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At least 433 records · Page 24

Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications

To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.

Array↗

2022 Component Innovation Awardee: Pecos Wind Power

Distributed wind energy systems supply electricity for nearby homes and businesses, often in remote and rural regions. Systems capable of capturing energy in areas with low wind speeds make it possible to deploy these clean energy systems in many geographic locations. Pecos Wind Power's 85-kilowatt (kW) horizontal-axis PW85 wind turbine shows potential to reduce the levelized cost of energy by approximately 55% when compared to typical small wind systems while achieving utility-scale capacity factors of more than 40% in areas with lower wind speeds. To maximize cost competitiveness, the Pecos Wind Power turbine needs a blade optimized for low wind speeds. Through its 2022 Competitiveness Improvement Project (CIP) award, Pecos Wind Power and partner Wetzel Wind Energy Services will model, optimize, fabricate, and validate a 14.5-meter wind turbine blade made specifically for these applications. Three prior CIP awards supported Pecos Wind Power's initial development of the PW85 turbine.

CIP↗

Inlet Performance of the NFAC 1/50th-scale 80- by 120- Foot Wind Tunnel

The National Full-Scale Aerodynamics Complex 80- by 120-Foot Wind Tunnel (80x120) was dedicated in 1987 and rated at 100 knots for full-scale aircraft testing at NASA Ames Research Center. The 80x120 is the world’s largest wind tunnel, designed as an open circuit tunnel with a large aerodynamically treated inlet open to the ambient atmospheric air. In 2017, damage was sustained within the wind tunnel drive system, opening a window to do testing using the existing 1/50th-scale model of the 80- by 120-Foot Wind Tunnel within the full-scale 80- by 120-Foot Wind tunnel test section. The objective of the research was to quantify the turbulence levels within the 1/50th-scale test section as a function of onset atmospheric wind direction (± 90 deg from tunnel center-line), variable test section speed (5 – 50 m/s) and purposeful obtrusion of wind flow into the inlet. The model wind tunnel inlet, contraction, and test section are geometrically identical to that of the full-scale wind tunnel and model testing provides aerodynamic performance characteristics under controlled test conditions allowing for insight into the full-scale test section flow quality. The test section turbulence levels are minimally affected by the onset direction of the ambient atmospheric wind, but are dramatically affected by the speed in the tunnel while operated in the presence of winds. Original design specifications were axial/vertical/lateral turbulence ≤ 0.5% at maximum test section speed, though early full scale tunnel testing determined that lateral turbulence would be ≤ 0.6%. For test section speeds ≥ 30 m/s the tunnel is within the design specification limits. Between 5 m/s and 30 m/s, the test section turbulence levels are dependent on the onset wind direction and test section speed where test section turbulence in the axial, vertical and lateral directions was seen to be between 0.5% and 1% and, at times, greater than 1%. Finally, testing was performed with blockage designs at the inlet to disrupt the wind flow quality entering the tunnel contraction zone in an attempt to create higher levels of turbulence for high turbulent test conditions simulating the earth’s boundary layer. The highest turbulence levels measured were 6% in the axial direction by use of large spires designed to obstruct ≈ 50% of the inlet area.

Inlet Performance↗

Parker Solar Probe Observations of High Plasma β Solar Wind From the Streamer Belt

In general, slow solar wind from the streamer belt forms a high plasma β equatorial plasma sheet around the heliospheric current sheet (HCS) crossing, namely, the heliospheric plasma sheet (HPS). Current Parker Solar Probe (PSP) observations show that the HCS crossings near the Sun could be full or partial current sheet (PCS) crossings, and they share some common features but also have different properties. In this work, using the PSP observations from encounters 4–10, we identify streamer belt solar wind from enhancements in plasma β, and we further use electron pitch angle distributions to separate it into HPS solar wind around the full HCS crossings and PCS solar wind in the vicinity of PCS crossings. Based on our analysis, we find that the PCS solar wind has different characteristics as compared with HPS solar wind: (a) the PCS solar wind could be non-pressure-balanced structures rather than magnetic holes, and the total pressure enhancement mainly results from the less reduced magnetic pressure; (b) some of the PCS solar wind is mirror-unstable; and (c) the PCS solar wind is dominated by very low helium abundance but varied alpha–proton differential speed. We suggest that the PCS solar wind could originate from coronal loops deep inside the streamer belt, and it is pristine solar wind that still actively interacts with ambient solar wind; thus, it is valuable for further investigations of the heating and acceleration of slow solar wind.

