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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1990

A global view of lander-to-orbiter communications accessibility for a Mars Global Network Mission

Given the mission objective to deploy a number of small landers to the surface of Mars at various latitude/longitude locations, it is of interest to obtain a global perspective of the communications link geometry between the landers and a data relay orbiter. Specifically, the question to be answered is what is the total time interval over one Martian day (1 sol) that a lander at any given latitude and longitude can communicate data to the orbiter. Results should be obtained for more than one elevation angle constraint (lander antenna design issue), and also for several time points into the mission since the orbiter's periapsis location moves under the influence of Mars' oblateness perturbation. Such information is presented in terms of global contour maps of available communications time per sol summed over all orbiter pases on that day. Global data of this type complements more detailed local site data such as communications range and elevation vs time per pass. Communications time contour maps are included here for sol grids of 180, 232, 318, 361, and 404 corresponding to orbiter periapsis latitudes of 35 S, 90 S, equatorial, 45 N, and 90 N. For each of these days, there is a map for both a 15 deg and 45 deg minimum elevation constraint on the lander-to-orbiter line of sight. The jagged appearance of the contour lines is due to computational resolution in interpolating between a finite number of latitude/longitude grid points. Although the contours should really be smooth, the general information content is represented by the lower resolution maps shown here. An example of the tabulated, finite-grid data points is also given. Communication with all sites is possible at the 15 deg elevation constraint, at times only for several minutes per sol but more generally for a much longer time up to 14 hours per sol. Significantly less time is available with a 45 deg elevation constraint, and at certain times in the mission some localized regions of the planet are inaccessible. Still, one may conclude that the reference orbit selection will support a more than adequate communications link through the mission timeline with landers emplaced at any desired location on Mars.

Friedlander, Alan L.↗
NASA NTRS2018

Decadal Seasonal Shifts of Precipitation and Temperature in TRMM and AIRS Data

We present results from an analysis of seasonal phase shifts in the global precipitation and surface temperatures. We use data from the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Algorithm (TMPA), and the Atmospheric Infrared Sounder (AIRS) on Aqua satellite, all hosted at NASA Goddard Earth Science Data and Information Services Center (GES DISC). We explore the information content and data usability by first aggregating daily grids from the entire records of both missions to pentad (5-day) series which are then processed using Singular Value Decomposition approach. A strength of this approach is the normalized principal components that can then be easily converted from real to complex time series. Thus, we can separate the most informative, the seasonal, components and analyze unambiguously for potential seasonal phase drifts. TMPA and AIRS records represent correspondingly 20 and 15 years of data, which allows us to run simple “phase learning†from the first 5 years of records and use it as reference. The most recent 5 years are then phase-compared with the reference. We demonstrate that the seasonal phase of global precipitation and surface temperatures has been stable in the past two decades. However, a small global trend of delayed precipitation, and earlier arrival of surface temperatures seasons, are detectable at 95% confidence level. Larger phase shifts are detectable at regional level, in regions recognizable from the Eigen vectors to having strong seasonal patterns. For instance, in Central North America, including the North American Monsoon region, confident phase shifts of 1-2 days per decade are detected at 95% confidence level. While seemingly symbolic, these shifts are indicative of larger changes in the Earth Climate System. We thus also demonstrate a potential usability scenario of Earth Science Data Records curated at the NASA GES DISC in partnership with Earth Science Missions.

surface temperatures↗
NASA NTRS2005

Data Format Standardization of Space Weather Model Output at the Community Coordinated Modeling Center

The disparate nature of space weather model output provides many challenges with regards to the portability and reuse of not only the data itself, but also any tools that are developed for analysis and visualization. We are developing and implementing a comprehensive data format standardization methodology that allows heterogeneous model output data to be stored uniformly in any common science data format. We will discuss our approach to identifying core meta-data elements that can be used to supplement raw model output data, thus creating self-descriptive files. The meta-data should also contain information describing the simulation grid. This will ultimately assists in the development of efficient data access tools capable of extracting data at any given point and time. We will also discuss our experiences standardizing the output of two global magnetospheric models, and how we plan to apply similar procedures when standardizing the output of the solar, heliospheric, and ionospheric models that are also currently hosted at the Community Coordinated Modeling Center.

