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NASA NTRS1999

Installed Transonic 2D Nozzle Nacelle Boattail Drag Study

The Transonic Nozzle Boattail Drag Study was initiated in 1995 to develop an understanding of how external nozzle transonic aerodynamics effect airplane performance and how strongly those effects are dependent on nozzle configuration (2D vs. axisymmetric). MDC analyzed the axisymmetric nozzle. Boeing subcontracted Northrop-Grumman to analyze the 2D nozzle. AU participants analyzed the AGARD nozzle as a check-out and validation case. Once the codes were checked out and the gridding resolution necessary for modeling the separated flow in this region determined, the analysis moved to the installed wing/body/nacelle/diverter cases. The boat tail drag validation case was the AGARD B.4 rectangular nozzle. This test case offered both test data and previous CFD analyses for comparison. Results were obtained for test cases B.4.1 (M=0.6) and B.4.2 (M=0.938) and compared very well with the experimental data. Once the validation was complete a CFD grid was constructed for the full Ref. H configuration (wing/body/nacelle/diverter) using a combination of patched and overlapped (Chimera) grids. This was done to ensure that the grid topologies and density would be adequate for the full model. The use of overlapped grids allowed the same grids from the full configuration model to be used for the wing/body alone cases, thus eliminating the risk of grid differences affecting the determination of the installation effects. Once the full configuration model was run and deemed to be suitable the nacelle/diverter grids were removed and the wing/body analysis performed. Reference H wing/body results were completed for M=0.9 (a=0.0, 2.0, 4.0, 6.0 and 8.0), M=1.1 (a=4.0 and 6.0) and M=2.4 (a=0.0, 2.0, 4.4, 6.0 and 8.0). Comparisons of the M=0.9 and M=2.4 cases were made with available wind tunnel data and overall comparisons were good. The axi-inlet/2D nozzle nacelle was analyzed isolated. The isolated nacelle data coupled with the wing/body result enabled the interference effects of the installed nacelles to be determined. Isolated nacelle mm were made at M=0.9 and M=1.1 for both the supersonic and transonic nozzle settings. AU of the isolated nacelle cases were run at alpha=0. Full configuration runs were to be made at Mach numbers of 0.9, 1.1, and 2.4 (the same as the wing/body and isolated nacelles). Both the isolated nacelles and installed nacelles were run with inlet conditions designed to give zero spillage. This was to be done in order to isolate the boattail effects as much as possible. Full configuration runs with the supersonic nozzles were completed for M=0.9 and 1.1 at a=4.0 and 6.0 (4 runs total) and with the transonic nozzles at M=0.9 and 1.1 at a=2.0, 4.0 and 6.0 (6 runs total). Drag breakdowns were completed for the M=0.9 and M= 1.1 showing favorable interference drag for both cases.

Malone, Michael B.↗
NASA NTRS2001

Rollout and Turnoff (ROTO) Guidance and Information Displays: Effect on Runway Occupancy Time in Simulated Low-Visibility Landings

This report examines a rollout and turnoff (ROTO) system for reducing the runway occupancy time for transport aircraft in low-visibility weather. Simulator runs were made to evaluate the system that includes a head-up display (HUD) to show the pilot a graphical overlay of the runway along with guidance and steering information to a chosen exit. Fourteen pilots (airline, corporate jet, and research pilots) collectively flew a total of 560 rollout and turnoff runs using all eight runways at Hartsfield Atlanta International Airport. The runs consisted of 280 runs for each of two runway visual ranges (RVRs) (300 and 1200 ft). For each visual range, half the runs were conducted with the HUD information and half without. For the runs conducted with the HUD information, the runway occupancy times were lower and more consistent. The effect was more pronounced as visibility decreased. For the 1200-ft visibility, the runway occupancy times were 13% lower with HUD information (46.1 versus 52.8 sec). Similarly, for the 300-ft visibility, the times were 28% lower (45.4 versus 63.0 sec). Also, for the runs with HUD information, 78% (RVR 1200) and 75% (RVR 300) had runway occupancy times less than 50 sec, versus 41 and 20%, respectively, without HUD information.

