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NASA NTRS2015

Interannual Variability of Tropospheric Trace Gases and Aerosols: The Role of Biomass Burning Emissions

Fires are responsible for a range of gaseous and aerosol emissions. However, their influence on the interannual variability of atmospheric trace gases and aerosols has not been systematically investigated from a global perspective. We examine biomass burning emissions as a driver of interannual variability of large-scale abundances of short-lived constituents such as carbon monoxide (CO), hydroxyl radicals (OH), ozone and aerosols using the GISS ModelE composition-climate model and a range of observations, with an emphasis on satellite information. Our model captures the observed variability of the constituents examined in most cases, but with substantial underestimates in boreal regions. The strongest interannual variability on a global scale is found for carbon monoxide (10 for its global annual burden), while the lowest is found for tropospheric ozone (10 for its global annual burden). Regionally, aerosol optical depth shows variability that exceeds 50. Areas of strong variability include the tropical land regions and northern high latitudes, while even regions in the northern midlatitudes experience substantial inteannual variability of aerosols. Ozone variability peaks over Equatorial Asia in boreal autumn, partly due to varying biomass burning emissions, and over the western and central Pacific in the rest of the year, mainly due to meteorological fluctuations. We find that biomass burning emissions are almost entirely responsible for global CO interannual variability, and similarly important for OH variability. The same is true for global and regional aerosol variability, especially when not taking into account dust and sea-salt particles. We show that important implications48 can arise from such interannual influences for regional climate and air quality.

aerosols↗

NASA NTRSDate not supplied

CALIGOLA: A New Spaceborne Lidar Mission

Agenzia Spaziale Italiana (ASI) and NASA are partnering on an exciting new spaceborne lidar mission known as CALIGOLA (i.e., the Cloud and Aerosol Lidar for Global Scale observations of the Ocean-Land-Atmosphere) that seeks to significantly advance our understanding of Earth’s coupled ocean-land-atmosphere systems. By flying an innovative multi-wavelength, polarization-sensitive Raman lidar that will simultaneously acquire elastic backscatter and fluorescence measurements, CALIGOLA will provide new insights into the processes that influence climate, weather, and air quality by vertically profiling microphysical and optical properties of clouds and aerosols. The mission will also probe phytoplankton biomass and zooplankton dynamics and provide observations of surface vegetation, ice, and snow. The mission is planned for launch early next decade and baselined to fly in an orbit that is compatible with NASA’s AOS-Sky mission. This formation flying configuration will enable synergistic retrievals between observations from CALIGOLA and the polarimeter and radar measurements planned for AOS-Sky and thus greatly enhance our ability to explore and characterize the intricate processes that govern the interactions between different components of the Earth system. The presentation will highlight CALIGOLA’s significant science objectives and describe the pioneering instrument that will enable us to achieve these objectives.

Chip Trepte↗

NASA NTRSDate not supplied

Enhanced Monitoring and Forecasting of South Asian Air Quality Episodes with Multi-Sensor Satellite Products and Dispersion Modeling

Air pollution and particulates represent a serious environmental, public health, and overarching societal concern in the Hindu Kush Himalaya (HKH) region of south-central Asia, especially during the boreal winter and spring months. Frequent contributors to poor air quality of the region include dust and particulate matter transport from the Middle East region and western India, persistent nocturnal fog and smog during the stable dry monsoon months, and biomass burning during the pre-monsoon months of boreal spring. Nocturnal and multi-day persistent stable fog and smog events can occur during the stable conditions of the dry monsoon months that lead to hazardous visibility reductions (e.g., at major airports such as Delhi, India)and poor air quality, particularly in urban corridors.Our project goal is therefore to design and implement a robust air quality and chemistry observation and modeling system using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. This system effectively assimilates aerosol and trace gas retrievals from geostationary and polar-orbiting satellites to advance the monitoring and prediction capabilities of air quality and visibility reductions throughout the HKH region. For this presentation, we highlight several[multi-spectral] geostationary satellite products and dispersion modeling capabilities being developed and implemented for improved situational awareness and forecasting of air quality episodes.Launched in December 2018, the Advanced Meteorological Imager (AMI) aboard the Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, or GK2A) provides 16 channels of multi-spectral information similar to the U.S. GOES-16/17 satellites. Proven community recipes for generating multi-spectral Red-Green-Blue (RGB) composite products to detect specific meteorological phenomena are applied to the AMI data for the HKH region. We implemented four such RGB products consisting of truecolor with Rayleigh correction to aid in smoke detection; dust RGB for monitoring larger particulate matter; nighttime microphysics for fog/smog detection;and natural color fire RGB and the shortwave 3.8 micron channel for fire hot-spot detection.We additionally configured HYSPLIT for dust/sand dispersion and concentration forecasts over HKH. The HYSPLIT dispersion simulations are initially being conducted with two different methodologies: (1) dust/sand lofting based on initializing plume releases from visual inspection of the dust RGB imagery; and (2) dust/sand release and dispersion using the internal HYSPLIT algorithm that determines lofting from input wind speeds and prescribed land use. The goal is to develop a near real-time HYSPLIT modeling solution for routinely simulating the transport and dispersion of dust plumes across the HKH region. This presentation illustrates these products and capabilities for various use cases from the 2019 to 2021 period, highlighting a particularly unhealthy episode during late March 2021 across Nepal.

