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Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.
Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.
Vibration characteristics of a deployable controllable-geometry truss boom
An analytical study was made to evaluate changes in the fundamental frequency of a two dimensional cantilevered truss boom at various stages of deployment. The truss could be axially deployed or retracted and undergo a variety of controlled geometry changes by shortening or lengthening the telescoping diagonal members in each bay. Both untapered and tapered versions of the truss boom were modeled and analyzed by using the finite element method. Large reductions in fundamental frequency occurred for both the untapered and tapered trusses when they were uniformly retracted or maneuvered laterally from their fully deployed position. These frequency reductions can be minimized, however, if truss geometries are selected which maintain cantilever root stiffness during truss maneuvers.
Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery
An algorithm for automatic atmospheric correction of satellite imagery of the earth's surface is proposed which is applicable to low-resolution and high-resolution imagery of land areas. The algorithm is based on the satellite image being corrected and on the climatology of the area, and it requires that some pixels in the image correspond to dense dark vegetation as the surface cover. The algorithm is sensitive to the assumed reflectance of the dense dark vegetation, and the accuracy of the corrected surface reflectance is expected to be + or - 0.01. Using the method, aerosol optical thicknesses were derived from clear and hazy Landsat MSS images in the Washington, D.C. and Chesapeake Bay region, and the results are found to agree well with simultaneous sunphotometer ground measurements.
Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences
A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results—especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes’ rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated ‘engine’ capable of computationally executing analysis decisions currently made by visual inspection across the sciences.
Crystal-Chemical Analysis Martian Minerals in Gale Crater
The CheMin instrument on the Mars Science Laboratory rover Curiosity performed X-ray diffraction analyses on scooped soil at Rocknest and on drilled rock fines at Yellowknife Bay (John Klein and Cumberland samples), The Kimberley (Windjana sample), and Pahrump (Confidence Hills sample) in Gale crater, Mars. Samples were analyzed with the Rietveld method to determine the unit-cell parameters and abundance of each observed crystalline phase. Unit-cell parameters were used to estimate compositions of the major crystalline phases using crystal-chemical techniques. These phases include olivine, plagioclase and clinopyroxene minerals. Comparison of the CheMin sample unit-cell parameters with those in the literature provides an estimate of the chemical compositions of the major crystalline phases. Preliminary unit-cell parameters, abundances and compositions of crystalline phases found in Rocknest and Yellowknife Bay samples were reported in. Further instrument calibration, development of 2D-to- 1D pattern conversion corrections, and refinement of corrected data allows presentation of improved compositions for the above samples.
Bioaerosols are the dominant source of warm-temperature immersion-mode INPs and drive uncertainties in INP predictability
Ice-nucleating particles (INPs) are rare atmospheric aerosols that initiate primary ice formation, but accurately simulating their concentrations and variability in large-scale climate models remains a challenge. Doing so requires both simulating major particle sources and parameterizing their ice nucleation (IN) efficiency. Validating and improving model predictions of INP concentrations requires measuring their concentrations delineated by particle type. We present a method to speciate INP concentrations into contributions from dust, sea spray aerosol (SSA), and bioaerosol. Field campaign data from Bodega Bay, California, showed that bioaerosols were the primary source of INPs between –12° and –20°C, while dust was a minor source and SSA had little impact. We found that recent parameterizations for dust and SSA accurately predicted ambient INP concentrations. However, the model did not skillfully simulate bioaerosol INPs, suggesting a need for further research to identify major factors controlling their emissions and INP efficiency for improved representation in models.
In Situ Water Quality Data for the Chesapeake Bay
This paper examines in situ water quality datameasured during2020-2021in the Chesapeake Bay for comparison with optical satellite data. Thiscollection was performed as part of a NASA project aiming to develop new methods for water quality monitoring from satellite remote sensingusing artificial intelligence. Our objective is to use insitu data as ground-truth to provide water quality classifications, or labels,to their overlapping (in time and location)satellite imagery. Having such labeled data, can help us achieve our project’s longer-termgoal:to train artificial intelligencemodelsto recognize features in spectral informationfor monitoringwater qualityfrom satellites. Because routine monitoring by state agencies is conducted at discrete locations, we obtained a flow-through system operated from small boats to measure waterquality parameters along transects for comparison with two-dimensional maps collected from space, with an initial focus on low oxygenevents, due to their large spatial extent and regular occurrence each summer.We also evaluated similar in situ data collected during 1984-2021by the Chesapeake Program.
