Cloud-Aerosol Lidar And Infrared Pathfinder Satellite Observations Data Research Articles

A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

Abstract. The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.

Theoretical derivation of aerosol lidar ratio using Mie theory for CALIOP-CALIPSO and OPAC aerosol models

Abstract. The extinction-to-backscattering ratio, popularly known as lidar (light detection and ranging) ratio of atmospheric aerosols is an important optical property, which is essential to retrieve the extinction profiles of atmospheric aerosols. Lidar satellite observations can provide the global coverage of atmospheric aerosols along with their vertical extent. NASA's Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is the only space-based platform available, so far, that provides the vertical profiles of extinction due to atmospheric aerosols. A physics-based theoretical approach is presented in the present paper that estimates lidar ratio values for CALIPSO aerosol models, which can be used as inputs to determine the extinction profiles of aerosols using CALIPSO data. The developed methodology was also qualified by comparing it with the lidar ratio values derived using AERONET (AErosol RObotic NETwork) datasets. Lidar ratio values for CALIPSO aerosol models were estimated in the range of 38.72 to 85.98 sr at 532 nm, whereas at 1064 nm lidar ratio varied between 20.11 to 71.11 sr depending upon the aerosol type and their size distributions. Aerosols are compositions of various particles; thus, the presence of water vapour in the atmosphere can affect the optical properties of the aerosols. Thus, the effect of relative humidity on lidar ratio was studied using Optical Properties of Aerosols and Clouds (OPAC) aerosol models, which are the standard aerosol models against the cluster-classified AERONET and CALIPSO aerosol models. Water-soluble particles contribute substantially in clean continental, clean marine, tropical marine and desert aerosol models and are hygroscopic in nature. Hygroscopic sulfate particles dominate the Antarctic aerosols during summertime. In the presence of relative humidity between 0 %–80 %, the lidar ratio values were observed to decrease from 53.59 to 47.13, from 53.66 to 47.15, from 53.70 to 47.16, and from 55.32 to 48.78 sr at 532 nm for clean continental, clean marine, tropical marine, and desert aerosols, respectively, whereas lidar ratio gradually increased from 47.13 to 51, from 47.15 to 51, from 47.16 to 51, and from 48.78 to 51.68 sr, respectively, for these aerosol models when relative humidity was between 80 %–99 %, due to constituent hygroscopic particles. In the case of Antarctic aerosols, the lidar ratio was observed to increase from 57.73 to 97.64 sr due to hygroscopic sulfate particles that backscattered heavily in the presence of water vapour at 532 nm. The soot particles dominate the polluted continental and polluted marine particles, causing an increase in lidar ratio over its corresponding clean counterpart. Similar results were observed at 1064 nm for OPAC aerosol models.

Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean.

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols' impact and trends.

Assessment of using spaceborne LiDAR to monitor the particulate backscatter coefficient on large, freshwater lakes: A test using CALIPSO on Lake Michigan

The Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was launched in 2006 with the primary goal of measuring the properties of clouds and aerosols in Earth’s atmosphere using LiDAR. Since then, numerous studies have shown the viability of using CALIPSO to observe day/night differences in subsurface optical properties of oceans and large seas from space. To date no studies have been done on using CALIPSO to monitor the subsurface optical properties of large, freshwater-lakes. This is likely due to the limited spatial resolution of CALIPSO, which makes the mapping of subsurface properties of regions smaller than large seas impractical. Still, CALIPSO does pass over some of the world’s largest, freshwater-lakes, yielding important information about the water. Here we use the entire CALIPSO data record (approximately 15 years) to measure the particulate backscatter coefficient (bbp, m−1) across Lake Michigan. We then compare the LiDAR derived values of bbp to optical imagery values obtained from MODIS and to in situ measurements. Critically, we find that the LiDAR derived bbp aligns better in non-summer months with in situ values when compared to the optically imagery. However, due to both high cloud coverage and high wind speeds on Lake Michigan, this comes with the caveat that the CALIPSO product is limited in its usability. We close by speculating on the roll that spaceborne LiDAR, including CALIPSO and other satitlites, have on the future of monitoring the Great Lakes and other large bodies of fresh water.