Remote Sensing↗

NASA NTRS2020

High productivity in hybrid-poplar plantations without isoprene emission to the atmosphere

Hybrid-poplar tree plantations provide a source for biofuel and biomass, but they also increase forest isoprene emissions. The consequences of increased isoprene emissions include higher rates of tropospheric ozone production, increases in the lifetime of methane, and increases in atmospheric aerosol production, all of which affect the global energy budget and/or lead to the degradation of air quality. Using RNA interference (RNAi) to suppress isoprene emission, we show that this trait, which is thought to be required for the tolerance of abiotic stress, is not required for high rates of photosynthesis and woody biomass production in the agroforest plantation environment, even in areas with high levels of climatic stress. Biomass production over 4 y in plantations in Arizona and Oregon was similar among genetic lines that emitted or did not emit significant amounts of isoprene. Lines that had substantially reduced isoprene emission rates also showed decreases in flavonol pigments, which reduce oxidative damage during extremes of abiotic stress, a pattern that would be expected to amplify metabolic dysfunction in the absence of isoprene production in stress-prone climate regimes. However, compensatory increases in the expression of other proteomic components, especially those associated with the production of protective compounds, such as carotenoids and terpenoids, and the fact that most biomass is produced prior to the hottest and driest part of the growing season explain the observed pattern of high biomass production with low isoprene emission. Our results show that it is possible to reduce the deleterious influences of isoprene on the atmosphere, while sustaining woody biomass production in temperate agroforest plantations.

oxidative stress↗

NASA NTRS2020

The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth’s Forests and Topography

Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a longsought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6 N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.

Dubayah, Ralph↗

NASA NTRS2010

Biomass Burning Emissions from Fire Remote Sensing

Knowledge of the emission source strengths of different (particulate and gaseous) atmospheric constituents is one of the principal ingredients upon which the modeling and forecasting of their distribution and impacts depend. Biomass burning emissions are complex and difficult to quantify. However, satellite remote sensing is providing us tremendous opportunities to measure the fire radiative energy (FRE) release rate or power (FRP), which has a direct relationship with the rates of biomass consumption and emissions of major smoke constituents. In this presentation, we will show how the satellite measurement of FRP is facilitating the quantitative characterization of biomass burning and smoke emission rates, and the implications of this unique capability for improving our understanding of smoke impacts on air quality, weather, and climate. We will also discuss some of the challenges and uncertainties associated with satellite measurement of FRP and how they are being addressed.

