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The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a NASA-sponsored field campaign to study wintertime snowstorms focusing on East Coast cyclones. This large cooperative effort takes place during the winters of 2020–23 to study precipitation variability in winter cyclones to improve remote sensing and numerical forecasts of snowfall. Snowfall within these storms is frequently organized in banded structures on multiple scales. The causes for the occurrence and evolution of a wide spectrum of snowbands remain poorly understood. The goals of IMPACTS are to characterize the spatial and temporal scales and structures of snowbands, understand their dynamical, thermodynamical, and microphysical processes, and apply this understanding to improve remote sensing and modeling of snowfall. The first deployment took place in January–February 2020 with two aircraft that flew coordinated flight patterns and sampled a range of storms from the Midwest to the East Coast. The satellite-simulating ER-2 aircraft flew above the clouds and carried a suite of remote sensing instruments including cloud and precipitation radars, lidar, and passive microwave radiometers. The in situ P-3 aircraft flew within the clouds and sampled environmental and microphysical quantities. Ground-based radar measurements from the National Weather Service network and a suite of radars located on Long Island, New York, along with supplemental soundings and the New York State Mesonet ground network provided environmental context for the airborne observations. Future deployments will occur during the 2022 and 2023 winters. The coordination between remote sensing and in situ platforms makes this a unique publicly available dataset applicable to a wide variety of interests.

Aerosol properties are fundamentally different near clouds than away from clouds. This paper reviews the current state of knowledge of aerosol properties in the near-low-cloud environment and quantitatively compares them with aerosols far from clouds, according to remote sensing observations. It interprets observations of aerosol properties from different sensors using satellite, aircraft, and ground-based observations. The correlation (and anticorrelation) between proximity to cloud and aerosol properties is discussed. Retrieval artifacts in the near-cloud environment are demonstrated and quantified for different sensor attributes and environmental conditions. Finally, the paper describes the possible corrections for near-cloud enhancement in remote sensing retrievals. This study is timely in view of science definition studies for NASA’s Aerosol, Cloud, Convection and Precipitation (ACCP) mission, which will also seek to directly link aerosol properties to nearby clouds.

The current study investigates the diurnal cycle of dust and dust mixture loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud-Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite observations, CATS aerosol and dust and dust mixture loading observations exhibit reasonable quality but significant day–night inconsistency. To account for this day–night inconsistency in CATS data quality, the diurnal variability in dust and dust mixture characteristics is currently examined separately for daytime and nighttime periods. Based on an analysis of variance (ANOVA) analytical framework, pronounced diurnal variations in dust and dust mixture loading are generally uncovered during daytime periods and over terrestrial areas. The current study identifies statistically significant diurnal variability in dust and dust mixture loading over key dust sources, including the Bodélé Depression, the West African El Djouf, Rub' al-Khali desert, and western and southern North America, confirming the previous observation-based findings regarding the diurnal cycle of dust emission and underlying meteorological processes in these regions. Significant seasonal and diurnal variability in dust and dust mixture is identified over the Iraqi and Thar deserts. The identified significant diurnal cycles in dust mixture loading over the vegetated regions in the Amazon and tropical southern Africa are hypothesized to be driven by enhanced dust emission due to wildfires.

Using collocated NASA Cloud Physics Lidar (CPL) and Research Scanning Polarimeter (RSP) data from the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, a new observational-based method was developed which uses aK-means clustering technique to classify ice crystal habit types into seven categories: column, plates, rosettes, spheroids, and three different type of irregulars. Intercompared with the collocated SPEC, Inc., Cloud Particle Imager (CPI) data, the frequency of the detected ice crystal habits from the proposed method presented in the study agrees within 5% with the CPI-reported values for columns, irregulars, rosettes, and spheroids, with more disagreement for plates. This study suggests that a detailed ice crystal habit retrieval could be applied to combined space-based lidar and polarimeter observations such asCALIPSOand POLDER in addition to future missions such as the Aerosols, Clouds, Convection, and Precipitation (A-CCP).

