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NASA NTRS2021

Dense 3D-Reconstruction from Monocular Image Sequences for Computationally Constrained UAS

The ability to find safe landing sites over complex 3D terrain is an essential safety feature for fully autonomous small unmanned aerial systems (UAS), which requires on-board perception for 3D reconstruction and terrain analysis if the overflown terrain is unknown. This is a challenge for UAS that are limited in size, weight and computational power, such as small rotorcrafts executing autonomous missions on Earth, or in planetary applications such as the Mars Helicopter. For such a computationally constraint system, we propose a structure from motion approach that uses inputs from a single downward facing camera to produce dense point clouds of the overflown terrain in real time. In contrast to existing approaches, our method uses metric pose information from a visual-inertial odometry algorithm as camera pose priors, which allows deploying a fast pose refinement step to align camera frames such that a conventional stereo algorithm can be used for dense 3D reconstruction. We validate the performance of our approach with extensive evaluations in simulation, and demonstrate the feasibility with data from UAS flights.

Brockers, Roland↗

NASA NTRS1990

FIRE extended time/limited area observations at Palisades, New York

Downwelling shortwave and longwave irradiation are being continuously monitored at Palisades, New York as part of the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Extended Time/Limited Area Initiative. In addition, fisheye (180 degree) sky photographs are taken at the times of NOAA 9 and LANDSAT satellite overpasses on select days, particularly when cirrus clouds are present. Measurements of incoming shortwave (0.28 to 2.80 microns) hemispheric and diffuse, hemispheric near infrared (0.7 to 2.80 microns), and downwelling hemispheric infrared (4.0 to 50.0 microns) irradiation have been made from a rooftop location on the grounds of the Lamont-Doherty Geological Observatory since December 1986. The three Eppley Precision Spectral Pyranometers and the Eppley Pyrgeometer used to measure these variables were calibrated with Colorado State University instruments at Madison, Wisconsin as part of the FIRE Intensive Laboratory. Pyrgeometer output contains an adjustment for body temperature but not for dome temperature. Data are transmitted to a Campbell CR-21 Digital Recorder, where one minute averages of ten second samples are stored and subsequently dumped to a cassette recorder. Using a Campbell C-20 Cassette Interface, these data are transferred to an Apple Macintosh computer for analysis and for archiving on floppy disks. In addition to the raw irradiances collected, variables derived from these data are generated and stored. These include: the ratio of near infrared irradiation to visible irradiation and the fraction of the full shortwave irradiation which is diffuse; and will soon include: shortwave transmissivity and optical depth in the shortwave. Sky photographs are taken with an Olympus OM2-N 35 mm camera and are timed to be coincident with overpassing NOAA and LANDSAT satellites. Palisades is within the field of view of the NOAA 9 daily in the middle to late afternoon. The satellite viewing angle is within 45 degrees of nadir over Palisades on approximately half of the passes.

Robinson, David A.↗

NASA NTRS1994

A statistical equilibrium analysis of interstellar CO toward zeta Ophiuchi as recorded by the Goddard High Resolution Spectrograph

High-resolution and high signal-to-noise ratio echelle observations made by the Goddard High Resolution Spectrograph (GHRS), aboard the Hubble Space Telescope, of molecular (12)C(16)O and (13)C(16)O in the absorbing gas toward zeta Oph are reported. A statistical equilibrium model, incorporating a different way of dealing with the radiation field in the interstellar clouds, is presented. Based upon previous observations, two velocity components of the gas at V(sub LSR) = - 0.79 and 0.39 km/s, corresponding to solar velocity = -14.6 and -13.4 km/s, together with three different sets of velocity dispersions are adopted in our models. The ratios of the computed emergent intensities (I(sub em)) resulting from the emission lines in the lowest transitions (J = 1 goes to 0, 2 goes to 1, 3 goes to 2) of ground state of CO are presented. The ratios for the two velocity components, (I(sub em)(1 goes to 0)(sub 1))/(I(sub em)(1 goes to 0)(sub 2)), and (I(sub em)(2 goes to 1)(sub 1))/I(sub em)(1 goes to 0)(sub 1)) are comparable to the observed corrected antenna temperature ratios. From the solutions to the statistical equilibrium equations including the six lowest rotational transitions of CO and from multiple Voigt profile fits, the observed (12)CO and (13)CO UV abroption features are successfully reproduced. Profile fitting and double cloud curve-of-growth analysis using the GHRS and Copernicus data indicate that the abundance of CO is (1.8 +/- 0.2) x 10(exp 15)/sq cm. The most recently measured wavelength of the (6 goes to 0) (13)CO band by Haridass & Huber (R(0) = 1370.616 A) shows that the tentative (13)CO features in the GHRS data are unquestionably identified. If there is no blending from any other unknown interstellar species, the isotope ratio of (12)CO/(13)CO is approximately 82 +/- 25. This result is close to the terrestrial value of 90 and implies that the CO is not substantially fractionated, and (12)C(16)O experiences little preferential self-shielding through the selective isotopic photodissociation process. This conclusion differs strongly with that of Sheffer et al. Other implications of the results are discussed.