Fog Detection and Estimation Using CALIPSO Lidar Observations

AbstractMost fog detection from space cannot differentiate fog and low stratus clouds, and cannot estimate fog deposition. This study assessed the feasibility of using spaceborne lidar observations from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in fog detection and estimation. We tested the method in the central Namib Desert, Namibia, where frequent fog events occur and fog observations are available. Results showed the CALIPSO backscatter signal at 532 nm can differentiate low clouds and fog due to its high‐resolution vertical profiles. Backscatter signals during fog events were significantly higher than those during non‐fog periods. The R2 between backscatter signals and fog observations was 0.85. Moreover, the backscatter signal was also sensitive to relative humidity variation (R2 = 0.66). These results indicate that the CALIPSO data are feasible to estimate fog occurrence and deposition, providing a new perspective for space‐based fog studies.

A novel method of identifying and analysing oil smoke plumes based on MODIS and CALIPSO satellite data

Abstract. Black carbon aerosols are the second largest contributor to global warming while also being linked to respiratory and cardiovascular disease. These particles are generally found in smoke plumes originating from biomass burning and fossil fuel combustion. They are also heavily concentrated in smoke plumes originating from oil fires, exhibiting the largest ratio of black carbon to organic carbon. In this study, we identified and analysed oil smoke plumes derived from 30 major industrial events within a 12-year timeframe. To our knowledge, this is the first study of its kind that utilized a synergetic approach based on satellite remote sensing techniques. Satellite data offer access to these events, which, as seen in this study, are mainly located in war-prone or hazardous areas. This study focuses on the use of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) products regarding these types of aerosol while also highlighting their intrinsic limitations. By using data from both MODIS instruments on board Terra and Aqua satellites, we addressed the temporal evolution of the smoke plume while assessing lidar-specific properties and plume elevation using CALIPSO data. The analysis method in this study was developed to better differentiate between oil smoke aerosols and the local atmospheric scene. We present several aerosol properties in the form of plume-specific averaged values. We believe that MODIS values are a conservative estimation of plume aerosol optical depth (AOD) since MODIS algorithms rely on general aerosol models and various atmospheric conditions within the look-up tables, which do not reflect the highly absorbing nature of these smoke plumes. Based on this study we conclude that the MODIS land algorithms are not yet suited for retrieving aerosol properties for these types of smoke plumes due to the strong absorbing properties of these aerosols. CALIPSO retrievals rely heavily on the type of lidar solutions showing discrepancy between constrained and unconstrained retrievals. Smoke plumes identified within a larger aerosol layer were treated as unconstrained retrievals and resulted in conservative AOD estimates. Conversely, smoke plumes surrounded by clear air were identified as opaque aerosol layers and resulted in higher lidar ratios and AOD values. Measured lidar ratios and particulate depolarization ratios showed values similar to the upper ranges of biomass burning smoke. Results agree with studies that utilized ground-based retrievals, in particular for Ångström exponent (AE) and effective radius (Reff) values. MODIS and CALIPSO retrieval algorithms disagree on AOD ranges, for the most part, due to the extreme light-absorbing nature of these types of aerosols. We believe that these types of studies are a strong indicator for the need of improved aerosol models and retrieval algorithms.

CALIPSO Aerosol-Typing Scheme Misclassified Stratospheric Fire Smoke: Case Study From the 2019 Siberian Wildfire Season

