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AMPR is an airborne instrument that flew aboard the NASA ER-2 during the OLYMPEX/RADEX field campaign in late 2015. This poster's goal is to explore how well the instrument can retrieve near-surface wind speed over the ocean.
As NASA prepares to launch ICESAT-II there are significant efforts underway to plan for aircraft under-flights to calibrate the onboard lasers and to validate data products. This presentation will begin with an overview of recent airborne science missions including Operation Ice Bridge that have focused observations on the arctic. This will be followed by results from recent NASA sponsored unmanned aircraft missions and a discussion how these new observing technologies can contribute to future research in the Arctic. Information on recent icing wind tunnel testing and comparisons with icing prediction models will also be shared leading to discussions on specific challenges in adapting systems to harsh arctic conditions.
NASA Thermal Infrared Multispectral Scanner (TIMS) and CSIRO Mid Infrared Airborne C02 Laser Spectrometer (MIRACO2LAS) data were acquired over the Mount Fitton area, South Australia, in order to evaluate their combined use for geological mapping and mineral exploration.
In the summer of 1994, the 0.07cm -1 resolution infrared Airborne Emission Spectrometer (AES) acquired spectral data over two wildfires, one in central Oregon on August 3 and the other near San Luis Obispo, California on August 15. The spectrometer was on board a NASA DC-8 research aircraft, flying at an altitude of 12 km.
The status of the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is reported at the beginning of the current flight season. Sensor performance based on laboratory measurements and trend data as well as a review of end of 1995 season performance is given as a measure of expected performance during the 1996 season which began in March of 1996. Recent improvements to the sensor's internal calibration source and measurements of instrument parameters are described. The effects of these enhancements on the reported data are also discussed along with and update onflight schedules and plans for 1996 flight operations.
In this paper, we describe an experiment to calibrate the Optical Sensor on board the Japanese Earth Resources Satellite-1 with data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This experiment establishes the suitability of AVIRIS for the calibration of spaceborne sensors in the 400 to 2500 nm spectral region.
The optically active component of dissolved organic material in aquatic ecosystems, or colored dissolved organic matter (CDOM), is represented by the coefficient of absorption due to the dissolved aquatic constituents at 440 nm, aCDOM(440). Remote sensing of aCDOM(440) enables characterization of ecosystem processes and aids in retrieval of chlorophyll a, a proxy for phytoplankton biomass. Spectrally adjacent band-ratio domains, e.g., blue to green, have previously been applied for remote sensing of aCDOM(440) in coastal and oceanic waters with similar results compared to more complex semi-analytical algorithms. Estimation of aCDOM(440) from ratios of the most spectrally separated ocean color wavebands (end members), e.g., ultraviolet (UV) to near-infrared (NIR), termed end-member analysis (EMA), has previously been shown to increase the accuracy of global aCDOM(440) retrievals from in-water observations of diffuse attenuation and to enable a unified algorithmic perspective without requiring regional adjustment of internal bio-optical parameters. EMA of above-water observations is evaluated herein, with a focus on coastal and inland waters in which increasing optical complexity and likelihood of bottom reflectance challenge the oceanic algorithms developed for deep and optically simple (case-1) waters. Analysis herein of three independent, in situ, bio-optical datasets indicates significant correlation between aCDOM(440) and end-member band ratios (next-generation 320 and 780 nm or legacy 412 and 670 nm ratios) with a coefficient of determination, R2, of 0.87 (log-scale) or higher based on a dataset spanning the dynamic range of global, conservative water bodies. For applicable wavelengths, EMA algorithms are shown to agree with case-1 relationships and to produce consistent log-scale uncertainties across more than three orders of magnitude in aCDOM(440) values (0.001–2.305 m1). EMA using UV and NIR wavelengths (320 and 780 nm) is applied to low-altitude airborne observations and satisfies 25% uncertainty based on unbiased percent differences (UPDs) within each of three dissimilar match-up sites ranging in aCDOM(440) from 0.02–0.57 m1. Results demonstrate that EMA is a useful and robust approach for the remote sensing of aCDOM(440) in coastal and inland waters, which are generally shallower, contain more optically complex environments, and span a greater range in aCDOM(440) than oceanic waters.
