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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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At least 595 records · Page 33
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NASA NTRS1992

The BL Lacertae object PKS 1413 + 135 - Is it within or behind a spiral galaxy?

New observations of the very red quasar or BL Lac object PKS 1413 + 135 are presented. Optical imaging and spectroscopy confirm the claim that the host galaxy to this BL Lac is a spiral, and H-band imaging shows the point source centered in the galaxy to within 0.1 arcsec. A reanalysis of Einstein X-ray data finds evidence for an extremely large column density in this source. While the astrometric evidence supports the hypothesis that PKS 1413 + 135 is the very unusual case of a BL Lac object in a spiral galaxy, a highly extincted nuclear source would produce other observable features in this galaxy that are not observed: narrow emission-line region and large thermal IR flux. Their absence argues for this source being background to the spiral.

Stocke, John T.↗
NASA NTRS1998

Intercomparisons of Total Precipitable Water from Satellite and Other Long Term Data Sets

Global water vapor data sets from satellite (NVAP) and reanalysis (NCEP and DAO) are intercompared for a 5 year period (1988-1992). Global average indicates that the NCEP and DAO reanalyses are dryer than NVAP over much of the period. Spatial patterns of the NCEP/NVAP and DAO/NVAP differences show regional variations in the 60 month climatogical fields. For example, in the Eastern Pacific just south of the equator NVAP is much dryer than the NCEP and more moist than DAO. Rather large discrepancies exist in other regions as well. North Africa, Saudi Arabia, India, and Australia show that NVAP TPW values are more moist than the NCEP and DAO model analyses by 4-8 mm. In general, the NCEP and DAO exhibits a tendency to be dryer than NVAP over the tropical ocean region. Over the Americas little differences exist except over the west coast. The shapes of the differences fields over the Eastern Pacific region are significantly different between NCEP/NVAP and DAO/NVAP differences. Over South America and Central Africa, DAO TPW values are significantly higher than those of NVAP. These difference fields show monthly and seasonal variability as well. These results will be highlighted in the paper and on the poster.

Chang, Fong-Chiau↗
NASA NTRS2013

Global Data Assembly Center (GDAC) Report to the GHRSST Science Team

In 2012-2013 the Global Data Assembly Center (GDAC) at NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) continued its role as the primary clearinghouse and access node for operational GHRSST data streams, as well as its collaborative role with the NOAA Long Term Stewardship and Reanalysis Facility (LTSRF) for archiving. Our presentation reported on our data management activities and infrastructure improvements since the last science team meeting in 2012.

user reports↗
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 NTRS2019

A Global Climatology of Extratropical Transition Part I: Characteristics Across Basins

The authors present a global climatology of tropical cyclones (TCs) that undergo extratropical transition (ET). ET is objectively defined based on a TC's trajectory through the cyclone phase space (CPS), which is calculated using storm tracks from 1979-2017 best-track data and geopotential height fields from reanalysis datasets. Two reanalyses are used and compared for this purpose, the Japanese 55-year Reanalysis and the ECMWF Interim Reanalysis. The results are used to study the seasonal and geographical distributions of storms undergoing ET and inter-basin differences in the statistics of ET occurrence. About 50% of all TCs in the North Atlantic and the Western North Pacific undergo ET. In the southern hemisphere, ET fractions range from about 20% in the South Indian Ocean and the Australian region to 45% in the South Pacific. In the majority of ETs, TCs become thermally asymmetric before forming a cold core. However, a substantial fraction of TCs takes the reverse pathway, developing a cold core before becoming thermally asymmetric. This pathway is most common in the Eastern North Pacific and the North Atlantic. Different ET pathways can be linked to different geographical trajectories and environmental settings. In ETs over warmer sea-surface temperatures, TCs tend to lose their thermal symmetry while still maintaining a warm core. Landfalls by TCs undergoing ET occur 3-4 times per year in the North Atlantic and 7-10 times per year in the Western North Pacific, while coastal regions in the Australian region are affected once every 1-2 years.

Melanie Bieli↗
NASA NTRS2012

The GEOS-iODAS: Description and Evaluation

This report documents the GMAO's Goddard Earth Observing System sea ice and ocean data assimilation systems (GEOS iODAS) and their evolution from the first reanalysis test, through the implementation that was used to initialize the GMAO decadal forecasts, and to the current system that is used to initialize the GMAO seasonal forecasts. The iODAS assimilates a wide range of observations into the ocean and sea ice components: in-situ temperature and salinity profiles, sea level anomalies from satellite altimetry, analyzed SST, and sea-ice concentration. The climatological sea surface salinity is used to constrain the surface salinity prior to the Argo years. Climatological temperature and salinity gridded data sets from the 2009 version of the World Ocean Atlas (WOA09) are used to help constrain the analysis in data sparse areas. The latest analysis, GEOS ODAS5.2, is diagnosed through detailed studies of the statistics of the innovations and analysis departures, comparisons with independent data, and integrated values such as volume transport. Finally, the climatologies of temperature and salinity fields from the Argo era, 2002-2011, are presented and compared with the WOA09.