In August 2019, a 4-km thick wildfire smoke layer was observed in the lower stratosphere over Leipzig, Germany, with a ground-based multiwavelength Raman lidar. The smoke was identified by the smoke-specific spectral dependence of the extinction-to-backscatter ratio (lidar ratio) measured with the Raman lidar. The spaceborne CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) detected the smoke and classified it as sulfate aerosol layer (originating from the Raikoke volcanic eruption). In this article, we discuss the reason for this misclassification. Two major sources for stratospheric air pollution were active in the summer of 2019 and complicated the CALIPSO aerosol typing effort. Besides intense forest fires at mid and high northern latitudes, the Raikoke volcano erupted in the Kuril Islands. We present two cases observed at Leipzig, one from July 2019 and one from August 2019. In July, pure volcanic sulfate aerosol layers were found in the lower stratosphere, while in August, wildfire smoke dominated in the height range up to 4–5 km above the local tropopause. In both cases, the CALIPSO aerosol typing scheme classified the layers as sulfate aerosol layers. The aerosol identification algorithm assumes non-spherical smoke particles in the stratosphere as consequence of fast lifting by pyrocumulonimbus convection. However, we hypothesize (based on presented simulations) that the smoke ascended as a results of self-lifting and reached the tropopause within 2–7 days after emission and finally entered the lower stratosphere as aged spherical smoke particles. These sphercial particles were then classified as liquid sulfate particles by the CALIPSO data analysis scheme. We also present a successful case of smoke identification by the CALIPSO retrieval method.

Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model

Based on the three-dimensional variational assimilation (3DVAR) algorithm, the extinction coefficient profiles of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite were assimilated for the first time on the basis of the aerosol variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the Weather Research and Forecasting-Chemistry (WRF-Chem) model. A light aerosol pollution episode (case1) and a heavy pollution event (case2) were selected to simulate and predict using the WRF-Chem model. Two sets of data were assimilated: CALIPSO derived aerosol extinction coefficient profiles and surface PM mass concentration data obtained from ground monitoring stations across China. Then, various aerosol data sources including ground-based lidar in Beijing area, Himawari-8 (H-8) satellite aerosol optical depth (AOD) and surface PM monitors were used for comprehensive evaluate the impact of CALIPSO data assimilation on the model performance. The results showed that simultaneous assimilation of the CALIPSO extinction coefficient profiles and surface PM mass concentration data can effectively improve the model aerosol initial conditions and forecasting accuracy of PM2.5, PM10 and AOD. The positive impact on PM2.5 and PM10 forecasting by the assimilation can be maintained for more than 14 h for the case1 and 24 h for the case2, and the initial field of AOD in assimilation experiment has been greatly improved comparing the model control run (cn, without data assimilation). For the simulation of the case2, the assimilation of CALIPSO extinction coefficient profiles reduced the root mean square error (RMSE), mean fraction (MFE) and bias (BIAS) of the AOD in the initial field of the model by 0.08 (19.5%), 0.34 (55.7%) and 0.16 (48.5%) compared to the cn experiment, respectively. When the CALIPSO extinction coefficient profiles and surface PM mass concentration data were assimilated simultaneously, the reduction of the RMSE, MFE and BIAS of the AOD was 0.6 (14.6%), 0.32 (52.5%) and 0.12 (36.4%), respectively. Additionally, aerosol extinction coefficient are remarkably underestimated in the cn experiment, particularly during heavy aerosol pollution case. Assimilating surface PM data can improve the extinction coefficient distribution below 500 m. The simulated aerosol extinction coefficient distribution at 1 km to 2 km can be significantly improved in both cases by assimilating the CALIPSO extinction coefficient profiles, and the value of model simulated extinction coefficient is nearly doubled compared to the cn experiment. Finally, the evaluation with independent ground-based lidar measurements indicates that combined assimilation of surface PM mass concentration and CALIPSO extinction coefficient profile can effectively improve the aerosol distribution near the ground and in the upper layer, and thus generate a more accurate 3D aerosol analysis and forecast field.

