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NASA NTRS2016

Observations of Stratiform Lightning Flashes and Their Microphysical and Kinematic Environments

During the Midlatitude Continental Convective Clouds Experiment (MC3E), combined observations of clouds and precipitation were made from airborne and ground-based in situ and remote sensing platforms. These observations were coordinated for multiple mesoscale convective systems (MCSs) that passed over the MC3E domain in northern Oklahoma. Notably, during a storm on 20 May 2011 in situ and remote sensing airborne observations were made near the times and locations of stratiform positive cloud-to-ground (+CG) lightning flashes. These +CGs resulted from extremely large stratiform lightning flashes that were hundreds of km in length and lasted several seconds. This dataset provides an unprecedented look at kinematic and microphysical environments in the vicinity of large, powerful, and long-lived stratiform lightning flashes. We will use this dataset to understand the influence of low liquid water contents (LWCs) in the electrical charging of MCS stratiform regions.

Lightning↗
NASA NTRS2017

Observations of Stratiform Lightning Flashes and Their Microphysical and Kinematic Environments

During the Midlatitude Continental Convective Clouds Experiment (MC3E), combined observations of clouds and precipitation were made from airborne and ground-based in situ and remote sensing platforms. These observations were coordinated for multiple mesoscale convective systems (MCSs) that passed over the MC3E domain in northern Oklahoma. Notably, during a storm on 20 May 2011 in situ and remote sensing airborne observations were made near the times and locations of stratiform positive cloud-to-ground (+CG) lightning flashes. These +CGs resulted from extremely large stratiform lightning flashes that were hundreds of km in length and lasted several seconds. This dataset provides an unprecedented look at kinematic and microphysical environments in the vicinity of large, powerful, and long-lived stratiform lightning flashes. We will use this dataset to understand the influence of low liquid water contents (LWCs) in the electrical charging of MCS stratiform regions.

Lightning↗
NASA NTRS2017

Exploring the Elevated Water Vapor Signal Associated with Biomass Burning Aerosol over the Southeast Atlantic Ocean

The quantification of radiative forcing due to the cumulative effects of aerosols, both directly and on cloud properties, remains the biggest source of uncertainty in our understanding of the physical climate. How the magnitude of these effects may be modified by meteorological conditions is an important aspect of this question. The Southeast Atlantic Ocean (SEA), with seasonal biomass burning (BB) smoke plumes overlying a persistent stratocumulus cloud deck, offers a perfect natural observatory in which to study the complexities of aerosol-cloud interactions. The NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign consists of three field deployments over three years (2016-2018) with the goal of gaining a better understanding of the complex processes (direct and indirect) by which BB aerosols affect clouds. We present results from the first ORACLES field deployment, which took place in September 2016 out of Walvis Bay, Namibia. Two NASA aircraft were flown with a suite of aerosol, cloud, radiation, and meteorological instruments for remote-sensing and in-situ observations. A strong correlation was observed between the aircraft-measured pollution indicators (carbon monoxide and aerosol properties) and atmospheric water vapor content, at all altitudes. Atmospheric reanalysis indicates that convective dynamics over the continent, near likely contribute to this elevated signal. Understanding the mechanisms by which water vapor covaries with plume strength is important to quantifying the magnitude of the aerosol direct and semi-direct effects in the region.

Pistone, Kristina↗
NASA NTRS2018

Results from ORACLES-2016: Observed Biomass Burning Aerosol Properties over the Southeast Atlantic Ocean and the Relationship to Meteorology