Hueschen, Richard M.↗
NASA NTRS2007

The Effect of Increasing Inertia upon Vertical Ground Reaction Forces during Locomotion

The addition of inertia to exercising astronauts could increase ground reaction forces and potentially provide a greater health benefit. However, conflicting results have been reported regarding the adaptations to additional mass (inertia) without additional net weight (gravitational force) during locomotion. We examined the effect of increasing inertia while maintaining net gravitational force on vertical ground reaction forces and kinematics during walking and running. Vertical ground reaction force was measured for ten healthy adults (5 male/5 female) during walking (1.34 m/s) and running (3.13 m/s) using a force-measuring treadmill. Subjects completed locomotion at normal weight and mass, and at 10, 20, 30, and 40% of added inertial force. The added gravitational force was relieved with overhead suspension, so that the net force between the subject and treadmill at rest remained equal to 100% body weight. Peak vertical impact forces and loading rates increased with increased inertia during walking, and decreased during running. As inertia increased, peak vertical propulsive forces decreased during walking and did not change during running. Stride time increased during walking and running, and contact time increased during running. Vertical ground reaction force production and adaptations in gait kinematics were different between walking and running. The increased inertial forces were utilized independently from gravitational forces by the motor control system when determining coordination strategies.

DeWitt, John K.↗
NASA NTRS2016

Successful Validation of Sample Processing and Quantitative Real-Time PCR Capabilities on the International Space Station

The WetLab-2 system was developed by NASA Ames Research Center to offer new capabilities to researchers. The system can lyse cells and extract RNA (Ribonucleic Acid) on-orbit from different sample types ranging from microbial cultures to animal tissues. The purified RNA can then either be stabilized for return to Earth or can be used to conduct on-orbit quantitative Reverse Transcriptase PCR (Polymerase Chain Reaction) (qRT-PCR) analysis without the need for sample return. The qRT-PCR results can be downlinked to the ground a few hours after the completion of the run. The validation flight of the WetLab-2 system launched on SpaceX-8 on April 8, 2016. On orbit operations started on April 15th with system setup and was followed by three quantitative PCR runs using an E. coli genomic DNA template pre-loaded at three different concentrations. These runs were designed to discern if quantitative PCR functions correctly in microgravity and if the data is comparable to that from the ground control runs. The flight data showed no significant differences compared to the ground data though there was more variability in the values, this was likely due to the numerous small bubbles observed. The capability of the system to process samples and purify RNA was then validated using frozen samples prepared on the ground. The flight data for both E. coli and mouse liver clearly shows that RNA was successfully purified by our system. The E. coli qRT-PCR run showed successful singleplex, duplex and triplex capability. Data showed high variability in the resulting Cts (Cycle Thresholds [for the PCR]) likely due to bubble formation and insufficient mixing during the procedure run. The mouse liver qRT-PCR run had successful singleplex and duplex reactions and the variability was slightly better as the mixing operation was improved. The ability to purify and stabilize RNA and to conduct qRT-PCR on-orbit is an important step towards utilizing the ISS as a National Laboratory facility. The ability to get on-orbit data will provide investigators with the opportunity to adjust experimental parameters in real time without the need for sample return and re-flight. The WetLab-2 Project is supported by the Research Integration Office in the ISS Program.

gene expression↗
NASA NTRS2016

Successful Validation of Sample Processing and Quantitative Real-Time PCR Capabilities on the International Space Station