Maddox, M.↗
NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗
NASA NTRS2018

CALIPSO Lidar Level 3 Aerosol Profile Product: Version 3 Algorithm Design

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) level 3 aerosol profile product reports globally gridded, quality-screened, monthly mean aerosol extinction profiles retrieved by CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization). This paper describes the quality screening and averaging methods used to generate the version 3 product. The fundamental input data are CALIOP level 2 aerosol extinction profiles and layer classification information (aerosol, cloud, and clear-air). Prior to aggregation, the extinction profiles are quality-screened by a series of filters to reduce the impact of layer detection errors, layer classification errors, extinction retrieval errors, and biases due to an intermittent signal anomaly at the surface. The relative influence of these filters are compared in terms of sample rejection frequency, mean extinction, and mean aerosol optical depth (AOD). The “extinction QC flag” filter is the most influential in preventing high-biases in level 3 mean extinction, while the “misclassified cirrus fringe” filter is most aggressive at rejecting cirrus misclassified as aerosol. The impact of quality screening on monthly mean aerosol extinction is investigated globally and regionally. After applying quality filters, the level 3 algorithm calculates monthly mean AOD by vertically integrating the monthly mean quality-screened aerosol extinction profile. Calculating monthly mean AOD by integrating the monthly mean extinction profile prevents a low bias that would result from alternately integrating the set of extinction profiles first and then averaging the resultant AOD values together. Ultimately, the quality filters reduce level 3 mean AOD by −24 and −31 % for global ocean and global land, respectively, indicating the importance of quality screening.

Tackett, Jason L.↗
NASA NTRS2013

The Collection 6 'dark-target' MODIS Aerosol Products

Aerosol retrieval algorithms are applied to Moderate resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua, creating two streams of decade-plus aerosol information. Products of aerosol optical depth (AOD) and aerosol size are used for many applications, but the primary concern is that these global products are comprehensive and consistent enough for use in climate studies. One of our major customers is the international modeling comparison study known as AEROCOM, which relies on the MODIS data as a benchmark. In order to keep up with the needs of AEROCOM and other MODIS data users, while utilizing new science and tools, we have improved the algorithms and products. The code, and the associated products, will be known as Collection 6 (C6). While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. In its entirety, the C6 algorithm is comprised of three sub-algorithms for retrieving aerosol properties over different surfaces: These include the dark-target DT algorithms to retrieve over (1) ocean and (2) vegetated-dark-soiled land, plus the (3) Deep Blue (DB) algorithm, originally developed to retrieve over desert-arid land. Focusing on the two DT algorithms, we have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to 84) to increase pole-ward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such as topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence in the retrieval, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time as we have introduced algorithm changes, we have also accounted for upstream changes including: new instrument calibration, revised land-sea masking, and changed cloud masking. Upstream changes also impact the coverage and global statistics of the retrieved AOD. Although our responsibility is to the DT code and products, we have also added a product that merges DT and DB product over semi-arid land surfaces to provide a more gap-free dataset, primarily for visualization purposes. Preliminary validation shows that compared to surface-based sunphotometer data, the C6, Level 2 (along swath) DT-products compare at least as well as those from C5. C6 will include new diagnostic information about clouds in the aerosol field, including an aerosol cloud mask at 500 m resolution, and calculations of the distance to the nearest cloud from clear pixels. Finally, we have revised the strategy for aggregating and averaging the Level 2 (swath) data to become Level 3 (gridded) data. All together, the changes to the DT algorithms will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. Changes in calibration will have more impact to Terras time series, especially over land. This will result in a significant reduction in artificial differences in the Terra and Aqua datasets, and will stabilize the MODIS data as a target for AEROCOM studie