We investigate the dust forecasts from two operational global atmospheric models in comparison with in situ and remote sensing measurements obtained during the AERosol properties – Dust (AER-D) field campaign. Airborne elastic backscatter lidar measurements were performed on board the Facility for Airborne Atmospheric Measurements during August 2015 over the eastern Atlantic, and they permitted us to characterise the dust vertical distribution in detail, offering insights on transport from the Sahara. They were complemented with airborne in situ measurements of dust size distribution and optical properties, as well as datasets from the Cloud–Aerosol Transport System (CATS) spaceborne lidar and the Moderate Resolution Imaging Spectroradiometer (MODIS). We compare the airborne and spaceborne datasets to operational predictions obtained from the Met Office Unified Model (MetUM) and the Copernicus Atmosphere Monitoring Service (CAMS). The dust aerosol optical depth predictions from the models are generally in agreement with the observations but display a low bias. However, the predicted vertical distribution places the dust lower in the atmosphere than highlighted in our observations. This is particularly noticeable for the MetUM, which does not transport coarse dust high enough in the atmosphere or far enough away from the source. We also found that both model forecasts underpredict coarse-mode dust and at times overpredict fine-mode dust, but as they are fine-tuned to represent the observed optical depth, the fine mode is set to compensate for the underestimation of the coarse mode. As aerosol–cloud interactions are dependent on particle numbers rather than on the optical properties, this behaviour is likely to affect their correct representation. This leads us to propose an augmentation of the set of aerosol observations available on a global scale for constraining models, with a better focus on the vertical distribution and on the particle size distribution. Mineral dust is a major component of the climate system; therefore, it is important to work towards improving how models reproduce its properties and transport mechanisms.

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

The current study investigates the diurnal cycle of dust loading across the global tropics, sub-tropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud–Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite observations, CATS aerosol and dust loading observations exhibits reasonable quality but significant day–night inconsistency. To account for this day–night inconsistency in CATS data quality, the diurnal variability in dust characteristics are currently examined separately for daytime and nighttime periods. Based on an analysis of variance analytical framework, pronounced diurnal variations in dust loading are generally uncovered during daytime periods and over terrestrial areas. The current study identifies statistically significant diurnal variability in dust loading over key dust sources, including the Bodélé Depression, the West African El Djouf, Rub-al Khali Desert, and western and southern North America, confirming the previous observation-based findings regarding the diurnal cycle of dust emission and underlying meteorological processes in these regions. Significant seasonal dust diurnal variability is identified over the Iraqi and Thar deserts. The identified significant diurnal cycles in dust loading over the rainforests in Amazon and tropical southern Africa are hypothesized to be driven by enhanced dust emission due to wildfires.

An optically thin cirrus cloud was profiled concurrently with nadir-pointing 1,064 nm lidars on 11 August 2017 over eastern Texas, including NASA's airborne Cloud Physics Lidar (CPL) and space-borne Clouds and Aerosol Transport System (CATS) instruments. Despite resolving fewer (37% vs. 94%) and denser (i.e., more emissive) clouds (average cloud optical depth of 0.10 vs. 0.03, respectively), CATS data render a near-equal estimate of the top-of-atmosphere (TOA) net cloud radiative forcing (CRF) versus CPL. The sample-relative TOA net CRF solved from CPL is 1.39 W/m(2), which becomes 1.32 W/m(2)after normalizing by occurrence frequency. Since CATS overestimates extinction for this case, the sample-relative TOA net forcing is similar to 3.0 W/m(2)larger than CPL, with the absolute value reduced to within 0.3 W/m(2)of CPL due its underestimation of cloud occurrence. We discuss the ramifications of thin cirrus cloud detectability from satellite and its impact on attempts at TOA CRF closure.

