Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Global Model”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

The Transit-Time Distribution from the Northern Hemisphere Midlatitude Surface

The distribution of transit times from the Northern Hemisphere (NH) midlatitude surface is a fundamental property of tropospheric transport. Here we present an analysis of the transit time distribution (TTD) since air last contacted the northern midlatitude surface layer, as simulated by the NASA Global Modeling Initiative Chemistry Transport Model. We find that throughout the troposphere the TTD is characterized by long flat tails that reflect the recirculation of old air from the Southern Hemisphere and results in mean ages that are significantly larger than the modal age. Key aspects of the TTD -- its mode, mean and spectral width -- are interpreted in terms of tropospheric dynamics, including seasonal shifts in the location and strength of tropical convection and variations in quasi-isentropic transport out of the northern midlatitude surface layer. Our results indicate that current diagnostics of tropospheric transport are insufficient for comparing model transport and that the full distribution of transit times is a more appropriate constraint.

model transport↗
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

GMI Capabilities

We describe the capabilities of the Global Modeling Initiative (GMI) chemical transport model (CTM) with a special focus on capabilities related to the Atmospheric Tomography Mission (ATom). Several science results based on GMI hindcast simulations and preliminary results from the ATom simulations are highlighted. We also discuss the relationship between GMI and GEOS-5.

modeling↗
NASA NTRS2015

How the Assumed Size Distribution of Dust Minerals Affects the Predicted Ice Forming Nuclei

The formation of ice in clouds depends on the availability of ice forming nuclei (IFN). Dust aerosol particles are considered the most important source of IFN at a global scale. Recent laboratory studies have demonstrated that the mineral feldspar provides the most efficient dust IFN for immersion freezing and together with kaolinite for deposition ice nucleation, and that the phyllosilicates illite and montmorillonite (a member of the smectite group) are of secondary importance.A few studies have applied global models that simulate mineral specific dust to predict the number and geographical distribution of IFN. These studies have been based on the simple assumption that the mineral composition of soil as provided in data sets from the literature translates directly into the mineral composition of the dust aerosols. However, these tables are based on measurements of wet-sieved soil where dust aggregates are destroyed to a large degree. In consequence, the size distribution of dust is shifted to smaller sizes, and phyllosilicates like illite, kaolinite, and smectite are only found in the size range 2 m. In contrast, in measurements of the mineral composition of dust aerosols, the largest mass fraction of these phyllosilicates is found in the size range 2 m as part of dust aggregates. Conversely, the mass fraction of feldspar is smaller in this size range, varying with the geographical location. This may have a significant effect on the predicted IFN number and its geographical distribution.An improved mineral specific dust aerosol module has been recently implemented in the NASA GISS Earth System ModelE2. The dust module takes into consideration the disaggregated state of wet-sieved soil, on which the tables of soil mineral fractions are based. To simulate the atmospheric cycle of the minerals, the mass size distribution of each mineral in aggregates that are emitted from undispersed parent soil is reconstructed. In the current study, we test the null-hypothesis that simulating the presence of a large mass fraction of phyllosilicates in dust aerosols in the size range 2 m, in comparison to a simple model assumption where this is neglected, does not yield a significant effect on the magnitude and geographical distribution of the predicted IFN number. Results from sensitivity experiments are presented as well.

Dust↗
NASA NTRS2015

Evaluation of the Surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States

We use surface fine particulate matter (PM2.5) measurements collected by the United States Environmental Protection Agency (US EPA) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks as independent validation for Version 1 of the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero) developed by the Global Modeling Assimilation Office (GMAO). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua satellites. By combining the spatial and temporal coverage of GEOS-5 with observational constraints on AOD, MERRAero has the potential to provide improved estimates of PM2.5 compared to the model alone and with greater coverage than available observations.Importantly, assimilation of AOD data constrains the total column aerosol mass in MERRAero subject to assumptions about optical properties for each of the species represented in GOGART. However, single visible wavelength AOD data does not contain sufficient information content to correct errors in either aerosol vertical placement or composition, critical elements for a proper characterization of surface PM2.5. Despite this, we find that the data-assimilation equipped version of GEOS-5 better represents observed PM2.5 between 2003 and 2012 compared to the same version of the model without AOD assimilation. Compared to measurements from the EPA-AQS network, MERRAero shows better PM2.5 agreement with the IMPROVE network measurements, which are composed essentially of rural stations. Regardless the data network, MERRAero PM2.5 are closer to observation values during the summer while larger discrepancies are observed during the winter. Comparing MERRAero to PM2.5 data collected by the Chemical Speciation Network (CSN) offers greater insight on the species MERRAero predicts well and those for which there are biases relative to the EPA observations. Analysis of this speciated data indicates that the lack of nitrate emissions in MERRAero and an underestimation of carbonaceous emissions in the Western US explains much of the reanalysis bias during the winter. To further understand discrepancies between the reanalysis and observations, we use complimentary data to assess two important aspects of MERRAero that are of relevance to the diagnosis of PM2.5, in particular AOD and vertical structure

