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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Multi-Decadal Records of Stratospheric Composition and Their Relationship to Stratospheric Circulation Change

Constituent evolution for 1990-2015 simulated using the Global Modeling Initiative chemistry and transport model driven by meteorological fields from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is compared with three sources of observations: ground-based column measurements of HNO3 and HCl from two stations in the Network for the Detection of Atmospheric Composition Change (NDACC, 1990- ongoing), profiles of CH4 from the Halogen Occultation Experiment (HALOE) on the Upper Atmosphere Research Satellite (UARS, 1992-2005), and profiles of N2O from the Microwave Limb Sounder on the Earth Observing System satellite Aura (2005- ongoing). The differences between observed and simulated values are shown to be time dependent, with better agreement after 2000 compared with the prior decade. Furthermore, the differences between observed and simulated HNO3 and HCl columns are shown to be correlated with each other, suggesting that issues with the simulated transport and mixing cause the differences during the 1990s and that these issues are less important during the later years. Because the simulated fields are related to mean age in the lower stratosphere, we use these comparisons to evaluate the time dependence of mean age. The ongoing NDACC column observations provide critical information necessary to substantiate trends in mean age obtained using fields from MERRA-2 or any other reanalysis products.

Profiles of N2O from the Microwave Limb Sounde↗
NASA NTRS2017

Evaluation of the Ozone Fields in NASA's MERRA-2 Reanalysis

The assimilated ozone product from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), produced at NASAs Global Modeling and Assimilation Office (GMAO) is summarized. The reanalysis begins in 1980 with the use of retrieved partial-column ozone concentrations from a series of Solar Backscatter Ultraviolet Radiometer (SBUV) instruments on NASA and NOAA spacecraft. Beginning in October 2004, retrieved ozone profiles from the Microwave Limb Sounder (MLS) and total column ozone from the Ozone Monitoring Instrument (OMI) on NASAs EOS Aura satellite are assimilated. While this change in data streams does lead to a discontinuity in the assimilated ozone fields in MERRA-2, making it not useful for studies in decadal (secular) trends in ozone, this choice was made to prioritize demonstrating the value NASAs high-quality research data in the reanalysis context. The MERRA-2 ozone is compared with independent satellite and ozonesonde data, focusing on the representation of the spatial and temporal variability of stratospheric and upper-tropospheric ozone. The comparisons show agreement within 10 (standard deviation of the difference) between MERRA-2 profiles and independent satellite data in most of the stratosphere. The agreement improves after 2004, when EOS Aura data are assimilated. The standard deviation of the differences between the lower-stratospheric and upper-tropospheric MERRA-2 ozone and ozonesondes is 11.2 and 24.5, respectively, with correlations of 0.8 and above. This is indicative of a realistic representation of the UTLS ozone variability in MERRA-2. After 2004, the upper tropospheric ozone in MERRA-2 shows a low bias compared to the sondes, but the covariance with independent observations is improved compared to earlier years. Case studies demonstrate the integrity of MERRA-2 analyses in representing important features such as tropopause folds.

Ozone↗
NASA NTRS2017

Description of the GMAO OSSE for Weather Analysis Software Package: Version 3

The Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Center has developed software and products for conducting observing system simulation experiments (OSSEs) for weather analysis applications. Such applications include estimations of potential effects of new observing instruments or data assimilation techniques on improving weather analysis and forecasts. The GMAO software creates simulated observations from nature run (NR) data sets and adds simulated errors to those observations. The algorithms employed are much more sophisticated, adding a much greater degree of realism, compared with OSSE systems currently available elsewhere. The algorithms employed, software designs, and validation procedures are described in this document. Instructions for using the software are also provided.