Recent fire seasons have featured volcanic-sized injections of smoke aerosols into the stratosphere where they persist for many months. Unfortunately, the aging and transport of these aerosols are not well understood. Using space-based lidar, the vertical and spatial propagation of these aerosols can be tracked and inferences can be made as to their size and shape. In this study, space-based CATS and CALIOP lidar were used to track the evolution of the stratospheric aerosol plumes resulting from the 2019–2020 Australian bushfire and 2017 Pacific Northwest pyrocumulonimbus events and were compared to two volcanic events: Calbuco (2015) and Puyehue (2011). The pyrocumulonimbus and volcanic aerosol plumes evolved distinctly, with pyrocumulonimbus plumes rising upwards of 10 km after injection to altitudes of 30 km or more, compared to small to modest altitude increases in the volcanic plumes. We also show that layer-integrated depolarization ratios in these large pyrocumulonimbus plumes have a strong altitude dependence with more irregularly shaped particles in the higher altitude plumes, unlike the volcanic events studied.

Biomass burning in Africa provides a prolific source of aerosols that are transported from the source region to distant areas, as far away as South America and Australia. Models have long predicted the primary outflow and transport routes. Over time, field studies have validated the basic production and dynamics that underlie these transport patterns. In more recent years, the advancement of spaceborne active remote-sensing techniques has allowed for more detailed verification of the models and, importantly, verification of the vertical distribution of the aerosols in the transport regions, particularly with respect to westerly transport over the Atlantic Ocean. The Cloud-Aerosol Transport System (CATS) lidar on the International Space Station has detection sensitivity that provides observations that support long-held theories of aerosol transport from the African subcontinent over the remote Indian Ocean and as far downstream as Australia. Significance: Biomass burning in Africa can have impacts as far away as Australia. Flow of aerosols from Africa towards Australia has long been postulated by transport models, but has been poorly characterised due to a lack of measurements. The CATS instrument on the International Space Station has detection sensitivity that captures aerosol transport from Africa over the Indian Ocean to Australia. Open data set: https://cats.gsfc.nasa.gov/

The current study investigates the diurnal cycle of dust loading across the global tropics, sub-tropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud-Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite observations, CATS aerosol and dust loading observations exhibits reasonable quality and insignificant day-night inconsistency, thereby supporting the current analysis of dust diurnal cycle using CATS data. Based on an analysis of variance analytical framework, statistically significant diurnal variability in dust loading is identified over key dust sources, including the Bodélé depression, West African El Djouf, Rub-al Khali desert, and western and southern North America, confirming the previous observation-based findings regarding the diurnal cycle of dust emission and underlying meteorological processes in these regions. Insignificant annual mean dust diurnal variability is identified over the Iraqi, Thar, and Taklamakan deserts. The currently identified significant diurnal cycles in dust loading over the rainforests in Amazon and tropical southern Africa, and drylands in South America and the central Australia, are hypothesized to be driven by enhanced dust emission due to wildfires and enhanced katabatic and frontal winds, respectively.

Constraint of the vertical distribution of aerosol particles is crucial for the study of aerosol plume structure, aerosol radiative effects, and ultimately monitoring surface air pollution. We developed an algorithm to retrieve the aerosol optical central height (AOCH) of absorbing aerosols by using, for the first time, the oxygen (O2) A and B absorption band measurements from the TROPOspheric Monitoring Instrument (TROPOMI) over dark targets. For the retrieval, narrow band radiance at seven channels ranging from ultraviolet (UV) to shortwave infrared (SWIR) are convolved from TROPOMI hyperspectral measurements. Subsequently, cloudy pixels are screened out by using the slope of spectral reflectance, while aerosol types (dust and smoke) are classified by the wavelength dependence of aerosol path radiance in conjunction with UV aerosol index. Surface reflectance over land is derived from the MODIS surface bi-directional reflectance climatology, and over water from the GOME-2 surface Lambert-equivalent reflectivity (LER) database. The aerosol optical depth (AOD) and AOCH are retrieved through an approach of look-up-table accounting for AERONET-based dust and smoke optical properties. For multiple smoke and dust plume events around the world, our retrieved AOCH values agree with space-borne lidar CALIOP counterparts, with a mean bias of

