Results of adaptive predictor studies, part 1
Adaptative predictor studies based on Tiros cloud picture video data, and relative merits of linear and Markov prediction data compression techniques
TIROS SATELLITE↗
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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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A harmonic analysis of lunar topography
A global lunar topographic map has been derived from existing earth-based and orbital observations supplemented in areas without data by a linear autocovariance predictor. Of 2592 bins, each 5 deg square, 1380 (64.7% by area) contain at least one measurement. A spherical harmonic analysis to degree 12 yields a mean radius of 1737.53 plus or minus 0.03 km (formal standard error) and an offset of the center of figure of 1.98 plus or minus 0.06 km toward (19 plus or minus 2) deg S, (194 plus or minus 1) deg E. A Bouguer gravity map, derived from a 12-degree free-air gravity model and the present topography data, is presented for an elevation of 100 km above the mean surface. It is confirmed that the low-degree gravity harmonics are determined primarily by surface height variations and only secondarily by lateral density variations.
Bills, B. G.↗
Optimal Prediction of Clocks from Finite Data
This talk is about optimal linear prediction of processes with stationary dth increments, which serve as a class of models for random clock disturbances. The predictor is obtained by orthogonal projection on the affine space of estimators whose errors are invariant to additive polynomials of degree < d. The projection conditions give a system of linear equations thatcan be solved straightforwardly for the regression coefficients. If the data are equally spaced, then the predictor can be obtained by an extension of Levinson's algorithm.
stationary increments↗
Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery
A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.
Kiely, Aaron B.↗
Predictability of the earth's polar motion
The results of a numerical experiment on the predictability of terrestrial polar motion using a Bureau International de l'Heure data set from 1967-83 are presented. A floating point predictor was defined by decomposing polar motion into secular motion and annual and Chandler wobbles. The secular term was linear and the others periodic, the former due to atmospheric mass transport and the latter to Eulerian nutation. A least squares estimator was employed with the data base to obtain constants for the model, which is given the name 'floating-point'. A fixed-period predictor was also devised and, in comparison with the floating point predictor in 6 yr estimates of the annual wobble period, failed after a given length of time. It is suggested that the failure is due to atmospheric motions.
Fong, B.↗
Peak Wind Tool for General Forecasting
The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.
Barrett, Joe H., III↗
Prediction of muscle performance during dynamic repetitive movement
BACKGROUND: During long-duration spaceflight, astronauts experience progressive muscle atrophy and often perform strenuous extravehicular activities. Post-flight, there is a lengthy recovery period with an increased risk for injury. Currently, there is a critical need for an enabling tool to optimize muscle performance and to minimize the risk of injury to astronauts while on-orbit and during post-flight recovery. Consequently, these studies were performed to develop a method to address this need. METHODS: Eight test subjects performed a repetitive dynamic exercise to failure at 65% of their upper torso weight using a Lordex spinal machine. Surface electromyography (SEMG) data was collected from the erector spinae back muscle. The SEMG data was evaluated using a 5th order autoregressive (AR) model and linear regression analysis. RESULTS: The best predictor found was an AR parameter, the mean average magnitude of AR poles, with r = 0.75 and p = 0.03. This parameter can predict performance to failure as early as the second repetition of the exercise. CONCLUSION: A method for predicting human muscle performance early during dynamic repetitive exercise was developed. The capability to predict performance to failure has many potential applications to the space program including evaluating countermeasure effectiveness on-orbit, optimizing post-flight recovery, and potential future real-time monitoring capability during extravehicular activity.
Physical Endurance/physiology↗
The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina
Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.
Nazmus Shams Sazib↗
Adaptive State Predictor Based Human Operator Modeling on Longitudinal and Lateral Control
Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to categorize these interactions of the pilot with an adaptive controller compensating during control surface failures. A general linear in-parameter model structure is used to represent a pilot. Three different estimation methods are explored. A gradient descent estimator (GDE), a least squares estimator with exponential forgetting (LSEEF), and a least squares estimator with bounded gain forgetting (LSEBGF) used the experiment data to predict pilot stick input. Previous results have found that the GDE and LSEEF methods are fairly accurate in predicting longitudinal stick input from commanded pitch. This paper discusses the accuracy of each of the three methods - GDE, LSEEF, and LSEBGF - to predict both pilot longitudinal and lateral stick input from the flight director's commanded pitch and bank attitudes.