Slow solar wind↗

High temporal frequency data from a four turbine, blade-resolved wind farm simulation with ExaWind

The data was generated with ExaWind (https://github.com/Exawind) which couples AMR-Wind (https://github.com/Exawind/amr-wind/), Nalu-Wind (https://github.com/Exawind/nalu-wind), TIOGA (https://github.com/Exawind/tioga), and OpenFAST (https://github.com/OpenFAST/openfast). This is a large-scale simulation of a blade-resolved wind farm using the ExaWind software stack. ExaWind couples together a background flow solver, AMR-Wind, and a near-body solver, Nalu-Wind, through an overset technique from the TIOGA application. Another application, OpenFAST, handles the structural dynamics of the turbine blades and towers, which informs the fluid-structure interaction of the wind turbines with the flow solvers. This particular simulation includes four blade-resolved wind turbines operating in a turbulent atmospheric boundary layer. The AMR-Wind solver uses 500 million cells and is being solved on 256 AMD GPUs of the Oakridge Leadership Computing Facility Frontier supercomputer. Each turbine is assigned its own Nalu-Wind solver with over 13 million elements per turbine and solved using 448 CPU cores, for a total of 1792 CPU cores. For each node, 56 cores contain Nalu-Wind, while 8 cores correspond to AMR-Wind operations on the GPUs. Consequently, ExaWind is entirely utilizing the CPUs and the GPUs of the nodes concurrently. The data used in the visualization is full flow field data output from the simulation. It is lossy-compressed to a specific accuracy using ZFP and written to disk every 16 time-steps to enable real-time flow visualization. The flow fields are sampled at a high temporal frequency to enable real-time, 24fps visualization. The flow fields are sampled every 12 simulation time steps (every 0.04132s).

17 WIND ENERGY↗

Comparative Evaluation of the Ability of the MYNN‐EDMF PBL Scheme in WRF Model to Reproduce Near Surface Wind Speed Over Different Topographical Types

Abstract This study systematically evaluates the performance of the Mellor‐Yamada‐Nakanishi‐Niino‐Eddy‐Diffusion‐Mass‐Flux planetary boundary layer (PBL) scheme within the Weather Research and Forecasting (WRF) model in simulating near‐surface wind speeds across various topographies in New York State (NYS). Simulated wind speeds are compared with in‐situ measurements from 22 surface sites, grouped into six topographic categories: continental plain (CT), lakeside (LS), river valley (RV), Long Island (LI), Block Island (BI), and offshore ocean (OO). A quantitative evaluation based on Relative Euclidean Distance shows that wind speeds at the OO site are the most accurately reproduced, followed by those at LI sites, while the model performs less accurately for the remaining topographic groups. Wind speeds over CT sites tend to be overestimated by approximately 1 m/s, although their diurnal variability (DV) is well captured. In contrast, the model underestimates wind DV at LS, RV, LI, and BI sites, with the largest biases occurring at LI and BI, resulting in underestimated daytime wind speed and/or overestimated nighttime wind speed. The OO winds exhibit minimal diurnal variation, accurately captured by our WRF model. The surface wind diurnal variation is closely linked to PBL development. Among the indicators of PBL development, surface potential temperature biases most strongly correlate with wind speed biases. Our WRF model faces challenges in capturing the distinctions between winds influenced by local circulations and those over continental plains, and the significantly stronger winds at OO compared to BI. Potential causes for these biases are discussed, offering pathways for improving surface wind simulations in future.

54 ENVIRONMENTAL SCIENCES↗

A theoretical analysis of airplane longitudinal stability and control as affected by wind shear

The longitudinal equations of motion with wind shear terms were used to analyze the stability and motions of a jet transport. A positive wind shear gives a decreasing head wind or changes a head wind into a tail wind. A negative wind shear gives a decreasing tail wind or changes a tail wind into a head wind. It was found that wind shear had very little effect on the short period mode and that negative wind shear, although it affected the phugoid, did not cause stability problems. On the other hand, it was found that positive wind shear can cause the phugoid to become aperiodic and unstable. In this case, a stability boundary for the phugoid was found that is valid for most aircraft at all flight speeds. Calculations of aircraft motions confirmed the results of the stability analysis. It was found that a flight path control automatic pilot and an airspeed control system provide good control in all types of wind shear. Appendixes give equations of motion that include the effects of downdrafts and updrafts and extend the longitudinal equations of motion for shear to six degrees of freedom.