Aerosol retrieval algorithms↗
NASA NTRS2016

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗
NASA NTRS2012

Integrated Modeling of Aerosol, Cloud, Precipitation and Land Processes at Satellite-Resolved Scales

In this talk, I will present recent results from a project led at NASA/GSFC, in collaboration with NASA/MSFC and JHU, focused on the development and application of an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satellite-resolved scales. The project, known as the NASA Unified WRF (NU-WRF), is funded by NASA's Modeling and Analysis Program, and leverages prior investments from the Air Force Weather Agency and NASA's Earth Science Technology Office (ESTO). We define "satellite-resolved" scales as being within a typical mesoscale atmospheric modeling grid (roughly 1-25 km), although this work is designed to bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the standard NCAR Advanced Research WRF model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the Goddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (SDSU), and boundary/initial condition preprocessors for MERRA and GEOS-5 into a single software release (with source code available by agreement with NASA/GSFC). I will show examples where the full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local, regional, and global water and energy cycles, including some high-impact phenomena such as floods, hurricanes, mesoscale convective systems, droughts, and monsoons.

Peters-Lidard, Christa↗
NASA NTRS2015

Remote Sensing of Cloud Top Heights Using the Research Scanning Polarimeter

Clouds cover roughly two thirds of the globe and act as an important regulator of Earth's radiation budget. Of these, multilayered clouds occur about half of the time and are predominantly two-layered. Changes in cloud top height (CTH) have been predicted by models to have a globally averaged positive feedback, however observational changes in CTH have shown uncertain results. Additional CTH observations are necessary to better and quantify the effect. Improved CTH observations will also allow for improved sub-grid parameterizations in large-scale models and accurate CTH information is important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. RSP scans along the aircraft track and obtains measurements at 152 viewing angles at any aircraft location. The approach presented here aggregates measurements from multiple scans to a single location at cloud altitude using a correlation function designed to identify the location-distinct features in each scan. During NASAs SEAC4RS air campaign, the RSP was mounted on the ER-2 aircraft along with the Cloud Physics Lidar (CPL), which made simultaneous measurements of CTH. The RSPs unique method of determining CTH is presented. The capabilities of using single and combinations of channels within the approach are investigated. A detailed comparison of RSP retrieved CTHs with those of CPL reveal the accuracy of the approach. Results indicate a strong ability for the RSP to accurately identify cloud heights. Interestingly, the analysis reveals an ability for the approach to identify multiple cloud layers in a single scene and estimate the CTH of each layer. Capabilities and limitations of identifying single and multiple cloud layers heights are explored. Special focus is given to sources of error in the method including optically thin clouds, physically thick clouds, multi-layered clouds as well as cloud phase. When determining multi-layered CTHs, limits on the upper clouds opacity are assessed.

Cloud height indicators↗
NASA NTRS2019

Impact of Assimilating AIRS Cloud Cleared Radiances on the Analysis and Forecast of Polar Lows

Previous work by this team (Reale et al. 2018) has found that the current assimilation of AIRS (Atmospheric InfraRed Sounder) radiances on a regularly spaced thinning grid is suboptimal, probably because of horizontal error correlation over meteorologically inactive areas. Moreover, cloud-cleared radiances appear to be a better product than clear-sky radiances, but need to be assimilated at a much lower density globally, because of the higher information content. Specifically: 1. Assimilation of AIRS cloud-cleared radiances at a density of about one quarter of the clear-sky radiances improves global forecast skill; 2. An adaptive thinning strategy assimilating cloud-cleared radiances at reduced density globally except around tropical cyclones (TCs), leads to substantial improvement in the structure and intensity forecast of TCs without damaging global skill.