The WetLab-2 system was developed by NASA Ames Research Center to offer new capabilities to researchers. The system can lyse cells and extract RNA (Ribonucleic Acid) on-orbit from different sample types ranging from microbial cultures to animal tissues. The purified RNA can then either be stabilized for return to Earth or can be used to conduct on-orbit quantitative Reverse Transcriptase PCR (Polymerase Chain Reaction) (qRT-PCR) analysis without the need for sample return. The qRT-PCR results can be downlinked to the ground a few hours after the completion of the run. The validation flight of the WetLab-2 system launched on SpaceX-8 on April 8, 2016. On orbit operations started on April 15th with system setup and was followed by three quantitative PCR runs using an E. coli genomic DNA template pre-loaded at three different concentrations. These runs were designed to discern if quantitative PCR functions correctly in microgravity and if the data is comparable to that from the ground control runs. The flight data showed no significant differences compared to the ground data though there was more variability in the values, this was likely due to the numerous small bubbles observed. The capability of the system to process samples and purify RNA was then validated using frozen samples prepared on the ground. The flight data for both E. coli and mouse liver clearly shows that RNA was successfully purified by our system. The E. coli qRT-PCR run showed successful singleplex, duplex and triplex capability. Data showed high variability in the resulting Cts (Cycle Thresholds [for the PCR]) likely due to bubble formation and insufficient mixing during the procedure run. The mouse liver qRT-PCR run had successful singleplex and duplex reactions and the variability was slightly better as the mixing operation was improved. The ability to purify and stabilize RNA and to conduct qRT-PCR on-orbit is an important step towards utilizing the ISS as a National Laboratory facility. The ability to get on-orbit data will provide investigators with the opportunity to adjust experimental parameters in real time without the need for sample return and re-flight. The WetLab-2 Project is supported by the Research Integration Office in the ISS Program.

quantitative PCR↗
NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗
NASA NTRS2022

Air Traffic Management TestBed: Messaging Performance

The Air Traffic Management (ATM) TestBed is an air traffic management modeling and simulation platform and framework developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The communication middleware, implemented in the TestBed framework layer, is a core feature for data message exchange. The feature provides an abstraction layer called Messaging Support to allow switching one middleware to another without a need to rebuild the simulation components. Messaging performance such as latencies, run durations, and throughputs are important factors. Low latencies can produce accurate results in high-fidelity and visualization models. Short run durations are preferred because better run efficiency can be achieved. High throughputs allow more runs to be executed concurrently. This technical memorandum studies and compares the messaging performance by running a full-day, fast-time simulation using three communication middleware as well as tweaking the default communication middleware settings used by the TestBed. Results indicate that the messaging performance could be improved by disabling either compression or persistence settings, while the run duration and throughput could be further improved by disabling both settings with a tradeoff of the message latencies increased by a factor of ten.

Chok Fung Lai↗
NASA NTRS1988

Photon degradation of AlGaAs/GaAs solar cells

The behavior of GaAs solar cells after photon illumination for a prolonged exposure time is presented. More than 55 OMCVD AlGaAs/GaAs solar cells were exposed for over 400 h to AM0 photons at 28 C in three separate, well-controlled runs. Significant degradation of solar cell efficiency was observed in two out of three runs. Although noticeable losses in the open-circuit voltage, fill factor, and maximum power were observed, no change in the short-circuit current was found. In one of the runs, no change was seen in either the test cells or the control cells. Each cell in this run was protected with a coverglass. The cells in this run had thicker buffer layers and thinner window layers than the cells in the other two runs.

Anspaugh, Bruce↗
NASA NTRS1994

Numerical simulation of three-dimensional self-gravitating flow

The three-dimensional flow of a self-gravitating fluid is numerically simulated using a Fourier pseudospectral method with a logarithmic variable formulation. Two cases with zero total angular momentum are studied in detail, a 32(exp 3) simulation (Run A) and a 64(exp 3) simulation (Run B). Other than the grid size, the primary differences between the two cases are that Run A modeled atomic hydrogen and had considerably more compressible motion initially than Run B, which modeled molecular hydrogen. ('Compressible motion' is that part of the velocity which has zero curl, but non-zero divergence). The numerical results indicate that gravitational collapse can proceed in a variety of ways. In Run A, collapse led to an elongated tube-like structure, while in Run B, collapse led to a flatter, disk-like structure.

Shebalin, J. V.↗
NASA NTRS2001

Thresholds in Atmosphere-Soil Moisture Interactions: Results from Climate Model Studies

