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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 NTRS2022

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

John L. Schnase↗
NASA NTRS2021

Gravity Wave Weakening During the 2019 Antarctic Stratospheric Sudden Warming

A rare Antarctic stratospheric sudden warming (SSW) occurred on August 30, 2019, and was a minor warming event. We investigated variations in gravity wave (GW) activity before and after this Antarctic SSW event using two satellite measurements (AIRS and CIPS) and reanalysis data (GEOS-5 FP). GW activity over the Andes decreased after August 30, although the westerly wind was 40–60 m/s and cannot filter out GWs with small zonal phase speed. This decline over the Andes was probably caused by wave saturation. Zonal mean GW activity over Antarctica and the Southern Ocean likewise decreased, with a weakening of zonal wind. The zonal mean GW activity further decreased around September 8 which coincided with a reversal of the zonal mean zonal wind at 40 km. The decline in the zonal mean GW activity after August 30 was probably caused by wind filtering and polar night jet breaking.

Gravity waves↗
NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗
NASA NTRS2022

Potential Link Between Ice Nucleation and Climate Model Spread in Arctic Amplification

Arctic amplification (AA) is simulated by all global climate models, however the spread in the degree of projected warming is large and the underlying mechanisms driving it are poorly understood. The impact of the temperature dependence of immersion freezing on cloud feedbacks and AA is studied using NASA's GEOS-5 model. Parameterizations that exhibit low ice-nucleating particle (INP) concentrations in the high Arctic during summer are found to weaken the cloud-phase feedback. This allows sunlight to readily melt sea-ice in the summer, which decreases the stability of the lower troposphere, causing a decrease in wintertime cloud fraction over open ocean. Arctic amplification was found to span from ∼1.4 to >2.6, which spans 30% of the spread in AA in the coupled model intercomparison project models, depending on the temperature dependence of immersion freezing. These results suggest that summertime INP concentrations may provide an observational constraint on AA.

Ice Nucleation↗
NASA NTRS2022

3-D Atmospheric Modeling of the Global Budget of N2O and its Isotopologues for 1980-2019: The Impact of Anthropogenic Emissions

Nitrous oxide (N2O) is the third most important anthropogenic greenhouse gas (GHG) and a major ozone-depleting substance (ODS). Its main sources include anthropogenic activities (mostly agriculture) and natural emissions from ocean and soils. However, emission estimates for individual sources are highly variable due to uncertainties in N2O lifetime estimates and partitioning among sources. We derive annual global N2O emissions for 1990-2019 using NOAA Global Monitoring Laboratory (GML) surface N2O observations and the N2O lifetime calculated in the NASA GEOS-5 chemistry climate model (CCM). The inferred global mean N2O emissions has gradually increased from ~15.8 TgN/yr in the early 1990s to ~17.8 TgN/yr in the 2010s. This implies that anthropogenic N2O emissions have grown rapidly from ~6.7 TgN/yr in the 1990s to about ~8.7 TgN/yr in the 2010s, a ~30% increase. With specially designed N2O isotopic tracers in 3-D GEOSCCM, we estimate that, on global average, stratospheric enrichment contributes about +7.7‰/yr, +7.6‰/yr, +8.0‰/yr to tropospheric N(sup α)-δ(sup 15), N(sup β)-δ(sup 15), and O-δ(sup 18) budget, respectively. To balance the global mean isotopic signature for pre-industrial terrestrial sources of N(sup α)-δ(sup 15) ~ 6.7‰, N(sup β)-δ(sup 15) ~ -12.6‰, O-δ(sup 18) ~ 35.4‰, our 3-D isotopic budget simulation using the GEOSCCM suggests global mean anthropogenic isotopic signatures in the recent decades are N(sup α)-δ(sup 15) ~ -18‰, N(sup β)-δ(sup 15) ~ -20‰, O-δ(sup 18) ~ 19‰. These anthropogenic isotopic estimates are significantly lighter than results from one-box atmospheric model-based estimates with the largest difference seen for N(sup β)-δ(sup 15). More surface isotopic measurements are needed to better quantify the N2O isotopic signatures.