Polar Low↗
NASA NTRS2007

The Global Precipitation Climatology Project (GPCP): Results, Status and Future

The Global Precipitation Climatology Project (GPCP) is one of a number of long-term, satellite-based, global analyses routinely produced under the auspices of the World Climate Research Program (WCRP) and its Global Energy and Watercycle EXperiment (GEWEX) program. The research quality analyses are produced a few months after real-time through the efforts of scientists at various national agencies and universities in the U.S., Europe and Japan. The primary product is a monthly analysis of surface precipitation that is globally complete and spans the period 1979-present. There are also pentad analyses for the same period and a daily analysis for the 1997-present period. Although generated with somewhat different data sets and analysis schemes, the pentad and daily data sets are forced to agree with the primary monthly analysis on a grid box by grid box basis. The primary input data sets are from low-orbit passive microwave observations, geostationary infrared observations and surface raingauge information. Examples of research with the data sets are discussed, focusing on tropical (25N-25s) rainfall variations and possible long-term changes in the 28-year (1979-2006) monthly dataset. Techniques are used to discriminate among the variations due to ENSO, volcanic events and possible long-term changes for rainfall over both land and ocean. The impact of the two major volcanic eruptions over the past 25 years is estimated to be about a 5% maximum reduction in tropical rainfall during each event. Although the global change of precipitation in the data set is near zero, a small upward linear change over tropical ocean (0.06 mm/day/l0yr) and a slight downward linear change over tropical land (-0.03 mm/day/l0yr) are examined to understand the impact of the inhomogeneity in the data record and the length of the data set. These positive changes correspond to about a 5% increase (ocean) and 3% increase (ocean plus land) during this time period. Relations between variations in surface temperature and precipitation are analyzed on seasonal to inter-decadal time scales. A new, version 3 of GPCP is being planned to incorporate new satellite information (e.g., TRMM) and provide higher spatial and temporal resolution for at least part of the data record. The goals and plans for that GPCP re-processing will be outlined.

Adler, Robert F.↗
NASA NTRSDate not supplied

Machine Learning Emulators and Empirical Models Combining Climate and Global Crop Models for Seasonal Agricultural Production

We present results from several connected efforts to apply machine learning methods to estimates of seasonal agricultural production anomalies around the world. First, we apply the XGBoost Random Forest method to fit emulators that mimic global crop models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI). These are the same models used in the agricultural sector simulations of the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). These emulators use 8 climate variables split across 5 sub-seasonal representations of the growing season for each ½ degree grid cell around the world for maize, wheat, rice and soybeans. Emulators are useful for estimating conditions that have not already been simulated by GGCMI (e.g., in a seasonal prediction model) and also to diagnose model differences and capabilities. For example, emulators of the pDSSAT maize model tend to be more reliant on mean temperatures than the LPJmL model, and few models have strong responses to cold extremes. Second, we use a similar XGBoost approach to fit empirical models for national production data for the top 20 producing countries according to the United Nations Food and Agricultural Organization (FAO). Models utilize both climate observations and the GGCM models as predictors, resulting in skillful models for many (but not all) top producing-countries. The patterns of climate and crop model features selected indicate regions and systems that are better or worse simulated by the GGCMs. For example, information in cold extreme predictors is often combined with GGCM output predictors to provide sensitivity that models may underrepresent.

machine learning↗
NASA NTRS2017

Optimal A-Train Data Utilization: A Use Case of Aura OMI L2G and MERRA-2 Aerosol Products

Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission measures ozone column and profile, aerosols, clouds, surface UV irradiance, and the trace gases including NO2, SO2, HCHO, BrO, and OClO using UltraViolet electromagnetic spectrum (280 - 400 nm) with a daily global coverage and a pixel spatial resolution of 13 km × 24 km at nadir, and it's been one of the key instruments to study the Earth's atmospheric composition and chemistry. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is NASA's atmospheric reanalysis using an upgraded version of Goddard Earth Observing System Model, version 5 (GEOS-5) data assimilation system. Compared to its predecessor MERRA, MERRA-2 is enhanced with more aspects of the Earth system among which is aerosol assimilation. When comparing between satellite pixel measurements and modeled grid data, how to properly handle counterpart pairing is critical considering their spatial and temporal variations. The comparison between satellite and model data by simply using Level 3 (L3) products may result biases due to lack of detailed temporal information. It has been preferred to inter-compare or implement satellite derived physical quantity (i.e., Level 2 (L2) Swath type) directly with/to model measurements with higher temporal and spatial resolution as possible. However, this has posed a challenge in the community to handle. Rather than directly handling the L2 or L3 data, there is a Level 2G (L2G) product conserving L2 pixel scientific data quality but in Grid type with the global coverage. In this presentation, we would like to demonstrate the optimal utilization of OMI L2G daily aerosol products by comparing with MERRA-2 hourly aerosol simulations matched well in both space and time.