A simple multiscale layer detection algorithm for CALIPSO measurements

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is unique in its ability to perform profiling measurements of aerosol and cloud layers globally. Detecting the layer boundaries of aerosols and clouds is a crucial step in CALIPSO data retrieval. The CALIPSO team uses the selective iterated boundary location (SIBYL) algorithm based on threshold arrays to find aerosol and cloud layers at different horizontal resolutions. However, threshold arrays could obstruct the detection of optically tenuous layers at a high resolution and may cause overestimation when averaging signals of layer and clear air at a low resolution. Here, a multiscale algorithm using a series of sliding window sizes without threshold setting is proposed based on a pre-defined probability. The results over land and marine areas show that the multiscale algorithm detected 37.41% and 16.36% more layer area than the SIBYL at 1–80 km resolutions at daytime and 1–5 km resolutions at night time, respectively. This indicates that the multiscale algorithm does not need a threshold array, allowing more tenuous layers to be detected, especially at low signal to noise ratios (SNRs). In contrast, the SIBYL detects 4.40% more layer area than the multiscale algorithm at 1–80 km resolutions at nighttime, mainly caused by the large proportion of layer area detected by SIBYL at 20 and 80 km resolutions. This implies possible noteworthy overestimation by the SIBYL at low resolutions. Additionally, the evaluation using the depolarization ratio of ice clouds shows that the extra detected layers by the multiscale algorithm are reliable. Besides, simulation tests show that the multiscale and SIBYL algorithms achieve a 100% true detection rate when SNR is approximately 2 and 4, respectively. The new multiscale algorithm could upgrade the resolution and accuracy of the layer detection of space lidars and reduce the underestimation of layer optical depth due to missing layers.

Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat–CALIPSO Radar–Lidar Observations

The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of a combination of infrequent sampling, and a very narrow, pencil-like swath. This study provides the first global assessment of these uncertainties, which are quantified using bootstrapped confidence intervals. Rather than focusing on a purely theoretical discussion, we investigate empirical data that span a five-year period between 2006 and 2011. We examine the 2B-Geometric Profiling (GEOPROF)-LIDAR cloud product, at typical spatial resolutions found in global grids (1.0°, 2.5°, 5.0°, and 10.0°), four confidence levels (0.85, 0.90, 0.95, and 0.99), and three time scales (annual, seasonal, and monthly). Our results demonstrate that it is impossible to estimate, for every location, a five-year mean cloud amount based on CloudSat–CALIPSO data, assuming an accuracy of 1% or 5%, a high confidence level (&gt;0.95), and a fine spatial resolution (1°–2.5°). In fact, the 1% requirement was only met by ~6.5% of atmospheric volumes at 1° and 2.5°, while the more tolerant criterion (5%) was met by 22.5% volumes at 1°, or 48.9% at 2.5° resolution. In order for at least 99% of volumes to meet an accuracy criterion, the criterion itself would have to be lowered to ~20% for 1° data, or to ~8% for 2.5° data. Our study also showed that the average confidence interval: decreased four times when the spatial resolution increased from 1° to 10°; doubled when the confidence level increased from 0.85 to 0.99; and tripled when the number of data-months increased from one (monthly mean) to twelve (annual mean). The cloud regime arguably had the most impact on the width of the confidence interval (mean cloud amount and its standard deviation). Our findings suggest that existing uncertainties in the CloudSat–CALIPSO five-year climatology are primarily the result of climate-specific factors, rather than the sampling scheme. Results that are presented in the form of statistics or maps, as in this study, can help the scientific community to improve accuracy assessments (which are frequently omitted), when analyzing existing and future CloudSat–CALIPSO cloud climatologies.

Estimation of cross-boundary aerosol flux over the Edmonton-Calgary Corridor in Canada based on CALIPSO and MERRA-2 data during 2011–2017

Via combining aerosol vertical profiles from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and assimilated multi-layer wind profiles from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the horizontal aerosol cross-boundary transport for the Edmonton-Calgary Corridor (ECC) during 2011–2017 was characterized on the basis of aerosol extinction flux. The western and northern boundaries (WB and NB) were revealed as the two aerosol importing gateways for ECC, with the annual mean cross-boundary efficiencies of 0.286 ± 0.231 hr−1 for WB and 0.050 ± 0.025 hr−1 for NB. The annual mean efficiency of the cross eastern boundary (EB) aerosol transport was −0.267 ± 0.243 hr−1, indicating an overall aerosol exporting effect for ECC. More efficient cross southern boundary (SB) aerosol importing processes were found in July (0.049 ± 0.071 hr−1) and August (0.043 ± 0.058 hr−1), which favor the outbreak of Pacific Northwest wildfires. The aerosol emissions from Athabasca oil sands region (AOSR) in Alberta are unlikely to have significant impact on ECC via the cross-NB aerosol transport pathway. The annual mean aerosol extinction flow of 202 ± 2244 km2/h suggested the cross-boundary aerosol importing effect for ECC. The most significant aerosol importing effect for ECC was found in fall, indicated by seasonal mean aerosol extinction flow of 680 ± 1122 km2/h. The observed seasonal variation implied the noticeable influences of aerosol local emissions and cloud-aerosol interactions on ECC aerosol loadings in winter and summer. To obtain a complete picture of aerosol loading budget over ECC, further investigations on the dynamics of aerosol emissions and depositions happened in the ECC atmosphere remain.