The Southeast Atlantic Ocean (SEA), with seasonal biomass burning (BB) smoke plumes overlying persistent stratocumulus cloud deck, offers an excellent natural laboratory to make the observations necessary to understand the complexities of aerosol-cloud-radiation interactions. The first field deployment of the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign was conducted in September of 2016 out of Walvis Bay, Namibia. During this deployment, two NASA aircraft (a P-3 and an ER-2) were flown with a suite of aerosol, cloud, radiation, and meteorological instruments for remote-sensing and in-situ observations. In this talk, I will first present results from an intercomparison of the different instrument retrievals for the 2016 flights, with a specific focus on the measures of aerosol absorption. In ORACLES, data collected by eight different instrument teams are used to derive aerosol properties over this region by independent methods. We focus on the single scattering albedo (SSA), a measure of the relative absorption versus total extinction by aerosols, as the main comparable property between all methods. I will also discuss comparisons of the retrieved aerosol absorption parameters, as well as the agreement and the variability seen between different instruments, and spatially over the study region. Another important factor in understanding aerosol radiative effects is how the magnitude of these effects may be modified by meteorological conditions. I will next describe observations collected from the P-3 aircraft (from near-surface up to 6-7 kilometers) which show a strong correlation between the in-situ pollution indicators (carbon monoxide and aerosol properties) and atmospheric water vapor content, seen at all altitudes above the boundary layer. This condition is seen to persist over all flights, with minimal detrainment during advection from the continental source. In exploring the potential causal factors behind and implications of this relationship, we see indications that convective dynamics over the continent likely contribute to this elevated signal, but neither meteorological reanalysis nor trajectory analysis fully capture the magnitude and vertical structure of the observed elevated signal. Finally, I will discuss the radiative implications of the observed correlations, a topic of ongoing analysis in ORACLES: understanding the mechanisms which cause water vapor to covary with plume strength is important to quantifying the radiative effects (direct and semi-direct) of biomass burning aerosol in the region.

Pistone, Kristina↗
NASA NTRSDate not supplied

Flood Detection with Synthetic Aperture Radar: A Case Study of Houston, Texas Following Hurricane Harvey (2017) using C- and X-Band Observations

Hurricane Harvey produced record-breaking rainfall of up to 60 inches resulting in extensive flooding in Houston, Texas, in late August and early September of 2017. The slow forward motion of the storm following landfall left much of the area unobservable to optical remote sensing instruments for several days due to cloud cover. The active nature of Synthetic Aperture Radar (SAR) instruments allows for observations through clouds, which can supplement efforts to estimate hurricane-induced flood impacts. A growing fleet of SAR constellations has helped lower the latency of imagery following a hurricane, allowing for more timely detections of flooding to help support emergency response efforts. In this study, we leverage publicly available C-band SAR observations from the European Space Agency’s Sentinel-1B (S1B) satellite, collected on 30 August, and X-band SAR imagery collected on 1 September by the Airbus TanDEM-X (TDX) satellite made available through the NASA Commercial Smallsat Data Acquisition (CSDA) program. For each dataset, one co-polarized, StripMap, Radiometric Terrain Corrected (RTC) image was used to create a binary water/no water classification map by referencing permanent water in the Cropland Data Layer (CDL) dataset to determine thresholds. A validation dataset of randomly distributed “ground truth” points was generated using optical imagery from PlanetScope on 31 August, where the domain had relatively little cloud cover. We found that the S1B- and TDX-derived open water maps achieved overall accuracies of 94.17% and 91.57%, respectively. The variation in performance is attributed to both the penetrative abilities of C- and X-band SAR wavelengths in vegetated areas and the increased spatial resolution of the commercial SAR (~3 m) over Sentinel-1 (30 m). While publicly available Sentinel-1 observations are commonly relied upon in response and recovery efforts, these results suggest that including commercial X-band SAR imagery could be beneficial by providing both increased spatial resolution and more frequent revisits when deriving post-event flood mapping products.

Alexander M Melancon↗
NASA NTRS2022

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities.