Trujillo, Anna C.↗
Use of Minute-by-Minute Cardiovascular Measurements During Tilt Tests to Strengthen Inference on the Effect of Long-Duration Space Flight on Orthostatic Hypotension
Typical methodology for evaluating the effects of spaceflight on orthostatic hypotension (OH) has been survival analysis of tolerance times from 80 head-up tilt tests. However when scheduled test durations are short, there may not be enough failures to allow survival analysis to adequately estimate and compare the effects of flight phase (e.g. pre-flight, number of days post-flight), flight duration, and their interaction, as well as interactions with effects of interventions or countermeasures. The problem is exacerbated in the presence of a repeated measures design, in which subjects participate in tilt tests during various flight phases. Here we show how it is possible to dramatically improve the efficiency of statistical inference in this setting by making use of the additional information contained in minute-by-minute observations of cardiovascular parameters thought to be reflective of progression towards presyncope during tilt testing. Methods: We retrospectively examined operational tilt test (OTT; 10 -min 80 head-up tilt) data from 20 International Space Station (ISS) and 66 Shuttle astronauts 10 d before launch (L-10), on landing day (R+0) and during recovery (R+1, R+3, R+6-10) depending on the level of participation. Data from 5 ISS astronauts tested on R+0 or R+1 who used non-standard countermeasures were excluded. In addition to OTT survival time, 8 cardiovascular parameters (CP: heart rate, systolic, diastolic, and mean arterial blood pressure, pulse pressure, stroke volume, cardiac output, and total peripheral resistance) that might be predictive of progression towards presyncope were measured every minute of each OTT. Statistical analysis was predicated on a two ]stage model of causation. In the first stage, flight duration and time from landing affect the astronauts' degree of OH, which is manifested in the time trends and variation of the above CPs during OTTs. In the second stage, the behavior of these parameters directly affects OTT survival time. Actual analysis proceeded in the opposite direction. First we identified those CPs or linear combinations that best predicted OTT survival regardless of what spaceflight conditions led to OTT completion or presyncope. From these, we calculated a summary statistic (one per OTT) that best predicted survival. We then used mixed ]model regression analysis to relate changes in the summary statistic to flight phase and duration. Inference on the effects of phase, duration, and their interaction on OH follows directly from this second analysis. Results: A linear combination (W) of diastolic blood pressure (DBP) and stroke volume (SV) was found to be the best predictor of OTT survival using the complete data set of minute-by-minute observations of CPs for each OTT. Furthermore, the log-transformed standard deviation of W (Z = log SW) was found to be a strong predictor of survival in the reduced data set consisting of one observation per OTT. In other words, this measure of variability of W during an OTT was the best indicator of whether or not the subject could complete the 10-min test, with higher variability (i.e. higher values of Z) being associated with greater probability of failure. In the mixed-model regression analysis where Z was now treated as a outcome with flight phase and duration groups (ISS and STS) as predictors, we found that there was a significantly more variability in W (higher values of Z) for both groups at R+0, but with no evidence of an interaction until R+3, when the ISS group still had inflated variability, but not the STS group. Conclusions: Variability of the cardiovascular index W recovers more slowly after long-compared to short-duration spaceflight. Since high variability of W has also been shown to be predictive of OTT failure, a primary manifestation of OH, a logical conclusion is that recovery from OH also is slower after long-duration compared to short-duration spaceflights.
Feiveson, Alan H.↗
A predictive monsoon signal in the surface level thermal field over India
The mean monthly surface thermal field over India during the premonsoon months March-May, based on mean monthly-temperature/minimum-temperature/maximum-temperature data from the network of 119 stations, was examined with the objective of locating significant predictor parameters for forecasting Indian monsoon rainfall. The results of linear correlation analysis bring out three areas for which the relationships between Indian monsoon rainfalls and the area average of mean monthly minimum temperature for April or for May were found to be significant. The best correlation was found for the southern peninsular area, where the mean May minimum temperature is significantly related to the 500 mB April ridge, a tendency in the Southern Oscillation Index, and a tendency in eastern equatorial Pacific SST.
Mooley, D. A.↗
Analysis of nonlifting and lifting airfoils in transonic flow by parametric differentiation
A method is presented which allows one to solve nonlinear transonic flow problems by analyzing a sequence of linear equations. The small disturbance formulation of steady transonic flow over airfoils is linearized by considering the perturbations due to small changes in airfoil thickness ratio and angle of attack. Repeatedly incrementing those parameters results in a series of nonlinear solutions and cumulatively determines the effects of large changes in airfoil geometry. Successive line overrelaxation is used to solve the associated linear equations and is coupled with predictor-corrector methods to yield series of nonlinear solutions. Computed pressure distributions on biconvex airfoils show good agreement with experimental data and other transonic prediction methods. Possible extensions to unsteady and/or three-dimensional transonic flow problems are briefly discussed.
Whitlow, W., Jr.↗
Demonstration of Linear Covariance Analysis Techniques to Evaluate Entry Descent and Landing Guidance Algorithms, Vehicle Configurations, Analysis Techniques, and Trajectory Profiles
Linear covariance analysis techniques have been previously developed to analyze closed-loop entry, descent, and landing (EDL) scenarios and the initial validation efforts are under-way confirming the generated GN&C system performance results. Given both the theoretical foundation and previous conceptual demonstration, this work begins to flex the potential of linear covariance analysis for atmospheric flight and highlight its versatility and reliability by evaluating multiple entry guidance algorithms, vehicle configurations, trajectory profiles, environment conditions, and analysis techniques for a variety of trade studies. To demonstrate the benefit linear covariance analysis can provide in producing rapid yet accurate performance data, two entry profiles are adopted including the NASA Mars Science Laboratory (MSL) and Exploration Flight Test-1 (EFT-1) while utilizing two different guidance algorithms, the Apollo Final Phase (AFP) and the Fully Numeric Predictor-Corrector Entry Guidance (FNPEG) with different navigation sensor suites in a 6 degree-of-freedom (6-DOF) simulation environment. Results are shown using both linear covariance and Monte Carlo analysis techniques to high-light the consistency between the two methodologies and continue the validation maturation of linear covariance analysis for entry, descent, and landing.
