Cloud-Aerosol Transport System Research Articles

Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations.

A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3×10−4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6×10−4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products.

Seasonal vertical distributions of diurnal variation of ice cloud frequency by CATS measurements over a global region (51°S-51°N)

Ice clouds can strongly affect the radiation budget through the reflection (absorption) of solar shortwave (longwave) radiation in the Earth-atmosphere system. Unlike water clouds, ice clouds present a longer lifetime in the upper troposphere. Therefore, the vertical patterns of diurnal variation of ice clouds are of very importance to understand climate change in the globe. The present paper investigates the diurnal variation of ice cloud frequency (ICF) from March 2015 to October 2017 over the global scale (51 ° S and 51 ° N) by CATS (Cloud-Aerosol Transport System) LiDAR onboard the International Space Station (ISS) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) LiDAR measurements. Our results suggest that: (1) ICF derived from the CATS and CALIPSO LiDAR presents the similar geographical variations. (2) The vertical distribution of ICF in land area over the North Hemisphere (NH) is found larger than in the South Hemisphere (SH) likely due to the more dust aerosols as ice nuclei to influence ice cloud formation over the NH. (3) ICF at nighttime (00:00–06:00 and 18:00–23:00) exhibits higher value than daytime (06:00–18:00), especially in high altitude (more than 12 km) ice clouds in the tropics. (4) The number of ice cloud samples is higher in spring and winter compared to summer and autumn, and the highest amount of ice cloud samples in spring(winter) occurs during nighttime(daytime) for most hours. These findings provide significant insights that may help improving the ice cloud's representation in climate models.

Sensitivity analysis of the WRF simulated planetary boundary layer height to synoptic conditions over eastern China

Accurately representing the planetary boundary layer height (PBLH) and its thermal/dynamic structures within is essential for simulating meteorological and environmental conditions in numerical models. However, how accurately model reproduces PBL processes under diverse synoptic conditions remains under-explored. Using the Weather Research Forecasting model that configured with the widely-used YSU boundary layer scheme, the present study conducted a nearly three-year PBL simulation to examine its sensitivity to synoptic conditions in eastern China. Nine types of synoptic patterns were identified by combining T-mode principal component analysis with K-means clustering. The simulated PBLH varied significantly with synoptic patterns, with the PBLH magnitude over land following the order: quasi-summer monsoon > transition > quasi-winter monsoon. In terms of simulation biases, the model presented an overall overestimation of PBLH over most land and an underestimation over the ocean compared with two spaceborne lidars (Cloud Aerosol Transport System; Cloud Aerosol Lidar with Orthogonal Polarization) and radiosondes; The overestimation and underestimation were amplified under quasi-summer monsoon types. As the synoptic patterns were characterized by different levels of pressure, wind, temperature, and humidity. This study further explored the relationship between model biases and meteorological factors. The results showed that the simulated PBLH biases in unstable conditions were linearly dependent on thermal factors, and showed less relationship with dynamical factors. The negative PBLH biases under warm, dry, and unstable conditions can be attributed to the model's underestimation of virtual potential temperature at lowest model level. While the positive PBLH biases under cold, wet, and stable conditions were caused by warm bias of virtual potential temperature profile at lower levels.

An Investigation of Non‐Spherical Smoke Particles Using CATS Lidar

AbstractSmoke particles originating from biomass burning events are typically assumed to be spherical, yet non‐spherical smoke particles are also reported from in situ observations. The spatial and temporal distributions of non‐spherical smoke particles, which could have impacts on passive‐ and active‐based satellite aerosol retrievals, are not yet well understood. In this analysis, using NASA's Cloud Aerosol Transport System (CATS) lidar data during the biomass burning season over Africa and South America from 2015 to 2017, we studied the frequency and distribution of non‐spherical smoke particles. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non‐spherical smoke particles which could otherwise be misclassified as dust or dust mixture using the CATS standard aerosol typing algorithm. Approximately 30% of smoke layers over Africa and South America are non‐spherical (depolarization ratio >0.1) and align with dry biomes of low soil moisture values. Conversely, spherical smoke layers (depolarization ratio <0.1) are in moist regions. The modified algorithm with improved discrimination of non‐spherical smoke detection using CATS depolarization ratio was further verified with the National Oceanic and Atmospheric Administration Hybrid Single‐Particle Lagrangian Integrated Trajectory model, Aerosol Robotic Network Ångström exponent retrievals, and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis soil moisture data. This study highlights the limitations of current aerosol typing algorithms and the potential of algorithms employing ancillary data to improve aerosol typing such as multi‐wavelength volume depolarization ratio measurements or synergy with passive sensors to further discriminate between aerosol types from spaceborne elastic backscatter lidar.

