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NASA NTRS2023

Southern Rockies Western Slope Agriculture: Identifying Drivers of Rangeland Production for Drought Planning on the Western Slope of the Southern Rockies

Over the last decade, the southern Rocky Mountains of the United States experienced severe and variable drought. Local ranchers and landowners have reported strain on their operations, citing decreasing forage for their cattle and a need to adjust their business models. This study identified Major Land Resource Area-48 (MLRA-48) and northwestern Colorado as the key region for analysis. NASA DEVELOP partnered with the BLM Colorado River Field Office, Colorado State University Extension, USDA Forest Service, and the National Drought Mitigation Center to address concerns regarding the efficacy of remotely sensed rangeland production platforms and identify early warning climatic indicators of drought. The study identified two key platforms, The Rangeland Productivity Monitoring Service (RPMS) and Rangeland Analysis Platform (RAP), which use NASA Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2 to estimate rangeland biomass. We regressed these with in situ biomass data to validate their efficacy and found that RAP was more effective than RPMS in estimating rangeland biomass, though it presents a tendency to overestimate. Our study performed a random forest analysis, comparing monthly RAP biomass estimates to a variety of climate variables, including mean precipitation, temperature, Palmer Drought Severity Index, snow water equivalent, snow persistence from Terra MODIS, wind speed and direction, and vapor pressure deficit. We determined that vapor pressure deficit and precipitation are key indicators in predicting forage production in MLRA-48. Our climate analysis provided our partners with greater understanding of the influence of various climate variables in determining rangeland production and allows them to assist land managers in drought mitigation.

remote sensing↗
NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular inefficiency that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based metering of internal departures, or TMA scheduling. Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot be easily fitted into the overhead stream. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. Depending on how the double delay is defined between 0.3 percent and 0.8 percent of arrivals at EWR experienced double delays in this period. However, this represents between 21 percent and 62 percent of all internal departures in GDP and TMA scheduling. A deep dive into the data reveals that two causes of high internal departure scheduling delays are upstream flights making up time between their estimated departure clearance times (EDCTs) and entry into time based metering, which undermines the sequencing and spacing underlying the flight EDCTs, and high demand on TMA, when TMA airborne metering delays are high. Data mining methods (currently) including logistic regression, support vector machines and K-nearest neighbors are used to predict the occurrence of double delays and high internal departure scheduling delays with accuracies up to 0.68. So far, key indicators of double delay and high internal departure scheduling delay are TMA virtual runway queue size, and the degree to which estimated runway demand based on TMA estimated times of arrival has changed relative to the estimated runway demand based on EDCTs. However, more analysis is needed to confirm this.

traffic management advisor↗
NASA NTRSDate not supplied

Developing Rationale for Experimental Designs Employed During Forthcoming NASA Quesst Mission Community Noise Campaigns

Since the publication of R.A. Fisher’s “The Arrangement of Field Experiments” in 1926, the enumerated principles that contributed to the improvement of agricultural field experiments in the early 20th century (randomization, replication, blocking, and appropriate variation of factors) remain hallmarks of all well-designed clinical trials and scientific endeavors in the 21st century. Beginning in 2026, NASA will conduct community noise campaigns with the first-of-kind X-59 experimental aircraft to 1) demonstrate the possibility of quiet supersonic flight over land and 2) to collect live data about annoyance (a categorical response) in relation to estimated noise levels (an experimental factor) produced by the new noise phenomenon, a low-noise “sonic thump”. The resulting predicted relationship between perceptual response and estimated noise level, e.g., mixed logistic regression or related models in the class of generalized linear (mixed) models, is believed to be a useful policy tool that domestic and international regulators can use when deciding whether existing prohibitions of supersonic flight over land can be replaced with a noise-based limit. The X-59 is engineered with the expectation that annoyance in the tested ranges should be a rare outcome, and, consequently, NASA convened an interdisciplinary team to examine and refine the rationale underlying the experimental design for future community campaigns. In this presentation, we review relevant literature on experiments with rare binary outcomes, relate the deliberations of the NASA team to tenets of good experimental design, and highlight several challenges and operational realities of this ambitious campaign.

