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324 records · Page 18

How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America

Fires and the aerosols that they emit impact air quality, health, and climate, but the abundance and properties of carbonaceous aerosol (both black carbon and organic carbon) from biomass burning (BB) remain uncertain and poorly constrained. We aim to explore the uncertainties associated with fire emissions and their air quality and radiative impacts from underlying dry matter consumed and emissions factors. To investigate this, we compare model simulations from a global chemical transport model, GEOS-Chem, driven by a variety of fire emission inventories with surface and airborne observations of black carbon (BC) and organic aerosol (OA) concentrations and satellite-derived aerosol optical depth (AOD). We focus on two fire-detection-based and/or burned-area-based (FD-BA) inventories using burned area and active fire counts, respectively, i.e., the Global Fire Emissions Database version 4 (GFED4s) with small fires and the Fire INventory from NCAR version 1.5 (FINN1.5), and two fire radiative power (FRP)-based approaches, i.e., the Quick Fire Emission Dataset version 2.4 (QFED2.4) and the Global Fire Assimilation System version 1.2 (GFAS1.2). We show that, across the inventories, emissions of BB aerosol (BBA) differ by a factor of 4 to 7 over North America and that dry matter differences, not emissions factors, drive this spread. We find that simulations driven by QFED2.4 generally overestimate BC and, to a lesser extent, OA concentrations observations from two fire-influenced aircraft campaigns in North America (ARCTAS and DC3) and from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, while simulations driven by FINN1.5 substantially underestimate concentrations. The GFED4s and GFAS1.2-driven simulations provide the best agreement with OA and BC mass concentrations at the surface (IMPROVE), BC observed aloft (DC3 and ARCTAS), and AOD observed by MODIS over North America. We also show that a sensitivity simulation including an enhanced source of secondary organic aerosol (SOA) from fires, based on the NOAA Fire Lab 2016 experiments, produces substantial additional OA; however, the spread in the primary emissions estimates implies that this magnitude of SOA can be neither confirmed nor ruled out when comparing the simulations against the observations explored here. Given the substantial uncertainty in fire emissions, as represented by these four emission inventories, we find a sizeable range in 2012 annual BBA PM2.5 population-weighted exposure over Canada and the contiguous US (0.5 to 1.6 µg/cu. m). We also show that the range in the estimated global direct radiative effect of carbonaceous aerosol from fires (−0.11 to −0.048 W/sq. m) is large and comparable to the direct radiative forcing of OA (−0.09 W/sq. m) estimated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Our analysis suggests that fire emissions uncertainty challenges our ability to accurately characterize the impact of smoke on air quality and climate.

North America↗

The Prevalence of Precipitation From Polar Supercooled Clouds

Supercooled clouds substantially impact polar surface energy budgets, but large-scale models often underestimate their occurrence, which motivates accurately establishing metrics of basic processes. An analysis of long-term measurements at Utqiaġvik, Alaska, and McMurdo Station, Antarctica, combines lidar-validated use of soundings to identify supercooled cloud layers and colocated ground-based profiling radar measurements to quantify cloud base precipitation. We find that more than 85 % (75 %) of sampled supercooled layers are precipitating over the Arctic (Antarctic) site, with more than 75 % (50 %) precipitating continuously to the surface. Such high frequencies can be reconciled with substantially lesser spaceborne estimates by considering differences in radar hydrometeor detection sensitivity. While ice precipitation into supercooled clouds from aloft is common, we also find that the great majority of supercooled cloud layers without ice falling into them are themselves continuously generating precipitation. Such sustained primary ice formation is consistent with continuous activation of immersion-mode ice-nucleating particles (INPs), suggesting that supercooled cloud formation is a principal gateway to ice formation at temperatures greater than ∼−38 ∘C over polar regions. The prevalence of weak precipitation fluxes is also consistent with supercooled cloud longevity and with well-observed and widely simulated case studies. An analysis of colocated microwave radiometer retrievals suggests that weak precipitation fluxes can be nonetheless consequential to moisture budgets for supercooled clouds owing to small liquid water paths. The results here also demonstrate that the observed abundance of mixed-phase clouds can vary substantially with instrument sensitivity and methodology. Finally, we suggest that these ground-based precipitation rate statistics offer valuable guidance for improving the representation of polar cloud processes in large-scale models.