Ichoku, Charles↗

NASA NTRS2019

2018 NISAR Applications Workshop: Forest and Disturbance; Workshop Report

Forest lands cover the globe and are important sources for providing ecosystem services including: carbon sequestration, biodiversity, timber, air and water quality. As such, counties around the world have dedicated programs for managing them. Accurate and timely information concerning the status of these forests (moisture, biomass, disturbance type, etc.) is essential to those Nations’ human and ecological health as well as economy. The joint NASA/US Forest Service workshop focused on arming forest land managers with observations and remote sensing information from the upcoming NASA-ISRO (Indian Space Research Organization) SAR (Synthetic Aperture Radar) (NISAR) satellite mission (expected to launch early 2022). Participants included representatives from different US Federal Agencies, private sector, and non-governmental organizations (NGO) that are key players in facilitating integration of Earth Observations (EO) into forest management and decision support workflows. They included scientists, technicians, and program managers with a responsibility for data acquisition and exploitation such as product development, delivery, and use, and capacity building. Discussions were held over two days to convey the broader forest and disturbance community information needs for various representative participants and programs and to facilitate the delivery of NISAR mission geospatial products and observational capabilities. Case studies were presented to demonstrate the current state of practice in the use of SAR remote sensing for applications of direct importance to forest and disturbance land management community. Eleven organizations presented their information requirements in response to a set of questions provided by the NASA team, then the NASA team responded by describing the degree to which NISAR could meet these requirements. Discussion ensued about needed data product specifications to increase utility (e.g., projection, latency, etc.), tools and capacity building. The general findings of this workshop were that (a) NISAR observations will be particularly useful to the global forest carbon and disturbance monitoring applications, but that certain data product design decisions (projections and radiometric and terrain corrections) need to be considered to increase utility; b) the biomass and disturbance detection algorithms meet many of the community needs, however there are other information products of value (e.g., soil moisture or disturbance classification, not just detection) and all products should be compliant with existing community standards for reporting uncertainty; c) providing SAR education to the community will be key specifically thinking about putting the information first and the SAR theory second, providing a simple guide of standard data processing steps (e.g., dB (decibel) to power conversion and speckle filtering); d) the community needs a user-friendly interface for finding free, archived data over their geographic regions of interest; e) user-friendly tools that connect to open-sources GIS (Global Information System) software (e.g., QGIS (Quantum GIS)) that include a graphical user interface (GUI) for SAR processing that enables both download and cloud processing. To integrate these findings and prepare the community before NISAR launches, it was suggested that there be a dedicated NISAR Forest and Disturbance Applications Working Group (as per the specifications in the NISAR Utilization Plan). After launch, it was decided that the community continue capacity building activities.

Stavros, Natasha↗

NASA NTRS2014

Airborne In-Situ Trace Gas Measurements of Multiple Wildfires in California (2013-2014)

Biomass burning emissions are an important source of a wide range of trace gases and particles that can impact local, regional and global air quality, climate forcing, biogeochemical cycles and human health. In the western US, wildfires dominate over prescribed fires, contributing to atmospheric trace gas budgets and regional and local air pollution. Limited sampling of emissions from wildfires means western US emission estimates rely largely on data from prescribed fires, which may not be a suitable proxy for wildfire emissions.We report here in-situ measurements of carbon dioxide, methane, ozone and water vapor from the plumes of a variety of wildfires sampled in California in the fire seasons of 2013 and 2014. Included in the analysis are the Rim Fire (August October 2013, near Yosemite National Park), the Morgan Fire (September 2013, near Clayton, CA), and the El Portal Fire (July August 2014, in Yosemite National Park), among others. When possible, fires were sampled on multiple days.Emission ratios and estimated emission factors will be presented and discussed in the context of fuel composition, plume structure, and fire phase. Correlations of plume chemical composition to MODISVIIRS Fire Radiative Power (FRP) and other remote sensing information will be explored. Furthermore, the role of plumes in delivery of enhanced ozone concentrations to downwind municipalities will be discussed.

Gas↗

NASA NTRS2020

Current and Future Remote Sensing of Harmful Algal Blooms in the Chesapeake Bay to Support the Shellfish Industry

Harmful algal bloom (HAB) species in the Chesapeake Bay can negatively impact fish, shellfish, and human health via the production of toxins and the degradation of water quality. Due to the deleterious effects of HAB species on economically and environmentally important resources, such as oyster reef systems, Bay area resource managers are seeking ways to monitor HABs and water quality at large spatial and fine temporal scales. The use of satellite ocean color imagery has proven to be a beneficial tool for resource management in other locations around the world where high-biomass, nearly monospecific HABs occur. However, remotely monitoring HABs in the Chesapeake Bay is complicated by the presence of multiple, often co-occurring, species and optically-complex waters. Here we present a summary of common marine and estuarine HAB species found in the Chesapeake Bay, Alexandrium monilatum, Karlodinium veneficum, Margalefidinium polykrikoides, and Prorocentrum minimum, that have been detected from space using multispectral data products from the Ocean and Land Color Imager (OLCI) sensor on the Sentinel-3 satellites and identified based on historic phytoplankton data, in situ sampling, and ecological associations. We review how future hyperspectral instruments will improve discrimination of potentially harmful species from other phytoplankton communities and present a framework in which satellite data products could aid Chesapeake Bay resource managers with monitoring water quality and protecting shellfish resources.

