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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Airborne 2-Micron Double-Pulsed Integrated Path Differential Absorption Lidar for Column CO2 Measurement

Double-pulse 2-micron lasers have been demonstrated with energy as high as 600 millijouls and up to 10 Hz repetition rate. The two laser pulses are separated by 200 microseconds and can be tuned and locked separately. Applying double-pulse laser in DIAL system enhances the CO2 measurement capability by increasing the overlap of the sampled volume between the on-line and off-line. To avoid detection complicity, integrated path differential absorption (IPDA) lidar provides higher signal-to-noise ratio measurement compared to conventional range-resolved DIAL. Rather than weak atmospheric scattering returns, IPDA rely on the much stronger hard target returns that is best suited for airborne platforms. In addition, the IPDA technique measures the total integrated column content from the instrument to the hard target but with weighting that can be tuned by the transmitter. Therefore, the transmitter could be tuned to weight the column measurement to the surface for optimum CO2 interaction studies or up to the free troposphere for optimum transport studies. Currently, NASA LaRC is developing and integrating a double-Pulsed 2-micron direct detection IPDA lidar for CO2 column measurement from an airborne platform. The presentation will describe the development of the 2-micron IPDA lidar system and present the airborne measurement of column CO2 and will compare to in-situ measurement for various ground target of different reflectivity.

Singh, Upendra N.↗
NASA NTRS2013

Discriminating Phytoplankton Functional Types (PFTs) in the Coastal Ocean Using the Inversion Algorithm Phydotax and Airborne Imaging Spectrometer Data

There is a need in the Biological Oceanography community to discriminate among phytoplankton groups within the bulk chlorophyll pool to understand energy flow through ecosystems, to track the fate of carbon in the ocean, and to detect and monitor-for harmful algal blooms (HABs). The ocean color community has responded to this demand with the development of phytoplankton functional type (PFT) discrimination algorithms. These PFT algorithms fall into one of three categories depending on the science application: size-based, biogeochemical function, and taxonomy. The new PFT algorithm Phytoplankton Detection with Optics (PHYDOTax) is an inversion algorithm that discriminates taxon-specific biomass to differentiate among six taxa found in the California Current System: diatoms, dinoflagellates, haptophytes, chlorophytes, cryptophytes, and cyanophytes. PHYDOTax was developed and validated in Monterey Bay, CA for the high resolution imaging spectrometer, Spectroscopic Aerial Mapping System with On-board Navigation (SAMSON - 3.5 nm resolution). PHYDOTax exploits the high spectral resolution of an imaging spectrometer and the improved spatial resolution that airborne data provides for coastal areas. The objective of this study was to apply PHYDOTax to a relatively lower resolution imaging spectrometer to test the algorithm's sensitivity to atmospheric correction, to evaluate capability with other sensors, and to determine if down-sampling spectral resolution would degrade its ability to discriminate among phytoplankton taxa. This study is a part of the larger Hyperspectral Infrared Imager (HyspIRI) airborne simulation campaign which is collecting Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery aboard NASA's ER-2 aircraft during three seasons in each of two years over terrestrial and marine targets in California. Our aquatic component seeks to develop and test algorithms to retrieve water quality properties (e.g. HABs and river plumes) in both marine and in-land water bodies. Results presented are from the 10 April 2013 overflight of the Monterey Bay region and focus primarily on the first objective - sensitivity to atmospheric correction. On-going and future work will continue to evaluate if PHYDOTax can be applied to historical (SeaWiFS and MERIS), existing (MODIS, VIIRS, and HICO), and future (PACE, GEO-CAPE, and HyspIRI) satellite sensors. Demonstration of cross-platform continuity may aid in calibration and validation efforts of these sensors.

Biological Oceanography↗
NASA NTRS2016

Preliminary Assessment of Operational Hazards and Safety Requirements for Airborne Trajectory Management (ABTM) Roadmap Applications

A set of five developmental steps building from the NASA TASAR (Traffic Aware Strategic Aircrew Requests) concept are described, each providing incrementally more efficiency and capacity benefits to airspace system users and service providers, culminating in a Full Airborne Trajectory Management capability. For each of these steps, the incremental Operational Hazards and Safety Requirements are identified for later use in future formal safety assessments intended to lead to certification and operational approval of the equipment and the associated procedures. Two established safety assessment methodologies that are compliant with the FAA's Safety Management System were used leading to Failure Effects Classifications (FEC) for each of the steps. The most likely FEC for the first three steps, Basic TASAR, Digital TASAR, and 4D TASAR, is "No effect". For step four, Strategic Airborne Trajectory Management, the likely FEC is "Minor". For Full Airborne Trajectory Management (Step 5), the most likely FEC is "Major".