OSSE↗
NASA NTRS2017

Effective Use Of Scatterometer Winds In Current and Future GMAO Reanalysis

Scatterometer-derived near-surface ocean vector wind retrievals provide global measurements complementary to the sparse conventional observing system which primarily consists of ships and buoys over water surfaces. The RapidScat instrument was flown on the International Space Station as a quick and low cost replacement of QuikScat and as a continuation of the NASA scatterometry data record. A unique characteristic of RapidScat was that it flew in a non-sun synchronous orbit at an inclination of 51.6 degrees. This orbit allowed for the collocation of measurements with other scatterometers as well as an ability to sample diurnal signals. In the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, the scatterometry record began with the ESA European Remote Sensing (ERS) scatterometer on 5 Aug 1991 and continued through today with the EUMETSAT Metop Advanced Scatterometer (ASCAT). RapidScat, however, was not used in the MERRA-2 system as development had been completed prior to the beginning of its data record. In this presentation, the RapidScat ocean vector winds will be compared to MERRA-2, both in terms of the analysis fields and in the context of its global observing system, to assess the viability of using the data in future reanalysis systems developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center.

Chattopadhyay, Mohar↗
NASA NTRS2017

An 8-Year, High-Resolution Reanalysis of Atmospheric CO2 Mixing Ratios Based on OCO-2 and GOSAT-ACOS Retrievals

The NASA GMAO (Global Modeling and Assimilation Office) reanalysis blends OCO-2 (Orbiting Carbon Observatory 2) and GOSAT-ACOS (Greenhouse Gases Observing Satellite-Atmospheric Carbon Observations from Space) retrievals (top) with GEOS (Goddard Earth Observing System) model predictions (bottom) to estimate the full 3D (three-dimensional) state of CO2 every 3 hours (middle). This poster describes monthly atmospheric growth rates derived from the reanalysis and an application to aircraft data with the potential to aid bias correction.

GEOS-Carb↗
NASA NTRS2018

Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit (AMSU-A), Microwave Humidity Sounder (MHS), and GMI on GEOS-5 analyses and forecasts of various hurricanes.

Kim, Min-Jeong↗
NASA NTRS2018

The Tangent Linear and Adjoint of the FV3 Dynamical Core: Development and Applications

GMAO (NASA's Global Modeling and Assimilation Office) has developed a highly sophisticated adjoint modeling system based on the most recent version of the finite volume cubed sphere (FV3) dynamical core. This provides a mechanism for investigating sensitivity to initial conditions and examining observation impacts. It also allows for the computation of singular vectors and for the implementation of hybrid 4DVAR (4-Dimensional Variational Assimilation). In this work we will present the scientific assessment of the new adjoint system and show results from a number of research application of the adjoint system.

FV3↗
NASA NTRS2018

Decline in Antarctic Ozone Depletion and Lower Stratospheric Chlorine Determined From Aura Microwave Limb Sounder Observations

Attribution of Antarctic ozone recovery to the Montreal protocol requires evidence that (1)Antarctic chlorine levels are declining and (2) there is a reduction in ozone depletion in response to achlorine decline. We use Aura Microwave Limb Sounder measurements of O3, HCl, and N2O to demonstratethat inorganic chlorine (Cly) from 2013 to 2016 was 223 ± 93 parts per trillion lower in the Antarctic lowerstratosphere than from 2004 to 2007 and that column ozone depletion declined in response. The mean Clydecline rate, ~0.8%/yr, agrees with the expected rate based on chlorofluorocarbon lifetimes. N2Omeasurements are crucial for identifying changes in stratospheric Cly loading independent of dynamicalvariability. From 2005 to 2016, the ozone depletion and Cly time series show matching periods of decline,stability, and increase. The observed sensitivity of O3 depletion to changing Cly agrees with the sensitivitysimulated by the Global Modeling Initiative chemistry transport model integrated

Ozone recovery↗
NASA NTRS2018

Near-Real Time Ocean-Atmosphere Skin Temperature as Part of NASA GMAO Atmospheric Data Assimilation System

The Global Modeling and Assimilation Office (GMAO) at NASA, GSFC is developing an integrated Earth system analysis (IESA). Integral to this IESA are ocean-atmosphere interface states, such as the skin SST. Recently the GMAO's near-real time operational weather analysis and prediction system implemented an analysis for skin SST along with the meteorological analysis since Jan, 2017. The skin SST is modeled and constrained using infrared radiometric observations. This poster describes some of the details of this development, its impact on forecasts, current and future developments towards the inclusion of microwave data (e.g. GPM-GMI).