Chesapeake Bay, satellite remote sensing, harmful ↗

NASA NTRS1986

Interpreting forest and grassland biome productivity utilizing nested scales of image resolution and biogeographical analysis

Data acquisition, initial site characterization, image and geographic information methods available, and brief evaluations of first-year for NASA's Thematic Mapper (TM) working group are presented. The TM and other spectral data are examined in order to relate local, intensive ecosystem research findings to estimates of carbon cycling rates over wide geographic regions. The effort is to span environments ranging from dry to moist climates and from good to poor site quality using the TM capability, with and without the inclusion of geographic information system (GIS) data, and thus to interpret the local spatial pattern of factors conditioning biomass or productivity. Twenty-eight TM data sets were acquired, archived, and evaluated. The ERDAS image processing and GIS system were installed on the microcomputer (PC-AT) and its capabilities are being investigated. The TM coverage of seven study areas were exported via ELAS software on the Prime to the ERDAS system. Statistical analysis procedures to be used on the spectral data are being identified.

Iverson, L. R.↗

NASA NTRSDate not supplied

CONUS and Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as wildlands. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have developed a carbon consumption database for the Continental United States (CONUS) and are developing a global carbon consumption database, both of which are directly link to distinct fuels within various fire danger categories. Fuel Characteristic Classification System (FCCS) fuels are used to parameterize biomass, and consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. Residual burned area is defined as burning in areas that have recently burned. We implemented the FCCS 30-meter CONUS fuelbed dataset during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets. The 300-meter global database builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized FCCS biomass parameters. These products can serve as valuable tools when used in conjunction with burned area data to rapidly and accurately estimate carbon consumed and released into the atmosphere.

FCCS↗

NASA NTRS2011

PMA-PhyloChip DNA Microarray to Elucidate Viable Microbial Community Structure

Since the Viking missions in the mid-1970s, traditional culture-based methods have been used for microbial enumeration by various NASA programs. Viable microbes are of particular concern for spacecraft cleanliness, for forward contamination of extraterrestrial bodies (proliferation of microbes), and for crew health/safety (viable pathogenic microbes). However, a "true" estimation of viable microbial population and differentiation from their dead cells using the most sensitive molecular methods is a challenge, because of the stability of DNA from dead cells. The goal of this research is to evaluate a rapid and sensitive microbial detection concept that will selectively estimate viable microbes. Nucleic acid amplification approaches such as the polymerase chain reaction (PCR) have shown promise for reducing time to detection for a wide range of applications. The proposed method is based on the use of a fluorescent DNA intercalating agent, propidium monoazide (PMA), which can only penetrate the membrane of dead cells. The PMA-quenched reaction mixtures can be screened, where only the DNA from live cells will be available for subsequent PCR reaction and microarray detection, and be identified as part of the viable microbial community. An additional advantage of the proposed rapid method is that it will detect viable microbes and differentiate from dead cells in only a few hours, as opposed to less comprehensive culture-based assays, which take days to complete. This novel combination approach is called the PMA-Microarray method. DNA intercalating agents such as PMA have previously been used to selectively distinguish between viable and dead bacterial cells. Once in the cell, the dye intercalates with the DNA and, upon photolysis under visible light, produces stable DNA adducts. DNA cross-linked in this way is unavailable for PCR. Environmental samples suspected of containing a mixture of live and dead microbial cells/spores will be treated with PMA, and then incubated in the dark. Thereafter, the sample is exposed to visible light for five minutes, so that the DNA from dead cells will be cross-linked. Following this PMA treatment step, the sample is concentrated by centrifugation and washed (to remove excessive PMA) before DNA is extracted. The 16S rRNA gene fragments will be amplified by PCR to screen the total microbial community using PhyloChip DNA microarray analysis. This approach will detect only the viable microbial community since the PMA intercalated DNA from dead cells would be unavailable for PCR amplification. The total detection time including PCR reaction for low biomass samples will be a few hours. Numerous markets may use this technology. The food industry uses spore detection to validate new alternative food processing technologies, sterility, and quality. Pharmaceutical and medical equipment companies also detect spores as a marker for sterility. This system can be used for validating sterilization processes, water treatment systems, and in various public health and homeland security applications.