Nitrous oxide↗
NASA NTRSDate not supplied

NASA GEOS Composition Forecast System: GEOS-CF

NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution analysis and forecasts for weather, aerosols, and air quality. Since 2019, the NASA Global Earth Observing System (GEOS) model provides global near-real-time historical estimates and daily 5-day forecasts of atmospheric composition to the public at unprecedented horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). The GEOS-CF is a tool for scientists and the public health community. This presentation will cover 1) an overview of the GEOS-CF modeling framework compared to the GEOS-5 Nature Run with Chemistry (used in the post-processing to make the TEMPO Proxy Data), 2) description of the file used to support the TEMPO retrieval team, and 3) research and development activities as the GEOS-CF system continues to evolve to include multi-constituent data assimilation.

K. Emma Knowland↗
NASA NTRSDate not supplied

Using the GEOS 5 Nature Run to Simulate 2053 nm Coherent Doppler Wind Lidar Observations

Wind observations are a critical part of the current global observation system used for Numerical Weather Prediction (NWP). Wind lidars have been cited as precise instruments that can provide 3-dimensional wind measurements. Several studies have conducted observing system experiments (OSEs) with existing lidar observations or observing system simulation experiments (OSSEs) with simulated lidar observations highlighting the benefits of wind lidar measurements to NWP. Previous studies using simulated lidar observations have typically tied aerosol optical properties to functions of relative humidity instead of to aerosol properties. A methodology is presented for simulating wind measurements from a novel 2053 nm lidar using aerosol properties derived using the GEOS-5 Nature Run, along with estimating winds derived from cloud information. Some assumptions regarding aerosol scattering and the distribution of clouds are explored, along with the role of observation weighting, and implications for representativeness error. Results from a preliminary OSSE are presented highlighting the importance of assumptions used to derive data from cloud returns and aerosol scattering. While a longer duration study is required, results show a general reduction in analysis error when lidar measurements are ingested.

Bryan M. Karpowicz↗
NASA NTRSDate not supplied

Enhancing Long-Term Trend Simulation of OH Through the Synergy of Model Simulations and Aura Ozone Monitoring Instrument (OMI) NO 2 and HCHO Retrievals

During the last few years, tremendous progress has been made to develop an efficient parameterization module using agile machine learning techniques. The aim of this module is to provide dynamic response of the tropospheric hydroxyl radical (OH) to its major drivers, including trace gases, aerosols, clouds, and meteorology. This module, named ECCOH (pronounced “echo”) and implemented in NASA’s GEOS-5 global model, offers an unrealized opportunity to unravel the convoluted response of OH to its underlying drivers while approaching the accuracy of full-chemistry without incurring excessive computational costs, making it suitable for climate models. However, the accurate representation of OH in ECCOH poses challenges due to the lack of representation of some of its critical inputs such as the abundance of NO 2 and HCHO concentrations. As such, we leverage the well-characterized satellite observations of NO2 and HCHO columns from Aura OMI to enhance their representation in ECCOH using an optimal interpolation method for the time period of 2005 - present. We show how the inclusion of OMI information can affect the spatiotemporal variability and long-term trends of OH, CO, and CH 4 across the globe. Additionally, we underscore the necessity of obtaining high-fidelity information regarding tropospheric ozone from the southern hemisphere from space, a region currently lacking full verification in models, posing a challenge to get a reasonable amount of chemical sink for CH 4 .