The potential predictability of the effects of warm season soil moisture anomalies over the central U.S. has been investigated using a series of GCM (Global Climate Model) experiments with the NCAR (National Center for Atmospheric Research) CCM3 (Community Climate Model version 3)/LSM (Land Surface Model). Three different types of experiments have been made, all starting in either March (representing precursor conditions) or June (conditions at the onset of the warm season): (1) 'anomaly' runs with large, exaggerated initial soil moisture reductions, aimed at evaluating the physical mechanisms by which soil moisture can affect the atmosphere; (2) 'predictability' runs aimed at evaluating whether typical soil moisture initial anomalies (indicative of year-to-year variability) can have a significant effect, and if so, for how long; (3) 'threshold' runs aimed at evaluating if a soil moisture anomaly must be of a specific size (i.e., a threshold crossed) before a significant impact on the atmosphere is seen. The 'anomaly' runs show a large, long-lasting response in soil moisture and also quantities such as surface temperature, sea level pressure, and precipitation; effects persist for at least a year. The 'predictability' runs, on the other hand, show very little impact of the initial soil moisture anomalies on the subsequent evolution of soil moisture and other atmospheric parameters; internal variability is most important, with the initial state of the atmosphere (representing remote effects such as SST anomalies) playing a more minor role. The 'threshold' runs, devised to help resolve the dichotomy in 'anomaly' and 'predictability' results, suggest that, at least in CCM3/LSM, the vertical profile of soil moisture is the most important factor, and that deep soil zone anomalies exert a more powerful, long-lasting effect than do anomalies in the near surface soil zone. We therefore suggest that soil moisture feedbacks may be more important in explaining prolonged decadal to century-long droughts evident in the historic and recent prehistoric records, but less important on a seasonal to interannual time-scale.

Oglesby, Robert J.↗
NASA NTRS1989

Effects of restricted feeding schedules on circadian organization in squirrel monkeys

Free running circadian rhythms of motor activity, food-motivated lever-pressing, and either drinking (N = 7) or body temperature (N = 3) were recorded from 10 squirrel monkeys maintained in constant illumination with unlimited access to food. Food availability was then restricted to a single unsignaled 3-hour interval each day. The feeding schedule failed to entrain the activity rhythms of 8 monkeys, which continued to free-run. Drinking was almost completely synchronized by the schedule, while body temperature showed a feeding-induced rise superimposed on a free-running rhythm. Nonreinforced lever-pressing showed both a free-running component and a 24-hour component that anticipated the time of feeding. At the termination of the schedule, all recorded variables showed free-running rhythms, but in 3 animals the initial phase of the postschedule rhythms was advanced by several hours, suggesting relative coordination. Of the remaining 2 animals, one exhibited stable entrainment of all 3 recorded rhythms, while the other appeared to entrain temporarily to the feeding schedule. These results indicate that restricted feeding schedules are only a weak zeitgeber for the circadian pacemaker generating free-running rhythms in the squirrel monkey. Such schedules, however, may entrain a separate circadian system responsible for the timing of food-anticipatory changes in behavior and physiology.

Non-NASA Center↗
NASA NTRS2009

Deployable Aeroshell Flexible Thermal Protection System Testing

Deployable aeroshells offer the promise of achieving larger aeroshell surface areas for entry vehicles than otherwise attainable without deployment. With the larger surface area comes the ability to decelerate high-mass entry vehicles at relatively low ballistic coefficients. However, for an aeroshell to perform even at the low ballistic coefficients attainable with deployable aeroshells, a flexible thermal protection system (TPS) is required that is capable of surviving reasonably high heat flux and durable enough to survive the rigors of construction handling, high density packing, deployment, aerodynamic loading and aerothermal heating. The Program for the Advancement of Inflatable Decelerators for Atmospheric Entry (PAIDAE) is tasked with developing the technologies required to increase the technology readiness level (TRL) of inflatable deployable aeroshells, and one of several of the technologies PAIDAE is developing for use on inflatable aeroshells is flexible TPS. Several flexible TPS layups were designed, based on commercially available materials, and tested in NASA Langley Research Center's 8 Foot High Temperature Tunnel (8ft HTT). The TPS layups were designed for, and tested at three different conditions that are representative of conditions seen in entry simulation analyses of inflatable aeroshell concepts. Two conditions were produced in a single run with a sting-mounted dual wedge test fixture. The dual wedge test fixture had one row of sample mounting locations (forward) at about half the running length of the top surface of the wedge. At about two thirds of the running length of the wedge, a second test surface drafted up at five degrees relative to the first test surface established the remaining running length of the wedge test fixture. A second row of sample mounting locations (aft) was positioned in the middle of the running length of the second test surface. Once the desired flow conditions were established in the test section the dual wedge test fixture, oriented at 5 degrees angle of attack down, was injected into the flow. In this configuration the aft sample mounting location was subjected to roughly twice the heat flux and surface pressure of the forward mounting location. The tunnel was run at two different conditions for the test series: 1) 'Low Pressure', and 2) 'High Pressure'. At 'Low Pressure' conditions the TPS layups were tested at 6W/cm2 and 11W/cm2 while at 'High Pressure' conditions the TPS layups were tested at 11W/cm2 and 20W/cm2. This paper details the test configuration of the TPS samples in the 8Ft HTT, the sample holder assembly, TPS sample layup construction, sample instrumentation, results from this testing, as well as lessons learned.

