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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
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 NTRS2016

ICN Deicing Operations Analysis

Aircraft deicing and anti-icing on airport ground in winter weather conditions is important to ensure safe departure takeoff and enroute flying. At the same time, it imposes additional workload to controllers and airport operators, and causes delays to airlines and passengers.This presentation shows the initial results of analyzing the deicing related operations at Incheon International Airport (ICN). It describes the deicing operations concept, procedures, and deicing making processes at ICN. It also looks into the three-month ICN operation data from Dec 2015 to Feb 2016 to identify the characteristics of the current day deicing operation.This work is under the joint collaboration between Korea Aerospace Research Institute and NASA. The objective is to develop a deicing operation simulation capability in NASA's fast time simulation environment to study and improve deicing operations efficiency.

Airport Surface ATM↗
NASA NTRS2019

Modeling Deicing Operations in Departure Scheduling Using Fast Time Simulation

In winter snow conditions, aircraft need inspection for deicing service before takeoff. Deicing service is a procedure to remove frost, ice, slush, or snow from aircraft for safe operation. Deicing operations vary by airport in many ways. Some airports have designated deicing zones, whereas some use a closed runway or terminal area to perform the procedure. Nonetheless, deicing operations add extra workloads to controllers, and cause increased taxi traffic on the ground. NASA and Korea Aerospace Research Institute (KARI) have been collaborating to model deicing operations at Incheon International Airport (ICN). This paper describes the deicing model and the study of deicing operations in departure scheduling using fast time simulations. The deicing model uses a heuristic algorithm for deicing zone assignment. In the fast time simulations, the model uses probability distributions derived from actual operation data to model deicing request and deicing zone time. It is envisioned that such a deicing model can be useful in airport surface scheduling to provide decision support and improve traffic management performance in winter snow operations.

modeling and simulation↗
NASA NTRS2019

Modeling Deicing Operations in Departure Scheduling using Fast Time Simulation

In winter snow conditions, aircraft need inspection for deicing service before takeoff. Deicing service is a procedure to remove frost, ice, slush, or snow from aircraft for safe operation. Deicing operations vary by airport in many ways. Some airports have designated deicing zones, whereas some use a closed runway or terminal area to perform the procedure. Nonetheless, deicing operations add extra workloads to controllers, and cause increased taxi traffic on the ground. NASA and Korea Aerospace Research Institute (KARI) have been collaborating to model deicing operations at Incheon International Airport (ICN). This paper describes the deicing model and the study of deicing operations in departure scheduling using fast time simulations. The deicing model uses a heuristic algorithm for deicing zone assignment. In the fast time simulations, the model uses probability distributions derived from actual operation data to model deicing request and deicing zone time. It is envisioned that such a deicing model can be useful in airport surface scheduling to provide decision support and improve traffic management performance in winter snow operations.

surface operation↗
NASA NTRS2019

A Tactical Scheduler for Surface Metering Under Minimum Departure Interval Restrictions

Minimum Departure Interval (MDI) and Miles-In-Trail (MIT) are common traffic management tools. They both require minimum separation between departures to meet specific traffic conditions. The MDI restriction is a time separation requirement between departures, usually on the same Standard Instrument Departure (SID), whereas the MIT restriction is a distance separation requirement between aircraft, including but not limited to departure flights, to meet specific criteria associated with flight path or destination. At Incheon International Airport (IATA code: ICN) in South Korea, MDIs are imposed on 92% of departure flights. They involve specific criteria including not only having identical SID, but also satisfying other conditions, imposed on the flight path and destination. To address complicated MDI constraints, Korea Aerospace Research Institute (KARI) has been developing and improving a tactical scheduler for surface metering at ICN to provide appropriate target times for pushbacks and takeoffs for departure flights under MDIs. This paper describes the MDI requirements at ICN, the development of a heuristic scheduling algorithm to work with the MDI restrictions, and the performance evaluation of the algorithm through fast-time simulations. The performance evaluation results indicate that the proposed heuristic algorithm can provide the surface metering schedules that comply with the MDI restrictions without significant performance degradation.