Venkateswaran, Kasthuri J.↗

NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide (NO 2 ), formaldehyde (CH2O), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ), and aerosol optical depth (AOD). Value-added products [e.g., Particulate matter with diameters less than 2.5 micrometers (PM2.5)] are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Air Quality↗

NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗

NASA NTRS1987

Wheat production in controlled environments

The present optimization study for maximum yield and quality conditions in the lunar or Martian Controlled Environment Life Support System (CELSS)-based growth of wheat has determined that, for 23-57 g/sq m per day of edible biomass, minimum CELSS size must be of the order of 12-30 sq m/person. About 600 W/sq m of electricity would be consumed by the artificial lighting required; temperature, irradiance, photoperiod, CO2 levels, humidity, and wind velocity are all controlled. A rock wool plant support allows direct seeding, and densities of up to 10,000 plants/sq m. Densities of up to 2000 plants/sq m appear to increase seed yields.

Salisbury, Frank B.↗

NASA NTRS2000

The Application of TOMS Ozone, Aerosol and UV-B Data to Madagascar Air Quality Determination

Total Ozone Mapping Spectrometer (TOMS) data products for the area of Madagascar are presented. In addition to total ozone, aerosols and UV-B tropospheric ozone results are shown from 1979 to the present. Tropospheric ozone over Africa and Madagascar is enhanced by 10 to 15 DU in October. This maximum coincides with the time of maximum biomass area burning in Africa and Madagascar. Ozone observations were made from 1979 to 1999 using the TOMS tropospheric ozone convective cloud differential method. As a result of easterly trade winds, ozone originating on Madagascar is transported to the west over the Mozambique Channel. In El Nino years higher level westerly winds descend to transport low level ozone easterly. This results in African continental ozone being transported east of Madagascar. Long range transport of African ozone is observed during El Nino periods. The potential of TOMS and other space data for use in public education and research on Madagascar air quality is demonstrated.

Aikin, A.C.↗

NASA NTRS2020

Wildfire Smoke Particle Properties and Evolution, from Space-Based Multi-Angle Imaging

Emitted smoke composition is determined by properties of the biomass burning source and ambient ecosystem. However, conditions that mediate the partitioning of black carbon (BC) and brown carbon (BrC) formation, as well as the spatial and temporal factors that drive particle evolution, are not understood adequately for many climate and air-quality related modeling applications. In situ observations provide considerable detail about aerosol microphysical and chemical properties, although sampling is extremely limited. Satellites offer the frequent global coverage that would allow for statistical characterization of emitted and evolved smoke, but generally lack microphysical detail. However, once properly validated, data from the National Aeronautics and Space Administration (NASA) Earth Observing System’s Multi-Angle Imaging Spectroradiometer (MISR) instrument can create at least a partial picture of smoke particle properties and plume evolution. We use in situ data from the Department of Energy’s Biomass Burning Observation Project (BBOP) field campaign to assess the strengths and limitations of smoke particle retrieval results from the MISR Research Aerosol (RA) retrieval algorithm. We then use MISR to characterize wildfire smoke particle properties and to identify the relevant aging factors in several cases, to the extent possible. The RA successfully maps qualitative changes in effective particle size, light absorption, and its spectral dependence, when compared to in situ observations. By observing the entire plume uniformly, the satellite data can be interpreted in terms of smoke plume evolution, including size-selective deposition, new-particle formation, and locations within the plume where BC or BrC dominates.

Noyes, Katherine Junghenn↗

NASA NTRS2012

Cloud Screening and Quality Control Algorithm for Star Photometer Data: Assessment with Lidar Measurements and with All-sky Images

This paper presents the development and set up of a cloud screening and data quality control algorithm for a star photometer based on CCD camera as detector. These algorithms are necessary for passive remote sensing techniques to retrieve the columnar aerosol optical depth, delta Ae(lambda), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of delta Ae() and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable Ae(lambda) and W fluctuations caused by the atmospheric turbulence, the data are averaged on 30 min. The algorithm is applied to the star photometer deployed in the city of Granada (37.16 N, 3.60 W, 680 ma.s.l.; South-East of Spain) for the measurements acquired between March 2007 and September 2009. The algorithm is evaluated with correlative measurements registered by a lidar system and also with all-sky images obtained at the sunset and sunrise of the previous and following days. Promising results are obtained detecting cloud-affected data. Additionally, the cloud screening algorithm has been evaluated under different aerosol conditions including Saharan dust intrusion, biomass burning and pollution events.

data quality control algorithm↗