Supercooled clouds↗

2020 Wildfire Plumes Observed by Satellite and Ground Sensors in the San Francesco Bay Area

The main purpose of this research is to study the spreading of the 2020 wildfire plumes in the San Francisco Bay Area. Last year’s fire plumes have caused severe impact on regional air quality and public health over large part of the west coast. We studied fire plumes with two datasets: aerosol optical depth (AOD) retrieved from MODIS sensor onboard two NASA satellites (Terra and Aqua), and surface PM2.5 measurements from US EPA. Satellite can monitor fire plumes from a top-down view, including active fire location, emission amount, spreading of the fire plume in both horizontal and vertical directions. EPA records air quality using the ground network of in-situ sensors. In general the two points of view are consistent with each other. But in the peak of the 2020 fire season, we found an episode where the AOD and PM2.5 are out-of-phase for two days. We explored the possible mechanism for this shift with available meteorological measurements, including both ground measurements and sounding data. By tracking the evolution of the sounding data, we concluded a heated near surface inversion layer might shield the region from aloft fire plumes for two days before they touch down and severely downgraded the air quality over the whole Bay area.

Hazem Mahmoud↗

Constraining Venus Cloud Habitability: Earth's Aerobiosphere as an Analogue Environment

The potential habitability of Venus's cloud and haze layers has been debated for decades, with interest fueled by observations including disequilibria in atmospheric chemistry, strongly UV-absorbing particles, and (most recently) a controversial phosphine detection. Venus cloud temperature and pressure are clement by Earth standards; however, the calculated high acid activity and low water activity may exceed the limits of Earth biochemistry, based on observed life in extreme environments. A significant challenge in understanding Venus's habitability is the lack of an appropriate terrestrial analogue environment. Life has not been found in hydrothermal systems approaching Venus aerosol acidities, but these systems also contain high levels of other harsh solutes. Earth's stratospheric sulfate layer is a partial match in terms of acidity, desiccation, size, and isolation from the surface, but the few samples returned from these altitudes have yielded only sparse, inactive cells. Earth's tropospheric cloud droplets have an active microbial presence, but are larger and far more water-rich than Venus aerosols; tropospheric times aloft are also similar to microbial generation times, making Earth's aerobiosphere dependent on continuous surface replenishment. Direct detection of potential biosignatures from a single transect is challenging; Earth's cloud microbiota yields 102 - 108 viable cells per mL, equivalent to one per 103 - 109 particles, with highly dynamic and heterogeneous distribution. The most important in situ measurement for Venus cloud habitability is detailed aerosol composition: confirmation of acid and water activity, presence of potentially bioavailable nutrients and energy, and potential presence of organic matter. Modeling Venus cloud aerosol residence time in comparison to periodic influxes of water and photochemical energy, and thus the ability of an ecosystem to maintain itself over time, would also address a significant habitability constraint particular to aerobiospheres.

Venus↗

Numerous Wildfire Smoke Plumes in Northwest Ontario, Canada

The height at which smoke is lofted into the atmosphere affects how long it will stay aloft, how far it will travel, and how much of an impact it will have on air quality downwind, and regional climate. Parallax, the change in apparent plume position relative to the surface, as observed by NASA's Multi0angle Imaging Spectroradiometer (MISR) instrument, makes it possible to map the height of smoke, dust and volcanic plumes near-source, where plume features are visible in the multi-angle views.

wildfire smoke↗

Improved parameterization of precipitation fluxes in the GPM combined algorithm to mitigate ground clutter effects

Ground clutter can significantly impact the accuracy of surface precipitation estimates from observations provided by the Global Precipitation Measurement (GPM) Dual Frequency Precipitation Radar (DPR). This is because the surface precipitation rates may be significantly and systematically different from the estimates derived from the lowest clutter-free observations. Corrections based on the climatologic relationships between surface precipitation and precipitation aloft work well in general, but such relationships are difficult to derive on a global scale. We use GPM combined precipitation rates estimated from near-nadir radar observations minimally affected by ground clutter to derive climatologic corrections applicable to observation profiles more significantly affected by clutter. Potential artifacts in the correction scheme stem in the fact that precipitation on the ground and in the lowest clutter-free bin may be associated with different phases. To mitigate such artifacts, we propose and investigate an improved phase and particle size distribution parameterization that ensures a seamless transition from the ice to the water phase.