Cloud top height (CTH) affects the radiative properties of clouds. Improved CTH observations will allow for improved parameterizations in large-scale models and accurate information on CTH is also important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. For the determination of CTH, a set of consecutive nadir reflectances is selected and the cross correlations between this set and collocated sets at other viewing angles are calculated for a range of assumed cloud top heights, yielding a correlation profile. Under the assumption that cloud reflectances are isotropic, local peaks in the correlation profile indicate cloud layers. This technique can be applied to every RSP footprint and we demonstrate that detection of multiple peaks in the correlation profile allows retrieval of heights of multiple cloud layers within single RSP footprints. This paper provides an in-depth description of the architecture and performance of the RSP's CTH retrieval technique using data obtained during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. RSP-retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar (CPL). The method's accuracy associated with the magnitude of correlation, optical thickness, cloud thickness and cloud height are explored. The technique is applied to measurements at a wavelength of 670 and 1880 nm and their combination. The 1880 nm band is virtually insensitive to the lower troposphere due to strong water vapor absorption. It is found that each band is well suitable for retrieving heights of cloud layers with optical thicknesses above about 0.1 and that RSP cloud layer height retrievals more accurately correspond to CPL cloud middle than cloud top. It is also found that the 1880 nm band yields the most accurate results for clouds at middle and high altitudes (4.0 to 17 km), while the 670 nm band is most accurate at low and middle altitudes (1.0–13.0 km). The dual band performs best over the broadest range and is suitable for accurately retrieving cloud layer heights between 1.0 and 16.0 km. Generally, the accuracy of the retrieved cloud top heights increases with increasing correlation value. Improved accuracy is achieved by using customized filtering techniques for each band with the most significant improvements occurring in the primary layer retrievals. RSP is able to measure a primary layer CTH with a median error of about 0.5 km when compared to CPL. For multilayered scenes, the second and third layer heights are determined median errors of about 1.5 and 2.0–2.5 km, respectively.

The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing rangeresolved vertical backscatter profiles of Earth’s atmosphere at 1064 and 532 nm. The CATS instrument design and ISS orbit lead to a higher 1064 nm signal-to-noise ratio than previous space-based lidars, allowing for direct atmospheric calibration of the 1064 nm signals. Nighttime CATS version 3- 00 data were calibrated by scaling the measured data to a model of the expected atmospheric backscatter between 22 and 26 km a.m.s.l. (above mean sea level). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) reanalysis data and aerosol scattering ratios measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The nighttime normalization altitude region was chosen to simultaneously minimize aerosol loading and variability within the CATS data frame, which extends from 28 to − 2 km a.m.s.l. Daytime CATS version 3-00 data were calibrated through comparisons with nighttime measurements of the layer-integrated attenuated total backscatter (iATB) from strongly scattering, rapidly attenuating opaque cirrus clouds. The CATS nighttime 1064 nm attenuated total backscatter (ATB) uncertainties for clouds and aerosols are primarily related to the uncertainties in the CATS nighttime calibration technique, which are estimated to be ∼ 9 %. Median CATS V3-00 1064 nm ATB relative uncertainty at night within cloud and aerosol layers is 7 %, slightly lower than these calibration uncertainty estimates. CATS median daytime 1064 nm ATB relative uncertainty is 21 % in cloud and aerosol layers, similar to the estimated 16 %– 18 % uncertainty in the CATS daytime cirrus cloud calibration transfer technique. Coincident daytime comparisons between CATS and the Cloud Physics Lidar (CPL) during the CATS-CALIPSO Airborne Validation Experiment (CCAVE) project show good agreement in mean ATB profiles for clearair regions. Eight nighttime comparisons between CATS and the PollyXT ground-based lidars also show good agreement in clear-air regions between 3 and 12 km, with CATS having a mean ATB of 19.7 % lower than PollyXT. Agreement between the two instruments (∼ 7 %) is even better within an aerosol layer. Six-month comparisons of nighttime ATB values between CATS and CALIOP also show that iATB comparisons of opaque cirrus clouds agree to within 19 %. Overall, CATS has demonstrated that direct calibration of the 1064 nm channel is possible from a space-based lidar using the atmospheric normalization technique.