Climate↗
DOE OSTI2022

Disentangling error structures of precipitation datasets using decision trees

Characterizing error structures in precipitation products not only facilitates their proper applications for scientific and practical purposes but also helps improve their retrieval algorithms and processing methods. Despite the fact that multiple precipitation products have been assessed in the literature, factors that affect their error structures remain inadequately addressed. By interpreting 60 binary decision trees, this study disentangles the error characteristics of precipitation products in terms of their spatiotemporal patterns and geographical factors. Three independent precipitation products - two satellite-based and one reanalysis datasets: the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) late run (IMERG-L), Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 uncorrected precipitation output (MERRA2-UC), are evaluated across the contiguous United States from 2010 to 2019. Here, the ground-based Stage IV precipitation dataset is used as the ground truth. Results indicate that the MERRA2-UC outperforms the IMERG-L and SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the analysis period. Decision trees cross-assess three spatiotemporal factors and find that the underestimation of MERRA2-UC occurs in the east of the Rocky Mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine different geographical characteristics, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover type, topography position index, and aspect are three relatively weak factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗
NASA NTRS1997

Studies of the Earth Energy Budget and Water Cycle Using Satellite Observations and Model Analyses

During this research period we have utilized the ERBE data set in comparisons to surface properties and water vapor observations in the atmosphere. A relationship between cloudiness and surface temperature anomalies was found. This same relationship was found in a general circulation model, verifying the model. The attempt to construct a homogeneous time series from Nimbus 6, Nimbus 7 and ERBE data is not complete because we are still waiting for the ERBE reanalysis to be completed. It will be difficult to merge the Nimbus 6 data in because its observations occurred when the average weather was different than the other periods, so regression adjustments are not effective.

Campbell, G. G.↗
NASA NTRSDate not supplied

Effect of Rain-Adjusted Satellite Sea Surface Salinity on ENSO Predictions from the GMAO S2S Forecast System

ENSO has a significant impact on climate variability throughout the world and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of altimetry and in situ data lead to improved short-term forecasts of the coupled system. However, few studies have focused on improving the near-surface density/mixing through assimilation of satellite sea surface salinity (SSS). For expediency, most projects that do assimilate SSS do so as if these data were observed at the top model layer instead of at the surface. In rainy regions where buoyant water sits as a fresh lens at the surface, this assumption is likely invalid. Therefore, we adjust SSS so that it more accurately represents the salinity at the first model layer. The Rain Impact Model (RIM) uses a simple diffusion model to determine the near surface salinity gradient (i.e., 1 cm to 5 m). Satellite SSS data are modified using this near-surface salinity gradient, so the salinity values are now valid at the first model layer (we call this SSS@5m). We assess the impact of satellite SSS observations for near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts using the NASA GMAO S2S coupled system. For all reanalysis experiments, all available along-track sea level and in situ observations are assimilated for 2011 to 2020 using the LETKF scheme (Penny et al., 2013). One reanalysis assimilates Aquarius /SMAP SSS as before. An additional reanalysis is performed assimilating the SSS@5m data. Validation statistics are compared for experiments that assimilate SSS (sub-optimally as before) versus the SSS@5m. We also compare results of coupled forecasts that are initialized from these reanalyses in spring. For all but the big La Niña in 2017, all NINO3.4 forecasts were improved by using the RIM. We will show that improved SSS estimates upgrades density and near-surface mixing leading to more accurate coupled air/sea interaction and better forecasts.