Cotton, William B.↗
NASA NTRS2015

An Overview of the Challenges With and Proposed Solutions for the Ingest and Distribution Processes for Airborne Data Management

The current data management practices for NASA airborne field projects have successfully served science team data needs over the past 30 years to achieve project science objectives, however, users have discovered a number of issues in terms of data reporting and format. The ICARTT format, a NASA standard since 2010, is currently the most popular among the airborne measurement community. Although easy for humans to use, the format standard is not sufficiently rigorous to be machine-readable. This makes data use and management tedious and resource intensive, and also create problems in Distributed Active Archive Center (DAAC) data ingest procedures and distribution. Further, most DAACs use metadata models that concentrate on satellite data observations, making them less prepared to deal with airborne data.

Beach, Aubrey↗
NASA NTRS2016

Investigating Ozone Sources in California Using AJAX Airborne Measurements and Models: Implications for Stratospheric Intrusion and Long Range Transport

High ozone concentrations at low altitudes near the surface were detected from airborne Alpha Jet Atmospheric eXperiment (AJAX) measurements on May 30, 2012. We investigate the causes of the elevated ozone concentrations using the airborne measurements and various models. GEOSchem and WRF-STILT model simulations show that the contribution from local sources is small. From MERRA reanalysis, it is found that high potential vorticity (PV) is observed at low altitudes. This high PV appears to be only partially coming through the stratospheric intrusions because the air inside the high PV region is moist, which shows that mixing appears to be enhanced in the low altitudes. Considering that diabatic heating can also produce high PV in the lower troposphere, high ozone is partially coming through stratospheric intrusion, but this cannot explain the whole ozone concentration in the target areas of the western U.S. A back-trajectory model is utilized to see where the air masses originated. The air masses of the target areas came from the lower stratosphere (LS), upper (UT), mid- (MT), and lower troposphere (LT). The relative number of trajectories coming from LS and UT is low (7.7% and 7.6%, respectively) compared to that from LT (64.1%), but the relative ozone concentration coming from LS and UT is high (38.4% and 20.95%, respectively) compared to that from LT (17.7%). The air mass coming from LT appears to be mostly coming from Asia. Q diagnostics show that there is sufficient mixing along the trajectory to indicate that ozone from the different origins is mixed and transported to the western U.S. This study shows that high ozone concentrations can be detected by airborne measurements, which can be analyzed by integrated platforms such as models, reanalysis, and satellite data.

gas composition↗
NASA NTRS2015

Investigation of Ozone Sources in California Using AJAX Airborne Measurements and Models: Implications for Stratospheric Intrusion and Long Range Transport

High ozone concentrations at low altitudes near the surface were detected from airborne Alpha Jet Atmospheric eXperiment (AJAX) measurements on May 30, 2012. We investigate the causes of the elevated ozone concentrations using the airborne measurements and various models. GEOS-chem and WRF-STILT model simulations show that the contribution from local sources is small. From MERRA reanalysis, it is found that high potential vorticity (PV) is observed at low altitudes. This high PV appears to be only partially coming through the stratospheric intrusions because the air inside the high PV region is moist, which shows that mixing appears to be enhanced in the low altitudes. Considering that diabatic heating can also produce high PV in the lower troposphere, high ozone is partially coming through stratospheric intrusion, but this cannot explain the whole ozone concentration in the target areas of the western U.S. A back-trajectory model is utilized to see where the air masses originated. The air masses of the target areas came from the lower stratosphere (LS), upper (UT), mid- (MT), and lower troposphere (LT). The relative number of trajectories coming from LS and UT is low (7.7 and 7.6, respectively) compared to that from LT (64.1), but the relative ozone concentration coming from LS and UT is high (38.4 and 20.95, respectively) compared to that from LT (17.7). The air mass coming from LT appears to be mostly coming from Asia. Q diagnostics show that there is sufficient mixing along the trajectory to indicate that ozone from the different origins is mixed and transported to the western U.S. This study shows that high ozone concentrations can be detected by airborne measurements, which can be analyzed by integrated platforms such as models, reanalysis, and satellite data.