We present a comparison of 1064 nm aerosol optical depth (AOD) and aerosol extinction profiles from the Cloud-Aerosol Transport System (CATS) Level 2 aerosol product with collocated Aerosol Robotic Network (AERONET) AOD, Aqua and Terra Moderate Imaging Spectroradiometer (MODIS) Dark Target (AOD) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD and extinction data for the period of Feb. 2015–Oct. 2017. Upon quality assurance checks of CATS data, reasonable agreements are found between aerosol data from CATS and other sensors. Using quality assured CATS aerosol data, for the first time, variations in AODs and aerosol extinction profiles are evaluated at 00, 06, 12, and 18 UTC (and/or 0:00 am, 6:00 am, 12:00 pm and 6:00 pm local solar times) on both regional and global scales. This study suggests that marginal variations are found in AOD from a global mean perspective, with the maximum and minimum aerosol vertical profiles found at local noon and 6:00 pm local time respectively, for both the June–November and December–May seasons. Strong diurnal variations are found over North Africa and India for the December–May season, and over North Africa, Middle East, and India for the June–November season. In particular, over North Africa, during the June-November season, a diurnal peak in aerosol extinction profile of 20 % larger than daily mean is found at 6:00 am (early morning local time), which may possibly be associated with dust generation through the breaking down of low level jet during morning hours.

Supercooled liquid water (SLW) clouds, where liquid droplets exist at temperatures below 0°C present a well-known aviation hazard through aircraft icing, in which SLW accretes on the airframe. SLW clouds are common over the Southern Ocean, and climate-induced changes in their occurrence is thought to constitute a strong cloud feedback on global climate. The two recent NASA field campaigns POlarimeter Definition EXperiment (PODEX, based in Palmdale, California, January–February 2013) and Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS, based in Houston, Texas in August–September 2013) provided a unique opportunity to observe SLW clouds from the high-altitude airborne platform of NASA's ER-2 aircraft. We present an analysis of measurements made by the Research Scanning Polarimeter (RSP) during these experiments accompanied by correlative retrievals from other sensors. The RSP measures both polarized and total reflectance in 9 spectral channels with wavelengths ranging from 410 to 2250 nm. It is a scanning sensor taking samples at 0.8° intervals within 60° from nadir in both forward and backward directions. This unique angular resolution allows for characterization of liquid water droplet size using the rainbow structure observed in the polarized reflectances in the scattering angle range between 135° and 165°. Simple parametric fitting algorithms applied to the polarized reflectance provide retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, Rainbow Fourier Transform (RFT), which allows retrieval of the droplet size distribution without assuming a size distribution shape. We present an overview of the RSP campaign datasets available from the NASA GISS website, as well as two detailed examples of the retrievals. In these case studies we focus on cloud fields with spatial features varying between glaciated and liquid phases at altitudes as high as 10 km, which correspond to temperatures close to the homogeneous freezing temperature of pure water drops (about -35°C or colder). The multimodal droplet size distributions retrieved from RSP data in these cases are consistent with the multi-layer cloud structure observed by correlative Cloud Physics Lidar (CPL) measurements.