Air Pollution↗
NASA NTRS2016

Analysis of Ozone in Cloudy Versus Clear Sky Conditions

Convection impacts ozone concentrations by transporting ozone vertically and by lofting ozone precursors from the surface, while the clouds and lighting associated with convection affect ozone chemistry. Observations of the above-cloud ozone column (Ziemke et al., 2009) derived from the OMI instrument show geographic variability, and comparison of the above-cloud ozone with all-sky tropospheric ozone columns from OMI indicates important regional differences. We use two global models of atmospheric chemistry, the GMI chemical transport model (CTM) and the GEOS-5 chemistry climate model, to diagnose the contributions of transport and chemistry to observed differences in ozone between areas with and without deep convection, as well as differences in clean versus polluted convective regions. We also investigate how the above-cloud tropospheric ozone from OMI can provide constraints on the relationship between ozone and convection in a free-running climate simulation as well as a CTM.

Cloud↗
NASA NTRS2016

The NASA Airborne Tropical TRopopause EXperiment (ATTREX):High-Altitude Aircraft Measurements in the Tropical Western Pacific

The February through March 2014 deployment of the NASA Airborne Tropical TRopopause EXperiment (ATTREX) provided unique in situ measurements in the western Pacific Tropical Tropopause Layer (TTL). Six flights were conducted from Guam with the long-range, high-altitude, unmanned Global Hawk aircraft. The ATTREX Global Hawk payload provided measurements of water vapor, meteorological conditions, cloud properties, tracer and chemical radical concentrations, and radiative fluxes. The campaign was partially coincident with the CONTRAST and CAST airborne campaigns based in Guam using lower-altitude aircraft The ATTREX dataset is being used for investigations of TTL cloud, transport, dynamical, and chemical processes as well as for evaluation and improvement of global-model representations of TTL processes.

tropopause↗
NASA NTRS2016

Ensemble-Based Assimilation of Aerosol Observations in GEOS-5

MERRA-2 is the latest Aerosol Reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO) from 1979 to present. This reanalysis is based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from AVHRR over ocean, MODIS sensors on both Terra and Aqua satellites, MISR over bright surfaces and AERONET data. In order to assimilate lidar profiles of aerosols, we are updating the aerosol component of our assimilation system to an Ensemble Kalman Filter (EnKF) type of scheme using ensembles generated routinely by the meteorological assimilation. Following the work performed with the first NASA's aerosol reanalysis (MERRAero), we first validate the vertical structure of MERRA-2 aerosol assimilated fields using CALIOP data over regions of particular interest during 2008.

LDE↗
NASA NTRS2015

Evaluating Surface Flux Results from CERES-FLASHFlux

The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide a rapid release version of the Clouds and Earth's Radiant Energy System (CERES) results, which could be made available to the research and applications communities within one week of the satellite observations by exchanging some accuracy for speed of processing. Unlike standard CERES products, FLASHFlux does not maintain a long-term consistent record. Therefore the latest algorithm changes and input data can be incorporated into processing. FLASHFlux released Version3A (January 2013) and Version 3B (August 2014) which include the latest meteorological product from Global Modeling and Assimilation Office (GMAO), GEOS FP-IT (5.9.1), the latest spectral response functions and gains for the CERES instruments, and aerosol climatology based on the latest MATCH data. Version 3B included a slightly updated calibration and some changes to the surface albedo over snow/ice. Typically FLASHFlux does not reprocess earlier versions when a new version is released. The combined record of Time Interpolated Space Averaged (TISA) surface flux results from Versions3A and 3B for July 2012 to October 2015 have been compared to the ground-based measurements. The FLASHFlux results are also compared to two other CERES gridded products, SYN1deg and EBAF surface fluxes.