This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar – third generation (APR-3) passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ∼10 %–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two-thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g/sq. m and almost all CWPs (99 %) between 6 to 445 g/sq. m. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP), with more than 70 % of raining clouds having less than 10 g/sq. m of rainwater. In heavier warm rain (i.e., rain rate exceeding 40 mm/h or 1000 mm/d), however, RWP is observed to exceed 2500 g/sq. m. CWP (RWP) is found to be approximately 30 g/sq. m (7 g/sq. m) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud–precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.
Alaska’s boreal forest is facing unprecedented challenges under rapid climate warming (increasingly severe fires, droughts, pest/disease outbreaks) that may destabilize its function as a global carbon sink. Forests near Fairbanks may be especially vulnerable, impacting air quality and ecosystem services. We combined GT (ground truthing) with Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) images collected by the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) program (2017-2019) to assess landcover change at five recently burned sites (2001-2019) of different fire severities and moisture regimes within 30 miles of Fairbanks. GT included tree seedling counts, understory % cover and >50% leaf canopy color assessment. 36 circular plots (1/30 ha radius) including 6 moderate to severely burned plots were selected across sites. 31 additional sites including 12 burned sites were geotagged in photos. AVIRIS images were processed from 29 spectral bands selected to identify changes in chlorophyll and water content. Images were segmented into natural boundaries (polygons) using ENVI 5.5 software. A spectral library of 8 AVIRIS bands with high between-class/low within-class variation was used in two random forest models to predict vegetation classes (model 1: 12 classes, model 2: 14 classes) in each AVIRIS scene, using 20% of the data as training data. Model 2 classified 20% more polygons overall, but only 42% of GT/geotagged polygons were correctly classified by both models. More forest sites were correctly classified (63%) than open vegetation (32%) or post-fire sites (46%). 50% of aspen forest and post-fire polygons were misclassified as shrubland. GT revealed that post-fire plots supported 134,000 (± 48,000) tree seedlings and saplings ha-1 (0.2 - 4 m height, 64% deciduous) versus 2500 (± 2100) shrubs ha-1 (1-6 m height). > 50% canopy browning was observed in conifer forest (8 plots) with no signs of insect infestation. Canopy herbivory > 50% (leaf miner, leaf beetle) and moose herbivory of tree bark was seen across aspen sites. Our study suggests: 1) low canopy vegetation presents challenges for improved landcover classification, and 2) aspen forest should be differentiated in vegetation maps which would aid in tracking herbivory.