Lyu, Cheng-Hsuan↗

NASA NTRS1997

Microphysical Retrievals Over Stratiform Rain Using Measurements from an Airborne Dual-Wavelength Radar-Radiometer

The need to understand the complementarity of the radar and radiometer is important not only to the Tropical Rain Measuring Mission (TRMM) program but to a growing number of multi-instrumented airborne experiment that combine single or dual-frequency radars with multichannel radiometers. The method of analysis used in this study begins with the derivation of dual-wavelength radar equations for the estimation of a two-parameter drop size distribution (DSD). Defining a "storm model" as the set of parameters that characterize snow density, cloud water, water vapor, and features of the melting layer, then to each storm model there will usually correspond a set of range-profiled drop size distributions that are approximate solutions of the radar equations. To test these solutions, a radiative transfer model is used to compute the brightness temperatures for the radiometric frequencies of interest. A storm model or class of storm models is considered optimum if it provides the best reproduction of the radar and radiometer measurements. Tests of the method are made for stratiform rain using simulated storm models as well as measured airborne data. Preliminary results show that the best correspondence between the measured and estimated radar profiles usually can be obtained by using a moderate snow density (0.1-0.2 g/cu cm), the Maxwell-Garnett mixing formula for partially melted hydrometeors (water matrix with snow inclusions), and low to moderate values of the integrated cloud liquid water (less than 1 kg/sq m). The storm-model parameters that yield the best reproductions of the measured radar reflectivity factors also provide brightness temperatures at 10 GHz that agree well with the measurements. On the other hand, the correspondence between the measured and modeled values usually worsens in going to the higher frequency channels at 19 and 34 GHz. In searching for possible reasons for the discrepancies, It is found that changes in the DSD parameter Mu, the radar constants, or the path-integrated attenuation can affect the high frequency channels significantly. In particular, parameters that cause only modest increases in the median mass diameter of the snow, and which have a minor effect on the radar returns or the low frequency brightness temperature, can produce a strong cooling of the 34 GHz brightness temperature.

Meneghini, Robert↗

NASA NTRSDate not supplied

Implementing Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) Cloud-Clearing Algorithm into the NASA GEOS: Focus on the 2017 Atlantic Tropical Cyclone Season

Numerical Weather Prediction (NWP) centers assimilate cloud-free infrared (IR) radiances because the assimilation of all-sky IR radiances is not yet operationally achievable. The cloud-clearing procedure offers a simpler, but effective strategy that produces cloud-affected radiances suitable for assimilation in partially cloudy regions. Several studies conducted by this team have demonstrated that IR Cloud-Cleared Radiances (CCRs), if thinned more aggressively than clear-sky radiances, can improve analysis and forecasts, particularly in meteorologically active areas. However, CCRs are not used by operational centers due partly to the thought that the process of cloud-clearing may affect latency and introduce difficult-to-control external dependencies. This study presents the results of implementing an Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) cloud-clearing procedure into the NASA Goddard Earth Observing System (GEOS) to demonstrate the portability of the procedure. The AIRS and CrIS cloud-clearing algorithms have been deprived of external dependencies, made customizable to any specific model, and the computational efficiency has been improved via parallelization. The revised AIRS and CrIS cloud-clearing algorithms allow a customized choice of channel selection, the use of a user-specified model's fields as first guess, and can perform in real time. Data assimilation experiments with the hybrid 4DEnVar GEOS system were successfully performed for the 2017 tropical cyclones (TC) season with a focus on three major hurricanes (Harvey, Irma, and Maria). This study shows that assimilation of locally-generated CCRs have a positive impact on both global skill and TC representation, compared to the assimilation of AIRS and CrIS clear-sky radiances, and a comparable or slightly improved impact compared to assimilation of CCRs produced by external sources, such as NASA's Distributed Active Archive Centers and NOAA’s Comprehensive Large Array-data Stewardship System. The customization and computational efficiency of the revised procedure would enable its usability in a real-time forecast context.