low altitude↗
NASA NTRS2017

A Study of Reflected Sonic Booms Using Airborne Measurements

In support of ongoing efforts to bring commercial supersonic flight to the public, the Sonic Booms in Atmospheric Turbulence (SonicBAT) flight test was conducted at NASA Armstrong Flight Research Center. During this test, airborne sonic boom measurements were made using an instrumented TG-14 motor glider, called the Airborne Acoustic Measurement Platform (AAMP).During the flight program, the AAMP was consistently able to measure the sonic boom wave that was reflected off of the ground, in addition to the incident wave, resulting in the creation of a completely unique data set of airborne sonic boom reflection measurements. This paper focuses on using this unique data set to investigate the ability of sonic boom modeling software to calculate sonic boom reflections. Because the algorithms used to model sonic boom reflections are also used to model the secondary carpet and over the top booms, the use of actual flight data is vital to improving the understanding of the effects of sonic booms outside of the primary carpet. Understanding these effects becomes especially important as the return of commercial supersonic approaches, as well as ensuring the accuracy of mission planning for future experiments.

flight research↗
NASA NTRS2016

Lidar-Based Estimates of Above-Ground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 +/- 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 +/- 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 +/- 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log-linear model result (63.29 +/-1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log-linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon,−40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., N2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10 +/- 0.88 t/ha, and the total biomass for the country, given a total forest area of 688,096 sq km, is 3.65 +/- 0.06 Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152 Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06 Gt) by 19%.

Hybrid 3-Phase sampling↗
NASA NTRS2017

Airborne Hyperspectral Sensing of Monitoring Harmful Algal Blooms in the Great Lakes Region: System Calibration and Validation

Harmful algal blooms (HABs) in Lake Erie have been prominent in recent years. The bloom in 2014 reached a severe level causing the State of Ohio to declare a state of emergency. At that time NASA Glenn Research Center was requested by stakeholders to help monitor the blooms in Lake Erie. Glenn conducted flights twice a week in August and September and assembled and distributed the HAB information to the shoreline water resource managers using its hyperspectral imaging sensor (in development since 2006), the S􀂱3 Viking aircraft, and funding resources from the NASA Headquarters Earth Science Division. Since then, the State of Ohio, National Oceanic and Atmospheric Administration (NOAA), and U.S. Environmental Protection Agency (EPA) have elevated their funding and activities for observing, monitoring, and addressing the root cause of HABs. Also, the communities and stakeholders have persistently requested NASA Glenn􀂶s participation in HAB observation. Abundant field campaigns and sample analyses have been funded by Ohio and NOAA, which provided a great opportunity for NASA to advance science and airborne hyperspectral remote sensing economically. Capitalizing on this opportunity to advance the science of algal blooms and remote sensing, NASA Glenn conducted the Airborne Hyperspectral Observation of harmful algal blooms campaign in 2015 that was, in many respects, twice as large as the 2014 campaign. Focusing mostly on Lake Erie, but also including other small inland lakes and the Ohio River, the campaign was conducted in partnership with a large number of partners specializing in marine science and remote sensing. Airborne hyperspectral observation of HABs holds promise to distinguish potential HABs from nuisance blooms, determine their concentrations, and delineate their movement in an augmented spatial and temporal resolution and under clouds􀂲all of which are excellent complements to satellite observations. Working with collaborators at several Ohio and Michigan institutions as well as one in South Dakota and one in Alabama, this effort was able to provide next-day georeferenced estimates of cyanobacteria and scum concentrations. Very prompt processing and analysis of the hyperspectral imagery is necessary for the information to be acted upon. For example, a next-day report of an overflight over the Ohio River indicated that a bloom could be present as far downstream as the Cincinnati intake, but the Ohio EPA had not received visual reports of a bloom that far downstream. Water samples were obtained at the Cincinnati water intake, based on the flight data, and detected microcystins in the source water. The flight data helped State and municipal authorities realize the potential extent of that bloom, and triggered response sampling, before the visual river-wide scums started forming. The present document describes the process that was utilized to take raw remote sensing data and create information products; this includes system calibration and validation, efforts to correct atmospheric effects, and algorithms that produce the data products. Furthermore, successful research into improved algorithms for expanding the capability to delineate in water constituents is included. Finally, comparisons that show expected relationships between ground-based measurements and hyperspectral imager version 2 (HSI2) data results are presented, giving confidence in the remote sensing products.