Sherman, W. L.↗

Determination of wind from NIMBUS 6 satellite sounding data

Objective methods of computing upper level and surface wind fields from NIMBUS 6 satellite sounding data are developed. These methods are evaluated by comparing satellite derived and rawinsonde wind fields on gridded constant pressure charts in four geographical regions. Satellite-derived and hourly observed surface wind fields are compared. Results indicate that the best satellite-derived wind on constant pressure charts is a geostrophic wind derived from highly smoothed fields of geopotential height. Satellite-derived winds computed in this manner and rawinsonde winds show similar circulation patterns except in areas of small height gradients. Magnitudes of the standard deviation of the differences between satellite derived and rawinsonde wind speeds range from approximately 3 to 12 m/sec on constant pressure charts and peak at the jet stream level. Fields of satellite-derived surface wind computed with the logarithmic wind law agree well with fields of observed surface wind in most regions. Magnitudes of the standard deviation of the differences in surface wind speed range from approximately 2 to 4 m/sec, and satellite derived surface winds are able to depict flow across a cold front and around a low pressure center.

Carle, W. E.↗

New algorithms for microwave measurements of ocean winds

Improved second generation wind algorithms are used to process the three month SEASAT SMMR and SASS data sets. The new algorithms are derived without using in situ anemometer measurements. All known biases in the sensors prime measurements are removed, and the algorithms prime model functions are internally self-consistent. The computed SMMR and SASS winds are collocated and compared on a 150 km cell-by-cell basis, giving a total of 115444 wind comparisons. The comparisons are done using three different sets of SMMR channels. When the 6.6H SMMR channel is used for wind retrieval, the SMMR and SASS winds agree to within 1.3 m/s over the SASS primary swath. At nadir where the radar cross section is less sensitive to wind, the agreement degrades to 1.9 m/s. The agreement is very good for winds from 0 to 15 m/s. Above 15 m/s, the off-nadir SASS winds are consistently lower than the SMMR winds, while at nadir the high SASS winds are greater than SMMR's. When 10.7H is used for the SMMR wind channel, the SMMR/SASS wind comparisons are not quite as good. When the frequency of the wind channel is increased to 18 GHz, the SMMR/SASS agreement substantially degrades to about 5 m/s.

Wentz, F. J.↗

New algorithms for microwave measurements of ocean winds Applications to Seasat and the special sensor microwave imager

Improved second generation wind algorithms are used to process the three month Seasat SMMR and SASS data sets. The new algorithms are derived without using in situ anemometer measurements. All known biases in the sensors prime measurements are removed, and the algorithms prime model functions are internally self-consistent. The computed SMMR and SASS winds are collocated and compared on a 10 km cell-by-cell basis, giving a total of 115444 wind comparisons. The comparisons are done using three different sets of SMMR channels. When the 6.6H SMMR channel is used for wind retrieval, the SMMR and SASS winds agree to within 1.3 m/s over the SASS primary swath. At nadir where the radar cross section is less sensitive to wind, the agreement degrades to 1.9 m/s. The agreement is very good for winds from 0 to 15 m/s. Above 15 m/s, the off-nadir SASS winds are consistently lower than the SMMR winds, while at nadir the high SASS winds are greater than SMMR's. When 10.7H is used for the SMMR wind channel, the SMMR/SASS wind comparisons are not quite as good. When the frequency of the wind channel is increased to 18 GHz, the SMMR/SASS agreement substantially degrades to about 5 m/S.

Wentz, F. J.↗

The analysis sensitivity to tropical winds from the Global Weather Experiment

The global scale divergent and rotational flow components of the Global Weather Experiment (GWE) are diagnosed from three different analyses of the data. The rotational flow shows closer agreement between the analyses than does the divergent flow. Although the major outflow and inflow centers are similarly placed in all analyses, the global kinetic energy of the divergent wind varies by about a factor of 2 between different analyses while the global kinetic energy of the rotational wind varies by only about 10 percent between the analyses. A series of real data assimilation experiments has been performed with the GLA general circulation model using different amounts of tropical wind data during the First Special Observing Period of the Global Weather Experiment. In exeriment 1, all available tropical wind data were used; in the second experiment, tropical wind data were suppressed; while, in the third and fourth experiments, only tropical wind data with westerly and easterly components, respectively, were assimilated. The rotational wind appears to be more sensitive to the presence or absence of tropical wind data than the divergent wind. It appears that the model, given only extratropical observations, generates excessively strong upper tropospheric westerlies. These biases are sufficiently pronounced to amplify the globally integrated rotational flow kinetic energy by about 10 percent and the global divergent flow kinetic energy by about a factor of 2. Including only easterly wind data in the tropics is more effective in controlling the model error than including only westerly wind data. This conclusion is especially noteworthy because approximately twice as many upper tropospheric westerly winds were available in these cases as easterly winds.