Niama Boukachaba↗

DOE OSTI2023

PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis

Abstract. This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options, and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (∼100s m) to mesoscale (∼10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Three-dimensional phenotyping of peach tree-crown architecture utilizing terrestrial laser scanning

Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices have been developed to characterize tree-crown architecture. However, the required branching data (BD) to develop these indices are difficult to collect. Traditionally, BD have been collected manually, but this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial laser scanning (TLS) to obtain a digital twin of the real tree. TLS generates three-dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected 16 young peach trees scanned in 2021 and 2022. These 16 trees were then modeled and quantified using the open-source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree-crown architecture. The comparison and analysis of field measurements (in situ) and in silico BD, biometric data, and quantitative structural model branch uncertainty data were utilized to determine the reconstructive model’s reliability as a source substitute for field measurements. Mean average deviation when comparing young tree (YT) height was approx. 5.93%, with crown volume was approx. 13.26% across both 2021 and 2022. All point clouds of the YTs in 2022 showed residuals lower than 12 mm to cylinders fitted to all branches, and mean surface coverage greater than 40% for both the trunk and primary branching orders.

09 BIOMASS FUELS↗

NASA NTRS2019

Federated Cloud Challenges in NASA's Earth Science Data Systems (Why So Difficult?)

NASA is presented with a number of opportunities and challenges in federating its Earth Science Data Systems in the burgeoning world of cloud computing. Cloud hosting of Earth Science data provides a new way of bringing data together, at least from a virtual location sense, and is one of the main motives for NASA to host data there. However, NASA is also faced with a Big Data Variety challenge, brought on by the variety of the EO datasets in its archives. This diversity requires many diverse science archives to service the different science communities. As a result, nearly every major function in the Earth Observing System Data and Information System (EOSDIS) must also be federated across its data centers. This pattern is repeated with many of the outside agencies and organizations that EOSDIS federates with, such as the Committee for Earth Observing Satellites, leading to pioneering work on "deep federation" in a joint project with the European Space Agency to develop a Multi-Mission Algorithm and Analysis Platform.

Lynnes, Christopher↗

NASA NTRSDate not supplied

Ice Shape Analysis of an eVTOL Propeller in Forward Flight at the NASA Glenn Icing Research Tunnel

Advanced Air Mobility (AAM) introduces many novel electric vertical takeoff and landing (eVTOL) aircraft configurations for which the effect of icing is not well understood. While icing computational tools have often aided in the design and certification of conventional aircraft, experimental data is needed to support the development and validation of such tools for eVTOL applications. To investigate the icing phenomenon relevant to eVTOL aircraft, NASA Glenn Research Center developed a general-purpose propeller test stand for conducting fundamental icing research on electrically driven propellers in the Icing Research Tunnel (IRT). A 10-day test entry in the IRT was completed in March, 2023, with the goal of generating ice accretions on a non-proprietary propeller geometry under well-characterized conditions. Three carbon fiber propellers of diameters 0.610, 0.711, and 0.914 m (24, 28, and 36 in) were used during the test. Various parametric sweeps of cloud and operating conditions were performed to evaluate the sensitivity of the parameters on the resulting ice accretion characteristics such as ice thickness, mass, location (impingement limits), and type (glaze vs rime). Ice shapes were reviewed for their repeatability, correctness of trends, and sensitivity to parameter variations. The data from the test is being used to support the development of the ice accretion solver, GlennICE.

Icing↗

NASA NTRS2024

Ice Shape Analysis of an eVTOL Propeller in Forward Flight at the NASA Glenn Icing Research Tunnel

Icing↗

DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2005

Principal Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Sensors: Theoretical Concept - Part I

Modern infrared satellite sensors such as Atmospheric Infrared Sounder (AIRS), Cosmic Ray Isotope Spectrometer (CrIS), Thermal Emission Spectrometer (TES), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and Infrared Atmospheric Sounding Interferometer (IASI) are capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, super fast radiative transfer models are needed. This paper presents a novel radiative transfer model based on principal component analysis. Instead of predicting channel radiance or transmittance spectra directly, the Principal Component-based Radiative Transfer Model (PCRTM) predicts the Principal Component (PC) scores of these quantities. This prediction ability leads to significant savings in computational time. The parameterization of the PCRTM model is derived from properties of PC scores and instrument line shape functions. The PCRTM is very accurate and flexible. Due to its high speed and compressed spectral information format, it has great potential for super fast one-dimensional physical retrievals and for Numerical Weather Prediction (NWP) large volume radiance data assimilation applications. The model has been successfully developed for the National Polar-orbiting Operational Environmental Satellite System Airborne Sounder Testbed - Interferometer (NAST-I) and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is able to include multiple scattering calculations to account for clouds and aerosols.