The development of a method for predicting the noise exposure of payloads in the space shuttle orbiter vehicle
The development of an analytical model for the prediction of sound levels in the payload bay of the space shuttle orbiter vehicle is outlined. Formulation of the analytical model and its validation by means of model scale and full scale tests are included. It is shown that the approach used in the development effort has resulted in a prediction procedure which can be expected to give reliable estimates of payload bay sound levels, even when a payload is present. Furthermore, the analytical model has the capability of being readily modified to include other excitations such as turbulent boundary layers and propeller near-field pressures, and to other aerospace vehicles.
A recent case study in system identification
Results of a recent study of a ten-bay truss structure at the NASA Langley Research Center are reported. First, the conditioning of complex eigenvectors derived by the ERA method is discussed. Results of parameter estimation using the SSID (Structural System Identification) code are then presented. Based on the results of the study, it is concluded that (1) parameter estimation based on modal data should include eigenvectors as well as eigenvalues; (2) the eigenvectors should be orthogonalized when orthogonality is poor due to closely spaced modes; and (3) the parameters used in the estimation should enable the model to match the data.
NASA Kennedy Space Center Swamp Works 10th Anniversary: Innovative Research & Technology Development Summary
Kennedy Space Center’s (KSC) Swamp Works, provides government and commercial space ventures with the technologies required for working and living on the surfaces of the Moon or other planets and bodies in our solar system. The Swamp Works team establishes rapid, innovative and cost-effective exploration mission solutions through leveraging of partnerships across NASA, industry and academia. Concepts start small and build up fast, with lean development processes and a hands-on approach. Testing is performed in early stages to drive design improvements and progressively increase Technology Readiness Levels (TRL). Swamp Works provides concepts, architecture studies and trades, designs, data, technology development, technology demonstration hardware, flight hardware, testing, flight support and knowledge in support of the development of surface systems. It consists of several teams with associated laboratories and test capabilities. The Granular Mechanics and Regolith Operations (GMRO) Laboratory and the Electrostatics and Surface Physics Laboratory (ESPL) are co-located in the Engineering Development Lab (EDL) facility high bay. The Applied Chemistry Lab (ACL) is in an adjacent facility and other KSC labs are being influenced by the innovation methods pioneered at the Swamp Works. The Swamp Works was founded in January 2013 by a group of scientists and engineers at KSC with the over-arching vision of expanding humanity and civilization into the solar system by the use of space resources via advanced technology. Ultimately, this will create a solar system economy that will improve the human condition due to the abundance of energy and resources. This paper will summarize the projects and technology development that have been performed by the Swamp Works to celebrate its 10th Anniversary of innovation success.
Swamp Works Technology Development 10th Anniversary: 2013-2023
Kennedy Space Center’s (KSC) Swamp Works, provides government and commercial space ventures with the technologies required for working and living on the surfaces of the Moon or other planets and bodies in our solar system. The Swamp Works team establishes efficient, innovative and cost-effective exploration mission solutions through leveraging of partnerships across NASA, industry and academia. Concepts start small and build up momentum, with lean development processes and a hands-on approach. Testing is performed in early stages to drive design improvements and progressively increase Technology Readiness Levels (TRL). Swamp Works provides concepts, architecture studies and trades, designs, data, technology development, technology demonstration hardware, flight hardware, testing, flight support and knowledge in support of the development of surface systems. It consists of several teams with associated laboratories and test capabilities. The Granular Mechanics and Regolith Operations (GMRO) Laboratory and the Electrostatics and Surface Physics Laboratory (ESPL) are co-located in the Engineering Development Lab (EDL) facility high bay. The Applied Chemistry Lab (ACL) is in an adjacent facility and other KSC labs are being influenced by the innovation methods pioneered at the Swamp Works. The Swamp Works was founded in January 2013 by a group of scientists and engineers at KSC with the over-arching vision of expanding humanity and civilization into the solar system by the use of space resources via advanced technology. Ultimately, this will create a solar system economy that will improve the human condition due to the abundance of energy and resources. This presentation will summarize the projects and technology development that have been performed by the Swamp Works to celebrate its 10th Anniversary of innovation success.