Paegle, J.↗

The wavenumber spectra of scatterometer-derived winds

The accuracy of scatterometer-derived winds using a frequency-domain analysis and simulation is studied. The wavenumber spectra of the Seasat-A-scatterometer (SASS)-derived wind fields have been observed to be accentuated relative to the input wind field. The results of extensive simulations designed to test this observation are reported. Actual SASS measurements of the normalized radar backscatter (NRB) over an orbit (rev) are used as a template to generate simulated NRB measurements. Pointwise estimation of winds from the simulated NRB measurements is accomplished with the ambiguity closest to the true wind selected as the unique wind vector estimate. For comparison, winds are also estimated using a new model-based approach. After wind retrieval, the spectra of the estimated wind fields are computed and compared to the input wind field. The high wavenumber portion of the spectra of the pointwise estimated winds was higher than the spectra of the true winds by an amount which depends on the wind speed variance.

Long, D. G.↗

Comparison of TOPEX/Poseidon Sea Level and Linear Model Results forced by Various Wind Products for the Tropical Pacific

The goal of this paper is to compare TOPEX/Posaidon (T/P) sea level with sea level results from linear ocean model experiments forced by several different wind products for the tropical Pacific. During the period of this study (October 1992 - October 1995), available wind products include satellite winds from the ERS-1 scatterometer product of [HALP 97] and the passive microwave analysis of SSMI winds produced using the variational analysis method (VAM) of [ATLA 91]. In addition, atmospheric GCM winds from the NCEP reanalysis [KALN 96], ECMWF analysis [ECMW94], and the Goddard EOS-1 (GEOS-1) reanalysis experiment [SCHU 93] are available for comparison. The observed ship wind analysis of FSU [STRI 92] is also included in this study. The linear model of [CANE 84] is used as a transfer function to test the quality of each of these wind products for the tropical Pacific. The various wind products are judged by comparing the wind-forced model sea level results against the T/P sea level anomalies. Correlation and RMS difference maps show how well each wind product does in reproducing the T/P sea level signal. These results are summarized in a table showing area average correlations and RMS differences. The large-scale low-frequency temporal signal is reproduced by all of the wind products, However, significant differences exist in both amplitude and phase on regional scales. In general, the model results forced by satellite winds do a better job reproducing the T/P signal (i.e. have a higher average correlation and lower RMS difference) than the results forced by atmospheric model winds.

Hackert, Eric C.↗

A Probabilistic Technique for Improving the Mid-Swath Wind Direction Accuracy of Scanning Pencil Beam Scatterometers

The SeaWinds on QuikSCAT scatterometer is a device developed by NASA JPL to measure the speed and direction of ocean surface winds. Simulations performed in order to determine the expected performance of the instrument prior to its launch have indicated that the accuracy of the retrieved wind vectors varies across the swath. In particular the mid-swath accuracy is worse than that of the rest of the swath. This behavior is a general characteristic of scanning pencil beam scatterometers. For SeaWinds, the accuracy of the rest of the swath, and the size of the swath are such that the instrument meets its science requirements despite mid-swath shortcomings. However, by understanding the problem at mid-swath, we can improve the performance there as well, yielding even better coverage and accuracy than was originally intended. The mid-swath performance degradation is due to suboptimal viewing geometry. In particular mid-swath measurements contains measurements from two azimuths, roughly 180 degrees apart. With the standard wind retrieval technique, mid-swath root mean square (RMS) wind direction errors are approximately 30 degrees for low wind speeds (3 - 5.5 m/s) and 20 degrees for moderate to high wind speeds (5.5 - 30 m/s). We discuss the underlying causes of this phenomenon in detail and propose a modification to the wind retrieval technique in order to improve mid-swath performance. By estimating the range of likely wind directions for each measurement cell, one can optimally apply information from neighboring cells in order to reduce random wind direction errors without significantly degrading the resolution of the resultant wind field. In this manner we are able to achieve mid-swath RMS wind direction errors as low as 15 degrees for low winds and 10 degrees for moderate to high winds, while at the same time preserving high resolution structures such as cyclones and fronts. Since our results are based on simulated data, we also outline a procedure for making use of real data to optimize the technique after launch.