Hughes, Stephen J.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2005

Improved Compression of Wavelet-Transformed Images

A recently developed data-compression method is an adaptive technique for coding quantized wavelet-transformed data, nominally as part of a complete image-data compressor. Unlike some other approaches, this method admits a simple implementation and does not rely on the use of large code tables. A common data compression approach, particularly for images, is to perform a wavelet transform on the input data, and then losslessly compress a quantized version of the wavelet-transformed data. Under this compression approach, it is common for the quantized data to include long sequences, or runs, of zeros. The new coding method uses prefixfree codes for the nonnegative integers as part of an adaptive algorithm for compressing the quantized wavelet-transformed data by run-length coding. In the form of run-length coding used here, the data sequence to be encoded is parsed into strings consisting of some number (possibly 0) of zeros, followed by a nonzero value. The nonzero value and the length of the run of zeros are encoded. For a data stream that contains a sufficiently high frequency of zeros, this method is known to be more effective than using a single variable length code to encode each symbol. The specific prefix-free codes used are from two classes of variable-length codes: a class known as Golomb codes, and a class known as exponential-Golomb codes. The codes within each class are indexed by a single integer parameter. The present method uses exponential-Golomb codes for the lengths of the runs of zeros, and Golomb codes for the nonzero values. The code parameters within each code class are determined adaptively on the fly as compression proceeds, on the basis of statistics from previously encoded values. In particular, a simple adaptive method has been devised to select the parameter identifying the particular exponential-Golomb code to use. The method tracks the average number of bits used to encode recent runlengths, and takes the difference between this average length and the code parameter. When this difference falls outside a fixed range, the code parameter is updated (increased or decreased). The Golomb code parameter is selected based on the average magnitude of recently encoded nonzero samples. The coding method requires no floating- point operations, and more readily adapts to local statistics than other methods. The method can also accommodate arbitrarily large input values and arbitrarily long runs of zeros. In practice, this means that changes in the dynamic range or size of the input data set would not require a change to the compressor. The algorithm has been tested in computational experiments on test images. A comparison with a previously developed algorithm that uses large code tables (generated via Huffman coding on training data) suggests that the data-compression effectiveness of the present algorithm is comparable to the best performance achievable by the previously developed algorithm.

Kiely, Aaron↗
NASA NTRS2013

Comparison of Four Precipitation Forcing Datasets in Land Information System Simulations over the Continental U.S.

The NASA Short ]term Prediction Research and Transition (SPoRT) Center in Huntsville, AL is running a real ]time configuration of the NASA Land Information System (LIS) with the Noah land surface model (LSM). Output from the SPoRT ]LIS run is used to initialize land surface variables for local modeling applications at select National Weather Service (NWS) partner offices, and can be displayed in decision support systems for situational awareness and drought monitoring. The SPoRT ]LIS is run over a domain covering the southern and eastern United States, fully nested within the National Centers for Environmental Prediction Stage IV precipitation analysis grid, which provides precipitation forcing to the offline LIS ]Noah runs. The SPoRT Center seeks to expand the real ]time LIS domain to the entire Continental U.S. (CONUS); however, geographical limitations with the Stage IV analysis product have inhibited this expansion. Therefore, a goal of this study is to test alternative precipitation forcing datasets that can enable the LIS expansion by improving upon the current geographical limitations of the Stage IV product. The four precipitation forcing datasets that are inter ]compared on a 4 ]km resolution CONUS domain include the Stage IV, an experimental GOES quantitative precipitation estimate (QPE) from NESDIS/STAR, the National Mosaic and QPE (NMQ) product from the National Severe Storms Laboratory, and the North American Land Data Assimilation System phase 2 (NLDAS ]2) analyses. The NLDAS ]2 dataset is used as the control run, with each of the other three datasets considered experimental runs compared against the control. The regional strengths, weaknesses, and biases of each precipitation analysis are identified relative to the NLDAS ]2 control in terms of accumulated precipitation pattern and amount, and the impacts on the subsequent LSM spin ]up simulations. The ultimate goal is to identify an alternative precipitation forcing dataset that can best support an expansion of the real ]time SPoRT ]LIS to a domain covering the entire CONUS.