Swamp Works Technology Development 10th Anniversary: 2013-2023 - Innovative Research & Technology Development Summary
Kennedy Space Center’s (KSC) Swamp Works, provides government and commercial space ventures with the technologies required for working and living on the surfaces of the Moon or other planets and bodies in our solar system. The Swamp Works team establishes rapid, innovative and cost-effective exploration mission solutions through leveraging of partnerships across NASA, industry and academia. Concepts start small and build up efficiently, with lean development processes and a hands-on approach. Testing is performed in early stages to drive design improvements and progressively increase Technology Readiness Levels (TRL). Swamp Works provides concepts, architecture studies and trades, designs, data, technology development, technology demonstration hardware, flight hardware, testing, flight support and knowledge in support of the development of surface systems. It consists of several teams with associated laboratories and test capabilities. The Granular Mechanics and Regolith Operations (GMRO) Laboratory and the Electrostatics and Surface Physics Laboratory (ESPL) are co-located in the Engineering Development Lab (EDL) facility high bay. The Applied Chemistry Lab (ACL) is in an adjacent facility and other KSC labs are being influenced by the innovation methods pioneered at the Swamp Works. The Swamp Works was founded in January 2013 by a group of scientists and engineers at KSC with the over-arching vision of expanding humanity and civilization into the solar system by the use of space resources via advanced technology. Ultimately, this will create a solar system economy that will improve the human condition due to the abundance of energy and resources. This presentation will summarize the projects and technology development that have been performed by the Swamp Works to celebrate its 10th Anniversary of innovation success.
Document Classification Techniques for Aviation Letters of Agreement
Often when working with technical documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques, including Transfer Learning, for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.
Document Classification Techniques for Aviation Letters of Agreement
Often when working with historic air traffic management (ATM) documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.
Document Classification Techniques for Aviation Letters of Agreement
Often when working with historic air traffic management (ATM) documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.
On the robustness of a Bayes estimate
This paper examines the robustness of a Bayes estimator with respect to the assigned prior distribution. A Bayesian analysis for a stochastic scale parameter of a Weibull failure model is summarized in which the natural conjugate is assigned as the prior distribution of the random parameter. The sensitivity analysis is carried out by the Monte Carlo method in which, although an inverted gamma is the assigned prior, realizations are generated using distribution functions of varying shape. For several distributional forms and even for some fixed values of the parameter, simulated mean squared errors of Bayes and minimum variance unbiased estimators are determined and compared. Results indicate that the Bayes estimator remains squared-error superior and appears to be largely robust to the form of the assigned prior distribution.
Evaluation of the procedure 1A component of the 1980 US/Canada wheat and barley exploratory experiment
Several techniques which use clusters generated by a new clustering algorithm, CLASSY, are proposed as alternatives to random sampling to obtain greater precision in crop proportion estimation: (1) Proportional Allocation/relative count estimator (PA/RCE) uses proportional allocation of dots to clusters on the basis of cluster size and a relative count cluster level estimate; (2) Proportional Allocation/Bayes Estimator (PA/BE) uses proportional allocation of dots to clusters and a Bayesian cluster-level estimate; and (3) Bayes Sequential Allocation/Bayesian Estimator (BSA/BE) uses sequential allocation of dots to clusters and a Bayesian cluster level estimate. Clustering in an effective method in making proportion estimates. It is estimated that, to obtain the same precision with random sampling as obtained by the proportional sampling of 50 dots with an unbiased estimator, samples of 85 or 166 would need to be taken if dot sets with AI labels (integrated procedure) or ground truth labels, respectively were input. Dot reallocation provides dot sets that are unbiased. It is recommended that these proportion estimation techniques are maintained, particularly the PA/BE because it provides the greatest precision.