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NASA NTRS2022

Vertical structure of biomass burning aerosol transported over the southeast Atlantic Ocean

Biomass burning in southwestern Africa produces smoke plumes that are transported over the Atlantic Ocean and overlie vast regions of stratocumulus clouds. This aerosol layer contributes to direct and indirect radiative forcing of the atmosphere in this region particularly during the months of August, September, and October. There was a multi-year international campaign to study this aerosol and its interactions with clouds. Here, we report on the evolution of aerosol distributions and properties as measured by the airborne high spectral resolution lidar (HSRL-2) during the ORACLES (Observations of Aerosols above Clouds and their intEractionS) campaign in September 2016. The NASA Langley HSRL-2 instrument was flown on the NASA ER-2 aircraft for several days in September 2016. Data were aggregated at two pairs of 2° × 2° grid boxes to examine the evolution of the vertical profile of aerosol properties during transport over the ocean. Results showed that the structure of the profile of aerosol extinction and microphysical properties is maintained over a 1 to 2 d timescale. In the 3–5 km altitude range, 95 % of the aerosol extinction was contributed by particles in the 0.05–0.50 µm radius size range with the aerosol in this size range having an average effective radius of 0.16 µm. This indicates that there is essentially no scavenging or dry deposition at these altitudes. Moreover, there is very little day-to-day variation in these properties, such that time sampling as happens in such campaigns may be representative of longer periods such as monthly means. Below 3 km, there is considerable mixing with larger aerosol, most likely continental source near land. Furthermore, these measurements indicated that there was often a distinct gap between the bottom of the aerosol layer and cloud tops at the selected locations as evidenced by a layer of several hundred meters that contained relatively low aerosol extinction values above the clouds.

lidar↗

NASA NTRSDate not supplied

Analysis of CO Transport from Australia Fires and Its Impact to Ozone, Water Vapor and Clouds Using CrIS Single-Field-of-View Sounder Products and Other Remote Sensing and MERRA-2 Reanalysis Data

Australia’s unprecedented fire disasters at the end of 2019 to early 2020 emitted huge amounts of carbon monoxide (CO) and fire aerosol particles, and the long-range transport of the smoke plumes poses great impact to climate change, such as altering the Antarctic ozone (O3) hole. Spaceborne observations from satellites have been proven successful in monitoring CO from wildfires in the past decade and can be used to examine the associated CO transport. The primary remote sensing data used in this study is the Single Field of View (SFOV) Sounder Atmospheric Product (SiFSAP) derived from the Cross-track Infrared Sounder (CrIS) on SNPP, a new product that will be generated at NASA and released to the public in a few months. SiFSAP has a high spatial resolution of ~14 km and includes atmospheric profiles of temperature, water vapor, CO, O3, as well as cloud products, and as the SiFSAP algorithm performs both day- and night-time retrievals, it provides an additional observation for night-time in comparison to the day-time-only solar polar satellite instruments such as MOPPIT, OMPS, and TROPOMI. Most recent significant improvements in the quality of this product plus its good spatial resolution (better than most global weather and climate models) enable us to conduct process-oriented analysis of CO transport, and the variation of water vapor, O3 and clouds (optical depth, effective radius and cloud height) within and around the fire plumes. The wind fields from MERRA-2 reanalysis will be used to help characterize the transport. Other remote sensing data, such as CO from AIRS, MOPPIT and TROPOMI, O3 from OMPS and TROPOMI, as well as the UV aerosol index from OMI and OMPS, will be used and compared with SiFSAP sounder products. Cross-validation of CO and O3 from these different sensors with the corresponding MERRA-2 products will also be carried out. Along the plume transport path, some enhancement of O3 and correlation between fire aerosol, cloud optical depth and water vapor have been found but need further investigation. Using the transport of fire plumes over the Southern Pacific Ocean as a unique testbed, this study will demonstrate the trace species monitoring capability of current satellite observations, in particular, for CO and O3, and hopefully, it will provide some insight on the impacts on fire emissions on air quality and climate.