DMAN (Departure Management)↗
NASA NTRS2020

Relative Importance of Greenhouse Gases, Sulfate, Organic Carbon, and Black Carbon Aerosol for South Asian Monsoon Rainfall Changes

The contribution of individual aerosol species and greenhouse gases to precipitation changes during the South Asian summer monsoon is uncertain. Mechanisms driving responses to anthropogenic forcings need further characterization. We use an atmosphere‐only climate model to simulate the fast response of the summer monsoon to different anthropogenic aerosol types and to anthropogenic greenhouse gases. Without normalization, sulfate is the largest driver of precipitation change between 1850 and 2000, followed by black carbon and greenhouse gases. Normalized by radiative forcing, the most effective driver is black carbon. The precipitation and moisture budget responses to combinations of aerosol species perturbed together scale as a linear superposition of their individual responses. We use both a circulation‐based and moisture budget‐based argument to identify mechanisms of aerosol and greenhouse gas induced changes to precipitation, and find that in all cases the dynamic contribution is the dominant driver to precipitation change in the monsoon region.

climate↗
NASA NTRS2020

Revised and extended benchmark results for Rayleigh scattering of sunlight in spherical atmospheres

While most of traditional Earth-atmosphere satellite remote sensing relies on radiative transfer in the plane parallel geometry, effects of sphericity are important at high sun and view zenith angles. Broad understanding of these effects is limited and, contrary to the plane-parallel case, finding accurate numerical results to test spherical RT codes is not easy. This paper aims to partially fill in this gap. Using the full-spherical RT code MYSTIC (Monte Carlo), and the plane-parallel RT code VLIDORT (discrete ordinates) corrected for atmospheric sphericity in the single and multiple scattering, we reproduced with better accuracy and extended the benchmark results by Adams & Kattawar [1978].

spherical atmospheres↗
NASA NTRS2021

The 2019 Southern Hemisphere Stratospheric Polar Vortex Weakening and Its Impacts

This study offers an overview of the low-frequency (i.e., monthly to seasonal) evolution, dynamics, predictability, and surface impacts of a rare Southern Hemisphere (SH) stratospheric warming that occurred in austral spring 2019. Between late August to mid- September 2019, the stratospheric circumpolar westerly jet weakened rapidly, and Antarctic stratospheric temperatures rose dramatically. The deceleration of the vortex at 10 hPa was as drastic as that of the first ever observed major sudden stratospheric warming in the SH during 2002, while the mean Antarctic warming over the course of spring 2019 broke the previous record of 2002 by ~50% in the mid-stratosphere. This event was preceded by a poleward shift of the SH polar night jet in the uppermost stratosphere in early winter, which was then followed by record-strong planetary wave-one activity propagating upward from the troposphere in August that acted to dramatically weaken the polar vortex throughout the depth of the stratosphere. The weakened vortex winds and elevated temperatures moved downward to the surface from mid-October to December, promoting a record strong swing of the Southern Annular Mode (SAM) to its negative phase. This record-negative SAM appeared to be a primary driver of the extreme hot and dry conditions over subtropical eastern Australia that accompanied the severe wildfires that occurred in late spring 2019. State-of-the-art dynamical seasonal forecast systems skillfully predicted the significant vortex weakening of spring 2019 and subsequent development of negative SAM from as early as late July.

Antarctic Oscillation↗
NASA NTRSDate not supplied

The Antarctic Stratospheric Warming and its Impacts in 2019

We explore the mechanism, predictability and hemispheric-wide surface impacts of a rare stratospheric warming that occurred above Antarctica during austral spring (September to November) 2019, using the Japanese 55-year reanalysis set for the period 1979-2019. From late August to mid-September, the stratospheric polar vortex suddenly weakened, and the stratospheric temperatures dramatically rose over the Antarctic polar cap. The deceleration of the vortex observed at 10 hPa was as drastic as that of the first ever observed major sudden stratospheric warming in the SH during 2002, while the mean warming in the mid-stratosphere (~30hPa) over the course of spring 2019 broke the previous record of 2002 by ~50%. The key mechanism for this event was a poleward shift of the polar night jet near the stratopause during mid-winter and subsequent record strong planetary wavenumber-one activity propagating from the troposphere in August, which acted to dramatically weaken the polar vortex. The easterly wind anomalies and positive temperature anomalies moved downward to the surface during October to December, causing the index polarity of the Southern Annular Mode (SAM) to become record-negative for the season. The record negative SAM played a key role in inducing significant local climate extremes over eastern Australia, southern New Zealand, eastern South America and western Patagonia. Especially, the strong negative SAM was the key driver of the extreme hot and dry conditions over subtropical eastern Australia in late spring 2019 that, in turn, were conducive for the severe wildfires that occurred during that time. State-of-the-art dynamical sub-seasonal to seasonal forecast systems skilfully predicted the upward propagating wavenumber-one activity in August, the significant vortex weakening of austral spring 2019, and subsequent development of negative SAM from late July.

