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At least 811 records · Page 45
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NASA NTRS2017

GEOS S2S-2_1: GMAO's New High Resolution Seasonal Prediction System

A new version of the modeling and analysis system used to produce sub-seasonal to seasonal forecasts has just been released by the NASA Goddard Global Modeling and Assimilation Office. The new version runs at higher atmospheric resolution (approximately 12 degree globally), contains a substantially improved model description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter. Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will also present results from a free-running coupled simulation with the new system and results from a series of retrospective seasonal forecasts. Results from retrospective forecasts show significant improvements in surface temperatures over much of the northern hemisphere and a much improved prediction of sea ice extent in both hemispheres. The precipitation forecast skill is comparable to previous S2S systems, and the only trade off is an increased double ITCZ, which is expected as we go to higher atmospheric resolution.

Molod, Andrea↗
NASA NTRS2017

HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

Wang, Panshi↗
NASA NTRS2017

CIMI Simulations with Newly Developed Multiparameter Chorus and Plasmaspheric Hiss Wave Models

Numerical simulation studies of the Earth's radiation belts are important to understand the acceleration and loss of energetic electrons. The Comprehensive Inner Magnetosphere-Ionosphere (CIMI) model considers the effects of the ring current and plasmasphere on the radiation belts to obtain plausible results. The CIMI model incorporates pitch angle, energy, and cross diffusion of electrons, due to chorus and plasmaspheric hiss waves. These parameters are calculated using statistical wave distribution models of chorus and plasmaspheric hiss amplitudes. However, currently, these wave distribution models are based only on a single-parameter, geomagnetic index (AE) and could potentially underestimate the wave amplitudes. Here we incorporate recently developed multiparameter chorus and plasmaspheric hiss wave models based on geomagnetic index and solar wind parameters. We then perform CIMI simulations for two geomagnetic storms and compare the flux enhancement of megavolt electrons with data from the Van Allen Probes and Akebono satellites. We show that the relativistic electron fluxes calculated with multiparameter wave models resemble the observations more accurately than the relativistic electron fluxes calculated with single-parameter wave models. This indicates that wave models based on a combination of geomagnetic index and solar wind parameters are more effective as inputs to radiation belt models.

Aryan, Homayon↗
NASA NTRS2018

NASA GMAO GEOS S2S Prediction System: Metrics, Post-Processing and Products

In this presentation we present an overview of the GMAO Sub-Seasonal and Seasonal Prediction System, current users and products, and methods for validation and evaluation of the system. Methods for evaluation include baseline evaluations metrics, the ability to simulate key modes of variability, and evaluation of new development areas.

Molod, Andrea M.↗
NASA NTRS2018

NASA GMAO S2S Prediction System Hindcast and Near-Real Time Operations Strategy

In this presentation we present an overview of the GMAO Sub-Seasonal and Seasonal Prediction System with a focus on the computing time and resources and actual time it takes to complete a full set of hindcasts. The goal is to come up with some solutions to allow us to run more ensemble members for the next version of the system which will be higher resolution and take many more resources.

Kovach, Robin↗
NASA NTRS2018

Improved Detection of Low Stratus and Fog at Dawn from Dual Geostationary (COMS and FY-2D) Satellites

A novel method was proposed to detect low stratus and fog (LSF) at dawn during the summertime using near simultaneous observations from dual geostationary-orbit satellites (GEOs): the Korean Communication, Ocean and Meteorological Satellite (COMS; 128.2 degrees East) and the Chinese Feng-Yun-2D (FY-2D; 86.5 degrees East). The orbital positions of the GEOs provided a large difference (around 46.5 degrees) in the viewing zenith angle (VZA) in the study region (122-132 degrees East, 32.5-42.5 degrees North) and high contrast observations at dawn. Numerical simulations were carried out to derive the optical properties of the LSF and the radiative differences between the GEOs due to both the VZA and spectral response function (SRF). The conventional threshold method, which used the visible reflectance at 0.67 microns (R (sub 0.67)) and the brightness temperature difference between 3.7 microns and 11 microns (BTD (sub 3.7-11), had limitations in detecting LSF at dawn due to weak visible light and fast night-to-day transition. We utilized the observed stereo differences (delta R (sub 0.67), delta BTD (sub 3.7-11) between the two GEOs to improve LSF detection. The dual-satellite observations were verified with ground-based data from 45 stations in South Korea co-located with each GEO pixel. The delta R (sub 0.67) threshold value showed better accuracy (78 percent vs. 67 percent) than the conventional R (sub 0.67) threshold method. In addition, the delta BTD (sub 3.7-11) threshold was better (55 percent vs. 38 percent) than the BTD (sub 3.7-11) threshold. The dual-satellite method allowed more reliable LSF detection using the combination of delta R (sub 0.67) and R (sub 0.67), particularly for LSF without cumuliform or high clouds. Our method is applicable to multiple geostationary satellites for continuous LSF monitoring.