ENSO↗
NASA NTRSDate not supplied

Reanalysis Activities at the NASA Global Modeling and Assimilation Office

This talk presents an overview of recent reanalysis activities at the NASA Global Modeling and Assimilation Office (GMAO) as part of a multi-faceted strategy towards an Integrated Earth System retrospective analysis, coupling components of the atmosphere, ocean, chemistry, land, and ice. While elements of the atmosphere-ocean coupled Goddard Earth Observing System (GEOS) model and data assimilation are being actively developed, a suite of reanalysis products is designed to provide further understanding of key aspects of Earth system coupling in a reanalysis context: The baseline atmospheric reanalysis, the GEOS Retrospective analysis for the early 21st Century (GEOS-R21C), features recent advances in the GEOS model and data assimilation, and targets the NASA’s Earth Observing System EOS and post-EOS satellite observations; GEOS-IT, a user-tailored low-resolution atmospheric reanalysis, serves as a second baseline to the NASA Instrument Teams for validation and calibration and drives a one-way coupled ocean reanalysis, GEOSIT-Ocean; PolarMERRA, a high-resolution downscaled product for the polar regions, focuses on improving the representation of polar atmospheric processes with an assessment of current cryospheric biases, and targeted improvements to surface sea ice and glacier conditions; Finally, R21C-Chem, an off-line atmospheric chemistry and composition reanalysis, includes both tropospheric and stratospheric trace gases. The diversity of these reanalysis activities presents unique opportunities for collaborations cross-teams/institutions, with new commercial data partners, and with end-user groups. This talk will discuss these opportunities and explore leveraging the lessons learned along the way on key drivers in Earth system interactions as we converge towards the next generation of the Modern-Era Retrospective analysis for Research and Applications (MERRA) suite.

Amal El Akkraoui↗
NASA NTRSDate not supplied

Cold-Point Tropopause Temperature Bias in Reanalyses Modulated By Equatorial Waves

In this reanalysis inter-comparison study, we compare the modeled Cold-point tropopause temperature (CPT from model level data) near the equator to that of multi-mission GNSS-RO within a preliminary time period of 2007-2011. The known reanalysis CPT temperature warm bias is studied relative to the phase of equatorial waves filtered from GNSS-RO at tropopause level. This uncovers a common denominator in the CPT temperature bias behavior in all reanalyses: - When equatorial waves are present, within their cold phase the reanalysis bias is increased by over 1K, i.e. >1K on top of the reanalysis average warm bias. - Within an equatorial wave’s warm phase, the opposite is observed, with the reanalysis CPT temperature bias even being reversed at higher equatorial wave amplitudes, i.e. the modeled CPT becoming too cold in these cases. - With no equatorial wave anomalies present, the reanalysis bias remains at the average reported in previous inter-comparisons that looked at zonal-mean CPT biases. Inertia-Gravity and Mixed Rossby-Gravity waves show this behavior most clearly, Kelvin waves show a similar modulation but with half of the magnitude, and equatorial Rossby wave modulation of CPT reanalysis bias is still apparent but with further decreased magnitude. This hints on a time-space scale-dependence, with medium-smaller-scale and faster equatorial waves influencing CPT reanalysis bias the most. For comparison, the large-scale and slow-moving MJO does not show any of the effects described above. Current work focuses on the data assimilation response of the reanalysis systems, again relative to the equatorial wave’s phases.

Tropopause↗
NASA NTRS2007

Cooling of the North Atlantic by Saharan Dust

Using aerosol optical depth, sea surface temperature, top-of-the-atmosphere solar radiation flux, and oceanic mixed-layer depth from diverse data sources that include NASA satellites, NCEP reanalysis, in situ observations, as well as long-term dust records from Barbados, we examine the possible relationships between Saharan dust and Atlantic sea surface temperature. Results show that the estimated anomalous cooling pattern of the Atlantic during June 2006 relative to June 2005 due to attenuation of surface solar radiation by Saharan dust remarkably resemble observations, accounting for approximately 30-40% of the observed change in sea surface temperature. Historical data analysis show that there is a robust negative correlation between atmospheric dust loading and Atlantic SST consistent with the notion that increased (decreased) Saharan dust is associated with cooling (warming) of the Atlantic during the early hurricane season (July- August-September).

Lau, K. M.↗
NASA NTRS2000

A New Neural Network Approach Including First-Guess for Retrieval of Atmospheric Water Vapor, Cloud Liquid Water Path, Surface Temperature and Emissivities Over Land From Satellite Microwave Observations

The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of efficient retrieval methods for non-linear cases, however, first-guess estimates, which are used in variational methods to avoid problems of solution non-uniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first-guess. Conceptual bridges are established between the neural network and variational methods. The new neural method retrieves the surface skin temperature, the integrated water vapor content, the cloud liquid water path and the microwave surface emissivities between 19 and 85 GHz over land from SSM/I observations. The retrieval, in parallel, of all these quantities improves the results for consistency reasons. A data base to train the neural network is calculated with a radiative transfer model and a a global collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data and from microwave emissivity atlases previously calculated. The results of the neural network inversion are very encouraging. The r.m.s. error of the surface temperature retrieval over the globe is 1.3 K in clear sky conditions and 1.6 K in cloudy scenes. Water vapor is retrieved with a r.m.s. error of 3.8 kg/sq m in clear conditions and 4.9 kg/sq m in cloudy situations. The r.m.s. error in cloud liquid water path is 0.08 kg/sq m . The surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.