Hyperspectral Imaging↗
NASA NTRS2017

A Study of Reflected Sonic Booms Using Airborne Measurements

In support of ongoing efforts to bring commercial supersonic flight to the public, the Sonic Booms in Atmospheric Turbulence (SonicBAT) flight test conducted at NASA Armstrong Flight Research Center. During this test, airborne sonic boom measurements were made using an instrumented TG-14 motor glider, called the Airborne Acoustic Measurement Platform (AAMP).During the flight program, the AAMP was consistently able to measure the sonic boom wave that was reflected off of the ground, in addition to the incident wave, resulting in the creation of a completely unique data set of airborne sonic boom reflection measurements.

sonic booms↗
NASA NTRS2018

Measurement of Atmospheric CO2 Column Concentrations to Cloud Tops With a Pulsed Multi-Wavelength Airborne Lidar

We have measured the column-averaged atmospheric CO2 mixing ratio to a variety of cloud tops by using an airborne pulsed multi-wavelength integrated-path differential absorption (IPDA) lidar. Airborne measurements were made at altitudes up to 13 km during the 2011, 2013 and 2014 NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) science campaigns flown in the United States West and Midwest and were compared to those from an in situ sensor. Analysis of the lidar backscatter profiles shows the average cloud top reflectance was approx. 5% for the CO2 measurement at 1572.335 nm except to cirrus clouds, which had lower reflectance. The energies for 1 micro-s wide laser pulses reflected from cloud tops were sufficient to allow clear identification of CO2 absorption line shape and then to allow retrievals of atmospheric column CO2 from the aircraft to cloud tops more than 90% of the time. Retrievals from the CO2 measurements to cloud tops had minimal bias but larger standard deviations when compared to those made to the ground, depending on cloud top roughness and reflectance. The measurements show this new capability helps resolve CO2 horizontal and vertical gradients in the atmosphere. When used with nearby full-column measurements to ground, the CO2 measurements to cloud tops can be used to estimate the partial-column CO2 concentration below clouds, which should lead to better estimates of surface carbon sources and sinks. This additional capability of the range-resolved CO2 IPDA lidar technique provides a new benefit for studying the carbon cycle in future airborne and space-based CO2 missions.

atmospheric↗
NASA NTRS2017

Airborne Demonstration of Atmospheric Oxygen Optical Depth Measurements with an Integrated Path Differential Absorption Lidar

We report on an airborne demonstration of atmospheric oxygen (O2) optical depth measurements with an Integrated Path Differential Absorption (IPDA) lidar using a fiber-based laser transmitter and photon counting detectors. Accurate atmospheric temperature and pressure measurements are needed for NASA's Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) mission. Since O2 is uniformly mixed in the atmosphere, its spectrum can be used to estimate the dry mixing ratio of CO2. In its airborne configuration, the IPDA lidar uses an Erbium Doped Fiber amplifier, a frequency doubler and single photon counting detectors to measure O2 absorption at multiple wavelengths near 765 nm. This instrument was deployed in 2013 and 2014 aboard NASA's DC-8 airborne laboratory as part of two campaigns to measure CO2 mixing ratios over a wide range of topography and weather conditions from altitudes ranging between 3 km and 13 km. In this paper we will review a summary of the results from our flights, discuss the errors that limit the precision and accuracy of the measurement and identify possible areas of improvement.