Mircea Grecu↗

2020 Wildfire Plumes Observed by Satellite and Ground Sensors at San Francisco Bay

The main purpose of this research is to study the spreading of the 2020 wildfire plumes in the San Francisco Bay Area. Last year’s fire plumes have caused severe impact on regional air quality and public health over large part of the west coast. We studied fire plumes with two datasets: aerosol optical depth (AOD) retrieved from MODIS sensor onboard two NASA satellites (Terra and Aqua), and surface PM2.5 measurements from US Environmental Protection Agency (EPA). Satellite can monitor fire plumes from a top-down view, including active fire location, emission amount, spreading of the fire plume in both horizontal and vertical directions. EPA records air quality using the ground network of in-situ sensors. In general, the two points of view are consistent with each other. But in the peak of the 2020 fire season, we found an episode where the AOD and PM2.5 are out-of-phase for two days, in addition data from NASA satellite AERONET was use to corroborate AOD information for the episode, yielding similar results. Thereafter, we explored the possible mechanism for this shift with available meteorological measurements, including both ground measurements and sounding data. By tracking the evolution of the sounding data, we concluded a heated near surface inversion layer might shield the region from aloft fire plumes for two days before they touch down and severely downgraded the air quality over the whole Bay area.

Hazem Mahmoud↗

The Future Intensification of the North Atlantic Winter Storm Track: The Key Role of Dynamic Ocean Coupling

Climate models project an intensification of the wintertime North Atlantic storm track, over its downstream region, by the end of this century. Previous studies have suggested that ocean-atmosphere coupling plays a key role in this intensification, but the precise role of the different components of the coupling has not been explored and quantified. In this paper, using a hierarchy of ocean coupling experiments, we isolate and quantify the respective roles of thermodynamic (changes in surface heat fluxes) and dynamic (changes in ocean heat flux convergence) ocean coupling in the projected intensification of North Atlantic transient eddy kinetic energy (TEKE). We show that dynamic coupling accounts for nearly all of the future TEKE strengthening as it overcomes the much smaller effect of surface heat flux changes to weaken the TEKE. We further show that by reducing the Arctic amplification in the North Atlantic, ocean heat flux convergence increases the meridional temperature gradient aloft, causing a larger eddy growth rate, and resulting in the strengthening of North Atlantic TEKE. Our results stress the importance of better monitoring and investigating the changes in ocean heat transport, for improving climate change adaptation strategies.

North Atlantic winter storms↗

The Radiative Effect on Cloud Microphysics from the Arctic to the Tropics

Cloud representation is one of the largest uncertainties in the current weather and climate models. In this article, the observations and modeling of the radiative effect on (cloud) microphysics (REM) from the Arctic to the Tropics are overviewed, providing a new direction to meet the challenge of cloud representation. REM deals with the radiation-induced temperature difference between cloud particles and air. It leads to two common phenomena observed at the surface—dew and frost—and impacts clouds aloft significantly, which is noticed via the wide occurrence of horizontally oriented ice crystals (HOICs). However, REM has been overlooked by all of the operational weather and climate models. Based on the bin model of REM and the global distribution of radiative cooling/warming, the observations of REM from several platforms (e.g., aircrafts, field campaigns, and satellites) are coordinated in this article, yielding a global picture on REM. As a result, the picture is compatible with the global distribution of HOICs and other ice crystal characteristics obtained from various clouds on the globe, such as diamond dust (or clear-sky precipitation) in the Arctic, sub-visual cirrus clouds in the tropical tropopause layer, and other cirrus clouds from the low to high latitudes. In addition, ice crystals possess relatively strong REM compared to liquid drops because their aspect ratio is usually not one. The global picture on REM can be used by the weather and climate modelers to diagnose their cloud representation biases. It can also be used to improve the atmospheric ice retrieval algorithm from satellite observations.

cloud microphysics↗

Polarization as a Discriminator of Light-Absorbing Impurities in or Above Snow

This conceptual study presents advanced radiative transfer computations of light polarization originating from a snowpack consisting of nonspherical grains and variable content of light-absorbing impurities, either embedded in the snowpack or (with the same optical properties) lofted above it in the form of atmospheric aerosols. The results highlight the importance of considering shapes other than spherical for the snow grains, which otherwise can lead to non-negligible errors in the retrieval of snow albedo from remote sensing observations. More importantly, it is found that polarimetric measurements provide a means to partition light-absorbing impurities embedded in the snowpack from absorbing aerosols aloft, a task traditionally prohibitive for sensors capable exclusively of measurements of total reflectance. Heritage techniques to obtain snow grain size from shortwave infrared observations of total reflectance are well established, as are those that leverage polarimetric, multiangular observations across the entire optical spectrum to characterize the optical and microphysical properties of atmospheric aerosols. The polarization signatures of near-infrared (e.g., 864 nm) observations carry critical information on snow grain shape. The prospected launch of space-borne polarimeters with proven accuracy, therefore, advocates for the development of data inversion schemes, to boost the accuracy of simultaneous retrievals of atmospheric and surface parameters in the polar and snow-covered regions, critical to climate studies.