Eun-Pa Lim↗
NASA NTRS2021

Recovery: Fast and Slow—Vegetation Response During the 2012–2016 California Drought

The 2012–2016 California Drought severely impacted natural vegetation across a wide range of environmental gradient. Although several studies have reported an increase in plant water stress and mortality, the spatiotemporal variations of ecosystem productivity responses and the associated environmental and biological drivers remain unclear. Here, using Enhanced Vegetation Index from the Moderate resolution imaging spectrometer, we found that 45% of the natural ecosystems showed an abrupt change (breakpoint [BP]) in productivity during 2012–2016. There were three major contrasting temporal patterns of productivity responses: (i) a steady increase under higher temperature followed by a decline due to accumulated moisture depletion (high elevation forest) or temperature decrease (high elevation nonforest), (ii) gradual decline during the drought followed by a rapid recovery within 1 year after drought stress was partially relieved, and (iii) both a gradual decline and an abrupt decline. The magnitude of abrupt changes was negatively correlated (r = −0.80, p < 0.001) with initial gradual changes. Overall, changes during BP offset, on average, 57% of the preceding gradual responses. The spatial variability in ecosystem response patterns is driven by both environmental and biological factors. Particularly, for forests, positive BP was driven by increasing rainfall and decreasing temperature, while negative BP was mainly driven by the precipitation anomaly. By 2019, 33% of the natural vegetation have recovered to the level of EVI in 2010. Ecosystem responses to multiyear droughts can influence ecosystem dynamics in a complex pattern. Multiple ecohydrological factors should be considered to understand and predict the long-term drought impacts on ecosystems.

Xi Yang↗
NASA NTRSDate not supplied

Decoupling the Effects of Anthropogenic Emission Reductions from the Meteorology and Natural Emissions in TROPOMI NO 2 Retrievals During the 2020 COVID-19 Lockdowns

Satellite measurements during the COVID-19 lockdowns that began in 2020 revealed unprecedented reductions in NO 2 tropospheric vertical column densities (VCD). These reductions have largely been attributed to reduced anthropogenic emissions associated with abrupt decreases in road traffic and other power consuming business activities. Although decreased emissions tended to be the main contributor to the observed NO 2 VCD reduction, meteorological variability also played a role. Whereas the observed VCD changes were predominantly negative in places where public health policies were strictly enforced, meteorology had both positive and negative effects over short time intervals. Here, we present results from a global study of the NO 2 reductions aimed at disentangling the meteorological and natural emission variability from the anthropogenic emissions over the world’s most populated megacities. For this study, NASA’s TROPOMI NO 2 algorithm was used in conjunction with the Global Modeling Initiative (GMI) chemical transport model to separate the contributions due emissions and meteorology. A priori NO 2 profiles were generated from two GMI simulations performed for 2020 at a resolution of 0.25° longitude x 0.25° latitude. The first simulation used updated, COVID-impacted NOX emissions based on Forster et al. (2020), while the second simulation used the 2019 emissions with the 2020 meteorology, referred to here as 2020BAU. The 2020BAU data set allowed for the decoupling of the emission component from the meteorology. When compared to the same period in 2019, NO 2 column amounts during the lockdowns were reduced in 35 out of 36 cities. While reduced emissions contributed most to the observed total change in NO 2 during the lockdowns, the effects of meteorology were significant, ranging between 40% (Chennai) and 15% (Beijing). In China, an increase in NO 2 levels due to meteorology were observed in five out of the seven cities considered in the study. Use of different a-priori NO 2 profiles from the two simulations in our TROPOMI retrievals allowed for a determination of retrieval errors that ranged from -1.7% to -11.0%. We used the quality assurance flag > 0.75 to select the highest quality scenes in the study period. Using the GMI, we estimated the sampling biases to be in the range -11% to 10% of the total change for the cities in our study.