Xiaozhen (Shawn) Xiong↗

NASA NTRSDate not supplied

Model Evaluation and Intercomparison Using Data Collected by the Langley Mobile Ozone Lidar in Hampton, Virginia

Throughout the year 2022, the Langley Mobile Ozone Lidar (LMOL) has been frequently collecting ozone and aerosol measurements in the lower troposphere. The lidar is located in the parking lot behind the Atmospheric Sciences building at NASA Langley in Hampton, Virginia, approximately 37.095 N, -76.389 W. The data was collected during a range of several distinct atmospheric conditions, including stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. We use this data to evaluate and intercompare the forecast accuracy of two atmospheric chemistry models: the GEOS Composition Forecasting model (GEOS-CF) and the Weather Research and Forecasting model with Chemistry (WRF-Chem). Both models make daily three-dimensional forecasts of many trace gases and species for the contiguous United States. Over a range of ten distinct collection events, each spanning 36 hours to nearly seven days, the forecast models predict tropospheric ozone at NASA Langley with reasonable accuracy. The models best predict the timing and shape of the observed stratospheric intrusions in the middle troposphere but often vary in the magnitude of the ozone mixing ratios. For other atmospheric conditions, there is more variability in the model accuracy. Here, we summarize the accuracy of the models for all events and investigate reasons for differences between the models and the lidar.

Daniel B. Phoenix↗

NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.

Thomas Grubb↗

NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

In this presentation, we will discuss our work in adapting the NASA open source XR software, the Mixed Reality Exploration Toolkit (MRET), to an earth science domain. MRET is a NASA open source XR software for rapidly building extended reality (XR) environments for NASA domain problems, e.g., pulling in CAD models of thermal vac chamber and Roman Space Telescope to do fit checks. Primarily used for hardware integration & test, we have been adapting and extending MRET for science problems. Traditionally, scientists view and analyze the result of calculated or measured observables with static 1-D, 2-D or 3-D plots. It can be difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. Additionally, numerical models, such as the NASA GEOS climate model, are almost exclusively formulated and analyzed on Eulerian grids with points fixed in space and time. However, atmospheric phenomena such as convective clouds, hurricanes and wildfire smoke plumes move with the 3-D flow field, and it is often difficult and unnatural to understand these phenomena in an Eulerian reference frame as opposed to the Lagrangian reference frame in which nature operates. As part of an Earth Science Technology Office (ESTO) proposal, we have been adapting MRET to be a scientific exploration and analysis XR tool with integrated Lagrangian Dynamics (LD) for the Goddard Earth Observing System (GEOS) numerical weather prediction model. We believe this will help scientists identify, track, and understand the evolution of Earth Science phenomena. This presentation will discuss current results in our work in developing this XR tool for Earth Science.

Thomas G Grubb↗

NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

Thomas Grubb↗

NASA NTRSDate not supplied

Global Carbon Consumption Database for Wildland Fire

Fire significantly impacts both human-altered and wild landscapes on national and global scales. Though often devastating, wildland fires naturally reduce fuels, preventing larger wildfires. However, their smoke can harm human health locally and globally. To understand its impact on air quality and health, quantifying the emissions released into the atmosphere is crucial. To accurately model fire emissions, understanding fuel characteristics and burning conditions is crucial, as they vary with the fuelbed type and fire weather. For example, savanna fires have lower carbon loading and intensity but spread quickly, while boreal forest fires burn longer and release more carbon due to higher loading. Hotter, drier conditions increase fuel consumption and smoke plume height. Fire weather and available fuel are the key drivers of emissions.

Emily Gargulinski↗

NASA NTRSDate not supplied

Atmospheric Composition Forecast Model Evaluation Using Ozone Measurements Collected by the Langley Mobile Ozone Lidar

The Langley Mobile Ozone Lidar (LMOL) is a mobile ground based lidar system based at NASA Langley in Hampton, Virginia. Between 2022 and 2024, LMOL collected over 2500 hours of ozone measurements for a range of different atmospheric conditions, including calm days, stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. Here, the data is used to evaluate the forecast accuracy of NASA’s Global GEOS Composition Forecasting (GEOS-CF) model. GEOS-CF makes daily three-dimensional forecasts of trace gases and aerosol species. Overall, for calm periods, the forecast model predicts lower tropospheric ozone at NASA Langley with reasonable accuracy (within 20%). The model best predicts the timing and extent of stratospheric intrusions but often vary in the magnitude of the ozone mixing ratio. Among the other types of atmospheric conditions, there is more variability in the model forecasts. Based on this analysis, model forecasts are utilized to determine future data acquisition opportunities with the goal of providing feedback to the modeling teams, thereby enabling them to better understand the model biases and improve the model forecasts of ozone during these different atmospheric conditions.