Liu, Xu↗

NASA NTRS2001

Microphysical Interpretation of Cirrus Measurements With Lidar

Cirrus measurements obtained with a ground-based polarization Raman lidar at 67.9 N in January 1997 reveal a strong correlation between the particle optical properties, specifically depolarization ratio delta and extinct ion-to-backscatter ratio S, for ambient cloud temperatures above approximately -45 C (delta less than approximately 40%), and an anti-correlation for colder temperatures (delta greater than approximately 40%). Over the length of the measurements (4-7.5 hours) the particle properties vary systematically: Initially, delta approximately equal to 60% and S approximately equal to 10sr are observed. Then, with decreasing delta, S first increases to approximately 27 sr(delta approximately equal to 40%) before decreasing to values around 10 sr again (delta approximately equal to 20%). The particle optical properties distinctly depend on the ambient temperature. For the microphysical analysis of the lidar observations. ray-tracing computations of particle scattering properties and a size-distribution resolving cirrus model with explicit microphysics have been used. The theoretical studies show that the optical properties and their temporal evolution can be interpreted in terms of size, shape, and growth of the cirrus particles: Near the cloud top in the early stage of the cirrus development, light scattering by small hexagonal columns with aspect ratios close to one is dominant. Over time the cloud base height extends to lower altitudes with warmer temperatures, the ice particles grow and get morphologically diverse (the scattering contributions of hexagonal columns and plates are roughly the same for large S and depolarization values of approximately 40%). In the lower ranges of the cirrus clouds, light scattering is predominantly by plate-like or complex ice particles. Mid-latitude cirrus data measured with the same instrument at 53.4 N between 1994 and 1996 follow closely the correlation between delta and S found in the warmer regions of the Arctic cirrus clouds (delta less than approximately 40%). Cirrus clouds with higher depolarization ratios are rarely observed, even for very cold ambient temperatures. Atmospheric parameters other than temperature, e.g., the availability of water vapor, are also important for the growth and the morphology of cirrus particles.

Reichardt, Jens↗

NASA NTRS2015

High Resolution Nature Runs and the Big Data Challenge

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

big data analysis↗

NASA NTRS2015

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Meng, Huan↗

NASA NTRSDate not supplied

Efficient Access to MERRA-2 Reanalysis Data from the Cloud Archives

The Modern-Era Retrospective analysis for Research and Applications, Version (MERRA-2) dataset is now available in the AWS West Region-2 cloud. The free data downloading policy remains unchanged. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) offers cloud services such as OPeNDP subsetting, and time series services. This presentation will showcase use cases that testing the procedures and find the most efficiency workflows to access and process the data for finding climate extremes. The use cases include a) accessing and processing data directly in the cloud S3 bucket without downloading it, and b) downloading subset data to process on the user’s local system.

data access, cloud computing, climate extremes↗

NASA NTRS2011

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

Molthan, Andrew L.↗

NASA NTRS1997

Molecular Hydrogen in Diffuse Interstellar Clouds of Arbitrary Three-Dimensional Geometry

We have constructed three-dimensional models for the equilibrium abundance of molecular hydrogen in diffuse interstellar clouds of arbitrary geometry that are illuminated by ultraviolet radiation. The position-dependent photodissociation rate of H2 in such clouds was computed with a 26 ray approximation to model the attenuation of the incident ultraviolet radiation field by dust and by H2 line absorption. We have applied our modeling technique to the isolated diffuse cloud G236+39, assuming that the cloud has a constant density and that the thickness of the cloud along the line of sight is at every point proportional to the 100 micron continuum intensity measured by IRAS. We find that our model can successfully account for observed variations in the ratio of 100 micron continuum intensity to H I column density, with larger values of that ratio occurring along lines of sight in which the molecular hydrogen fraction is expected to be the largest. Using a standard chi square analysis to assess the goodness of fit of our models, we find (at the 60 a level) that a three-dimensional model is more successful in matching the observational data than a one-dimensional model in which the geometrical extent of the cloud along the line of sight is assumed to be much smaller than its extent in the plane of the sky. If D is the distance to G236 + 39, and given standard assumptions about the rate of grain-catalyzed H2 formation, we find that the cloud has an extent along the line of sight that is 0.9 +/- 0.1 times its mean extent projected onto the plane of the sky and a gas density of 53 +/- 8 (100 pc/D) H nuclei/cc and is illuminated by a radiation field of 1.1 +/- 0.2 (100 pc/D) times the mean interstellar radiation field. The derived 100 micron emissivity per nucleon is 1.13 +/- 0.06 x 10(exp -20) MJy/sr sq cm.

Spaans, Marco↗