Wilber, Anne C.↗
NASA NTRS2016

Characterizing background error with the NASA GMAO OSSE

Understanding the characteristics of background error is vital to the design and performance of data assimilation systems. However, the true state of the atmosphere is unknown, and qualitative determination of background error is thus a challenge. We present an exploration of background error characteristics using the NASA Global Modeling and Assimilation Office Observing System Simulation Experiment framework, where the presence of the Nature Run allows direct calculation of the background error. The behavior of the data assimilation system (DAS) can be compared with the character of the background error, and performance of the DAS will be investigated in a series of both idealized and realistic experiments.

Nature Run↗
NASA NTRS2016

Low Ozone over Europe Doesn't Mean the Sky Is Falling, Its Actually Rising

Data Sources: NASA Aura Microwave Limb Sounder (MLS) (O3 profiles and columns), NASA Global Modeling Initiative (GMI) Chemistry and Transport Model (calculated O3depletion), and MERRA Tropopause Heights. Technical Description of Figures: The left graphics show MLS northern hemisphere stratospheric column ozone on Feb. 1, 2016. Very low columns are seen over the UK and Europe (<225 DU, inside dashed circle). The lower graphic shows the GMI-calculated O3 depletion. It's very small, suggesting the low O3 does not indicate significant depletion. The right graphics show how the high tropopause height in this region explains the observed low ozone. The lower panel shows that the high tropopause on Feb. 1 lifts the O3 profile compared to a typical profile found earlier in winter. This motion lifts the profile to lower pressures thus reducing the total column. The GMI Model shows only 4 Dobson Units (DU) of O3 depletion even though the column is more than 100 DU lower than one month earlier. Scientific significant and societal relevance: To quantitatively understand anthropogenic impacts to the stratospheric ozone layer, we must be able to distinguish between low ozone caused by ozone depleting substances and that caused by natural dynamical variability in the atmosphere. Observations and realistic simulations of atmospheric composition are both required in order to separate natural and anthropogenic ozone variability.

Stratospheric↗
NASA NTRS2016

Chemistry Simulations Using MERRA-2 Reanalysis with the GMI CTM and Replay in Support of the Atmospheric Composition Community

Simulations using reanalyzed meteorological conditions have been long used to understand causes of atmospheric composition change over the recent past. Using the new Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorology, chemistry simulations are being conducted to create products covering 1980-2016 for the atmospheric composition community. These simulations use the Global Modeling Initiative (GMI) chemical mechanism in two different models: the GMI Chemical Transport Model (CTM) and the GEOS-5 model developed Replay mode. Replay mode means an integration of the GEOS-5 general circulation model that is incrementally adjusted each time step toward the MERRA-2 analysis. The GMI CTM is a 1 x 1.25 simulation and the MERRA-2 GMI Replay simulation uses the native MERRA-2 approximately horizontal resolution on the cubed sphere. The Replay simulations is driven by the online use of key MERRA-2 meteorological variables (i.e. U, V, T, and surface pressure) with all other variables calculated in response to those variables. A specialized set of transport diagnostics is included in both runs to better understand trace gas transport and changes over the recent past.

Chemistry-Climate Model↗
NASA NTRS2017

Chemistry Simulations using the MERRA-2 Reanalysis with the GMI CTM and Replay in Support of the Atmospheric Composition Community

Simulations using reanalysis meteorological fields have long been used to understand the causes of atmospheric composition change in the recent past. Using the new MERRA-2 reanalysis, we are conducting chemistry simulations to create products covering 1980-2016 for the atmospheric composition community. These simulations use the Global Modeling Initiative (GMI) chemical mechanism in two different models: the GMI Chemical Transport Model (CTM) and the GEOS-5 model in Replay mode. Replay mode means an integration of the GEOS-5 general circulation model that is incrementally adjusted each time step toward the MERRA-2 reanalysis. The GMI CTM is a 1 deg x 1.25 deg simulation and the MERRA-2 GMI Replay simulation uses the native MERRA-2 grid of approximately 1/2 deg horizontal resolution on the cubed sphere. A specialized set of transport diagnostics is included in both runs to better understand trace gas transport and its variability in the recent past.