A novel technique is used to estimate derivatives of ice effective radius with respect to height near convective cloud tops (dre /dz) from airborne shortwave reflectance measurements and lidar. Values of dre /dz are about -6 μm/km for cloud tops below the homogeneous freezing level, increasing to near 0 μm/km above the estimated level of neutral buoyancy. Retrieved dre /dz compares well with previously documented remote sensing and in situ estimates. Effective radii decrease with increasing cloud top height, while cloud top extinction increases. This is consistent with weaker size sorting in high, dense cloud tops above the level of neutral buoyancy where fewer large particles are present, and with stronger size sorting in lower cloud tops that are less dense. The results also confirm that cloud-top trends of effective radius can generally be used as surrogates for trends with height within convective cloud tops. These results provide valuable observational targets for model evaluation.

We present retrievals of water cloud properties from the measurements made by the Research Scanning Polarimeter (RSP) during the Polarimeter Definition Experiment (PODEX) held between January 14 and February 6, 2013. The RSP was onboard the high-altitude NASA ER-2 aircraft based at NASA Dryden Aircraft Operation Facility in Palmdale, California. The retrieved cloud characteristics include cloud optical thickness, effective radius and variance of cloud droplet size distribution derived using a parameter-fitting technique, as well as the complete droplet size distribution function obtained by means of Rainbow Fourier Transform. Multi-modal size distributions are decomposed into several modes and the respective effective radii and variances are computed. The methodology used to produce the retrieval dataset is illustrated on the examples of a marine stratocumulus deck off California coast and stratus/fog over California's Central Valley. In the latter case the observed bimodal droplet size distributions were attributed to two-layer cloud structure. All retrieval data are available online from NASA GISS website.

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

We demonstrate that multi-angle polarization measurements in the near-UV and blue part of the spectrum are very well suited for passive remote sensing of aerosol layer height. For this purpose we use simulated measurements with different set-ups (different wavelength ranges, with and without polarization, different polarimetric accuracies) as well as airborne measurements from the Research Scanning Polarimeter (RSP) obtained over the continental USA. We find good agreement of the retrieved aerosol layer height from RSP with measurements from the Cloud Physics Lidar (CPL) showing a mean absolute difference of less than 1 km. Furthermore, we found that the information on aerosol layer height is provided for large part by the multi-angle polarization measurements with high accuracy rather than the multi-angle intensity measurements. The information on aerosol layer height is significantly decreased when the shortest RSP wavelength (410 nm) is excluded from the retrieval and is virtually absent when 550 nm is used as shortest wavelength.

A novel technique is used to estimate derivatives of ice effective radius with respect to height near convective cloud tops (dre/dz) from airborne shortwave reflectance measurements and lidar. Values of dre/dz are about −6 μm/km for cloud tops below the homogeneous freezing level, increasing to near 0 μm/km above the estimated level of neutral buoyancy. Retrieved dre/dz compares well with previously documented remote sensing and in situ estimates. Effective radii decrease with increasing cloud top height, while cloud top extinction increases. This is consistent with weaker size sorting in high, dense cloud tops above the level of neutral buoyancy where fewer large particles are present, and with stronger size sorting in lower cloud tops that are less dense. The results also confirm that cloud-top trends of effective radius can generally be used as surrogates for trends with height within convective cloud tops. These results provide valuable observational targets for model evaluation.

The Airborne Cloud–Aerosol Transport System (ACATS) is a Doppler wind lidar system that has recently been developed for atmospheric science capabilities at the NASA Goddard Space Flight Center (GSFC). ACATS is also a high-spectral-resolution lidar (HSRL), uniquely capable of directly resolving backscatter and extinction properties of a particle from a high-altitude aircraft. Thus, ACATS simultaneously measures optical properties and motion of cloud and aerosol layers. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS HSRL retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions, such as the Cloud–Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such as dust or smoke transport and convective outflow in anvil cirrus clouds.

In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements – combining neural networks and an iterative scheme based on Phillips–Tikhonov regularization – is described. The algorithm – which is an extension of a scheme previously designed for ground-based retrievals – is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips–Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals.