Design of Experiments; blocking; randomization; co↗
NASA NTRS2002

Multi-Image Registration for an Enhanced Vision System

An Enhanced Vision System (EVS) utilizing multi-sensor image fusion is currently under development at the NASA Langley Research Center. The EVS will provide enhanced images of the flight environment to assist pilots in poor visibility conditions. Multi-spectral images obtained from a short wave infrared (SWIR), a long wave infrared (LWIR), and a color visible band CCD camera, are enhanced and fused using the Retinex algorithm. The images from the different sensors do not have a uniform data structure: the three sensors not only operate at different wavelengths, but they also have different spatial resolutions, optical fields of view (FOV), and bore-sighting inaccuracies. Thus, in order to perform image fusion, the images must first be co-registered. Image registration is the task of aligning images taken at different times, from different sensors, or from different viewpoints, so that all corresponding points in the images match. In this paper, we present two methods for registering multiple multi-spectral images. The first method performs registration using sensor specifications to match the FOVs and resolutions directly through image resampling. In the second method, registration is obtained through geometric correction based on a spatial transformation defined by user selected control points and regression analysis.

Hines, Glenn↗
NASA NTRS2008

A VARI-Based Relative Greenness from MODIS Data for Computing the Fire Potential Index

The Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate the VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R2 of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FP outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.

Schneider, P.↗
NASA NTRS2004

NASA Tech Briefs, March 2004

Topics covered include: 1) Advanced Signal Conditioners for Data-Acquisition Systems; 2) Downlink Data Multiplexer; 3) Viewing ISS Data in Real Time via the Internet; 4) Autonomous Environment-Monitoring Networks; 5) Readout of DSN Monitor Data; 6) Parallel-Processing Equalizers for Multi-Gbps Communications; 7) AIN-Based Packaging for SiC High-Temperature Electronics; 8) Software for Optimizing Quality Assurance of Other Software; 9) The TechSat 21 Autonomous Sciencecraft Experiment; 10) Software for Analyzing Laminar-to-Turbulent Flow Transitions; 11) Elastomer Filled With Single-Wall Carbon Nanotubes; 12) Modifying Ship Air-Wake Vortices for Aircraft Operations; 13) Strain-Gauge Measurement of Weight of Fluid in a Tank; 14) Advanced Docking System With Magnetic Initial Capture; 15) Blade-Pitch Control for Quieting Tilt-Rotor Aircraft; 16) Solar Array Panels With Dust-Removal Capability; 17) Aligning Arrays of Lenses and Single-Mode Optical Fibers; 18) Automatic Control of Arc Process for Making Carbon Nanotubes; 19) Curved-Focal-Plane Arrays Using Deformed-Membrane Photodetectors; 20) Role of Meteorology in Flights of a Solar-Powered Airplane; 21) Model of Mixing Layer With Multicomponent Evaporating Drops; 22) Solution-Assisted Optical Contacting; 23) Improved Discrete Approximation of Laplacian of Gaussian; 24) Utilizing Expert Knowledge in Estimating Future STS Costs; 25) Study of Rapid-Regression Liquefying Hybrid Rocket Fuels; and 26) More About the Phase-Synchronized Enhancement Method.

Source record↗
NASA NTRS2010

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

Barrett, Joe H., III↗
NASA NTRS2011

Sensitivity of C-Band Polarimetric Radar-Based Drop Size Distribution Measurements to Maximum Diameter Assumptions