Sergii Skakun↗
NASA NTRS2023

Toward Physics-informed Neural Networks for 3D Multi-layer Cloud Mask Reconstruction

Three-dimensional (3D) cloud retrievals are critical for understanding their impact on climate and other applications such as aviation safety, weather prediction, and remote sensing. However, obtaining high-resolution and accurate vertical representation of clouds remains unsolved due to the limitations imposed by satellite instrumentation, viewing conditions, and the complexity of cloud dynamics. Cloud masks are essential for comprehending various cloud vertical properties, but deriving accurate 3D cloud masks from 2D satellite imagery data is a challenging task. To tackle these challenges, we introduce a physics-informed loss function for training deep learning models that can extend 2D cloud images into 3D cloud masks. The proposed loss, called CloudMask Loss, is composed of two domain knowledge-informed loss terms: one for evaluating cloud position and thickness, and the other for measuring the number of layers. By combining these loss terms, we improve the trainability of the deep learning models for more accurate and meaningful results. We apply the proposed loss function to different neural networks and demonstrate significant improvements in multi-layer cloud mask reconstruction. Utilizing the same neural network architecture, our proposed loss outperforms standard binary crossentropy loss in terms of multi-layer cloud classification accuracy, number of layers accuracy, and thickness mean absolute error (MAE). The proposed loss function can be readily integrated into various neural network architectures, resulting in substantial performance gains in 3D cloud mask generation.

multi-layer clouds↗
NASA NTRS1987

Remote sensing: Earth's surface and atmosphere; Proceedings of Workshop X and the Topical Meeting of the 26th COSPAR Plenary Meeting, Toulouse, France, June 30-July 11, 1986

The present conference on space-based remote sensing of the earth's surface and atmosphere addresses the two broad issues of remote sensing activities of interest to developing countries and the results obtained to date by the International Satellite Cloud Climatology Project, the Earth Radiation Budget Experiment, and the Stratospheric Aerosol and Gas Experiment (SAGE). Attention is given to the remote sensing of environmental factors affecting health, applications of satellite microwave radiometry, earth science missions for the NASA Space Station, and digitally produced Landsat map images. Also discussed are time-accumulated visible and IR histograms used as cloud cover descriptors, the estimation of the radiation budget's sensitivity to cloud variations, monitoring global surface temperature variations using cloud data sets, and an analysis of preliminary SAGE II data on ozone and NO2.

Carter, W. D.↗
NASA NTRS1997

CERES and the S'COOL Project

The first Clouds and the Earth's Radiant Energy System (CERES) instrument will be launched on the Tropical Rainfall Measuring Mission (TRMM) spacecraft from a Japanese launch site in November 1997. This instrument is a follow-on to the Earth Radiation Budget Experiment (ERBE) begun in the 1980's. The instrument will measure the radiation budget - incoming and outgoing radiant energy - of the Earth. It will establish a baseline and look for climatic trends. The major feature of interest is clouds, which play a very strong role in regulating our climate. CERES will identify clear and cloudy regions and determine cloud physical and microphysical properties using imager data from a companion instrument. Validation efforts for the remote sensing algorithms will be intensive. As one component of the validation, the S'COOL (Students' Cloud Observations On-Line) project will involve school children from around the globe in making ground truth measurements at the time of a CERES overpass. Their observations will be collected at the NASA Langley Distributed Active Archive Center (DAAC) and made available over the Internet for educational purposes as well as for use by the CERES Science Team in validation efforts. Pilot testing of the S'COOL project began in January 1997 with two local schools in Southeastern Virginia and one remote site in Montana. This experience is helping guide the development of the S'COOL project. National testing is planned for April 1997, international testing for July 1997, and global testing for October 1997. In 1998, when the CERES instrument is operational, a global observer network should be in place providing useful information to the scientists and learning opportunities to the students.

Chambers, Lin H.↗
NASA NTRS2026

Ice in Convective Storm Environments: Assessing Impacts of Microphysics on Simulated Reflectivities