Diurnal variation in the near-global planetary boundary layer height from satellite-based CATS lidar: Retrieval, evaluation, and influencing factors

Diurnal variations in planetary boundary layer height (PBLH) are essential for regulating the diffusion of pollutants and controlling heat and moisture exchange in the lower atmosphere. However, owing to the lack of continuous large-scale observations, the global behavior of diurnal variation in the PBLH remains poorly understood. This study seeks to bridge this knowledge gap by examining 33 months of profiles from Cloud Aerosol Transport System (CATS) lidar to retrieve the PBLH on a near-global scale. The retrievals were compared with other datasets, including radiosonde, CALIPSO, reanalysis, and CMIP6. The results suggest that the CATS retrievals were significantly consistent with the radiosonde and CALIPSO retrievals, with correlations of 0.6 (clear-sky) and 0.74, respectively. Spatially, the CATS PBLH exhibits correlations of 0.11–0.72 with several CMIP6 and reanalysis outputs. For diurnal variations, the CATS PBLH showed a higher correlation (reaching 0.7) with reanalysis outputs during midday and early afternoon. However, there were considerable differences between them in the morning and late afternoon, owing to the influences of the residual layer and algorithm differences. Notably, deserts, plateaus and tropical areas exhibited more discrepancies between the CATS and reanalysis PBLH, which may be attributed to multilayer aerosol structures, heterogeneous surfaces, and deep convection. Based on the CATS derived PBLH dataset, we further investigated the meteorological impacts on interannual variability in the near-global PBLH using multiple stepwise regression and contribution calculations. The results indicate that meteorological factors explain 39–58% of the interannual variability in the CATS PBLH, and their contributions are land cover dependent and exhibit diurnal variations. Lower tropospheric stability dominated the interannual variability over land, with a diurnal pattern of its contribution aligned with the PBLH itself, whereas humidity factors contributed the most over oceans and wetlands. These findings have important implications for understanding the diurnal and temporal variability in the near-global PBLH, as well as the PBL parameterization.

Diurnal vertical distribution and transport of dust aerosol over and around Tibetan Plateau from lidar on International Space Station

Tibetan Plateau (TP) and its surroundings play a vital role in the emission and transport of dust in Eastern Asia. Using the CATS (Cloud-Aerosol Transport System) retrievals during 2015–2017, the spatiotemporal distributions and diurnal variations of dust over the study region were presented. The results showed that the dust could exceed 6 km aloft Taklimakan Desert (TD) in spring and summer, and could easily be transported to TP. The dust of Thar Desert and Indo Gangetic Plains (TDIP) reached the maximum of intensity and uplift height in summer, and was transported to TP mostly through the Yarlung Zangbo Grand Canyon aera in the longitudes of 90°−100°E. A long-standing dust layer with thickness of 1–2 km appeared over the whole region, and it was thicker over TD and TDIP. The thickest dust layer over TD appeared in the southeast part. The dense dust top showed a consistency with boundary layer height (BLH), both of which started to increase after sunrise and reached the maximum after the noon. The dust of TD appeared easier to break through the boundary layer top during daytime mainly due to stronger updraft, while it was confined below the BLH over TDIP. High wind speed and CAPE benefitted the emission and vertical transport of dust. The mountain-valley circulation also played an important role in the diurnal variation of dust. The valley wind facilitated to form a thicker dust layer during daytime, while the mountain wind helped to accumulate dust in lower level during nighttime. It showed a similar diurnal change of dust aerosol optical depth (DAOD) for CATS and MERRA-2, especially in the regions with high dust loading.

Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product

Abstract. Despite significant advances in atmospheric measurements and modeling, clouds' response to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. The launch of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in 2006 started the era of long-term spaceborne optical active sounding of Earth's atmosphere, which continued with the CATS (Cloud-Aerosol Transport System) lidar on board the International Space Station (ISS) in 2015 and the Atmospheric Laser Doppler Instrument (ALADIN) lidar on board Aeolus in 2018. The next important step is the Atmospheric Lidar (ATLID) instrument from the EarthCARE (Earth Clouds, Aerosols and Radiation Explorer) mission, expected to launch in 2024. In this article, we define the ATLID Climate Product, Short-Term (CLIMP-ST) and ATLID Climate Product, Long-Term (CLIMP-LT). The purpose of CLIMP-ST is to help evaluate the description of cloud processes in climate models, beyond what is already done with existing space lidar observations, thanks to ATLID's new capabilities. The CLIMP-LT product will merge the ATLID cloud observations with previous space lidar observations to build a long-term cloud lidar record useful to evaluate the cloud climate variability predicted by climate models. We start with comparing the cloud detection capabilities of ATLID and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) in day- and nighttime, on a profile-to-profile basis in analyzing virtual ATLID (355 nm) and CALIOP (532 nm) measurements over synthetic cirrus and stratocumulus cloud scenes. We show that solar background noise affects the cloud detectability in daytime conditions differently for ATLID and CALIPSO. We found that the simulated daytime ATLID measurements have lower noise than the simulated daytime CALIOP measurements. This allows for lowering the cloud detection thresholds for ATLID compared to CALIOP and enables ATLID to better detect optically thinner clouds than CALIOP in daytime at high horizontal resolution without false cloud detection. These lower threshold values will be used to build the CLIMP-ST (Short-Term, related only to the ATLID observational period) product. This product should provide the ability to evaluate optically thin clouds like cirrus in climate models compared to the current existing capability. We also found that ATLID and CALIPSO may detect similar clouds if we convert ATLID 355 nm profiles to 532 nm profiles and apply the same cloud detection thresholds as the ones used in GOCCP (GCM-Oriented CALIPSO Cloud Product; general circulation model). Therefore, this approach will be used to build the CLIMP-LT product. The CLIMP-LT data will be merged with the GOCCP data to get a long-term (2006–2030s) cloud climate record. Finally, we investigate the detectability of cloud changes induced by human-caused climate warming within a virtual long-term cloud monthly gridded lidar dataset over the 2008–2034 period that we obtained from two ocean–atmosphere coupled climate models coupled with a lidar simulator. We found that a long-term trend of opaque cloud cover should emerge from short-term natural climate variability after 4 years (possible lifetime) to 7 years (best-case scenario) for ATLID merged with CALIPSO measurements according to predictions from the considered climate models. We conclude that a long-term lidar cloud record built from the merging of the actual ATLID-LT data with CALIPSO-GOCCP data will be a useful tool for monitoring cloud changes and evaluating the realism of the cloud changes predicted by climate models.

Spatiotemporal distribution of dust aerosol optical properties from CALIPSO and CATS observations in Xinjiang, China

In the current study, the spatiotemporal distribution of optical properties of desert dust aerosols (DD) are investigated in Xinjiang Basin located in Northwest China, using Cloud-Aerosol Transport System (CATS) lidar onboard International Space Station (ISS) during 2015–2017, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar level 3 product during 2007–2021. CALIPSO and CATS are in good agreement to capture the high aerosol loading in this dust-dominant region. From the horizontal scale, the dust aerosol optical depth (DAOD) presents the seasonal variations (Spring > Summer > Autumn > Winter) observed by CALIPSO. Moreover, DAOD suggests the south-to-north with the declined gradient, likely due to the different scale of dust sources and climate conditions between Southern Xinjiang (SXJ) and Northern Xinjiang (NXJ). From the vertical scale, the dust extinction coefficient (DEC) is gradually decreased with the height in the four seasons, possibly attributed to the larger and heavier dust particles near the low-level contributing to the larger DEC. Compared with SXJ, the DEC has the slighter decreased gradient with height, and the optically thin DD occupies the higher ratio in NXJ observed by CALIPSO. In addition, the diurnal cycle of DD is insignificant in SXJ obtained from CATS, which is likely associated with the dust emission from Taklimakan Desert mainly driven by frontal passage, further causing the high wind and deep convection activities. Meantime, the insignificant diurnal variation of DD is also found in NXJ based on CATS.