From June to October, low-level clouds in the Southeast (SE) Atlantic often underlie seasonal aerosol layers transported from African continent. Previously, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) 532 nm lidar observations have been used to estimate the relative vertical location of the above-cloud aerosols (ACA) to the underlying clouds. Here, we show new observations from NASA's Cloud-Aerosol Transport System (CATS) lidar. Two seasons of CATS 1064 nm observations reveal that the bottom of the ACA layer is much lower than previously estimated based on CALIPSO 532nm observations. For about 60% of CATS nighttime ACA scenes, the aerosol layer base is within 360 m distance to the top of the underlying cloud. Our results are important for future studies of the microphysical indirect and semi-direct effects of ACA in the SE Atlantic region.

An eruption of Italian volcano Mount Etna on 3 December 2015 produced fast-moving sulfur dioxide (SO2) and sulfate aerosol clouds that traveled across Asia and the Pacific Ocean, reaching North America in just 5 days. The Ozone Profiler and Mapping Suite's Nadir Mapping UV spectrometer aboard the U.S. National Polar-orbiting Partnership satellite observed the horizontal transport of the SO2 cloud. Vertical profiles of the colocated volcanic sulfate aerosols were observed between 11.5 and 13.5 km by the new Cloud Aerosol Transport System (CATS) space-based lidar aboard the International Space Station. Backward trajectory analysis estimates the SO2 cloud altitude at 7-12 km. Eulerian model simulations of the SO2 cloud constrained by CATS measurements produced more accurate dispersion patterns compared to those initialized with the back trajectory height estimate. The near-real-time data processing capabilities of CATS are unique, and this work demonstrates the use of these observations to monitor and model volcanic clouds.

Accurate knowledge of cloud optical properties, such as extinction-to-backscatter ratio and depolarization ratio, can have a significant impact on the quality of cloud extinction retrievals from lidar systems because parameterizations of these variables are often used in nonideal conditions to determine cloud phase and optical depth. Statistics and trends of these optical parameters are analyzed for 4 yr (2003–07) of cloud physics lidar data during five projects that occurred in varying geographic locations and meteorological seasons. Extinction-to-backscatter ratios (also called lidar ratios) are derived at 532 nm by calculating the transmission loss through the cloud layer and then applying it to the attenuated backscatter profile in the layer, while volume depolarization ratios are computed using the ratio of the parallel and perpendicular polarized 1064-nm channels. The majority of the cloud layers yields a lidar ratio between 10 and 40 sr, with the lidar ratio frequency distribution centered at 25 sr for ice clouds and 16 sr for altocumulus clouds. On average, for ice clouds the lidar ratio slightly decreases with decreasing temperature, while the volume depolarization ratio increases significantly as temperatures decrease. Trends for liquid water clouds (altocumulus clouds) are also observed. Ultimately, these observed trends in optical properties, as functions of temperature and geographic location, should help to improve current parameterizations of extinction-to-backscatter ratio, which in turn should yield increased accuracy in cloud optical depth and radiative forcing estimates.