Satellite radar retrievals of clouds and precipitation rely on assumptions about the microphysical properties of hydrometeors such as particle size distributions (PSDs) and massdimensional relationships, which have been shown to vary in distinct environments. A deeper understanding of linkages between mass-dimensional relationships, ice crystal shape, and radar reflectivities across cloud and precipitation regimes is necessary for developing new retrievals for upcoming satellite radar missions, including the INvestigation of Convective UpdraftS (INCUS), as well as future missions that will feature space-borne radars observing clouds, convection, and precipitation. This study investigates ice microphysical characteristics through aircraft in-situ and remote sensing observations from two convective events during the Mid-latitude Continental Convective Clouds Experiment (MC3E) in the Southern Great Plains, and tests implications for ice particle habit in forward modeled radar observations. During MC3E, the Ka/Ku- band High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) was onboard the NASA ER-2 aircraft, while in-situ measurements were taken onboard the University of North Dakota Citation. In-situ PSDs and particle shape information from optical array probe data are used to forward model radar reflectivities from particle scattering databases and are compared against HIWRAP reflectivities. Various particle combinations can be used to find agreement with HIWRAP observations, and the forward modeled reflectivities were sensitive to particle type. Varying the riming on the aggregate and graupel particles resulted in larger than 10 dB differences in reflectivities. The in-situ and radar observations imply the occurrence of aggregation, size sorting and lofting particles.

Julia A. Shates↗
NASA NTRS1984

A model for the estimation of the surface fluxes of momentum, heat and moisture of the cloud topped marine atmospheric boundary layer from satellite measurable parameters

A model is developed for the estimation of the surface fluxes of momentum, heat, and moisture of the cloud topped marine atmospheric boundary layer by use of satellite remotely sensed parameters. The parameters chosen for the problem are the integrated liquid water content, q sub li, the integrated water vapor content, q sub vi, the cloud top temperature, and either a measure of the 10 meter neutral wind speed or the friction velocity at the surface. Under the assumption of a horizontally homogeneous, well-mixed boundary layer, the model calculates the equivalent potential temperature and total water profiles of the boundary layer along with the boundary layer height from inputs of q sub li, q sub vi, and cloud top temperature. These values, along with the 10m neutral wind speed or friction velocity and the sea surface temperature are then used to estimate the surface fluxes. The development of a scheme to parameterize the integrated water vapor outside of the boundary layer for the cases of cold air outbreak and California coastal stratus is presented.

Allison, D. E.↗
NASA NTRS1998

The CERES S'COOL Project: Development and Operational Phases

As part of NASA's Mission to Planet Earth, the first Clouds and the Earth's Radiant Energy System (CERES) instrument will be launched on the Tropical Rainfall Measuring Mission (TRMM) spacecraft from the Tanegashima launch site in Japan in November 1997. The instrument will measure the radiation budget incoming and outgoing radiant energy - of the Earth. The major feature of interest is clouds, which play a very strong role in regulating our climate. CERES will identify clear and cloudy regions and determine cloud physical and microphysical properties using imager data from a companion instrument. Validation efforts for the remote sensing algorithms will be intensive. As one component of the validation, the S'COOL (Students' Cloud Observations On-Line) project will involve school children around the globe in making ground truth measurements at the time of a CERES overpass. They will report cloud type, height, fraction, and opacity, as well as the local surface conditions. Their observations will be collected at the NASA Langley Distributed Active Archive Center (DAAC) and made available over the Internet for educational purposes as well as for use by the CERES Science Team in validation efforts. Pilot testing of the S'COOL project began in January 1997 with two local schools in Southeastern Virginia and one remote site in Montana. National testing in April 1997 involved 8 schools (grades 3 to high school) across the United States. Global testing will be carried out in October 1997. Details of the S'COOL project, which is mainly Internet-based, are being developed in each of these phases according to feedback received from participants. In 1998, when the CERES instrument is operational, a global observer network should be in place providing useful information to the scientists and learning opportunities to the students. Broad participation in the S'COOL project is planned, both to obtain data from a wide range of geographic areas, and to involve as many students as possible in learning about clouds and atmospheric science. This paper reports on the development phase of the S'COOL project, including the reaction of the teachers and students who have been involved. It describes the operational state of the S'COOL network, and identifies opportunities for additional participants.