Stiles, Bryan↗

Simulation of Ground Winds Time Series for the NASA Crew Launch Vehicle (CLV)

Simulation of wind time series based on power spectrum density (PSD) and spectral coherence models for ground wind turbulence is described. The wind models, originally developed for the Shuttle program, are based on wind measurements at the NASA 150-m meteorological tower at Cape Canaveral, FL. The current application is for the design and/or protection of the CLV from wind effects during on-pad exposure during periods from as long as days prior to launch, to seconds or minutes just prior to launch and seconds after launch. The evaluation of vehicle response to wind will influence the design and operation of constraint systems for support of the on-pad vehicle. Longitudinal and lateral wind component time series are simulated at critical vehicle locations. The PSD model for wind turbulence is a function of mean wind speed, elevation and temporal frequency. Integration of the PSD equation over a selected frequency range yields the variance of the time series to be simulated. The square root of the PSD defines a low-pass filter that is applied to adjust the components of the Fast Fourier Transform (FFT) of Gaussian white noise. The first simulated time series near the top of the launch vehicle is the inverse transform of the adjusted FFT. Simulation of the wind component time series at the nearest adjacent location (and all other succeeding next nearest locations) is based on a model for the coherence between winds at two locations as a function of frequency and separation distance, where the adjacent locations are separated vertically and/or horizontally. The coherence function is used to calculate a coherence weighted FFT of the wind at the next nearest location, given the FFT of the simulated time series at the previous location and the essentially incoherent FFT of the wind at the selected location derived a priori from the PSD model. The simulated time series at each adjacent location is the inverse Fourier transform of the coherence weighted FFT. For a selected design case, the equations, the process and the simulated time series at multiple vehicle stations are presented.

Adelfang, Stanley I.↗

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

The peak winds near the surface are an important forecast element for Space Shuttle landings. As defined in the Shuttle Flight Rules (FRs), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMTJ) developed a personal computer based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak-wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center. However, the shuttle must land at Edwards Air Force Base (EAFB) in southern California when weather conditions at Kennedy Space Center in Florida are not acceptable, so SMG forecasters requested that a similar tool be developed for EAFB. Marshall Space Flight Center (MSFC) personnel archived and performed quality control of 2-minute average and 10-minute peak wind speeds at each tower adjacent to the main runway at EAFB from 1997- 2004. They calculated wind climatologies and probabilities of average peak wind occurrence based on the average speed. The climatologies were calculated for each tower and month, and were stratified by hour, direction, and direction/hour. For the probabilities of peak wind occurrence, MSFC calculated empirical and modeled probabilities of meeting or exceeding specific 10-minute peak wind speeds using probability density functions. The AMU obtained and reformatted the data into Microsoft Excel PivotTables, which allows users to display different values with point-click-drag techniques. The GUT was then created from the PivotTables using Visual Basic for Applications code. The GUI is run through a macro within Microsoft Excel and allows forecasters to quickly display and interpret peak wind climatology and likelihoods in a fast-paced operational environment. A summary of how the peak wind climatologies and probabilities were created and an overview of the GUT will be presented.

