines have resulted from major meteorological field programs. Increasingly sophisticated satellite-borne instruments in low and geosynchronous orbits have provided valuable measurements of these events. The major deficiencies in the measurements taken from geosynchronous orbit today, relative to guidelines, are (1) the lack of temperature profiles and moisture profiles below clouds, and the poor vertical resolution of these profiles; (2) the insufficient combination of spatial resolution and temporal resolution and spectral intervals available in generating images (imaging); and (3) the lack of accurate precipitation mapping. Considerably more-powerful instrumentation is possible on geosynchronous satellites that can substantially reduce these deficiencies. The capability of advanced geosynchronous observations and those expected with future instrumentation in low-orbit are evaluated with respect to tropical cyclone and severe local-storm observational guidelines. A high percentage of the guidelines are expected to be fulfilled with geosynchronous measurements: (1) using microwave instruments for imaging and for obtaining temperature profiles and moisture profiles, (2) very high spectral-resolution infrared profiling, (3) very high spatial-resolution and very high temporal-resolution imaging, and (4) ozone mapping.
ILEOS: A Novel Intelligent Observing System Enabled by High Altitude Long Endurance Uncrewed Aerial Systems
Most major global satellite surveyors of climate-relevant trace gases have relatively coarse spatial resolution or temporal sampling. While these data can be supplemented by fine-pointing satellites and aircraft, the spatial and temporal resolutions available from crewed aircraft is not sufficient to observe stochastic, ephemeral events that take place between observations. Emerging High Altitude Long Endurance (HALE) Uncrewed Aerial Systems (UAS) can operate for months at a time and loiter over targets to provide continuous daylight geostationary-like observations, allowing these new platforms to be integrated with existing satellites as part of a New Observing Strategy (NOS).To aid in the planning of future NOS missions, NASA is developing the Intelligent Long Endurance Observing System (ILEOS), a science activity planning system. ILEOS will help scientists build plans to improve spatio-temporal resolution of climate-relevant gases by fusing coarse-grained sensor data from satellites and other sources(e.g., terrain, forecasts), and plan HALE UAS flights to obtain finer-grain (high spatio-temporal) data. ILEOS will also enable observations for longer periods and of environments not accessible through in-situ observations and crewed aircraft field campaigns.
Multi-Dimensional Measurements of Combustion Species in Flame Tube and Sector Gas Turbine Combustors
The higher temperature and pressure cycles of future aviation gas turbine combustors challenge designers to produce combustors that minimize their environmental impact while maintaining high operation efficiency. The development of low emissions combustors includes the reduction of unburned hydrocarbons, smoke, and particulates, as well as the reduction of oxides of nitrogen (NO(x)). In order to better understand and control the mechanisms that produce emissions, tools are needed to aid the development of combustor hardware. Current methods of measuring species within gas turbine combustors use extractive sampling of combustion gases to determine major species concentrations and to infer the bulk flame temperature. These methods cannot be used to measure unstable combustion products and have poor spatial and temporal resolution. The intrusive nature of gas sampling may also disturb the flow structure within a combustor. Planar laser-induced fluorescence (PLIF) is an optical technique for the measurement of combustion species. In addition to its non-intrusive nature, PLIF offers these advantages over gas sampling: high spatial resolution, high temporal resolution, the ability to measure unstable species, and the potential to measure combustion temperature. This thesis considers PLIF for in-situ visualization of combustion species as a tool for the design and evaluation of gas turbine combustor subcomponents. This work constitutes the first application of PLIF to the severe environment found in liquid-fueled, aviation gas turbine combustors. Technical and applied challenges are discussed. PLIF of OH was used to observe the flame structure within the post flame zone of a flame tube combustor, and within the flame zone of a sector combustor, for a variety of fuel injector configurations. OH was selected for measurement because it is a major combustion intermediate, playing a key role in the chemistry of combustion, and because its presence within the flame zone can serve as a qualitative marker of flame temperature. All images were taken in the environment of actual engines during flight, using actual jet fuel. The results of the PLIF study led directly to the modification of a fuel injector.
Super Resolving Unrolled Neural Networks for Remote Sensing
In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.
Geostationary orbit Earth science platform concepts for global change monitoring
Functionality of a geostationary spacecraft to support Earth science regional process research is identified. Most regional process studies require high spatial and temporal resolution. These high temporal resolutions are on the order of 30 minutes and may be achievable with instruments positioned in a geostationary orbit. A complement of typical existing or near term instruments are identified to take advantage of this altitude. This set of instruments is listed, and the requirements these instruments impose on a spacecraft are discussed. A brief description of the geostationary spacecraft concepts which support these instruments is presented.
Exploring Mission Design for Imaging Spectroscopy Retrievals for Land and Aquatic Ecosystems
The retrieval algorithms used for optical remote sensing satellite data to estimate Earth's geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal-to-noise ratio (SNR) performance to meet biogeoscience objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8-day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit, and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.