geostationary satellite↗
NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

image registration↗
NASA NTRS2018

An Engergetic Electron Flux Dropout Due to Magnetopause Shadowing on 1 June 2013

We examine the mechanisms responsible for the dropout of energetic electron flux during 31 May to 1 June 2013 using Van Allen Probe (Radiation Belt Storm Probes (RBSP)) electron flux data and simulations with the Comprehensive Inner Magnetosphere-Ionosphere (CIMI) model. During the storm main phase, L-shells at RBSP locations are greater than ~8, which are connected to open drift shells. Consequently, diminished electron fluxes were observed over a wide range of energies. The combination of drift shell splitting, magnetopause shadowing, and drift loss all results in butterfly electron pitch angle distributions (PADs) at the nightside. During storm sudden commencement, RBSP observations display electron butterfly PADs over a wide range of energies. However, it is difficult to determine whether there are butterfly PADs during the storm main phase since the maximum observable equatorial pitch angle from RBSP is not larger than ~40° during this period. To investigate the causes of the dropout, the CIMI model is used as a global 4-D kinetic inner magnetosphere model. The CIMI model reproduces the dropout with very similar timing and flux levels and PADs along the RBSP trajectory for 593 keV. Furthermore, the CIMI simulation shows butterfly PADs for 593 keV during the storm main phase. Based on comparison of observations and simulations, we suggest that the dropout during this event mainly results from magnetopause shadowing.

Kang, Suk-Bin↗
NASA NTRS2018

AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear-Red Rock watershed, central Oklahoma between 2015-2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.

AMSR2; passive microwave soil moisture; soil moist↗
NASA NTRS2018

Automated Contingency Management for Water Recycling System

To enable effective management, planning, and operations for future missions that involve a crewed space habitat, operational support must be migrated from Earth to the habitat. Intelligent System Health Management technologies (ISHM) promise to enable the future space habitats to increase the safety and mission success while minimizing operational risks. In this paper, Water Recycling System (WRS) deployed at NASA Ames Research Center's Sustainability Base is used for verification and validation of the proposed solution. Our work includes the development of the WRS simulation model based on its dynamic physical characteristics and the design of Automatic Contingency Management (ACM) framework that integrates fault diagnosis and optimization. In WRS modeling, a nominal model with fault injectors is developed. Fault detection and isolation techniques are then developed for isolating causes and identifying the severity of the faults. Dynamic Programming (DP) based fault mitigation strategies are designed to accommodate the faults in the system. A series of simulations are presented with different fault modes and the results indicate that the proposed ACM system can alleviate the fault in the WRS optimally regarding energy consumption and effects of the fault.

Systems Health Management↗
NASA NTRS2018

Recombinant Spidroins Fully Replicate Primary Mechanical Properties of Natural Spider Silk

Dragline spider silk is among the strongest and toughest bio-based materials, capable of outperforming most synthetic polymers and even some metal alloys.1,2,3,4 These properties have gained spider silk a growing list of potential applications that, coupled with the impracticalities of spider farming, have driven a decades-long effort to produce recombinant spider silk proteins (spidroins) in engineered heterologous hosts.2 However, these efforts have so far been unable to yield synthetic silk fibers with mechanical properties equivalent to natural spider silk, largely due to an inability to stably produce highly repetitive, high molecular weight (MW) spidroins in heterologous hosts.1,5 Here we address these issues by combining synthetic biology techniques with split intein (SI)- mediated ligation for the bioproduction of spidroins with unprecedented MW (556 kDa), containing 192 repeat motifs of the Nephila clavipes MaSp1 dragline spidroin. Fibers spun from these synthetic spidroins display ultimate tensile strength (σ), modulus (E), extensibility (ε), and toughness (UT) of 1.03 +/- 0.11 GPa, 13.7 +/- 3.0 GPa, 18 +/- 6%, and 114 +/- 51 MJ/m3, respectively-equivalent to the performance of natural N. clavipes dragline silk.6 This work demonstrates for the first time that microbially produced synthetic silk fibers can match the performance of natural silk fibers by all common metrics (σ, E, ε, UT), providing a more dependable source of high-strength fibers to replace natural spider silks for mechanically demanding applications. Furthermore, our biosynthetic platform can be potentially expanded for the assembly and production of other protein-based materials with high MW and repetitive sequences that have so far been impossible to synthesize by genetic means alone.