The cloud drop effective radius (Re) of the drop size distribution derived from passive satellite sensors is a key variable used in climate research. Validation of these satellite products has often taken place under stratiform cloud conditions that favor the assumption of cloud horizontal homogeneity used by the retrieval techniques. However, many studies have noted concerns with respect to significant biases in retrieved Re arising from cloud heterogeneity, for example, in cumulus cloud fields. Here, we examine data collected during the 2019 “Cloud, Aerosol and Monsoon Processes Philippines Experiment” (CAMP2Ex), which, in part, targeted the objective of providing the first detailed evaluation of Re retrieved across multiple platforms and techniques in a cumulus and congestus cloud region. Our evaluation consists of cross-comparisons of Re between the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements from both the NASA P-3 and Learjet aircraft that are all taken in close spatiotemporal proximity to the same cloud fields. A particular advantage of our approach lies in the capability of the RSP to retrieve Re using a bi-spectral MODIS approach and a polarimetric approach, which allows for the evaluation of bi-spectral and polarimetric Re retrievals from an airborne perspective using the same samples. Averaged over all P-3 flight segments examined here for warm clouds, the RSP polarimetric method, the in situ method, and the bias-adjusted MODIS method of Fu et al. (2019) show a comparable median (mean ± standard deviation) for the Re samples of 9.6 (10.2 ± 4.0) µm, 11.0 (13.6 ± 11.3) µm, and 10.4 (10.8 ± 3.8) µm, respectively. These values are far lower than the values of 15.1 (16.2 ± 5.5) µm and 17.2 (17.7 ± 5.7) µm from the bi-spectral retrievals of RSP and MODIS, respectively. Similar results are observed when Re is segregated by cloud-top height and in detailed case studies. The clouds sampled during CAMP2Ex consist of mostly small (mean transect length ∼ 1.4 km) and low clouds (mean cloud-top height ∼ 1 km), which had more numerous small clouds than the trade wind cumuli sampled in past field campaigns such as Rain in Shallow Cumulus over the Ocean (RICO) and the Indian Ocean Experiment (INDOEX). The overestimates of Re from the RSP bi-spectral technique compared with the polarimetric technique increased as cloud size and cloud optical depth decreased. Drizzle, cloud-top bumpiness, and solar zenith angle, however, are not closely correlated with the overestimate of bi-spectral Re. For shallow clouds that dominated the liquid cloud cover for the CAMP2Ex region and period, we show that 3-D radiative transfer and cloud heterogeneity, particularly for the optically thin and small clouds, appear to be the leading cause of the large positive biases in bi-spectral retrievals. Because this bias varies with the underlying structure of the cloud field, caution continues to be warranted in studies that use bi-spectral Re retrievals in cumulus cloud fields.
During boreal summer, the circulation of the northern hemisphere upper troposphere and lower stratosphere (UTLS) is dominated by two quasi-stationary anticyclonic eddies driven by latent heat released in the troposphere: the Asian Monsoon Anticyclone (AMA) and the North American Monsoon Anticyclone (NAMA). Deep convection rapidly transports boundary layer air to the UTLS, and the monsoon anticyclones act to partially confine and isolate air from the surrounding atmosphere. The interactions between chemistry and circulation within these systems are crucial for understanding the sensitivity of these regions to changes in climate and air quality. Satellite observations show that within the summer monsoon anticyclones is a region of enhanced water vapor, aerosol, and trace gases of boundary layer origin near the tropopause level. However, satellite data do not provide necessary details for process studies and for diagnosing their representations in models. In response to this need, NASA conducted two airborne missions to investigate these summer monsoon systems and their roles in climate. NASA Earth Venture Suborbital 3 (EVS-3) mission called Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) completed two deployments in Salina, Kansas during summers 2021 and 2022 to study the NAMA. The Asian Summer Monsoon Chemical and Climate Impact Project (ACCLIP) was a multi-agency effort to investigate the AMA from Osan, South Korea in summer 2022.
Risks associated with dust hazards are often underappreciated, a gap between the knowledge pool and public awareness that can be costly for impacted communities. This study reviews the emission sources and chemical, physical, and biological characteristics of airborne soil particles (dust) and their effects on human and environmental health and safety in the Pan-American region. American dust originates from both local sources (western United States, northern Mexico, Peru, Bolivia, Chile, and Argentina) and long-range transport from Africa and Asia. Dust properties, as well as the trends and interactions with criteria air pollutants, are summarized. Human exposure to dust is associated with adverse health effects, including asthma, allergies, fungal infections, and premature death. In the Americas, a well-documented and striking effect of soil dust is its association with Coccidioidomycosis, commonly known as Valley fever, an infection caused by inhalation of soil-dwelling fungi unique to this region. Besides human health, dust affects environmental health through nutrients that increase phytoplankton biomass, contaminants that diminish water supply and affect food (crops/fruits/vegetables and ready-to-eat meat), spread crop and marine pathogens, cause Valley fever among domestic and wild animals, transport heavy metals, radionuclides and microplastics, and reduce solar and wind power generation. Dust is also a safety hazard to road transportation and aviation, in the southwestern US where blowing dust is one of the deadliest weather hazards. To mitigate the harmful effects, coordinated regional and international efforts are needed to enhance dust observations and prediction capabilities, soil conservation measures, and Valley fever and other disease surveillance.