Daniel B Phoenix↗

NASA NTRSDate not supplied

Spaceborne Lidar Retrievals of PM2.5 for Air Quality Studies and Applications

Fine particulate matter (PM2.5) substantially contributes to air pollution and negatively affects human health. While many studies have investigated the use of passive column-integrated aerosol optical depth to infer surface PM2.5, the use of lidar observations for air quality characterization is not nearly as extensive. Lidar measurements are critical, however, due to the vertical aerosol information they provide, including near the surface. In this presentation, we first provide an overview of various lidar-based approaches for estimating PM2.5 concentrations and then discuss how lidar measurements can assist other air quality applications. For example, estimates of PM2.5 have been obtained in a physics-based approach through CALIOP near-surface aerosol extinction retrievals, assumptions on the mass extinction efficiency, and incorporating other parameters (an aerosol hygroscopic growth factor and PM2.5/PM10 ratio). Application of this algorithm over the contiguous United States (CONUS) from 2006 to 2018 yielded larger PM2.5 values over the eastern and western CONUS (~10-15 μg/m³) and lower PM2.5 levels in the central CONUS (~5 μg/m³). These spatial patterns were similar to those from gridded PM2.5 concentrations obtained through in situ measurements at ground stations operated by the US Environmental Protection Agency. In another approach, the Cloud Aerosol Transport System (CATS) lidar was used with the Goddard Earth Observing System (GEOS) model in a 1D ensemble-based variational technique to obtain PM2.5 over the US and Europe, and the spatial patterns of the CATS/GEOS based PM2.5 concentrations generally captured those from surface stations (with corresponding hourly EPA PM2.5 vs CATS PM2.5 statistics of R=0.4 and bias=1.5 μg/m³). In our recent work, as part of the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group, we have applied both the CALIOP and CATS/GEOS based approaches over the highly polluted country of India during the post-monsoon season (September-October 2016). We derived elevated levels of two-month mean PM2.5 (~100 μg/m³) in northern India, especially near New Delhi. These high PM2.5 concentrations in the Indo-Gangetic plain are driven in large part from the seasonal burning of crop residue and meteorological conditions typical at this time of the year, such as low wind speeds and a shallow boundary layer. While the satellite-derived PM2.5 moderately replicates (R = ~0.7-0.9) the spatial variability in the two-month mean of surface in situ PM2.5 from monitoring sites operated by the Central and State Pollution Control Boards, we show results from specific scenes for which there are large deviations between the satellite-derived PM2.5 and in situ measurements. Other current work on this topic focuses on developing PM2.5 estimates using airborne high spectral resolution lidar measurements through machine learning regression algorithms and involves several parameters (e.g., aerosol extinction, color ratio, lidar ratio). Application of this method over major metropolitan areas in the US and Asia have resulted in high correlations (R = 0.93) with surface measurements. This airborne lidar approach can be adapted to spaceborne lidar measurements, and all three of these approaches can be applied to ESA’s EarthCARE Atmospheric Lidar instrument, setting the stage for the future Cloud Aerosol Lidar for Global Scale Observations of the Ocean-Land Atmosphere System (CALIGOLA) mission. Ultimately, beyond estimates of PM2.5, the aerosol vertical distribution from lidars can benefit studies involving passive sensor approaches for PM2.5 proxies (including from geostationary satellites), wildfire smoke plume injection heights, volcanic emissions (e.g., ash height retrievals), and aerosol/air quality model assimilation, evaluation, and forecasts.