Chambers, Lin H.↗
NASA NTRSDate not supplied

Aerosol Above Cloud Optical Depth in the Southeast Atlantic and Its Use in Continuity NASA Products

When aerosol overlie bright clouds, their radiative impact on the incident light can be either positive or negative. This difference in radiative impact promotes difficulties in remote sensing aerosol properties, which have shown biases in aerosol optical depth (AOD) retrievals when clouds are underneath. To better constrain this quantity, we have measured aerosols above clouds over the course of 3 years in the southeast Atlantic as part of the ObseRvations of CLouds above Aerosols and their intEractionS (ORACLES). These directly measured AOD above clouds are used to build and improve upon the current Near-Real-Time MODACAERO algorithm for above cloud AOD from MODIS, into a continuity product for NASA EOS/SNPP/JPSS. Here we present a summary of the above cloud aerosol optical depth measured during ORACLES, and that will be used for porting the MODACAERO algorithm to the continuity product. We use aerosol optical depth measured directly from sunlight attenuation using the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) and compare it to the active remote sensing, High Spectral Resolution Lidar-2 (HSRL-2). We present a combination of all 3 years of measurements during peak biomass burning season. We have observed average above cloud aerosol optical depth at 500 nm ranging from 0.28 to 0.37, with the maximum in August 2017. We emphasize the use of improved aerosol absorption and scattering models based on airborne measurements during ORACLES.

aerosol optical depth (AOD)↗
NASA NTRSDate not supplied

The PolCube CubeSat Polarimeter for Earth Science

PolCube is a 12U CubeSat + polarimeter instrument designed by NASA Langley and the Korea Astronomy and Space Science Institute (KASI) for Earth Science. PolCube is based on the PolCam polarimeter onboard the Korean Pathfinder Lunar Observatory (KPLO) that launched in August 2022. The objective of the PolCube instrument is to retrieve detailed fine-mode (pollution and smoke) and coarse-mode (sea-salt and dust) aerosol properties over the ocean for a range of light to heavy aerosol loadings using its polarimetric-imaging capability at multiple angles and wavelengths from 410 − 865 nm. An additional objective is to discriminate aerosols from thin clouds. We quantify the performance of aerosol and ocean remote sensing products from the PolCube polarimeter instrument using the Microphysical Aerosol Properties from Polarimetry (MAPP) remote sensing retrieval algorithm. PolCube’s accurate and high-resolution aerosol-retrieval products will provide unique spatial and temporal coverage of the Earth that can be used synergistically with other instruments, such as the PACE (Plankton, Aerosols, Clouds and Ecosystems) and GEMS (Geostationary Environmental Monitoring Spectrometer) mission to improve air-quality forecasting. We present the PolCube-MAPP retrieval algorithm, which used optimal estimation and artificial intelligence, as well as multiple powerful inherent optical property look-up-tables for the Earth’s aerosol, cloud, and hydrosol particles. We estimate that PolCube can retrieve total aerosol optical depth at 555 nm (AOD555) within ±0.068, fine-mode AOD555 within ±0.078, and fine-mode single-scattering albedo within ±0.036, where all uncertainties are expressed as one standard deviation (1σ).

Snorre Stamnes↗
NASA NTRS2017

Radiation, Smoke and Clouds Observed in the Southeastern Atlantic With the Research Scanning Polarimeter During ORACLES