MERRA-2↗
NASA NTRS2017

Experimental Study of Convective Heating on the Back Face and Payload of a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) Aeroshell

A wind tunnel test program has been conducted to define convective heating environments on the back-face of a Hypersonic Inflatable Aerodynamic Decelerator aeroshell. Wind tunnel testing was conducted at Mach 6 and Mach 10 at unit Reynolds numbers from 0.5×10(exp 6)/ft to 3.9×10(exp 6)/ft on a 6.3088 in diameter aeroshell model. Global heating data were obtained through phosphor thermography on the aeroshell back face, as well as on the payload and the aeroshell front face. For all test conditions, laminar flow was produced on the aeroshell front face, while the separated wake shear layer and aeroshell back-face boundary layer were transitional or turbulent. Along the leeward centerline of the aeroshell back face and payload centerbody, heating levels increased with both free stream Reynolds number and angle of attack. The Reynolds number dependency was due to increasing strength of wake turbulence with Reynolds number. The angle-of-attack dependency was due to movement of the wake-vortex reattachment point on the aeroshell back face. The maximum heating levels on the aeroshell back face and payload were approximately 5% to 6%, respectively, of the aeroshell front-face stagnation point. To allow for extrapolation of the ground test data to flight conditions, the back face and payload heating levels were correlated as a function of aeroshell front-face peak momentum thickness Reynolds numbers.

Hollis, Brian R.↗
NASA NTRS2017

An Introduction to Observing System Simulation Experiments

Observing System Simulation Experiments (OSSEs) are used to estimate the potential impact of proposed new instruments and data on numerical weather prediction. OSSEs can also be used to help design new observing platforms and to investigate the behavior of data assimilation systems. A basic overview of how to design and perform an OSSE will be given, as well as best practices and pitfalls. Some examples using the OSSE framework developed at the NASA Global Modeling and Assimilation Office will be shown.

OSSE↗
NASA NTRS2016

Aerosols in the Atmosphere: Sources, Transport, and Multi-decadal Trends

We present our recent studies with global modeling and analysis of atmospheric aerosols. We have used the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and satellite and in situ data to investigate (1) long-term variations of aerosols over polluted and dust source regions and downwind ocean areas in the past three decades and the cause of the changes and (2) anthropogenic and volcanic contributions to the sulfate aerosol in the upper tropospherelower stratosphere.

Chin, M.↗
NASA NTRS2017

The Role of DYNAMO in Situ Observations in Improving NASA Ceres-like Daily Surface and Atmospheric Radiative Flux Estimates

The daily surface and atmospheric radiative fluxes from NASA Clouds and the Earths RadiantEnergy System (CERES) Synoptic 1 degree (SYN1deg) Ed3A are among the most widely used data to studycloud-radiative feedback. The CERES SYN1deg data are based on Fu-Liou radiative transfer computations thatuse specific humidity (Q) and air temperature (T) from NASA Global Modeling and Assimilation Office (GMAO)reanalyses as inputs and are therefore subject to the quality of those fields. This study uses in situ Q and Tobservations collected during the Dynamics of the Madden-Julian Oscillation (DYNAMO) field campaign toaugment the input stream used in the NASA GMAO reanalysis and assess the impact on the CERES dailysurface and atmospheric longwave estimates. The results show that the assimilation of DYNAMOobservations considerably improves the vertical profiles of analyzed Q and T over and near DYNAMO stationsby moistening and warming the lower troposphere and upper troposphere and drying and cooling themid-upper troposphere. As a result of these changes in Q and T, the computed CERES daily surface downwardlongwave flux increases by about 5 W m(exp -2), due mainly to the warming and moistening in the lowertroposphere; the computed daily top-of-atmosphere (TOA) outgoing longwave radiation increases by2-3 W m(exp -2) during dry periods only. Correspondingly, the estimated local atmospheric longwave radiativecooling enhances by about 5 W m(exp -2) (7-8 W m(exp -2)) during wet (dry) periods. These changes reduce the bias inthe CERES SYN1deg-like daily longwave estimates at both the TOA and surface and represent animprovement over the DYNAMO region.

DYNAMO↗
NASA NTRS2017

Aerosol EnKF at GMAO

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASAs Global Modeling and Assimilation Office(GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to an ensemble square root filter(EnSRF; Whitaker and Hamill (2002)) type of scheme.We present a summary of our preliminary results of the assimilation of column integrated aerosol observations (Aerosol Optical Depth; AOD) using an Ensemble Square Root Filters (EnSRF) scheme and the ensemble members produced routinely by the meteorological assimilation.

MERRA-2 EnSRF↗