The estimation of rain drop size distribution (DSD) parameters from polarimetric radar observations is accomplished by first establishing a relationship between differential reflectivity (Z(sub dr)) and the central tendency of the rain DSD such as the median volume diameter (D0). Since Z(sub dr) does not provide a direct measurement of DSD central tendency, the relationship is typically derived empirically from rain drop and radar scattering models (e.g., D0 = F[Z (sub dr)] ). Past studies have explored the general sensitivity of these models to temperature, radar wavelength, the drop shape vs. size relation, and DSD variability. Much progress has been made in recent years in measuring the drop shape and DSD variability using surface-based disdrometers, such as the 2D Video disdrometer (2DVD), and documenting their impact on polarimetric radar techniques. In addition to measuring drop shape, another advantage of the 2DVD over earlier impact type disdrometers is its ability to resolve drop diameters in excess of 5 mm. Despite this improvement, the sampling limitations of a disdrometer, including the 2DVD, make it very difficult to adequately measure the maximum drop diameter (D(sub max)) present in a typical radar resolution volume. As a result, D(sub max) must still be assumed in the drop and radar models from which D0 = F[Z(sub dr)] is derived. Since scattering resonance at C-band wavelengths begins to occur in drop diameters larger than about 5 mm, modeled C-band radar parameters, particularly Z(sub dr), can be sensitive to D(sub max) assumptions. In past C-band radar studies, a variety of D(sub max) assumptions have been made, including the actual disdrometer estimate of D(sub max) during a typical sampling period (e.g., 1-3 minutes), D(sub max) = C (where C is constant at values from 5 to 8 mm), and D(sub max) = M*D0 (where the constant multiple, M, is fixed at values ranging from 2.5 to 3.5). The overall objective of this NASA Global Precipitation Measurement Mission (GPM/PMM Science Team)-funded study is to document the sensitivity of DSD measurements, including estimates of D0, from C-band Z(sub dr) and reflectivity to this range of D(sub max) assumptions. For this study, GPM Ground Validation 2DVD's were operated under the scanning domain of the UAHuntsville ARMOR C-band dual-polarimetric radar. Approximately 7500 minutes of DSD data were collected and processed to create gamma size distribution parameters using a truncated method of moments approach. After creating the gamma parameter datasets the DSD's were then used as input to a T-matrix model for computation of polarimetric radar moments at C-band. All necessary model parameterizations, such as temperature, drop shape, and drop fall mode, were fixed at typically accepted values while the D(sub max) assumption was allowed to vary in sensitivity tests. By hypothesizing a DSD model with D(sub max) (fit) from which the empirical fit to D0 = F[Z(sub dr)] was derived via non-linear least squares regression and a separate reference DSD model with D(sub max) (truth), bias and standard error in D0 retrievals were estimated in the presence of Z(sub dr) measurement error and hypothesized mismatch in D(sub max) assumptions. Although the normalized standard error for D0 = F[Z(sub dr)r] can increase slightly (as much as from 11% to 16% for all 7500 DSDs) when the D(sub max) (fit) does not match D(sub max) (truth), the primary impact of uncertainty in D(sub max) is a potential increase in normalized bias error in D0 (from 0% to as much as 10% over all 7500 DSDs, depending on the extent of the mismatch between D(sub max) (fit) and D(sub max) (truth)). For DSDs characterized by large Z(sub dr) (Z(sub dr) > 1.5 to 2.0 dB), the normalized bias error for D0 estimation at C-band is sometimes unacceptably large (> 10%), again depending on the extent of the hypothesized D(sub max) mismatch. Modeled errors in D0 retrievals from Z(sub dr) at C-band are demonstrated in detail and comparedo similar modeled retrieval errors at S-band and X-band where the sensitivity to D(sub max) is expected to be less. The impact of D(sub max) assumptions to the retrieval of other DSD parameters such as Nw, the liquid water content normalized intercept parameter, are also explored. Likely implications for DSD retrievals using C-band polarimetric radar for GPM are assessed by considering current community knowledge regarding D(sub max) and quantifying the statistical distribution of Z(sub dr) from ARMOR over a large variety of meteorological conditions. Based on these results and the prevalence of C-band polarimetric radars worldwide, a call for more emphasis on constraining our observational estimate of D(sub max) within a typical radar resolution volume is made

Carey, Lawrence D.↗
NASA NTRS2020

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates.As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.

Verification and Validation↗
NASA NTRS1977

The effects of participatory mode and task workload on the detection of dynamic system failures

The ability of operators to detect step changes in the dynamics of control systems is investigated as a joint function of, (1) participatory mode: whether subjects are actively controlling those dynamics or are monitoring an autopilot controlling them, and (2) concurrent task workload. A theoretical analysis of detection in the two modes identifies factors that will favor detection in either mode. Three subjects detected system failures in either an autopilot or manual controlling mode, under single-task conditions and concurrently with a subcritical tracking task. Latency and accuracy of detection were assessed and related through a speed accuracy tradeoff representation. It was concluded that failure detection performance was better during manual control than during autopilot control, and that the extent of this superiority was enhanced as dual-task load increased. Ensemble averaging and multiple regression techniques were then employed to investigate the cues utilized by the subjects in making their detection decisions.

Wickens, C. D.↗
NASA NTRS2023

Interpretation of Probabilistic Surface Ozone Forecasts: A Case Study for Philadelphia

The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000–11 ozone-season (1 May–30 September) data, calibrated using 2012–14 data, and evaluated using 2015–18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational “expert” forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.

Nikolay Balashov↗
NASA NTRS1980

Effects of primary rotor parameters on flapping dynamics

The effects of flapping dynamics of four main rotor design features that influence the agility, stability, and operational safety of helicopters are studied. The parameters include flapping hinge offset, flapping hinge restraint, pitch-flap coupling, and blade lock number. First, the flapping equations of motion are derived that explicitly contain the design parameters. The dynamic equations are then developed for the tip-path plane, and the influence of individual and combined variations in the design parameters determined. The steady state flapping response is examined with respect to control input and aircraft angular rate which leads to a feedforward control law for control decoupling through cross feed, and a feedback control law to decouple the steady state flapping response. The condition for achieving perfect decoupling of the flapping response due to aircraft pitch and roll rates without using feedback control is also found for the hover case. It is indicated that the frequency of the regressing flapping mode of the rotor system can become low enough to require consideration in the assessment of handling characteristics.