Aires, F.↗
NASA NTRS2007

Evidence for Interannual to Decadal Variations in Hadley and Walker Circulations and Links to Water and Energy Fluxes

Mass and energy transports associated with the Hadley and Walker circulations are important components of the earth s climate system and are strongly linked to hydrologic processes. Interannual to decadal variation in these flows likely signify a combination of natural climate noise as well as a response to anthropgenic forcing. There remains considerable uncertainty in quantifying variations in these flows. Evidence in the surface pressure record supports a weakening of the Walker circulation over the Pacific in recent decades. Conversely the NCEP / NCAR and ERA 40 reanalyses indicate that the Hadley circulation has increased in strength over the last two decades, though these analyses depict significantly different mass circulation changes. Interestingly, the NCEP - II / DOE reanalysis contains essentially no Hadley circulation changes. Most climate model integrations anticipate a weakening of both tropical circulations associated with stronger static stability. Clearly there is much uncertainty not only with the mass transports, but also how they are linked to water and energy balance of the planet through variations in turbulent heat and radiative fluxes and horizontal exports / imports of energy. Here we examine heat and water budget variations from a number of reanalysis products and focus on the linear and nonlinear response of ENSO warm and cold events as opportunities to study budget variations over the past 15-20 years. Our analysis addresses such questions as To what extent do Hadley and Walker Cell variations compensate each other on mass and energy transport? Do static stability adjustments appear to constrain fractional precipitation response vs. fractional water vapor response? We appeal to constraints offered by GPCP precipitation, SSWI ocean evaporation estimates, and ISCCP-FD radiative fluxes, and other satellite data sets to interpret and confirm reanalysis-based diagnostics. Using our findings we also attempt to place in context the recent findings that tropical ocean evaporation increased by order 5% or more during the 1990s, reconciling this with GPCP precipitation variations.

Robertson, Franklin↗
NASA NTRS2012

Evaluation of the Relative Contribution of Observing Systems in Reanalyses: Aircraft Temperature Bias and Analysis Innovations

Reanalyses have become important sources of data in weather and climate research. While observations are the most crucial component of the systems, few research projects consider carefully the multitudes of assimilated observations and their impact on the results. This is partly due to the diversity of observations and their individual complexity, but also due to the unfriendly nature of the data formats. Here, we discuss the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) and a companion dataset, the Gridded Innovations and Observations (GIO). GIO is simply a post-processing of the assimilated observations and their innovations (forecast error and analysis error) to a common spatio-temporal grid, following that of the MERRA analysis fields. This data includes in situ, retrieved and radiance observations that are assimilated and used in the reanalysis. While all these disparate observations and statistics are in a uniform easily accessible format, there are some limitations. Similar observations are binned to the grid, so that multiple observations are combined in the gridding process. The data is then implicitly thinned. Some details in the meta data may also be lost (e.g. aircraft or station ID). Nonetheless, the gridded observations should provide easy access to all the observations input to the reanalysis. To provide an example of the GIO data, a case study evaluating observing systems over the United States and statistics is presented, and demonstrates the evaluation of the observations and the data assimilation. The GIO data is used to collocate 200mb Radiosonde and Aircraft temperature measurements from 1979-2009. A known warm bias of the aircraft measurements is apparent compared to the radiosonde data. However, when larger quantities of aircraft data are available, they dominate the analysis and the radiosonde data become biased against the forecast. When AMSU radiances become available the radiosonde and aircraft analysis and forecast error take on an annual cycle. While this supports results of previous work that recommend bias corrections for the aircraft measurements, the interactions with AMSU radiances will also require further investigation. This also provides an example for reanalysis users in examining the available observations and their impact on the analysis. GIO data is presently available alongside the MERRA reanalysis.

Bosilovich, Michael G.↗
DOE OSTI2026

Strong coupling constant from 1-loop improved static energy

The static energy is an excellent observable for extracting the strong coupling $α_s$ on the lattice. For short distances, the static energy can be calculated both on the lattice using Wilson line correlators, and with perturbation theory up to three loop accuracy with leading ultrasoft log resummation. Comparing the perturbative expression and lattice data allows for precise determination of $α_s$. We present early results for 1-loop lattice perturbation theory improvement of the Wilson loop and show how it improves the $α_s$ extraction. We present a preliminary reanalysis of the TUMQCD (2+1)-flavor QCD data.

Leino, Viljami [U. Southern Denmark, QTC; Southern↗