Travis D Toth↗

NASA NTRS2021

Evaluation and Intercomparison of Wildfire Smoke Forecasts from Multiple Modeling Systems for the 2019 Williams Flats Fire

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

AOD↗

NASA NTRS2018

What's It like to See Earth from Space? Viewing Your World with NASA's Worldview!

When you first see Earth from space, you'll realize it's largely covered in white - our world is quite cloudy! Look closer and you'll discern landmasses, oceans, and regions covered in snow. Look closer still and you'll notice that our world is in constant motion - storms brewing and tracing paths over the oceans, plumes of smoke from wildfires and plumes of ash from volcanic eruptions billowing with the wind, dust storms blowing across the deserts, phytoplankton swirling in the oceans, icebergs floating in the oceans, and you'll see the human footprint on the earth's surface: cities connected by roads and vast swaths of agriculture. NASA's Worldview (https://worldview.earthdata.nasa.gov) interactive web map application provides a platform to view the world as it has been every day for the past 18 years, using data from NASA's fleet of Earth Observing System (EOS) satellites.This presentation will cover the history and development of the Worldview web map application; the 700+ imagery layers that are provided by the Global Imagery Browse Services (GIBS) (https://earthdata.nasa.gov/gibs); current and new features that are in Worldview to constantly improve the user experience; the interdisciplinary nature of the app and how it helps a broad range of user communities discover and interact with NASA satellite imagery; and ongoing efforts to improve Worldview and serve user communities.

Earth Science↗

NASA NTRS2008

MISR Instrument Data Visualization

The MISR Interactive eXplorer (MINX) software functions both as a general-purpose tool to visualize Multiangle Imaging SpectroRadiometer (MISR) instrument data, and as a specialized tool to analyze properties of smoke, dust, and volcanic plumes. It includes high-level options to create map views of MISR orbit locations; scrollable, single-camera RGB (red-greenblue) images of MISR level 1B2 (L1B2) radiance data; and animations of the nine MISR camera images that provide a 3D perspective of the scenes that MISR has acquired. NASA Tech Briefs, September 2008 55 The plume height capability provides an accurate estimate of the injection height of plumes that is needed by air quality and climate modelers. MISR provides global high-quality stereo height information, and this program uses that information to perform detailed height retrievals of aerosol plumes. Users can interactively digitize smoke, dust, or volcanic plumes and automatically retrieve heights and winds, and can also archive MISR albedos and aerosol properties, as well as fire power and brightness temperatures associated with smoke plumes derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Some of the specialized options in MINX enable the user to do other tasks. Users can display plots of top-of-atmosphere bidirectional reflectance factors (BRFs) versus camera-angle for selected pixels. Images and animations can be saved to disk in various formats. Also, users can apply a geometric registration correction to warp camera images when the standard processing correction is inadequate. It is possible to difference the images of two MISR orbits that share a path (identical ground track), as well as to construct pseudo-color images by assigning different combinations of MISR channels (angle or spectral band) to the RGB display channels. This software is an interactive application written in IDL and compiled into an IDL Virtual Machine (VM) ".sav" file.

Nelson, David↗

NASA NTRS2021

The Long-term Transport and Radiative Impacts of the 2017 British Columbia Pyrocumulonimbus Smoke Aerosols in the Stratosphere

Interactions of meteorology with wildfires in British Columbia, Canada during August 2017 led to three major pyrocumulonimbus (pyroCb) events that resulted in the injection of large amounts of smoke aerosols and other combustion products at the local upper troposphere and lower stratosphere (UTLS). These plumes of UTLS smoke with elevated values of aerosol extinction and backscatter compared to the background state were readily tracked by multiple satellite-based instruments as they spread across the Northern Hemisphere (NH). The plumes were observed in the lower stratosphere for about 8-10 months following the fire injections, with a stratospheric aerosol e-folding time of about 5 months. To investigate the radiative impacts of these events on the Earth system, we performed a number of simulations with the Goddard Earth Observing System (GEOS) atmospheric general circulation model (AGCM). Observations from multiple remote-sensing instruments were used to calibrate the injection parameters (location, amount, composition and heights) and optical properties of the smoke aerosols in the model. The resulting simulations of three-dimensional smoke transport were evaluated for a year from the day of injections using daily observations from OMPS-LP (Ozone Mapping Profiler Suite Limb Profiler). The model simulated rate of ascent, hemispheric spread and residence time (or e-folding time) of the smoke aerosols in the stratosphere are in close agreement with OMPS-LP observations. We found that both aerosol self-lofting and the large-scale atmospheric motion play important roles in lifting the smoke plumes from near the tropopause altitudes (~12 km) to about 22-23 km into the atmosphere. Further, our estimations of the radiative impacts of the pyroCb-emitted smoke aerosols showed that the smoke caused an additional warming of the atmosphere by about 0.6-1 W/m2 (zonal mean) that persisted for about 2-3 months after the injections in regions north of 40oN. The surface experienced a comparable magnitude of cooling. The atmospheric warming is mainly located in the stratosphere, coincident with the location of the smoke plumes, leading to an increase in zonal mean shortwave (SW) heating rates of 0.02-0.04 K/day during September 2017.