EDL↗
Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida
The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.
Lambert, Winfred↗
Global Variability in Sonic Boom Exposure due to Macroscopic Effects
Supersonic flight over land has been prohibited since 1973 due to the loudness of sonic booms. NASA is building the X-59 aircraft as part of its Quesst mission to demonstrate low-loudness shaped sonic booms, or “sonic thumps.” The Quesst mission will gather human perception data via a series of community noise surveys across the USA. The noise dose and perceptual response data will be provided to the International Civil Aviation Organization (ICAO) and the Federal Aviation Administration for use in determining potential future supersonic aircraft noise certification standards, effectively changing the prohibition from a speed limit to a noise limit. These noise regulations must be globally effective, as long travel distances see the largest benefit to supersonic flight. The state of the atmosphere through which a sonic boom travels affects the size of the region exposed to sound, the “carpet width” (CW), as well as the loudness. The focus of this dissertation is to understand and quantify the expected loudness and CW of sonic booms due to the macroscopic atmospheric effects around the world. A pair of large-scale propagation simulation studies were conducted using the NASA PCBoom code to compare predicted sonic boom loudness and CW statistics first across the USA and then across the world. For the USA study, near-field data of the X-59 in steady cruise was propagated at 4 cardinal headings at 138 locations through 5 years of Climate Forecast System Version 2 (CFSv2) atmospheric profiles. Results of a bootstrap forest predictor screening model indicated the importance of climate zone, latitude, ground elevation, season, and heading. It also noted the unimportance of time of day for predicting loudness and CW. The data is visualized in aggregate, and then broken out geographically, by season and heading, and by climate zone. Multiple linear regression models were fit to the data from the 138 locations so that estimates of the loudness and CW can be produced anywhere in the US. The results can aid in planning when and where to fly the X-59. For the global study, near-field data from three aircraft, the X-59 in a quiet and loud configuration, B-58, and Concorde, were propagated at four cardinal headings through data from three atmospheric models, the CFSv2, the Global Forecast System (GFS), and the ECMWF Reanalysis Version 5 (ERA5), at 100 global locations over 1 year. Results of a bootstrap forest predictor screening model indicated the importance of climate zone, ground elevation, season, and heading. Similar to the US study, the model indicated time of day was not an important predictor. The model also indicated that choice of weather model was not important, so the atmospheric model data are effectively interchangeable. The ERA5 model was chosen for use in an extension of the study to include 18 additional locations to ensure sampling of every climate zone. Loudness and CW results are shown in aggregate, and split geographically and by heading, season, and climate. Multiple linear regression models were fit to the data from the 118 locations so that estimates of loudness and CW can be produced around the world. N-waves and shaped booms did not have the same global variability. Koppen-Geiger climate zones were used as the climate zone definition for the global study. These are available as present-day and future climate projections. Making use of the multiple linear regression models, the future climate zones were input to estimate the effect of the changing climate on sonic boom loudness and CW. Results indicate that a changing climate would have little impact on the effectiveness of noise regulations.
X-59↗
Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.
Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.
Ohring, G.↗
Image coding of SAR imagery
Five coding techniques in the spatial and transform domains have been evaluated for SAR image compression: linear three-point predictor (LTPP), block truncation coding (BTC), microadaptive picture sequencing (MAPS), adaptive discrete cosine transform (ADCT), and adaptive Hadamard transform (AHT). These techniques have been tested with Seasat data. Both LTPP and BTC spatial domain coding techniques provide very good performance at rates of 1-2 bits/pixel. The two transform techniques, ADCT and AHT, demonstrate the capability to compress the SAR imagery to less than 0.5 bits/pixel without visible artifacts. Tradeoffs such as the rate distortion performance, the computational complexity, the algorithm flexibility, and the controllability of compression ratios are also discussed.
Chang, C. Y.↗
Suppression of pressure oscillations in an open cavity by passive pneumatic control
A computational investigation has been conducted to determine the effectiveness of a passive control technique in suppressing the oscillations in an open cavity exposed to a supersonic flow. Time-accurate solutions of the unsteady, Reynolds-averaged, Navier-Stokes equations were obtained with an explicit predictor-corrector algorithm; the passive control was implemented through the use of a simple linear pressure-velocity law along the porous cavity floor. The computational code was validated by comparisons with experimental data for the cavity flow without control. The computational results with control demonstrate that the oscillations are suppressed; the fluid dynamic mechanism of the control is seen to be a stabilization of the free-shear layer. Spectral analysis of the unsteady data shows that the resonant frequencies are essentially unchanged by the control. Beneficial reductions in the cavity resonant drag are also observed.
Chokani, N.↗