Dreher, Joseph↗

Mars Technologies Spawn Durable Wind Turbines

Sometimes referred to as regenerative life support systems, the concept includes an enclosed self-sufficient habitat that can independently support life for years on end. Such a system aims not only to produce its own food and water but to purify air and convert waste into useful byproducts. In the early 1990s, NASA was planning for an extended stay on Mars, and Bubenheim and his Ames colleagues were concentrating efforts on creating a complete ecological system to sustain human crewmembers during their time on the Red Planet. The main barrier to developing such a system, he says, is energy. Mars has no power plants, and a regenerative system requires equipment that runs on electricity to do everything from regulating humidity in the atmosphere to monitoring the quality of recycled water. The Ames group started looking at how to best make power on a planet that is millions of miles away from Earth and turned to a hybrid concept combining wind and solar power technologies. The reason was that Mars experiences frequent dust storms that can block nearly all sunlight. When theres a dust storm and the wind is blowing, the wind system could be the dominant power source. When the wind is not blowing and the sun is out, photovoltaics could be the dominant source, says Bubenheim.To develop and test the wind power technology, Ames turned to a remote, harsh environment here on Earth: the South Pole. The South Pole was a really good analog for Mars, says Bubenheim. The technology features for going to Mars were the same technology features needed to make something work at the South Pole.Around the same time that NASA started investigating energy technologies for the Red Planet, the National Science Foundation (NSF) was working on a redesign of their station at the South Pole. To power its operations, NSF used fuel that it flew to the remote location, but the Foundation recognized the benefits of also using onsite renewable energy technologies. In the winter they have small crews and their power requirements are less, says Bubenheim. In the summers, they bring in larger groups and photovoltaics could supply a lot of power. Using renewable energy technology could be a way of reducing the amount of fuel they have to fly in.Technology TransferTo advance wind turbine technology to meet the requirements of extremely harsh environments like that on Mars, Ames partnered with NSF and the Department of Energy. It was clear that a lot of the same features were also desirable for the cold regions of the Earth, says Bubenheim. NASA took the leadership on the team because we had the longest-term technology a Mars turbine. Years before, NSF had worked with a company called Northern Power Systems (NPS), based in Barre, Vermont, to deploy a 3-kilowatt wind turbine on Black Island off the coast of Antarctica.Sometimes referred to as regenerative life support systems, the concept includes an enclosed self-sufficient habitat that can independently support life for years on end. Such a system aims not only to produce its own food and water but to purify air and convert waste into useful byproducts. In the early 1990s, NASA was planning for an extended stay on Mars, and Bubenheim and his Ames colleagues were concentrating efforts on creating a complete ecological system to sustain human crewmembers during their time on the Red Planet. The main barrier to developing such a system, he says, is energy. Mars has no power plants, and a regenerative system requires equipment that runs on electricity to do everything from regulating humidity in the atmosphere to monitoring the quality of recycled water. The Ames group started looking at how to best make power on a planet that is millions of miles away from Earth and turned to a hybrid concept combining wind and solar power technologies. The reason was that Mars experiences frequent dust storms that can block nearly all sunlight. When there's a dust storm and the wind is blowing, the wind system could be the dominant power source. When the wind is not blowing and the sun is out, photovoltaics could be the dominant source, says Bubenheim.To develop and test the wind power technology, Ames turned to a remote, harsh environment here on Earth: the South Pole. The South Pole was a really good analog for Mars, says Bubenheim. The technology features for going to Mars were the same technology features needed to make something work at the South Pole.Around the same time that NASA started investigating energy technologies for the Red Planet, the National Science Foundation (NSF) was working on a redesign of their station at the South Pole. To power its operations, NSF used fuel that it flew to the remote location, but the Foundation recognized the benefits of also using onsite renewable energy technologies. In the winter they have small crews and their power requirements are less, says Bubenheim. In the summers, they bring in larger groups and photovoltaics could supply a lot of power. Using renewable energy technology could be a way of reducing the amount of fuel they have to fly in.Technology Transfer To advance wind turbine technology to meet the requirements of extremely harsh environments like that on Mars, Ames partnered with NSF and the Department of Energy. It was clear that a lot of the same features were also desirable for the cold regions of the Earth, says Bubenheim. NASA took the leadership on the team because we had the longest-term technology a Mars turbine.

Mars Technologies↗

Lidar and UAS Measurements of Winds for the NASA Advanced Air Mobility Mission

The envisioned future of Advanced Air Mobility involves low-altitude operation of a new class of air vehicles. The low altitudes of the flight paths would have these vehicles spending most of their operation in conditions where unpredictable wind and turbulence effects may occur. Wind measurements will hence be critical to ensure safe and efficient operations for AAM. Such wind measurement could be used as a monitoring system for warning of hazardous wind events, as data input to forecasting models, or as a research tool to understand wind effects in complex environments. To meet this need NASA is evaluating wind sensing technologies with a capability to probe the atmospheric boundary layer. Doppler lidar is a leading candidate ground-based sensor, as shown by the decades-long history of being an effective tool for many wind studies. However, there is a need to re-assess the Doppler wind lidars for the AAM application which involves evaluating wind effects as vehicles operate in and out of vertiports, notably regarding spatial resolution. Meeting the needs for spatial resolution may involve the use of dual-Doppler techniques, rather than just single Doppler lidar. The study was furthermore motivated by looking toward the future of AAM, in which the air vehicles involved can also provide wind measurements. Airborne wind measurements, obtained directly from vehicle-mounted anemometers or indirectly from vehicle navigation data, offer a means to compare remotely sensed wind lidar with in-situ measurements. The following sections report on results of wind measurements obtained using lidar and the small uninhabited aircraft systems (sUAS’s) operating in the same volume of air.

Adam Medina↗