Aerosol Detection from the Cloud–Aerosol Transport System on the International Space Station: Algorithm Overview and Implications for Diurnal Sampling

Concentrations of particulate aerosols and their vertical placement in the atmosphere determine their interaction with the Earth system and their impact on air quality. Space-based lidar, such as the Cloud–Aerosol Transport System (CATS) technology demonstration instrument, is well-suited for determining the vertical structure of these aerosols and their diurnal cycle. Through the implementation of aerosol-typing algorithms, vertical layers of aerosols are assigned a type, such as marine, dust, and smoke, and a corresponding extinction-to-backscatter (lidar) ratio. With updates to the previous aerosol-typing algorithms, we find that CATS, even as a technology demonstration, observed the documented seasonal cycle of aerosols, comparing favorably with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) space-based lidar and the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) model reanalysis. By leveraging the unique orbit of the International Space Station, we find that CATS can additionally resolve the diurnal cycle of aerosol altitude as observed by ground-based instruments over the Maritime Continent of Southeast Asia.

Exploring the Scientific Utility of Combined Spaceborne Lidar and Lightning Observations of Thunderstorms

AbstractApproximately 8 months of colocated spaceborne lidar and lightning observations were analyzed in a pathfinder study to understand the advantages and challenges of using these combined observations to understand thunderstorms. Data from the Lightning Imaging Sensor (LIS) and the Cloud‐Aerosol Transport System (CATS) lidar were used when they overlapped on the International Space Station during March–October 2017. Using simple matching criteria, 8246 LIS flashes occurred within 25 km of the CATS ground track. CATS cloud top heights near these flashes showed similar behavior with latitude when compared with a spaceborne radar‐based climatology, but the lidar cloud tops were approximately 2‐km higher than 20‐dBZ radar echo tops. CATS cloud phase near LIS flashes was consistent with ice‐ or mixed‐phase more than 90% of the time, showing the value of using lightning observations to validate lidar‐based feature masks. In addition, correlations between a proxy for LIS flash rate and CATS ice water path, cloud optical depth, and cloud top height were low (0.38–0.42) but positive and highly statistically significant (>99%), suggesting that lidar retrievals of cloud properties can be meaningfully compared with lightning observations despite lidar's known inability to penetrate deeply into optically thick clouds like thunderstorms. Finally, CATS was used to help diagnose LIS false alarms due to surface‐based glint. The false alarm rate was approximately 0.1%, which demonstrated the excellent performance of the surface glint filter in the LIS processing code. The results suggest that fruitful scientific insights can be expected from larger combined lidar/lightning datasets.

Planetary Boundary Layer Height Estimates From ICESat-2 and CATS Backscatter Measurements

The lowest layer of the atmosphere in which all human activity occurs is called the Planetary Boundary Layer (PBL). All physical interactions with the surface, such as heat and moisture transport, pollution dispersion and transport happen in this relatively shallow layer. The ability to understand and model the complex interactions that occur in the PBL is very important to air quality, weather prediction and climate modeling. A fundamental and physically important property of the PBL is its thickness or height. This work presents two methods to obtain global PBL height using satellite lidar data from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and the Cloud-Aerosol Transport System (CATS). The first method is a straightforward backscatter threshold technique and the second is a machine learning approach known as a Convolutional Neural Network. The PBL height retrievals from the two methods are compared with each other and with PBL height from the NASA GEOS MERRA-2 reanalysis. The lidar-retrieved PBL heights have a high degree of spatial correlation with the model heights but are generally higher over ocean (∼400 m) and over northern hemisphere high latitude regions (∼1,000 m). Over mid-latitude and tropical land areas, the satellite estimated PBL heights agree well with model mid-day estimates. This work demonstrates the feasibility of using satellite lidar backscatter measurements to obtain global PBL height estimates, as well as determining seasonal and regional variability of PBL height.

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

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.

Assessment and Error Analysis of Terra-MODIS and MISR Cloud-Top Heights Through Comparison With ISS-CATS Lidar.