This presentation outlines work to date analyzing airborne observations to improve a satellite observation-based hydroxyl radical (OH) column product by interrogating model chemistry processes. Anderson et al., 2023, established a machine learning method to combine satellite observations of O3, CO, NO2, HCHO, H2O, and aerosol optical depth, along with analyzed sea surface temperatures, for the prediction of tropospheric column OH (TCOH). Since the TCOH machine learning model was trained on output from the MERRA-2 GMI model simulation, we seek to identify if any model deficiencies may yield errors in the TCOH prediction. By evaluating F0AM box model simulations and neural networks trained to reproduce in situ OH concentrations, together with output reaction rates and interpretability metrics, respectively, we validate the Anderson et al. TCOH model and assess the largest contributors to uncertainties.
Chloride (Cl−) displacement from sea salt particles is an extensively studied phenomenon with implications for human health, visibility, and the global radiation budget. Past works have investigated Cl− depletion over the northwest Atlantic (NWA); however, an updated, multi-seasonal, and geographically expanded account of sea salt reactivity over the region is needed. This study uses chemically resolved mass concentrations and meteorological data from the airborne Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) to quantify seasonal, spatial, and meteorological trends in Cl− depletion and to explore the importance of quantifying (1) non-sea salt sources of Na+ and (2) mass concentrations of lost Cl− (instead of relative amounts displaced). Lost Cl− mass concentrations are lowest in December–February and March, moderate around Bermuda in June, and highest in May (median losses of 0.04, 0.04, 0.66, and 1.76µgm−3, respectively), with losses in May that are high enough to potentially accelerate tropospheric oxidation rates. Inorganic acidic species can account for all Cl− depletion in December–February, March, and June near Bermuda but none of the lost Cl− in May, suggesting that organic acids may be of importance for Cl− displacement in certain months. Contributions of dust to Na+ are not important seasonally but may cause relevant overestimates of lost Cl− in smoke and dust plumes. Higher percentages of Cl− depletion often do not correspond to larger mass concentrations of lost Cl−, so it is highly recommended to quantify the latter to place depletion reactions in context with their role in atmospheric oxidation and radiative forcing.
The Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) is a new imaging polarimeter instrument capable of sampling a single Earth target from up to 120 viewing angles, in four spectral channels, and in three linear polarization states across a 114° field of view (FOV). AirHARP is telecentric in the image space and simultaneously images three linear polarization states with no moving parts. These two aspects of the design allow for a simple and efficient quantitative calibration. Using coefficients derived at the center of the lens and the detector flatfields, we can calibrate the entire AirHARP sensor in a variety of laboratory, field, and space environments. We show that this telecentric calibration technique yields a 1σ absolute uncertainty of 0.25 % in degree of linear polarization (DOLP) in the laboratory for all channels and for pixels around the optical axis. To validate across the FOV, we compare our multi-angle reflectance and polarization data with the Research Scanning Polarimeter (RSP) over targets sampled during the NASA Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We use the error-normalized difference technique to estimate how well the instruments compare relative to their error models. We find that AirHARP and the RSP reasonably agree for reflectance and DOLP within 2 standard deviations of their mutual uncertainty at 550, 670, and 870 nm and over a limited set of ocean and desert scenes. This calibration technique makes the Hyper-Angular Rainbow Polarimeter (HARP) design attractive for new spaceborne climate missions: HARP CubeSat (2020–2022), HARP2 (2024–) on the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and beyond.