The ObseRvations of Aerosols above Clouds and their interactions (ORACLES) project is making a series of field deployments to the southeastern Atlantic with NASA ER-2 and P3 aircraft to acquire both detailed remote sensing observations and in situ measurements of the aerosols and clouds in that region. This area is home to one of the largest low-level cloud decks on Earth that is seasonally affected by vast plumes of smoke from biomass burning, which in effect provides a natural experiment testing the radiative and microphysical interactions between the smoke and the clouds. The downward solar radiation at the surface, or cloud top, is always reduced by the presence of smoke. However, whether the amount of sunlight reflected back out to space is increased, or decreased by the presence of smoke is sensitively dependent on the brightness of the clouds and the fraction of light that the smoke absorbs each time light hits a smoke particle. In this study we use data from the Research Scanning Polarimeter, an along track scanning instrument, that provides measurements of the Stokes parameters I, Q and U at 410, 470, 555, 670, 865, 960, 1590, 1880 and 2260 nm at 150 viewing angles over a range of +/- 60° from nadir for each contiguous sub-aircraft pixel (~ 300 m in size). A retrieval algorithm is applied to the data acquired with a table look up technique, similar to that of the operational POLDER algorithm, to provide a first guess of the complex refractive index, optical depth and size distribution of the smoke particles together with cloud droplet size and optical depth. A subsequent iterative fitting procedure, where the fact that the doubling/adding method allows the construction of the Green's function for the radiative transfer equation, is used to obtain an efficient and statistically optimal estimate of the aerosol and cloud retrieval parameters. These retrieval parameters are evaluated against in situ observations, when available, and the optical depth and intensive lidar variables that are measured by the High Spectral Resolution Lidar 2. Finally, the aerosol and cloud retrievals are used to evaluate the variations in top of the atmosphere, surface/cloud top shortwave radiative forcing and atmospheric absorption that are caused by variations in the smoke and clouds.

Cairns, Brian↗
NASA NTRS1992

Remote sensing of aerosols with the Earth Observing Scanning Polarimeter

Tropospheric aerosols have potential climate forcing roles through both the direct effect on solar radiation scattered and absorbed and the indirect effect as condensation nuclei for cloud particles. Present capabilities for characterizing the global aerosol climatology are limited by the existing satellite remote sensing measurements, making a quantitative determination of the climate forcing effects of aerosols rather difficult and uncertain. The Earth Observing Scanning Polarimeter (EOSP) instrument under consideration for Earth Observing System (EOS) mission platforms and for a proposed Climsat Earth Probe mission is intended to provide global mapping with multispectral photopolarimetry, which can be exploited for the more pronounced aerosol signature in the polarization of the scattered light as compared to the radiance. We describe the essential features and performance specifications of the EOSP instrument and discuss approaches for retrieving tropospheric aerosol properties from multispectral photopolarimetry.

Travis, Larry D.↗
NASA NTRS2016

Vertical Variation of Ice Particle Size in Convective Cloud Tops

A novel technique is used to estimate derivatives of ice effective radius with respect to height near convective cloud tops (dr(sub e)/dz) from airborne shortwave reflectance measurements and lidar. Values of dr(sub e)/dz are about −6 micrometer/km for cloud tops below the homogeneous freezing level, increasing to near 0 micrometer/km above the estimated level of neutral buoyancy. Retrieved dr(sub e)/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.

cloud height indicators↗
NASA NTRSDate not supplied

Characterizing the Seasonal and Diurnal Aerosol Cycles using CATS Space-Based Lidar

The broader effects aerosols have on the earth and climate system is determined by a host of factors including their composition and vertical distribution. Characterizing the vertical distribution of clouds and aerosols is especially important considering it remains one of the greatest uncertainties in climate predictions. Space-based lidar, such as Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud-Aerosol Transport System (CATS), is especially well suited as a remote sensing tool in determining this vertical distribution. CALIOP has been used for over a decade to observe the global and vertical distribution of clouds and aerosols but is limited by Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations’ (CALIPSO’s) polar orbit to set equatorial crossing times of ~1:30AM/PM. This limited temporal sampling limits the use of CALIOP in determining the full diurnal variability of clouds and aerosols. On the International Space Station (ISS), with its inclined orbit and variable equatorial crossing times, the CATS lidar provided an opportunity to study the vertical profile of clouds and aerosols at a variety of local times. With this 33-month CATS record, we show that CATS captured the seasonal changes in global aerosol distributions and compares favorably with other instruments and models. Specifically targeting biomass burning regions, we also find CATS could observe the documented diurnal cycle in aerosol altitudes with some caveats. With the next generation of proposed space-based lidars including designs in similar orbits as the ISS and CATS, future studies of the aerosol diurnal and seasonal cycles would also be possible.

Kenneth Christian↗