Case, Jonathan L.↗
NASA NTRS2013

Automatic Data Filter Customization Using a Genetic Algorithm

This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.

Mandrake, Lukas↗
NASA NTRS2014

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

Flooding and drought are two key forecasting challenges for the Kenya Meteorological Department (KMD). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the boundary layer of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-end events over east Africa. KMD currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Nonhydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over eastern Africa. Two organizations at the National Aeronautics and Space Administration Marshall Space Flight Center in Huntsville, AL, SERVIR and the Short-term Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMD for enhancing its regional modeling capabilities. To accomplish this goal, SPoRT and SERVIR will provide experimental land surface initialization datasets and model verification capabilities to KMD. To produce a land-surface initialization more consistent with the resolution of the KMD-WRF runs, the NASA Land Information System (LIS) will be run at a comparable resolution to provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Additionally, real-time green vegetation fraction data from the Visible Infrared Imaging Radiometer Suite will be incorporated into the KMD-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service. Finally, model verification capabilities will be transitioned to KMD using the Model Evaluation Tools (MET) package, in order to quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. The transition of these MET tools will enable KMD to monitor model forecast accuracy in near real time. This presentation will highlight preliminary verification results of WRF runs over east Africa using the LIS land surface initialization.

Case. Jonathan↗
NASA NTRS2014

Gas Chromatograph Method Optimization Trade Study for RESOLVE: 20-meter Column v. 8-meter Column

RESOLVE is the payload on a Class D mission, Resource Prospector, which will prospect for water and other volatile resources at a lunar pole. The RESOLVE payload's primary scientific purpose includes determining the presence of water on the moon in the lunar regolith. In order to detect the water, a gas chromatograph (GC) will be used in conjunction with a mass spectrometer (MS). The goal of the experiment was to compare two GC column lengths and recommend which would be best for RESOLVE's purposes. Throughout the experiment, an Inficon Fusion GC and an Inficon Micro GC 3000 were used. The Fusion had a 20m long column with 0.25mm internal diameter (Id). The Micro GC 3000 had an 8m long column with a 0.32mm Id. By varying the column temperature and column pressure while holding all other parameters constant, the ideal conditions for testing with each column length in their individual instrument configurations were determined. The criteria used for determining the optimal method parameters included (in no particular order) (1) quickest run time, (2) peak sharpness, and (3) peak separation. After testing numerous combinations of temperature and pressure, the parameters for each column length that resulted in the most optimal data given my three criteria were selected. The ideal temperature and pressure for the 20m column were 95 C and 50psig. At this temperature and pressure, the peaks were separated and the retention times were shorter compared to other combinations. The Inficon Micro GC 3000 operated better at lower temperature mainly due to the shorter 8m column. The optimal column temperature and pressure were 70 C and 30psig. The Inficon Micro GC 3000 8m column had worse separation than the Inficon Fusion 20m column, but was able to separate water within a shorter run time. Therefore, the most significant tradeoff between the two column lengths was peak separation of the sample versus run time. After performing several tests, it was concluded that better detection via good peak separation with a longer run time is a better asset than moderate peak separation with a shorter run time. Even given that RESOLVE is highly interested in water and that mission timeline is of significant importance given the short seven-to-ten-day mission timeline, worse detection with an 8m column may lead to overlooking other substances existing on the moon that could advance planetary science. Thus, I recommend the 20m column. However, if mission timeline and water separation are deemed the highest priority, the 8m column should be selected due to its ability to separate water within a shorter run time than the 20m column.

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