light polarization↗

The Jörmungandr Dark Flight Model for Meteorite and Orbital Debris Recovery

Jörmungandr is a model that replicates the flight paths of falling meteorites to facilitate rapid meteorite collection [e.g. 1-3]. By iteratively entering a range of meteorite masses, a polygonal landing field (or “strewn field”) is generated to simplify any search for meteorites. Jörmungandr uses winds-aloft data collected by weather balloons to accurately replicate lateral movement of falling meteorites. Jörmungandr is recently upgraded to v.2 with slightly more accurate fall modelling than v.1, and the math and operations of the model are described here. Jörmungandr data show that strewn field shapes are dominated by local wind conditions which can produce banana-shaped strewn fields in the event of strong crosswinds.

M D Fries↗

Recent Activities of the Marshall Space Flight Center Natural Environments Branch Terrestrial & Planetary Environments Team

- The Natural Environments (NE) Branch is within the MSFC Engineering Directorate’s Spacecraft and Vehicle System (S&VS) Department - S&VS plans, performs and directs the technical Design, Analysis, Test, Evaluation, Verification, Integration, and Applied Research and Development of future Spacecraft and Launch Vehicle Systems - Current programs we support - Space Launch System, Human Landing System, Mars Ascent Vehicle, Orion, Gateway - NE maintains expertise in terrestrial, space, and planetary natural environments definition, characterization, and analysis - Responsibility to disseminate this expertise to Agency programs and projects in support of environment definition, requirements development, vehicle development, operations, and sustaining engineering through program’s life cycle - Branch has two teams; Terrestrial and Planetary Environments (T&PE) and Space Environments (SE) - T&PE has extensive expertise characterizing terrestrial surface and atmospheric (aloft) environments - Winds, temperature, humidity, pressure, lightning, density, precipitation, radiant energy, aerosols, sea state, sea surface temperature - Maintains climatological databases for engineering analyses

Ryan K. Decker↗

Precipitation Science at NASA MSFC

The Precipitation Research Group in NASA MSFC’s Earth Science Branch (ST-11) focuses on observations of precipitation (rain, snow, and hail) from a variety of perspectives: ground-based radars, surface gauge networks, airborne instruments, and spaceborne measurements from onboard satellites. Current work includes identifying signatures of hail and strong thunderstorms from spaceborne measurements and assessing those signatures against multiple satellite datasets and ground-based radar observations. The Precipitation Team is also involved in the development and maintenance of NASA’s global-gridded multi-satellite precipitation product (IMERG) and operating and maintaining the GPM Validation Network (VN): a software package that geometrically matches the reference ground-based weather radar observations with GPM satellite observations in 3D. The team is also responsible for the Advanced Microwave Precipitation Radiometer (AMPR) used in airborne field campaign research, which recently was used to collect data on thunderstorms that produce terrestrial gamma-ray flashes (TGFs) in the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) field campaign. While the Precipitation Group largely supports NASA’s Global Precipitation Measurement (GPM) mission and Precipitation Science Team, the team also looks to the future Precipitation Measurement Mission (PMM) and Investigation of Convective Updrafts (INCUS) missions.

Sarah D Bang↗

Flight Test Evaluation of Automation-Induced Oscillations

Flight-test systems analysis techniques are applied to the case of aircraft automation-induced oscillations(AIO). Emerging technological advancements in flight control automation and artificial intelligence have created a need for evaluating the age-old aerial phenomenon of undesired aircraft oscillations. This paper is an overview on the study of AIO as interactions between human pilot, software engineer, and vehicle dynamics(e.g., latency) with the presence of winds aloft. Flight-test evaluation included a broad, non-comprehensive theory on cyclical and non-cyclical oscillations and a preliminary evaluation of the ecosystem from software engineer, airspace architect, and human test pilot.