Chen, R. T. N.↗
NASA NTRS2018

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗
NASA NTRS2022

Constraining Arctic Climate Projections of Wintertime Warming With Surface Turbulent Flux Observations and Representation of Surface-Atmosphere Coupling

The drivers of rapid Arctic climate change—record sea ice loss, warming SSTs, and a lengthening of the sea ice melt season—compel us to understand how this complex system operates and use this knowledge to enhance Arctic predictability. Changing energy flows sparked by sea ice decline, spotlight atmosphere-surface coupling processes as central to Arctic system function and its climate change response. Despite this, the representation of surface turbulent flux parameterizations in models has not kept pace with our understanding. The large uncertainty in Arctic climate change projections, the central role of atmosphere-surface coupling, and the large discrepancy in model representation of surface turbulent fluxes indicates that these processes may serve as useful observational constraints on projected Arctic climate change. This possibility requires an evaluation of surface turbulent fluxes and their sensitivity to controlling factors (surface-air temperature and moisture differences, sea ice, and winds) within contemporary climate models (here Coupled Model Intercomparison Project 6). The influence of individual controlling factors and their interactions is diagnosed using a multi-linear regression approach. This evaluation is done for four sea ice loss regimes, determined from observational sea ice loss trends, to control for the confounding effects of natural variability between models and observations. The comparisons between satellite- and model-derived surface turbulent fluxes illustrate that while models capture the general sensitivity of surface turbulent fluxes to declining sea ice and to surface-air gradients of temperature and moisture, substantial mean state biases exist. Specifically, the central Arctic is too weak of a heat sink to the winter atmosphere compared to observations, with implications to the simulated atmospheric circulation variability and thermodynamic profiles. Models were found to be about 50% more efficient at turning an air-sea temperature gradient anomaly into a sensible heat flux anomaly relative to observations. Further, the influence of sea ice concentration on the sensible heat flux is underestimated in models compared to observations. The opposite is found for the latent heat flux variability in models; where the latent heat flux is too sensitive to a sea ice concentration anomaly. Lastly, the results suggest that present-day trends in sea ice retreat regions may serve as suitable observational constraints of projected Arctic warming.

turbulent fluxes↗
NASA NTRS2010

Roles of Engineering Correlations in Hypersonic Entry Boundary Layer Transition Prediction

Efforts to design and operate hypersonic entry vehicles are constrained by many considerations that involve all aspects of an entry vehicle system. One of the more significant physical phenomenon that affect entry trajectory and thermal protection system design is the occurrence of boundary layer transition from a laminar to turbulent state. During the Space Shuttle Return To Flight activity following the loss of Columbia and her crew of seven, NASA's entry aerothermodynamics community implemented an engineering correlation based framework for the prediction of boundary layer transition on the Orbiter. The methodology for this implementation relies upon the framework of correlation techniques that have been in use for several decades. What makes the Orbiter boundary layer transition correlation implementation unique is that a statistically significant data set was acquired in multiple ground test facilities, flight data exists to assist in establishing a better correlation and the framework was founded upon state of the art chemical nonequilibrium Navier Stokes flow field simulations. The basic tenets that guided the formulation and implementation of the Orbiter Return To Flight boundary layer transition prediction capability will be reviewed as a recommended format for future empirical correlation efforts. The validity of this approach has since been demonstrated by very favorable comparison of recent entry flight testing performed with the Orbiter Discovery, which will be graphically summarized. These flight data can provide a means to validate discrete protuberance engineering correlation approaches as well as high fidelity prediction methods to higher confidence. The results of these Orbiter engineering and flight test activities only serve to reinforce the essential role that engineering correlations currently exercise in the design and operation of entry vehicles. The framework of information-related to the Orbiter empirical boundary layer transition prediction capability will be utilized to establish a fresh perspective on this role, to illustrate how quantitative statistical evaluations of empirical correlations can and should be used to assess accuracy and to discuss what the authors' perceive as a recent heightened interest in the application of high fidelity numerical modeling of boundary layer transition. Concrete results will also be developed related to empirical boundary layer transition onset correlations. This will include assessment of the discrete protuberance boundary layer transition onset data assembled for the Orbiter configuration during post-Columbia Return To Flight. Assessment of these data will conclude that momentum thickness Reynolds number based correlations have superior coefficients and uncertainty in comparison to roughness height based Reynolds numbers, aka Re(sub k) or Re(sub kk). In addition, linear regression results from roughness height Reynolds number based correlations will be evaluated, leading to a hypothesis that non-continuum effects play a role in the processes associated with incipient boundary layer transition on discrete protuberances.