pyrocumulonimbus (pyroCb) events↗

DOE OSTI2025

In Situ Atmospheric Plume Thermometry via Carbon Monoxide Spectral Profile: Laboratory and Field Validation

For small molecules with large rotational constants, knowing the relative intensities of the ro-vibrational transitions can be used to determine the temperature within a gas plume. We demonstrate the use of carbon monoxide (CO) as an in situ spectroscopic probe of gas plume temperature by application of both laboratory and standoff Fourier transform infrared spectroscopy to monitor the CO spectral response at different temperatures. Here, the measured CO rotational contours were analyzed using a simple Boltzmann model to deduce the population distribution of the J-levels, from which the in-plume temperature is deduced. The method was vetted by comparing deduced temperatures in both static laboratory measurements of known temperatures, as well as field measurements using a simulated smokestack release. For the smokestack experiments, spectroscopically deduced temperatures were compared to readings from a series of thermocouples placed at strategically sampled distances along the plume trajectory. Both the spectroscopically-derived and thermocouple-measured temperatures revealed an expansion-induced (mixing) rapid cooling of the plume, with the infrared thermometry values displaying greater temperature values which are believed to better represent the actual plume temperatures.

Analysis of rotational structure↗

NASA NTRSDate not supplied

Challenges in Observing, Modeling, and Forecasting the June 2023 Smoke Event Over the Northeast United States

In the first week of May 2023, boreal Canada began an early start to the biomass burning season when fires erupted across Alberta and western Saskatchewan. Smoke entered the troposphere and spread across the United States as biomass burning emissions in Canada quadrupled the prior maximum since the onset of the MODIS satellite record. Adding to the already existing smoke, wildfires ignited in Quebec on June 2, 2023, producing a heavy plume of aerosol over the northeastern part of the continent. Meteorological factors, including an area of low pressure situated, nearly stationary, over Maine, transported the smoke into a densely populated corridor containing the cities of Washington, DC, Baltimore, Philadelphia, and New York City. Air quality alerts became widespread as near-surface levels of fine particulate matter (PM2.5) exceeded harmful levels due the low altitude of the smoke plume. Forecasting the smoke transport and impacts of such events requires a complex combination of observed initial conditions for meteorology and aerosols, knowledge of the emissions from wildfires, and a state-of-the-art Earth System model that couples these components together. Using a case-study perspective with the Goddard Earth Observing System (GEOS), challenges associated with near real time forecasting of the smoke plume are presented. Analyzed and forecasted aerosol optical depth will be evaluated using available satellite and AERONET observations, while surface aerosol will be assessed relative to observations of PM2.5. Lacking real time estimates of biomass burning emissions for inputs to our forecast model, particular attention is given to the use of day-old biomass burning emissions throughout the forecast period covering the first ten days in June 2023, and the resulting reduction in forecast skill will be quantified.