David Zahn↗

Global Sensitivity Analysis of Simulated Remote Sensing Polarimetric Observations Over Snow

This study presents a detailed theoretical assessment of the information content of passive polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses the covariance of state parameters across their entire parameter space, hence favoring a more correct interpretation of inversion algorithms and the optimal design of their state vectors. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing particulates (LAPs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAPs in the VIS–NIR and to grain size at different depths in the snowpack in the NIR–SWIR. With adequate viewing geometries, mono-angle measurements of total reflectance in the VIS–SWIR (akin to those of the Moderate Resolution Imaging Spectroradiometer, MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS–NIR provides information on grain shape and microscale roughness. The simultaneous sensitivity in the VIS–NIR to both aerosols and snow-embedded impurities can be disentangled by extending the spectral range to the SWIR, which contains information on aerosol optical depth while remaining essentially unaffected when the same particulates are mixed with the snow. Multi-angle polarimetric observations can therefore (i) effectively partition LAPs between the atmosphere and the surface, which represents a notorious challenge for snow remote sensing based on measurements of total reflectance only and (ii) lead to better estimates of grain shape and roughness and, in turn, the asymmetry parameter, which is critical for the determination of albedo. The retrieval uncertainties are minimized when the degree of linear polarization is used in place of the polarized reflectance. The Sobol indices, which are the main metric for the GSA, were used to select the state parameters in retrievals performed on data simulated for multiple instrument configurations. Improvements in retrieval quality with the addition of measurements of polarization, multi-angle views, and different spectral channels reflect the information content, identified by the Sobol indices, relative to each configuration. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors. They can also aid flight planning activities, since the optimal exploitation of the information content of multi-angle measurements depends on the viewing geometry. The better characterization of surface and atmospheric parameters in snow-covered regions advances research opportunities for scientists of the cryosphere and ultimately benefits albedo estimates in climate models.

remote sensing↗

PM 2.5 Concentrations over Major Metropolitan Regions Inferred from Airborne High Spectral Resolution Lidar Measurements Using Machine Learning Regression

We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) to develop a machine learning regression methodology to infer PM2.5 concentrations at the surface and aloft. These airborne HSRL measurements were acquired over major metropolitan regions in the United States and Asia during more than 170 flights since 2010. Hourly surface PM2.5 measurements from the EPA air quality system and similar networks in other countries acquired within 10 km and 15 minutes of these near-surface HSRL measurements are used to train models that compute PM2.5 concentrations from the HSRL measurements. We examine several regression methods and find that exponential Gaussian Process algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. Model performance varies significantly depending on various combinations of HSRL aerosol measurements (e.g., aerosol backscatter, extinction, depolarization, backscatter color ratios, lidar ratios, aerosol optical thickness) and retrievals (e.g., mixed layer height, aerosol type) used in the regressions. Models that use near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS errors around 4 mg/m3 and correlation coefficients above 0.9. HSRL measurements were often acquired when the aircraft flew systematic “raster-scan” patterns for several hours over these cities. These flight patterns enabled measurements of the spatial, temporal, and vertical variabilities in the distributions of aerosol backscatter and aerosol intensive properties and allowed us to derive the corresponding variabilities in PM2.5 concentrations. We present examples of such variabilities over urban areas in the United States as well as Asia. We describe also how the distribution of surface PM2.5 varies with aerosol type and use these retrievals to examine model simulations of surface PM2.5 in these metropolitan regions. We also discuss how this methodology may be applied to measurements from satellite lidars such as CALIOP on CALIPSO and ATLID on EarthCARE.

lidar↗

Northern Winter Climate Change: Assessment of Uncertainty in CMIP5 Projections Related to Stratosphere-Troposphere Coupling

Future changes in the stratospheric circulation could have an important impact on northern winter tropospheric climate change, given that sea level pressure (SLP) responds not only to tropospheric circulation variations but also to vertically coherent variations in troposphere-stratosphere circulation. Here we assess northern winter stratospheric change and its potential to influence surface climate change in the Coupled Model Intercomparison Project-Phase 5 (CMIP5) multimodel ensemble. In the stratosphere at high latitudes, an easterly change in zonally averaged zonal wind is found for the majority of the CMIP5 models, under the Representative Concentration Pathway 8.5 scenario. Comparable results are also found in the 1% CO2 increase per year projections, indicating that the stratospheric easterly change is common feature in future climate projections. This stratospheric wind change, however, shows a significant spread among the models. By using linear regression, we quantify the impact of tropical upper troposphere warming, polar amplification, and the stratospheric wind change on SLP. We find that the intermodel spread in stratospheric wind change contributes substantially to the intermodel spread in Arctic SLP change. The role of the stratosphere in determining part of the spread in SLP change is supported by the fact that the SLP change lags the stratospheric zonally averaged wind change. Taken together, these findings provide further support for the importance of simulating the coupling between the stratosphere and the troposphere, to narrow the uncertainty in the future projection of tropospheric circulation changes.

climate change↗