Campbell, Charles H.↗
NASA NTRS2021

Bhutan Agriculture: Developing a Crop Mask for Rice and Creating a Data Collection Protocol Utilizing Remotely Sensed Data in Bhutan

Rice cultivation in Bhutan has been increasingly threatened by deteriorating soil health and outbreaks of diseases and pests associated with the global change in climate patterns. Field surveys, which the national government of Bhutan has relied on to monitor remote agricultural lands, are becoming increasingly overwhelmed by growing threats to agricultural health. To address these concerns, NASA DEVELOP partnered with the Department of Agriculture of Bhutan, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER) and worked to increase the government of Bhutan’s agricultural monitoring capacity. Utilizing Earth observations including Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Shuttle Radar Topography Mission (SRTM), and Planet imagery, the DEVELOP team worked with NASA SERVIR and created a sampling protocol to identify rice plantations and supplement field surveys for more efficient agriculture monitoring. The analysis focused on districts Paro, Punakha, Samtse, Sarpang, Trongsa, Zhemgang, Wangdue Phodrang, and Samdrup Jongkhar in the year 2020 during the period of transplantation (June) to harvesting of rice (November). The team provided the partners with a sampling protocol for integrating NASA Earth observations into their crop monitoring methods, as well as a crop mask for rice identification and to aid crop management. The crop mask for rice was developed using the Random Forest (RF) classifier for the eight districts of Bhutan. Visually, the random forest model has proved to be more accurate and precise than the classification and Regression Tree model. Statistically, the Random Forest model was 91.8% accurate in identifying rice in Bhutan.

Yeshey Seldon↗
NASA NTRS2018

Preliminary Error Characterization and Parametric Error Model for the Automatic Dependent Surveillance - Contract Extended Projected Profile Message

A critical component of Trajectory-Based Operations (TBO) is the ability for a consistent and accurate 4-dimensional trajectory (4DT) to be shared and synchronized between airborne and ground systems as well as amongst various ground automation systems. The Aeronautical Telecommunication Network—Baseline 2 (ATN-B2) standard defines the Extended Projected Profile (EPP) trajectory that can be sent via Automatic Dependent Surveillance-Contract (ADS-C) from an aircraft to ground automation. The EPP trajectory message contains a representation of the reference trajectory from an aircraft’s Flight Management System (FMS). In this work, a set of scenarios were run in a high-fidelity aircraft and FMS simulation to perform an initial characterization of EPP trajectory errors under a given set of conditions. The parameters investigated were the route length, route type, wind magnitude error, wind direction error, and with and without a required time-of-arrival (RTA) constraint. In addition, linear regression was used to identify a set of EPP error models for the cross-track, vertical, and time errors.

Guerreiro, Nelson M.↗
NASA NTRSDate not supplied

Cloud and Precipitation Analyses using Merged Datasets from Two Airborne Microwave Radiometers Covering 10–183 GHz

Microwave radiometers provide valuable insight into the structure and characteristics of clouds and precipitation. In NASA’s airborne remote-sensing arsenal, the Advanced Microwave Precipitation Radiometer (AMPR) and the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) have been used extensively in field campaigns throughout the world. AMPR operates with four channels between 10.7 and 85.5 GHz, while CoSMIR operates with nine channels ranging from 50.3 to 183.31 GHz. Although these datasets provide key information when used separately, the combination of these radiometers covers virtually the full range of frequencies used by the Global Precipitation Measurement (GPM) Microwave Imager (GMI), enabling suborbital observations to compare with GPM spaceborne measurements. This presentation will detail the merger of AMPR and CoSMIR data during two NASA airborne field campaigns: the Integrated Precipitation and Hydrology Experiment (IPHEx) and the Olympic Mountains Experiment / Radar Definition Experiment (OLYMPEX/RADEX). Other field campaigns, such as the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS), may be included as well. Using the merged brightness temperature dataset, features of precipitating and non-precipitating clouds containing liquid and/or ice hydrometeors will be discussed from selected flight segments. Geophysical retrievals derived from these brightness temperatures using a one-dimensional variational (1DVAR) technique and/or multi-linear regression equations will also be employed in these analyses. Observed transitions between precipitating and non-precipitating systems will be explored in greater detail. Dropsonde data will be used to provide environmental contexts throughout each flight, and additional observations (e.g., from airborne and/or land-based radar) will be incorporated to supplement the radiometer-based results. The broader implications of these results and pathways for future work will also be discussed.

Corey G. Amiot↗