Brad Fisher↗
NASA NTRSDate not supplied

Verification of TROPOMI NO2 Product Using OMI NO2 algorithm

We evaluated S5P TROPOspheric Monitoring Instrument (TROPOMI) operational nitrogen dioxide (NO2) product by comparing with the NO2 retrievals from Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite. We compared spatially matched NO2 vertical column density (VCD) data from OMI and TROPOMI to identify any discrepancies between the two operational products. We also applied OMI Air-Mass Factor (AMF) algorithm to TROPOMI Slant Column Density (SCD) data (v02.03.01) aiming to create long-term merged NO2 Earth Science Data Record (https://disc.gsfc.nasa.gov/datasets/TROPOMI_MINDS_NO2_1.1/summary ). The algorithm (version 4.0) uses new Geometry-dependent Lambertian Equivalent surface Reflectivity (GLER) product available for each TROPOMI pixel. GLER is pre-calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance based on MODIS gap-filled in-water chlorophyll-a data. The GLER data, combined with consistently retrieved cloud parameters, provide improved information for the calculation of the scattering weight profiles. Additional AMF improvements result from using high resolution (0.25o latitude x 0.25o longitude) a priori NO2 profile shapes and other auxiliary information from the Global Modeling Initiative (GMI) Replay simulation sampled using S5P orbital simulator. Our TROPOMI_MINDS_NO2 algorithm employs the stratosphere-troposphere separation scheme, de-striping, and surface snow/ice treatment consistently with the latest OMI NO2 re-processing (version 4). We evaluate the TROPOMI_MINDS_NO2 product by comparing with the S5P operational NO2 product as well as with independent NO2 observations from ground-based Pandora and aircraft observations. Our results indicate that the new TROPOMI_MINDS_NO2 retrievals are generally higher than the S5P operational NO2 product over polluted regions and show improved agreement with independent validation data.

TROPOMI↗
NASA NTRS2022

Climate Change-Induced Peatland Drying in Southeast Asia

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.

VIIRS↗
NASA NTRSDate not supplied

Boundary Layer Data Assimilation and Interaction with Parameterizations in the NASA GEOS Model

The NASA Global Modeling and Assimilation Office develops the Goddard Earth Observing System (GEOS), which assimilates a wide range of observations to support medium range and seasonal forecasts and production of reanalyses like MERRA-2. In this talk we report on recent efforts to assimilate boundary layer (PBL) height observations derived from radiosondes, GNSS radio occultation, space-based lidar (CALIPSO, CATS, IceSat-2) and ground-based lidar (MPLNET). A novel component of this project includes examining the influence of model parameterizations on PBL profile estimates. We will discuss physics-based parameterizations of the PBL and how they may benefit or distort representation of PBL profiles in data assimilation. In particular, we consider the tendency of PBL parameterizations to compensate for analysis updates of state variables, effectively reducing the information retained from observations. We will present efforts to reduce this compensation in GEOS by using the PBL height analysis to adjust length scales used in the model PBL parameterizations.

Nathan Arnold↗
NASA NTRSDate not supplied

Observation impacts in the lower troposphere and challenges of Planetary Boundary Layer data assimilation

The Goddard Earth Observing System (GEOS) developed by the NASA Global Modeling and Assimilation Office assimilates a wide range of observations to support various NASA Earth Science missions. To set the stage for follow-on Planetary Boundary Layer (PBL) science and prepare for future observing systems of the next decade, we have assessed the effectiveness of the use of existing observing systems in the lower troposphere in GEOS, and analyzed model responses to the incremental analysis update (IAU) forcing. With a better understanding of the GEOS data assimilation algorithms in the PBL, we have developed strategies for improved PBL data assimilation in GEOS. The strategies to enhance data usages in both the data assimilation system and forecast model will be presented, and the utilization of PBL height data from multiple observing systems will be discussed as well.

Yanqiu Zhu↗
NASA NTRSDate not supplied

Assimilation of PBL Height Data from Multiple Observing Systems in the GEOS System for Global PBL Height Analysis and Monitoring System

The Goddard Earth Observing System (GEOS) developed by the NASA Global Modeling and Assimilation Office provides the critical capability to assimilate a wide range of observations in producing a comprehensive PBL estimate consistent with model physics and observations. To generate global Planetary Boundary Layer height (PBLH) analysis and monitor PBLH data online, we have developed strategy and infrastructure for PBLH data assimilation in the GEOS system and ingested PBLH data derived from multiple observing systems including radiosonde, space- and ground-based LiDAR, and GNSS RO, and implemented corresponding thinning and quality control procedures. The evaluation of departures of model PBLH simulations from PBLH data for the period of 9 days in Aug 2015 will be presented, and different features of space-based backscattered-based PBLHs will be discussed.

Eun-Gyeong Yang↗