SST↗
NASA NTRS2017

High-Resolution NO2 Observations from the Airborne Compact Atmospheric Mapper: Retrieval and Validation

Nitrogen dioxide (NO2) is a short-lived atmospheric pollutant that serves as an air quality indicator and is itself a health concern. The Airborne Compact Atmospheric Mapper (ACAM) was flown on board the NASA UC-12 aircraft during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality Maryland field campaign in July 2011. The instrument collected hyperspectral remote sensing measurements in the 304-910 nm range, allowing daytime observations of several tropospheric pollutants, including nitrogen dioxide (NO2), at an unprecedented spatial resolution of 1.5 × 1.1 sq. km. Retrievals of slant column abundance are based on the differential optical absorption spectroscopy method. For the air mass factor computations needed to convert these retrievals to vertical column abundance, we include high-resolution information for the surface reflectivity by using bidirectional reflectance distribution function data from the Moderate Resolution Imaging Spectroradiometer. We use high-resolution simulated vertical distributions of NO2 from the Community Multiscale Air Quality and Global Modeling Initiative models to account for the temporal variation in atmospheric NO2 to retrieve middle and lower tropospheric NO2 columns (NO2 below the aircraft). We compare NO2 derived from ACAM measurements with in situ observations from NASA's P-3B research aircraft, total column observations from the ground-based Pandora spectrometers, and tropospheric column observations from the space-based Ozone Monitoring Instrument. The high-resolution ACAM measurements not only give new insights into our understanding of atmospheric composition and chemistry through observation of subsampling variability in typical satellite and model resolutions, but they also provide opportunities for testing algorithm improvements for forthcoming geostationary air quality missions.

Lamsal, L. N.↗
NASA NTRS2016

A42F-03: Small-Scale Variability in Tropical Tropopause Layer Humidity

Recent advances in statistical parameterizations of cirrus cloud processes for use in global models are highlighting the need for information about small-scale fluctuations in upper tropospheric humidity and the physical processes that control the humidity variability. To address these issues, we have analyzed high-resolution airborne water vapor measurements obtained in the Airborne Tropical TRopopause EXperiment (ATTREX) over the tropical Pacific between 14 and 20 km. Using accurate and precise 1-Hz water vapor measurements along approximately-level aircraft flight legs, we calculate structure functions spanning horizontal scales ranging from about 0.2 to 50 km, and we compare the water vapor variability in the lower (about 14 km) and upper (16-19 km) Tropical Tropopause Layer (TTL). We also compare the magnitudes and scales of variability inside TTL cirrus versus in clear-sky regions. The measurements show that in the upper TTL, water vapor concentration variance is stronger inside cirrus than in clear-sky regions. Using simulations of TTL cirrus formation, we show that small variability in clear-sky humidity is amplified by the strong sensitivity of ice nucleation rate to supersaturation, which results in highly-structured clouds that subsequently drive variability in the water vapor field. In the lower TTL, humidity variability is correlated with recent detrainment from deep convection. The structure functions indicate approximately power-law scaling with spectral slopes ranging from about minus 5 divided by 3, to minus 2.

water vapor↗
NASA NTRS2018

NASA GMAO Integrated Earth System Analysis

An Integrated Earth System Analysis (IESA) is the only possible way to assimilate all the available observations of separate components of the earth system (atmosphere, ocean, cryosphere, land, constituents, etc) into a single consistent Coupled Data Assimilation System (CDAS). The NASA Global Modeling and Assimilation Office (GMAO) is developing an IESA for Reanalysis in an incremental fashion by coupling different components- one at a time, for e.g., the MERRA-2 reanalysis coupled aerosol and meteorological data assimilation systems. Following a similar approach the ocean (including sea-ice) is being coupled to the atmosphere, starting with the air-sea interface. This presentation briefly outlines GMAO's reanalysis road map and summarizes our recent work in that direction.

