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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2000

A Comparison of the Automated Meteorological Profiling System High Resolution Flight Element to the Kennedy Space Center 50 MHz Doppler Wind Profiler

Wind profile measurement and the simulation of aerodynamic loads on a launch vehicle play an important role in determining launch capability and post launch assessment of the vehicle's performance. To date, all United States range certified wind profile measurement systems have been based on balloon tracking. Since the 1960's, the standard used by the National Aeronautics and Space Administration and the Air Force at the Cape Canaveral Air Station (CCAS) for detailed wind profile measurements has been the radar tracked, aerodynamically stabilized Jimsphere balloon system. Currently, the Air Force is nearing certification and operational implementation of the Automated Meteorological Profiling System (AMPS) at CCAS and Vandenburg Air Force Base (VAFB). AMPS uses the Global Positioning System for tracking the Jimsphere balloon. It is anticipated that the AMPS/Jimsphere, named the High Resolution Flight Element (HRFE), will have equivalent, or better resolution than the radar tracked Jimsphere, especially when the balloon is far downrange, at a low elevation angle. By the 1980's, the development of Doppler Wind Profilers (DWP) had become sufficiently advanced to justify an experimental measurement program at Kennedy Space Center (KSC). In 1989 a 50 MHz DWP was installed at KSC. In principal, the 50 MHz DWP has the capability to track the evolution of wind profile dynamics within 5 minutes of a launch. Because of fundamental differences in the measurement technique, there is a significant time and space differential between 50 MHz DWP and HRFE wind profiles. This paper describes a study to quantify these differences from a sample of 50 MHz DWP/HRFE pairs obtained during the AMPS certification test program.

Roberts, Barry C.↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
NASA NTRSDate not supplied

The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chain, TX

Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in central Texas, NASA DEVELOP partnered with LCRA and COA DWP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 MultiSpectral Instrument (MSI) data were used to provide a combined revisit time of up to ~3 days and < 30 m per pixel products. Chlorophyll a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations (mats). In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results show a good fit between the modeled output and in situ observations, suggesting that remote sensing data can be used to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatch between satellite and sampling data, and a relatively lower signal-to-noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.

Shuyu Chang↗