Deep convection in the Asian summer monsoon (ASM) rapidly transports boundary layer pollutants including ozone-depleting substances into the upper troposphere and lower stratosphere (UTLS). The confinement of the seasonal anticyclonic circulation in the UTLS over Asia initially traps the pollutants near the tropopause level, but air masses are subsequently transported throughout the global stratosphere and impact Earth’s climate. Characterizing the transport pathways and processes of these pollutants are critical for identifying source regions and quantifying regional contributions to global stratospheric concentrations. The Asian summer monsoon Chemical and Climate Impact Project (ACCLIP) was conducted in summer 2022 using two high-altitude aircraft (NSF NCAR Gulfstream V and the NASA WB-57) to obtain UTLS chemical composition and aerosol distribution for characterizing the impact of the ASM on global chemistry and climate. Backward trajectories from ACCLIP flight tracks were traced through time-varying, satellite-derived global convective cloud top altitude fields to investigate the convective air mass origins of the sampled air parcels. Analyses reveal that air masses lofted by convection within the ASM (South Asian summer monsoon) and near the edge of the of the ASM anticyclone (East Asian summer monsoon) contributed to the airborne observations. In this study, we present a summary of the transport statistics of UTLS air masses sampled during ACCLIP and discuss the implications of these results on our understanding of the role of ASM in global constituent transport.
Abstract Tweezers based on optical, electric, magnetic, and acoustic fields have shown great potential for contactless object manipulation. However, current tweezers designed for manipulating millimeter‐sized objects such as droplets, particles, and small animals exhibit limitations in translation resolution, range, and path complexity. Here, a novel acoustic vortex tweezers system is introduced, which leverages a unique airborne acoustic vortex end effector integrated with a three‐degree‐of‐freedom (DoF) linear motion stage, for enabling contactless, multi‐mode, programmable manipulation of millimeter‐sized objects. The acoustic vortex end effector utilizes a cascaded circular acoustic array, which is portable and battery‐powered, to generate an acoustic vortex with a ring‐shaped energy pattern. The vortex applies acoustic radiation forces to trap and spin an object at its center, simultaneously protecting this object by repelling other materials away with its high‐energy ring. Moreover, The vortex tweezers system facilitates contactless, multi‐mode, programmable object surfing, as demonstrated in experiments involving trapping, repelling, and spinning particles, translating particles along complex paths, guiding particles around barriers, translating and rotating droplets containing zebrafish larvae, and merging droplets. With these capabilities, It is anticipated that the tweezers system will become a valuable tool for the automated, contactless handling of droplets, particles, and bio‐samples in biomedical and biochemical research.
Stratigraphic units can influence the fate and transport of subsurface contaminants within groundwater. Units having coarse-grained sediments act as preferential flow pathways, and therefore can accelerate the transport of contaminants to reach human and ecological receptors. At legacy waste sites, detailed knowledge of subsurface stratigraphy can be used for effective monitoring and remediation planning to help minimize risk to human health and the environment. Airborne electromagnetic (AEM) methods can non-invasively provide information on kilometer-scale or larger subsurface stratigraphic features and fill informational gaps in directly sampled data from sparsely located boreholes. In this paper, we present inversion results of a 412 line-km frequency-domain AEM survey to delineate subsurface stratigraphic features at the Hanford Site, located in southeastern Washington State. The inversion was performed using a massively parallel 3D electromagnetic modeling and inversion code, where the modeling is based on solving frequency-domain Maxwell’s equations using an unstructured-mesh finite-element method and the inversion employs a Gauss-Newton optimization scheme. The results are compared to an underlying geologic framework model (GFM), built by interpolating contact depths of stratigraphic units interpreted from site borehole datasets. In areas with good borehole coverage, the inversion results show a good match with the GFM to a depth of about 60 m. Outside of these areas, the inversion results exhibit inconsistencies from the assumptions made to create the GFM, demonstrating that the AEM survey results can be used to improve the understanding of the geological conceptual model.