Akella, Santha↗
NASA NTRS2018

A Cloud Algorithm Based on the O2-O2 477 nm Absorption Band Featuring an Advanced Spectral Fitting Method and the Use of Surface Geometry-Dependent Lambertian-Equivalent Reflectivity

We discuss a new cloud algorithm that retrieves an effective cloud pressure, also known as cloud optical centroid pressure (OCP), from oxygen dimer (O2-O2) absorption at 477nm after determining an effective cloud fraction (ECF) at 466nm, a wavelength not significantly affected by trace-gas absorption and rotational Raman scattering. The retrieved cloud products are intended for use as inputs to the operational nitrogen dioxide (NO2) retrieval algorithm for the Ozone Monitoring Instrument (OMI) flying on the Aura satellite. The cloud algorithm uses temperature-dependent O2-O2 cross sections and incorporates flexible spectral fitting techniques that account for specifics of the surface reflectivity. The fitting procedure derives O2-O2 slant column densities (SCDs) from radiances after O3, NO2, and H2O absorption features have been removed based on estimates of the amounts of these species from independent OMI algorithms. The cloud algorithm is based on the frequently used mixed Lambertian-equivalent reflectivity (MLER) concept. A geometry-dependent Lambertian-equivalent reflectivity (GLER), which is a proxy of surface bidirectional reflectance, is used for the ground reflectivity in our implementation of the MLER approach. The OCP is derived from a match of the measured O2-O2 SCD to that calculated with the MLER method. Temperature profiles needed for computation of vertical column densities are taken from the Global Modeling Initiative (GMI) model. We investigate the effect of using GLER instead of climatological LER on the retrieved ECF and OCP. For evaluation purposes, the retrieved ECFs and OCPs are compared with those from the operational OMI cloud product, which is also based on the same O2-O2 absorption band. Impacts of the application of the newly developed cloud algorithm to the OMI NO2 retrieval are discussed.

cloud fraction and pressure↗
NASA NTRS2018

Link Between Arctic Tropospheric BrO Explosion Observed from Space and Sea-Salt Aerosols from Blowing Snow Investigated Using Ozone Monitoring Instrument BrO Data and GEOS-5 Data Assimilation System

Bromine radicals (Br + BrO) are important atmospheric species owing to their ability to catalytically destroy ozone as well as their potential impacts on the oxidative pathways of many trace gases, including dimethylsulfide and mercury. Using space-based observations of BrO, recent studies have reported rapid enhancements of tropospheric BrO over large areas (so called "BrO explosions") connected to near-surface ozone depletion occurring in polar spring. However, the source(s) of reactive bromine and mechanism(s) that initiate these BrO explosions are uncertain. In this study, we investigate the relationships between Arctic BrO explosions and two of the proposed sources of reactive bromine: sea-salt aerosol (SSA) generated from blowing snow and first-year (seasonal) sea ice. We use tropospheric column BrO derived from the Ozone Monitoring Instrument (OMI) in conjunction with the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation system provided by National Aeronautics and Space Administration Global Modeling and Assimilation Office. Case studies demonstrate a strong association between the temporal and spatial extent of OMI-observed BrO explosions and the GEOS-5 simulated blowing snow-generated SSA during Arctic spring. Furthermore, the frequency of BrO explosion events observed over the 11-year record of OMI exhibits significant correlation with a time series of the simulated SSA emission flux in the Arctic and little to no correlation with a time series of satellite-based first-year sea ice area. Therefore, we conclude that SSA generated by blowing snow is an important factor in the formation of the BrO explosion observed from space during Arctic spring.

blowing snow↗
NASA NTRS2018

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite Sea Surface Salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (National Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF ( Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive) V4 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction.