Cloud‐top heights (CTH) from the Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra constitute our longest‐running single‐platform CTH record from a stable orbit. Here, we provide the first evaluation of the Terra Level 2 CTH record against collocated International Space Station Cloud‐Aerosol Transport System (CATS) lidar observations between 50ºN and 50ºS. Bias and precision of Terra CTH relative to CATS is shown to be strongly tied to cloud horizontal and vertical heterogeneity and altitude. For single‐layered, unbroken, optically thick clouds observed over all altitudes, the uncertainties in MODIS and MISR CTH are −540 ± 690 m and −280 ± 370 m, respectively. The uncertainties are generally smaller for lower altitude clouds and larger for optically thin clouds. For multi‐layered clouds, errors are summarized herein using both absolute CTH and CATS‐layer‐altitude proximity to Terra CTH. We show that MISR detects the lower cloud in a two‐layered system, provided top‐layer optical depth <∼0.3, but MISR low‐cloud CTH errors are unaltered by the presence of thin cirrus. Systematic and random errors are propagated to explain inter‐sensor disagreements, as well as to provide the first estimate of the MISR stereo‐opacity bias. For MISR, altitude‐dependent wind‐retrieval bias (−90 to −110 m) and stereo‐opacity bias (−60 to −260 m) and for MODIS, CO2‐slicing bias due to geometrically thick cirrus leads to overall negative CTH bias. MISR’s precision is largely driven by precision in retrieved wind‐speed (3.7 m s−1), whereas MODIS precision is driven by forward‐modeling uncertainty.

Measurement Report: Determination of aerosol vertical features on different timescales over East Asia based on CATS aerosol products

Abstract. The Cloud-Aerosol Transport System (CATS) lidar, on board the International Space Station (ISS), provides a new opportunity for studying aerosol vertical distributions, especially the diurnal variations, from space observations. In this study, we investigate the seasonal variations and diurnal cycles in the vertical aerosol extinction coefficients (AECs) over East Asia by taking advantage of 32 months of continuous and uniform aerosol measurements from the CATS lidar. Over the Tibetan Plateau, a belt of AECs at approximately 6 km between 30 and 38∘ N persistently exists in all seasons with an obvious seasonal variation. In summer, the aerosols at 6 km are identified as a mixture of both anthropogenic aerosols transported from India and coarse dust particles from Asian dust sources. In addition, the high AECs up to 8 km in summer over the Tibetan Plateau are caused by smoke aerosols from thermal dynamic processes. In fall and winter, the northern slope of the plateau is continuously influenced by both dust aerosols and polluted aerosols transported upslope from cities located at lower elevations in northwestern Asia. The diurnal variation in AECs in North China is mainly related to the diurnal variations in the transported dust and local polluted aerosols. Below 2 km, the AEC profiles in North China at 06:00 and 12:00 CST (China standard time) are significantly higher than those at 00:00 and 18:00 CST, reaching a maximum at midday. The aerosol vertical profiles over the Tarim Desert region in summer have obvious diurnal variations, and the AECs at 12:00 and 18:00 CST are significantly higher than those at 00:00 and 06:00 CST, which are induced by the strong diurnal variations in near-surface wind speeds. In addition, the peak in the AEC profiles has a significant seasonal variation, which is mainly determined by the boundary layer height.

Comparison of ISS–CATS and CALIPSO–CALIOP Characterization of High Clouds in the Tropics

Clouds in the tropics have an important role in the energy budget, atmospheric circulation, humidity, and composition of the tropical-to-global upper-troposphere–lower-stratosphere. Due to its non-sun-synchronous orbit, the Cloud–Aerosol Transport System (CATS) onboard the International Space Station (ISS) provided novel information on clouds from space in terms of overpass time in the period of 2015–2017. In this paper, we provide a seasonally resolved comparison of CATS characterization of high clouds (between 13 and 18 km altitude) in the tropics with well-established CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) data, both in terms of clouds’ occurrence and cloud optical properties (optical depth). Despite the fact that cloud statistics for CATS and CALIOP are generated using intrinsically different local overpass times, the characterization of high clouds occurrence and optical properties in the tropics with the two instruments is very similar. Observations from CATS underestimate clouds occurrence (up to 80%, at 18 km) and overestimate the occurrence of very thick clouds (up to 100% for optically very thick clouds, at 18 km) at higher altitudes. Thus, the description of stratospheric overshoots with CATS and CALIOP might be different. While this study hints at the consistency of CATS and CALIOP clouds characterizaton, the small differences highlighted in this work should be taken into account when using CATS for estimating cloud properties and their variability in the tropics.