Ozone profiles are often used to investigate day-to-day and year-to-year variability in origins of free tropospheric ozone. With this in mind, more than 50 ozonesonde launches were conducted in Beltsville, MD, during the summers of 2004 through 2007. Budgets of free tropospheric ozone were calculated for each ozone profile in the four summers using a laminar identification (LID) method and unusual episodes were analyzed with respect to meteorological variables. The laminar method showed that stratosphere-to-troposphere transport (ST) accounted for greater than 50% of the free tropospheric ozone column on 17% of days sampled, a more pronounced influence than regional convective and lightning (RCL) sources. The ST origins were confirmed with trajectories, and tracers (water vapor and potential vorticity). The amount of free tropospheric ozone from ST and RCL sources varied from year-to-year (up to 13%) and can be explained by differences in mean meteorological patterns. On average, almost 30% of the free tropospheric column was attributed to ST influence, about twice as much as RCL, although the LID method may not capture weeks-old lightning influences as in a chemical model. The prevalence of ST ozone in summertime Beltsville soundings was similar to six sounding sites in the IONS-04 campaign [Thompson, A.M., et al., 2007b. Intercontinental Transport Experiment Ozonesonde Network Study (IONS, 2004): 1. Summertime upper tropospheric/lower stratosphere ozone over northeastern North America. J. Geophys. Res. 112, D12S12; Thompson, A.M., et al., 2007c. Intercontinental Transport Experiment Ozonesonde Network Study (IONS, 2004): 2. Tropospheric ozone budgets and variability over northeastern North America. J. Geophys. Res. 112, D12S13.] and to statistics from a 30 year climatology of European soundings [Collette, A., Ancellet, G., 2005. Impact of vertical transport processes on the tropospheric ozone layering above Europe. Part II: Climatological analysis of the past 30 years. Atmos. Environ. 39, 5423–5435]. The Beltsville record also demonstrated the value of soundings for air quality forecasting in an urban area. The 22 nighttime soundings collected over Beltsville in 2004–2007 can be divided into distinct polluted and unpolluted subsets, the former 20 ppbv higher in residual layer ozone (1 km) than the latter. These distinctions propagated to daytime differences of 10 ppbv at the surface in the Washington, DC, area, with the high-ozone residual layers leading to non-attainment of the National Ambient Air Quality Standard for ozone. More frequent ozone observations aloft appear essential for better understanding ozone variability and for enabling air quality modelers to achieve more accurate ozone forecasts.

The Cloud-Aerosol Transport System (CATS) is a multi-wavelength lidar instrument developed to enhance Earth Science remote sensing capabilities from the International Space Station. The CATS project was chartered to be an experiment in all senses: science, technology, and management. As a low-cost project following a strict build-to-cost/ build-to-schedule philosophy, CATS is following a new management approach while also serving as a technology demonstration for future NASA missions. This presentation will highlight the CATS instrument and science objectives with emphasis on how the ISS platform enables the specific objectives of the payload. The development process used for CATS and a look at data being produced by the instrument will also be presented.

With the recent launch of the Cloud-Aerosol Transport System (CATS) we have the opportunity to acquire a continuous record of space based lidar measurements spanning from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) era to the start of the EarthCARE mission. Utilizing existing well-validated science algorithms from the CALIPSO mission, we will ingest the CATS data stream and deliver high-quality lidar data sets to the user community at the earliest possible opportunity. In this paper we present an overview of procedures necessary to generate CALIPSO-like lidar level 2 data products from the CATS level 1 data products.

[1] The Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite was successfully launched in April 2006 to study cloud and aerosol layers using range-resolved laser remote sensing. Dedicated flights were conducted from July 26 to August 14, 2006 using the airborne Cloud Physics Lidar (CPL) to validate the CALIPSO lidar (CALIOP) data products. This paper presents results from coincident ice cloud measurements of lidar ratio, extinction coefficient, and optical depth. Flight segment case studies are shown as well as statistics for all coincident measurements during this CALIPSO-CloudSat Validation Experiment (CC-VEX). For the penetrated portion of opaque layers, CALIOP estimates of lidar ratio and extinction are substantially lower than the corresponding CPL values. Significant differences were also found for measurements of horizontally aligned ice, where different instrument viewing geometries precluded meaningful comparisons. After filtering the data set to exclude these discrepancies, overall CALIOP lidar ratio and extinction averages compared favorably to within 1% of overall CPL averages. When restricting the data further to exact coincident in-cloud point-pairs, CALIOP lidar ratios remained close to CPL values, averaging 2.1% below CPL, and the retrieved extinction and optical depth averaged 14.7% above CPL values, a result partially of higher average CALIOP attenuated backscatter but still a respectably close match.