Methane↗
NASA NTRS2018

Characterization of Wildfire Emissions in California: Analysis of Airborne Measurements of Trace Gases from 2013 to 2016

Biomass burning, which includes wildfires, prescribed, and agricultural fires, is an important source of trace gases and particles, and can influence air quality on a local, regional, and global scale. Biomass burning emissions are an important source of several key trace gases including carbon dioxide (CO2) and methane (CH4). With the threat of wildfire events increasing due to changes in land use, increasing population, and climate change, the importance of characterizing wildfire emissions is vital. In this work we characterize trace gas emissions from 9 wildfire events in California between 2013 – 2016, in some cases with multiple measurements performed during different burn periods of a specific wildfire. During this period airborne measurements of CO2, CH4, water vapor (H2O), ozone (O3), and formaldehyde (HCHO) were made by the Alpha Jet Atmospheric eXperiment (AJAX). Located in the Bay Area of California, AJAX is a joint effort between NASA Ames Research Center and H211, LLC. AJAX makes in-situ airborne measurements of trace gases 2-4 times per month, resulting in 229 flights to date since 2011. Results presented include emission ratios (ER) of trace gases measured by AJAX during fire flights, and comparisons of ERs are made for each fire, which differ in time, location, burning intensity, and fuel type. We also use our airborne measurements to compare with photochemical grid model results to assess model approximations of plume transport and chemical evolution from select wildfires.

Parworth, Caroline L.↗
NASA NTRS2019

Characterization and Measurement of Spacecraft Airborne Particulate Matter

The International Space Station (ISS) gives a 6-member astronaut crew the ability to live and work in low Earth orbit. It is a unique indoor environment, which has served as both home and workplace to over 230 people since the year 2000. In this low gravity environment, smoke does not rise and cookie crumbs do not settle the way they do on Earth, causing airborne particulate matter, or aerosols, to behave differently and pose unique hazards for crew members. In its existence, virtually the same volume of ISS air has been continuously conditioned and ‘revitalized,’ including the removal of particles by filtration. While gaseous constituents of ISS air are monitored meticulously, sparse data exists on the indoor aerosols. The quantity and types of ISS airborne debris have been investigated in NASA’s Aerosol Sampling Experiment. Both active and passive samplers successfully collected airborne particulate matter in U.S. segments of the ISS, which were returned to Earth for characterization by microscopy and other techniques. The resulting data has informed the design of candidate particle instruments for spacecraft. In 2020, a reference-quality aerosol instrument will be flown to ISS, and will provide real-time data of particle concentrations in various modules. Smaller, more compact instruments will be necessary in future space missions, for example, in smaller vehicles, in habitats on lunar and planetary surfaces with ubiquitous dust, and also for use as wearable technology throughout missions. Miniaturized aerosol sensors, though lower fidelity than reference-quality instruments, can monitor the environment well when calibrated appropriately. Indoor air quality in spacecraft is fundamentally important to human health and comfort, and several particulate monitoring technologies will be at sufficient technology readiness levels for operational use within the next two years. Results of the Aerosol Sampling Experiment will be presented, along with the status of NASA’s aerosol instrument technology demonstrations on ISS.

Meyer, Marit E.↗
NASA NTRS2020

Instrument Offset Optimization Technique for Airborne Doppler Wind Lidar Profiling Algorithm

This paper presents the instrument offset optimization technique for the Doppler Aerosol Wind Lidar (DAWN) profiling algorithm at NASA Langley Research Center (LaRC). The 2 um Doppler wind Lidar system at LaRC has a long history of research development and airborne campaigns. The core of the wind Lidar data processing is Airborne Wind Profiling Algorithm for Doppler Wind Lidar (APOLO), which was developed at LaRC. The extraction of wind parameters from the airborne Doppler wind Lidar data is challenging due to many factors such as the speed of aircraft, and the errors in instrument installation and calibration. It is unavoidable to prevent the adverse impact of calibration error and instrument installation offset on the performance of wind parameter estimation in the algorithm. Due to the unsteady environment where the data are collected, even a small offset will result in nonsensical results in the parameter estimation process. A brief introduction of APOLO and the overview of the optimization techniques are presented in this paper.