Airborne in situ measurements of aerosol size distributions and black carbon across the Indo-Gangetic Plain during SWAAMI–RAWEX

Abstract. During the combined South-West Asian Aerosol–Monsoon Interactions and Regional Aerosol Warming Experiment (SWAAMI–RAWEX), collocated airborne measurements of aerosol number–size distributions in the size (diameter) regime 0.5 to 20 µm and black carbon (BC) mass concentrations were made across the Indo-Gangetic Plain (IGP), for the first time, from three distinct locations, just prior to the onset of the Indian summer monsoon. These measurements provided an east–west transect of region-specific properties of aerosols as the environment transformed from mostly arid conditions of the western IGP (represented by Jodhpur, JDR) having dominance of natural aerosols to the central IGP (represented by Varanasi, VNS) having very high anthropogenic emissions, to the eastern IGP (represented by the coastal station Bhubaneswar, BBR) characterized by a mixture of the IGP outflow and marine aerosols. Despite these, the aerosol size distribution revealed an increase in coarse mode concentration and coarse mode mass fraction (fractional contribution to the total aerosol mass) with the increase in altitude across the entire IGP, especially above the well-mixed region. Consequently, both the mode radii and geometric mean radii of the size distributions showed an increase with altitude. However, near the surface and within the atmospheric boundary layer (ABL), the features were specific to the different subregions, with the highest coarse mode mass fraction (FMC∼72 %) in the western IGP and highest accumulation fraction in the central IGP with the eastern IGP in between. The elevated coarse mode fraction is attributed to mineral dust load arising from local production as well as due to advection from the west. This was further corroborated by data from the Cloud-Aerosol Transport System (CATS) on board the International Space Station (ISS), which also revealed that the vertical extent of dust aerosols reached as high as 5 km during this period. Mass concentrations of BC were moderate (∼1 µg m−3) with very little altitude variation up to 3.5 km, except over VNS where very high concentrations were seen near the surface and within the ABL. The BC-induced atmospheric heating rate was highest near the surface at VNS (∼0.81 K d−1), while showing an increasing pattern with altitude at BBR (∼0.35 K d−1 at the ceiling altitude).

Observation and quantification of aerosol outflow from southern Africa using spaceborne lidar

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.&#x0D; Significance:&#x0D; &#x0D; Biomass burning in Africa can have impacts as far away as Australia.&#x0D; 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.&#x0D; The CATS instrument on the International Space Station has detection sensitivity that captures aerosol transport from Africa over the Indian Ocean to Australia.&#x0D; &#x0D; Open data set: &#x0D; https://cats.gsfc.nasa.gov/

Cloud Occurrence Frequency at Puy de Dôme (France) Deduced from an Automatic Camera Image Analysis: Method, Validation, and Comparisons with Larger Scale Parameters

We present a simple algorithm that calculates the cloud occurrence frequency at an altitude site using automatic camera image analysis. This algorithm was applied at the puy de Dôme station (PUY, 1465 m. a.s.l., France) over 2013–2018. Cloud detection thresholds were determined by direct comparison with simultaneous in situ cloud probe measurements (particulate volume monitor (PVM) Gerber). The cloud occurrence frequency has a seasonal cycle, with higher values in winter (60%) compared to summer (24%). A cloud diurnal cycle is observed only in summer. Comparisons with the larger scale products from satellites and global model reanalysis are also presented. The NASA cloud-aerosol transport system (CATS) cloud fraction shows the same seasonal and diurnal variations and is, on average, 11% higher. Monthly variations of the ECMWF ERA-5 fraction of cloud cover are also highly correlated with the camera cloud occurrence frequency, but the values are 19% lower and up to 40% for some winter months. The METEOSAT-SEVIRI cloud occurrence frequency also follows the same seasonal cycle but with a much smaller decrease in summer. The all-sky imager cloud fraction (CF) presents larger variability than the camera cloud occurrence but also follows similar seasonal variations (67% in winter and 44% in summer). This automatic low-cost detection of cloud occurrence is of interest in characterizing altitude observation sites, especially those that are not yet equipped with microphysical instruments and can be deployed to other high-altitude sites equipped with cameras.

Cloud Aerosol Transport System (CATS) 1064 nm Calibration and Validation.

The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing range-resolved 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 above mean sea level (AMSL). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) re-analysis 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 km to -2 km AMSL. 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 clear-air regions. Eight nighttime comparisons between CATS and the PollyXT ground based lidars also show good agreement in clear-air regions between 3-12 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 the Cloud-Aerosol Lidar with Orthogonal Polarization (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.