spider silk↗
NASA NTRS2018

An Overview of the XRT Observations for the September 10 2017 X Flare

The September 10 2017 X8 flare was one of the biggest of the current solar cycle, and it was also incredibly well observed. This presentation will review the available XRT data for this flare. XRT observed the initial flux rope eruption, which was also well observed by AIA and SUVI. XRT data is missing for some of the impulsive phase of the flare, but the late phase shows some very interesting features. There is some nice XRT imaging of a plasma sheet above cusp-shaped loops between 16:47 and 18:39 UT. After 18:40 UT, a double loop structure becomes apparent, with a more rounded loop nested within a larger, cusp-shaped structure. The flare loops continue to grow, and at around 1 UT on September 11, supra-arcade down flows and shrinking loops become visible. There is also interesting data in the late phase of this flare from RHESSI and the Expanded Owens Valley Solar Array (EOVSA).

Reeves, Kathy↗
NASA NTRS2018

Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth's Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations

Earth's reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)'s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)'s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth's reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth's reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth's reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8% to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between −0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between −10% and 23%.

DSCOVR↗
NASA NTRS2015

Effects of Resolution and Spectral Nudging in Simulation the Effects of Wintertime Atmospheric River Landfalls in the Western US

Landfalling atmospheric rivers (ARs) play a crucial role in the climate of the US Pacific coast region as they are frequently related with heavy precipitation and flash flooding events. Thus, the capability of climate models to accurately simulate AR landfalls and their key hydrologic effects is an important practical concern for WUS, from flood forecasting to future water resources projections. In order to examine the effects of model configuration, including the resolution and spectral nudging, in simulating the climatology of key weather events in the conterminous US, a NASA team has performed a hindcast experiment using the GEOS5 global and the NU-WRF regional models for Nov 1999 - Oct 2010. This study examines the skill of these hindcasts, with different models and their configurations, in simulating key footprints of landfalling ARs in the WUS region. Using an AR-landfall chronology based on the vertically-integrated water vapor flux calculated from the MERRA2 reanalysis, we have analyzed the observed and simulated precipitation and temperature anomalies associated with wintertime AR landfalls along the US Pacific coast. Model skill is measured using metrics including regional means, a skill score based on correlations and mean-square errors, and Taylor diagrams in four WUS Bukovsky regions. Results show that the AR-related anomalies of precipitation is more reliable than of surface temperatures. Model skill also varies according to regions. The AR temperature anomalies are well simulated in most of the WUS region except PNW. For precipitation, simulations with finer spatial resolution tend to generate larger spatial variability and agree better with the PRISM data in most regions. Such a resolution dependence of spatial variability is not found for temperatures; e.g., the MERRA2 reanalysis often outperforms, with similar spatial variability and higher pattern correlations with the PRISM data, finer-resolution NU-WRF runs in simulating temperature variations within subregions. Results from this study will be summarized to assist future (regional) climate experiments for climate change impact assessments and developing adaptationmitigation strategies, the key elements of the National Climate Assessment.

Simulation↗
NASA NTRS2018

GEOS S2S-2_1: The GMAO High Resolution Seasonal Prediction System

A new version of the coupled modeling and analysis system used to produce near real time subseasonal to seasonal forecasts was recently released by the NASA/Goddard Global Modeling and Assimilation Office. The new version runs at higher atmospheric resolution than the previous, (approximately 1/2 degree globally), contains a substantially improved model description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter, and now includes the assimilation of along-track sea surface height. Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will also present results from a series of retrospective seasonal forecasts. Results show significant improvements in surface temperatures over much of the northern hemisphere and a much improved prediction of sea ice extent in both hemispheres. Analysis of the ensemble spread shows improvements relative to the previous system, including generally better reliability. The precipitation forecast skill is comparable to previous S2S systems, and the only tradeoff is an increased "double ITCZ", which is expected as we go to higher atmospheric resolution.