Hackert, E.↗
NASA NTRS2018

Observing System Simulation Experiments as Tools for Investigating the Behavior of Data Assimilation Systems

Data assimilation systems (DAS) are difficult to evaluate in part because there is limited independent data to use for verification of performance. In an Observing System Simulation Experiment (OSSE), the full true state is known exactly, in the form of the Nature Run. The availability of this truth allows the investigation of DAS characteristics in the OSSE framework that are not quantifiable in the real world. The synthetic observations can also be manipulated to test configurations that range from idealized to highly realistic. A sampling of OSSE investigations into the behavior of 3DVar and 4DEnVar DAS and adjoint observation impact estimation tools will be illustrated using the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE.

Prive, Nikki C.↗
NASA NTRS2018

Assimilating GCOM-W AMSR2 Radiance Data in Future GEOS Reanalysis

Surface and atmospheric radiation are being measured by space-borne conical scanning microwave radiometers with high accuracy and spatial resolution since 1980s'. These radiometers include Special Sensor Microwave Imager (SSM/I) onboard various NOAA's polar-orbiting satellites, Special Sensor Microwave Imager Sounder (SSMIS) onboard Defense Meteorological Satellite Program (DMSP) satellites, and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), and Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard NASA and JAXA's satellites. Atmospheric temperature, humidity, clouds, and precipitation are retrieved using brightness temperature data at frequencies between 22 GHz – 183 GHz. Some of the brightness temperature and retrieved rain rate data made by these imagers are assimilated in NASA Global Modelling and Assimilation Office (GMAO)'s weather and climate data sets including the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets. However, those radiance data are assimilated with the Goddard Earth Observing System model, version 5 (GEOS-5) only in clear-sky conditions. Recently, we started assimilate GPM/GMI radiance data with GEOS-5's atmospheric data assimilation system GSI under all-sky conditions in order to have better constraints in analyzed hydrological properties. GMAO plans to assimilate more microwave radiometers' brightness temperature data in its analyses. In this talk, we will discuss TMI and AMSR2 data's impact in temperature, humidity, clouds and precipitation under all-sky conditions in future GEOS reanalysis.

Jin, Jianjun↗
NASA NTRS2018

Improving NASA GEOS Atmospheric CO2 Simulations by Calibrating CASA Surface Fluxes with an Empirical Sink

With the adoption of the Paris climate accord, efforts to monitor and understand both anthropogenic and natural carbon sources and sinks are increasing across the world. Given their low latency and global coverage, satellite observations of atmospheric carbon dioxide (CO2) are poised to make important contributions to this field. The combination of satellite data and high resolution global models can be used to monitor changes in carbon fluxes and to evaluate the consistency of nationally reported emissions estimates in support of multiple stakeholder communities. However, a consistent challenge to such work has been the high latency of surface carbon flux estimates, which are often not available for a year or more. This presentation describes the construction of surface carbon flux estimates meant to improve the near real time simulation of atmospheric CO2 with NASA's Goddard Earth Observing System (GEOS) general circulation model. The surface flux estimates begin with a collection of bottom-up fluxes which incorporate satellite measurements in their construction, e.g. vegetation indices in the Carnegie-Ames-Stanford Approach (CASA) and nighttime lights in the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). From there, we take the additional step of using an empirical sink to calibrate terrestrial net biospheric exchange (NBE) to estimated values from atmospheric inversion systems. This approach removes a known, systematic bias in predicted atmospheric mixing ratios. Using these fluxes in a free running simulation, the model is able to reproduce in situ measurements with the same skill as when it uses gridded fluxes from a flux inversion system. Using these fluxes as a prior in an assimilation system, e.g. one incorporating retrievals of column CO2 from the Orbiting Carbon Observatory 2 (OCO-2), allows the analysis to capture variability in CO2 on scales that would be missed otherwise. This approach supports NASA's capability to forecast atmospheric CO2 up to two weeks in advance by leveraging a GEOS system used to produce quasi-operational weather analyses and forecasts, providing a valuable new tool to the carbon monitoring research and applications communities.

Weir, B.↗