MERRA-2 reanalysis↗
NASA NTRS2001

Global Digital Image Mosaics of Mars: Assessment of Geodetic Accuracy

A revised global image mosaic of Mars (MDIM 2.0) was recently completed by USGS. Comparison with high-resolution gridded Mars Orbiter Laser Altimeter (MOLA) digital image mosaics will allow us to quantify its geodetic errors; linking the next MDIM to the MOLA data will help eliminate those errors. Additional information is contained in the original extended abstract.

Kirk, R.↗
NASA NTRS2002

Grids for Dummies: Featuring Earth Science Data Mining Application

This viewgraph presentation discusses the concept and advantages of linking computers together into data grids, an emerging technology for managing information across institutions, and potential users of data grids. The logistics of access to a grid, including the use of the World Wide Web to access grids, and security concerns are also discussed. The potential usefulness of data grids to the earth science community is also discussed, as well as the Global Grid Forum, and other efforts to establish standards for data grids.

Hinke, Thomas H.↗
NASA NTRS1994

The Grid Analysis and Display System (GrADS)

During the period 1 September 1993 - 31 August 1994, further development of the Grid Analysis and Display System (GrADS) was conducted at the Center for Ocean-Land-Atmosphere Studies (COLA) of the Institute of Global Environment and Society, Inc. (IGES) under subcontract 5555-31 from the University Space Research Association (USRA) administered by The Center of Excellence in Space Data and Information Sciences (CESDIS). This final report documents progress made under this subcontract and provides directions on how to access the software and documentation developed therein. A short description of GrADS is provided followed by summary of progress completed and a summary of the distribution of the software to date and the establishment of research collaborations.

Kinter, James L., III↗
NASA NTRSDate not supplied

AN INTRODUCTION TO THE GEONEX LEVEL-1G PRODUCTS: TOP-OF-ATMOSPHERE REFLECTANCE AND BRIGHTNESS TEMPERATURE

This paper introduces the GeoNEX (Geostationary-NASA Earth eXchange) Level-1G products of top-of-atmosphere (TOA) reflectance and brightness temperature. The products use data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. The GeoNEX processing pipeline starts by converting digital numbers to physical quantities with the latest radiometric calibration information. It integrates algorithms to automatically detect and remove residual geolocation errors, to estimate the pixel-wise data-acquisition time, and to accurately calculate the solar illumination angles for each pixel in the domain at every time step. The outputs are reprojected to a globally tiled common grid in geographic coordinates designed to facilitate inter-comparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites. Therefore, the GeoNEX L1G products provide accurate and consistent TOA reflectance and brightness temperature datasets for scientific analyses and downstream product development.

Geostationary satellite, GOES-16, Himawari-8, NASA↗
NASA NTRS1991

Developing and utilizing an Euler computational method for predicting the airframe/propulsion effects for an aft-mounted turboprop transport. Volume 2: User guide

This manual explains how to use an Euler based computational method for predicting the airframe/propulsion integration effects for an aft-mounted turboprop transport. The propeller power effects are simulated by the actuator disk concept. This method consists of global flow field analysis and the embedded flow solution for predicting the detailed flow characteristics in the local vicinity of an aft-mounted propfan engine. The computational procedure includes the use of several computer programs performing four main functions: grid generation, Euler solution, grid embedding, and streamline tracing. This user's guide provides information for these programs, including input data preparations with sample input decks, output descriptions, and sample Unix scripts for program execution in the UNICOS environment.

Chen, H. C.↗