Allison Collow↗

NASA NTRSDate not supplied

Challenges in Observing, Modeling, and Forecasting the June 2023 Smoke Event over the Northeast United States

In the first week of May 2023, boreal Canada began an early start to the biomass burning season when fires erupted across Alberta and western Saskatchewan. Smoke entered the troposphere and spread across the United States as biomass burning emissions in Canada quadrupled the prior maximum since the onset of the MODIS satellite record. Adding to the already existing smoke, wildfires ignited in Quebec on June 2, 2023, producing a heavy plume of aerosol over the northeastern part of the continent. Meteorological factors, including an area of low pressure situated, nearly stationary, over Maine, transported the smoke into a densely populated corridor containing the cities of Washington, DC, Baltimore, Philadelphia, and New York City. Air quality alerts became widespread as near-surface levels of fine particulate matter (PM2.5) exceeded harmful levels due the low altitude of the smoke plume. Forecasting the smoke transport and impacts of such events requires a complex combination of observed initial conditions for meteorology and aerosols, knowledge of the emissions from wildfires, and a state-of-the-art Earth System model that couples these components together. Using a case-study perspective with the Goddard Earth Observing System (GEOS ), challenges associated with near real time forecasting of the smoke plume are presented. Analyzed and forecasted aerosol optical depth will be evaluated using available satellite and AERONET observations, while surface aerosol will be assessed relative to observations of PM2.5. Lacking real time estimates of biomass burning emissions for inputs to our forecast model, particular attention is given to the use of day-old biomass burning emissions throughout the forecast period covering the first ten days in June 2023, and the resulting reduction in forecast skill will be quantified.

Allison Collow↗

NASA NTRS2010

JMISR INteractive eXplorer

MISR (Multi-angle Imaging SpectroRadiometer) INteractive eXplorer (MINX) is an interactive visualization program that allows a user to digitize smoke, dust, or volcanic plumes in MISR multiangle images, and automatically retrieve height and wind profiles associated with those plumes. This innovation can perform 9-camera animations of MISR level-1 radiance images to study the 3D relationships of clouds and plumes. MINX also enables archiving MISR aerosol properties and Moderate Resolution Imaging Spectroradiometer (MODIS) fire radiative power along with the heights and winds. It can correct geometric misregistration between cameras by correlating off-nadir camera scenes with corresponding nadir scenes and then warping the images to minimize the misregistration offsets. Plots of BRF (bidirectional reflectance factor) vs. camera angle for points clicked in an image can be displayed. Users get rapid access to map views of MISR path and orbit locations and overflight dates, and past or future orbits can be identified that pass over a specified location at a specified time. Single-camera, level-1 radiance data at 1,100- or 275- meter resolution can be quickly displayed in color using a browse option. This software determines the heights and motion vectors of features above the terrain with greater precision and coverage than previous methods, based on an algorithm that takes wind direction into consideration. Human interpreters can precisely identify plumes and their extent, and wind direction. Overposting of MODIS thermal anomaly data aids in the identification of smoke plumes. The software has been used to preserve graphical and textural versions of the digitized data in a Web-based database.

Nelson, David L.↗

NASA NTRS2019

Differences in the Vertical and Microphysical Evolution of Volcanic and Pyrocumulonimbus Stratospheric Aerosol Plumes as Observed by CALIOP and CATS Satellite Lidar

For some time, volcanic eruptions have been thought to be the only significant direct injectors of aerosols in the stratosphere. However, recent fire seasons have featured fire events resulting in large volcanic-sized pyrocumulonimbus plumes of smoke aerosols reaching many kilometers into the lower stratosphere. To understand and model the effects of these pyrocumulonimbus events on stratospheric composition and climate, a natural analogy lies with better studied volcanic events; however, differences in plume composition may limit this comparison. Using satellite lidar from both CATS and CALIOP, we show that the stratospheric aerosol plumes from the record-setting Pacific Northwest pyrocumulonimbus event of 2017 and the Calbuco volcanic eruption of 2015 evolve differently both vertically and microphysically. Specifically, depolarization ratios indicate that this pyrocumulonimbus event’s aerosol particles became more irregularly shaped over time in contrast to volcanic aerosols which become more spherical over time. Accounting for these changes in aerosol properties may be significant in assessing the effects of pyrocumulonimbus events on the Earth’s radiative balance and aid in refining stratospheric aerosol typing algorithms to differentiate volcanic from pyrocumulonimbus plumes.

Christian, Kenneth↗