Molod, Andrea↗
NASA NTRS2015

3D Photocatalytic Air Processor for Dramatic Reduction of Life Support Mass and Complexity

To dramatically reduce the cost and risk of CO2 management systems in future extended missions, we have conducted preliminary studies on the materials and device development for advanced "artificial photosynthesis" reaction systems termed the High Tortuosity PhotoElectroChemical (HTPEC) system. Our Phase I studies have demonstrated that HTPEC operates in much the same way a tree would function, namely directly contacting the cabin air with a photocatalyst in the presence of light and water (as humidity) to immediately conduct the process of CO2 reduction to O2 and useful, "tunable" carbon products. This eliminates many of the inefficiencies associated with current ISS CO2 management systems. We have laid the solid foundation for Phase II work to employ novel and efficient reactor geometries, lighting approaches, 3D manufacturing methods and in-house grown novel catalytic materials.The primary objective of the proposed work is to demonstrate the scientific and engineering foundation for light-activated, compact devices capable of converting CO2 to O2 and mission-relevant carbon compounds. The proposed HTPEC CO2 management system will demonstrate a novel pathway with high efficiency and reliability in a compact, lightweight reactor architecture. The proposed HTPEC air processing concept can be developed in multiple architectures, such as centralized processing as well as "artificial leaves" distributed throughout the cabin that utilize pre-existing cabin ventilation (wind). Additionally, HTPEC can be deployed with spectrally tunable collection channels for selectable product generation. HTPEC employs light as its only energy source to remove and convert waste CO2 using a non-toxic composite catalyst.We have demonstrated in the Phase I studies the production, tunability and robustness of the novel composite catalysts following the preliminary work in the Chen laboratory. Additionally, we have designed, fabricated and tested all components of HTPEC device with active materials, including flow modeling to optimize flow mixing and pressure drop as well as the production of ethylene and other larger hydrocarbons. To best determine how this technology could be implemented, we also performed system integration optimization and trade studies. This includes parameters such as mass, volume, power in relation to selected mission configurations, CO2 delivery methods and light source/delivery approaches.

Artificial Photosynthesis↗
NASA NTRS2012

Near-Infrared (NIR) Raman Spectroscopy of Precambrian Carbonate Stromatolites with Post-Depositional Organic Inclusions

Raman spectroscopy has promising potential for future Mars missions as a non-contact detection technique for characterizing organic material and mineralogy. Such a capability will be useful for selecting samples for detailed analysis on a rover and for selecting samples for return to Earth. Stromatolites are important evidence for the earliest life on Earth and are promising targets for Mars investigations. Although constructed by microorganisms, stromatolites are organo-sedimentary structures that can be large enough to be discovered and investigated by a Mars rover. In this paper, we report the Raman spectroscopic investigations of the carbonate mineralogy and organic layering in a Precambrian (;1.5 Gyr old) stromatolite from the Crystal Spring Formation of Southern California. Ultraviolet (UV: 266 nm), visible (514 nm, 633 nm), and near-infrared (NIR: 785 nm, 1064 nm) Raman spectra are presented. We conclude that 1064 nm excitation is the optimal excitation wavelength for avoiding intrinsic fluorescence and detecting organic carbon within the carbonate matrix. Our results confirm that NIR Raman spectroscopy has important applications for future Mars missions.

Tanaka, Zuki↗
NASA NTRS2018

GOES-16 ABI Navigation Assessment

The US Geostationary Operational Environmental Satellite – R Series (GOES-R) was launched on November 19, 2016and was designated GOES-16 upon reaching geostationary orbit ten days later. After checkout and calibration, GOES-16 was relocated to its operational location of 75.2 degrees west and officially became GOES East on December 18, 2017. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R series for imaging Earth's surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. A team supporting the GOES-R Flight Project at NASA's Goddard Space Flight Center developed algorithms and software for independent verification of ABI Image Navigation and Registration (INR), which became known as the INR Performance Assessment Tool Set (IPATS). In this paper, we will briefly describe IPATS on top concept level, and then introduce the Landsat chips, chip registration algorithms, and how IPATS measurements are filtered. We present GOES-16 navigation (NAV) errors from flight data from January 2017 to May 2018. The results show a) IPATS characterized INR variations throughout the post-launch test phase; and b) ABI INR has improved over time as post-launch tests were performed and corrections applied. Finally, we will describe how estimated NAV errors have been used to assess and understand satellite attitude anomalies and scale errors etc. This paper shows that IPATS is an effective tool for assessing and improving GOES-16 ABI INR and is also useful for INR long-term monitoring.

GOES-R↗