Wind profile↗
NASA NTRSDate not supplied

Examining Soil Freeze/Thaw Remote Sensing Using NASA’s SLAPex Freeze/Thaw Airborne Campaign

Around half the exposed land of the Northern Hemisphere experiences seasonal freezing and thawing. The changes to the soil thermal, hydraulic, and mechanical properties are large and these, in turn, exert significant controls on the water, energy, and carbon cycles of the affected regions. Wide-area freeze/thaw discrimination via microwave remote sensing is possible using both passive and active techniques. We explore freeze/thaw remote sensing using observations from NASA’s Scanning L-band Active Passive (SLAP) airborne sensor during the SLAPex Freeze/Thaw campaign near Winnipeg, Canada in November, 2015. SLAP is an airborne simulator of the Soil Moisture Active Passive (SMAP) satellite mission. The SLAPex Freeze/Thaw dataset is a unique dataset that allows comparison of airborne-derived freeze/thaw state (plus soil moisture for thawed areas) against in-situ ground truth, satellite-derived freeze/thaw from SMAP, as well as tower-based passive microwave freeze/thaw observations. The sensitivity of passive and active microwave sensing to different depths of freezing and different dielectric models (when soil moisture is retrieved) is examined, and the spatial scaling of heterogeneous frozen/thawed pixels is also explored. Implications for the accuracy of satellite-based freeze/thaw discrimination are discussed.

Edward Kim↗
NASA NTRS2020

Reducing Errors in Velocity–Azimuth Display (VAD) Wind and Deformation Retrievals from Airborne Doppler Radars in Convective Environments

The present study describes methods to reduce the uncertainty of velocity–azimuth display (VAD) wind and deformation retrievals from downward-pointing, conically scanning, airborne Doppler radars. These retrievals have important applications in data assimilation and real-time data processing. Several error sources for VAD retrievals are considered here, including violations to the underlying wind field assumptions, Doppler velocity noise, data gaps, temporal variability, and the spatial weighting function of the VAD retrieval. Specific to airborne VAD retrievals, we also consider errors produced due to the radar scans occurring while the instrument platform is in motion. While VAD retrievals are typically performed using data from a single antenna revolution, other strategies for selecting data can be used to reduce retrieval errors. Four such data selection strategies for airborne VAD retrievals are evaluated here with respect to their effects on the errors. These methods are evaluated using the second hurricane nature run numerical simulation, analytic wind fields, and observed Doppler radar radial velocities. The proposed methods are shown to reduce the median absolute error of the VAD wind retrievals, especially in the vicinity of deep convection embedded in stratiform precipitation. The median absolute error due to wind field assumption violations for the along-track and for the across-track wind is reduced from 0.36 to 0.08 m s−1 and from 0.35 to 0.24 m s−1, respectively. Although the study focuses on Doppler radars, the results are equally applicable to conically scanning Doppler lidars as well.

Charles N Helms↗
NASA NTRS2020

Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016

In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. A strength of the comparison is a focus on the spatial distribution of a wider range of aerosol composition and optical properties than has been done previously. The sparse airborne observations are aggregated into approximately 2° grid boxes and into three vertical layers: 3–6 km, the layer from cloud top to 3 km, and the cloud-topped marine boundary layer. Simulated aerosol extensive properties suggest that the flight-day observations are reasonably representative of the regional monthly average, with systematic deviations of 30 % or less. Evaluation against observations indicates that all models have strengths and weaknesses, and there is no single model that is superior to all the others in all metrics evaluated. Whereas all six models typically place the top of the smoke layer within 0–500 m of the airborne lidar observations, the models tend to place the smoke layer bottom 300–1400 m lower than the observations. A spatial pattern emerges, in which most models underestimate the mean of most smoke quantities (black carbon, extinction, carbon monoxide) on the diagonal corridor between 16° S, 6° E, and 10° S, 0° E, in the 3–6 km layer, and overestimate them further south, closer to the coast, where less aerosol is present. Model representations of the above-cloud aerosol optical depth differ more widely. Most models overestimate the organic aerosol mass concentrations relative to those of black carbon, and with less skill, indicating model uncertainties in secondary organic aerosol processes. Regional-mean free-tropospheric model ambient single scattering albedos vary widely, between 0.83 and 0.93 compared with in situ dry measurements centered at 0.86, despite minimal impact of humidification on particulate scattering. The modeled ratios of the particulate extinction to the sum of the black carbon and organic aerosol mass concentrations (a mass extinction efficiency proxy) are typically too low and vary too little spatially, with significant inter-model differences. Most models overestimate the carbonaceous mass within the offshore boundary layer. Overall, the diversity in the model biases suggests that different model processes are responsible. The wide range of model optical properties requires further scrutiny because of their importance for radiative effect estimates.

Yohei Shinozuka↗