OH↗
NASA NTRSDate not supplied

Using the GEOS 5 Nature Run to Simulate 2053 nm Coherent Doppler Wind Lidar Observations

Wind observations are a critical part of the current global observation system used for Numerical Weather Prediction (NWP). Wind lidars have been cited as precise instruments that can provide 3-dimensional wind measurements. Several studies have conducted observing system experiments (OSEs) with existing lidar observations or observing system simulation experiments (OSSEs) with simulated lidar observations highlighting the benefits of wind lidar measurements to NWP. Previous studies using simulated lidar observations have typically tied aerosol optical properties to functions of relative humidity instead of to aerosol properties. A methodology is presented for simulating wind measurements from a novel 2053 nm lidar using aerosol properties derived using the GEOS-5 Nature Run, along with estimating winds derived from cloud information. Some assumptions regarding aerosol scattering and the distribution of clouds are explored, along with the role of observation weighting, and implications for representativeness error. Results from a preliminary OSSE are presented highlighting the importance of assumptions used to derive data from cloud returns and aerosol scattering. While a longer duration study is required, results show a general reduction in analysis error when lidar measurements are ingested.

Bryan M. Karpowicz↗
NASA NTRS2015

Sea Ice Outlook for September 2015 June Report - NASA Global Modeling and Assimilation Office

The recent decline in perennial sea ice cover in Arctic Ocean is a topic of enormous scientific interest and has relevance to a broad variety of scientific disciplines and human endeavors including biological and physical oceanography, atmospheric circulation, high latitude ecology, the sustainability of indigenous communities, commerce, and resource exploration. A credible seasonal prediction of sea ice extent would be of substantial use to many of the stakeholders in these fields and may also reveal details on the physical processes that result in the current trends in the ice cover. Forecasts are challenging due in part to limitations in the polar observing network, the large variability in the climate system, and an incomplete knowledge of the significant processes. Nevertheless it is a useful to understand the current capabilities of high latitude seasonal forecasting and identify areas where such forecasts may be improved. Since 2008 the Arctic Research Consortium of the United States (ARCUS) has conducted a seasonal forecasting contest in which the average Arctic sea ice extent for the month of September (the month of the annual extent minimum) is predicted from available forecasts in early June, July, and August. The competition is known as the Sea Ice Outlook (SIO) but recently came under the auspices of the Sea Ice Prediction Network (SIPN), and multi-agency funded project to evaluate the SIO. The forecasts are submitted based on modeling, statistical, and heuristic methods. Forecasts of Arctic sea ice extent from the GMAO are derived from seasonal prediction system of the NASA Goddard Earth Observing System model, version 5 (GEOS 5) coupled atmosphere and ocean general circulation model (AOGCM). The projections are made in order to understand the relative skill of the forecasting system and to determine the effects of future improvements to the system. This years prediction is for a September average Arctic ice extent of 5.030.41 million km2.

SIPN↗
NASA NTRS2016

Evaluation of MERRAero (MERRA Aerosol Reanalysis)

This presentation focuses on MERRA Aerosol Reanalysis (MERRAero) which is the first aerosol reanalysis produced at GMAO. This presentation involve an overview of MERRAero. The evaluation of MERRAero absorption and the evaluation of MERRAero Surface PM 2.5 will also be discussed.

Aerosol↗
NASA NTRS2016

Estimation of the Ocean Skin Temperature using the NASA GEOS Atmospheric Data Assimilation System

This report documents the status of the development of a sea surface temperature (SST) analysis for the Goddard Earth Observing System (GEOS) Version-5 atmospheric data assimilation system (ADAS). Its implementation is part of the steps being taken toward the development of an integrated earth system analysis. Currently, GEOS-ADAS SST is a bulk ocean temperature (from ocean boundary conditions), and is almost identical to the skin sea surface temperature. Here we describe changes to the atmosphere-ocean interface layer of the GEOS-atmospheric general circulation model (AGCM) to include near surface diurnal warming and cool-skin effects. We also added SST relevant Advanced Very High Resolution Radiometer (AVHRR) observations to the GEOS-ADAS observing system. We provide a detailed description of our analysis of these observations, along with the modifications to the interface between the GEOS atmospheric general circulation model, gridpoint statistical interpolation-based atmospheric analysis and the community radiative transfer model. Our experiments (with and without these changes) show improved assimilation of satellite radiance observations. We obtained a closer fit to withheld, in-situ buoys measuring near-surface SST. Evaluation of forecast skill scores corroborate improvements seen in the observation fits. Along with a discussion of our results, we also include directions for future work.

AGCM↗