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.

A Decadal Global Climatology of Ice Cloud Fraction with Their Microphysical and Optical Properties Inferred from the CALIPSO and Reanalysis Data

In the present study, the spatiotemporal and vertical distributions of ice cloud properties and their association with meteorological variables are analyzed for the period 2007–2016 using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Modern Era Retrospective-Analysis for Research (MERRA-2) reanalysis observations. The distribution of ice cloud fraction (ICF) with its peak does not overlap with that of the ice water content (IWC) peak during daytime and nighttime due to the sampling bias. Moreover, the vertical distributions of mean IWC exhibited a vaguely “sharp thorn” at an altitude of ~4 km in all seasons at the location of about ±40°, which can be caused by the artifacts. Furthermore, it is noted that different ice cloud optical depth (ICOD) presents significant changes observed in their diurnal variations in the heights of peaks. The maximum diurnal difference of ice cloud properties occurs in the tropical regions of the North Hemisphere (NH) during summer. We also investigated the relation between ICOD and the meteorological variables and found that the ICOD values are dependent on the meteorological parameters.

Vertical cloud structure of warm conveyor belts – a comparison and evaluation of ERA5 reanalysis, CloudSat and CALIPSO data

Abstract. Warm conveyor belts (WCBs) are important cyclone-related airstreams that are responsible for most of the cloud and precipitation formation in the extratropics. They can also substantially influence the dynamics of cyclones and the upper-level flow. So far, most of the knowledge about WCBs is based on model data from analyses, reanalyses and forecast data with only a few observational studies available. The aim of this work is to gain a detailed observational perspective on the vertical cloud and precipitation structure of WCBs during their inflow, ascent and outflow and to evaluate their representation in the new ERA5 reanalysis dataset. To this end, satellite observations from the CloudSat radar and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar are combined with an ERA5-based WCB climatology for nine Northern Hemisphere winters. Based on a case study and a composite analysis, the main findings can be summarized as follows. (i) WCB air masses are part of deep, strongly precipitating clouds, with cloud-top heights at 9–10 km during their ascent and an about 2–3 km deep layer with supercooled liquid water co-existing with ice above the melting layer. The maximum surface precipitation occurs when the WCB is at about 2–4 km height. (ii) Convective clouds can be observed above the inflow and during the ascent. (iii) At upper levels, the WCB outflow is typically located near the top of a 3 km deep cirrus layer. (iv) There is a large variability between WCBs in terms of cloud structure, peak reflectivity and associated surface precipitation. (v) The WCB trajectories with the highest radar reflectivities are mainly located over the North Atlantic and North Pacific, and – apart from the inflow – they occur at relatively low latitudes. They are associated with particularly deep and strongly precipitating clouds that occur not only during the ascent but also in the inflow and outflow regions. (vi) ERA5 represents the WCB clouds remarkably well in terms of position, thermodynamic phase and frozen hydrometeor distribution, although it underestimates the high ice and snow values in the mixed-phase clouds near the melting layer. (vii) In the lower troposphere, high potential vorticity is diabatically produced along the WCB in areas with high reflectivities and hydrometeor contents, and at upper levels, low potential vorticity prevails in the cirrus layer in the WCB outflow. The study provides important observational insight into the internal cloud structure of WCBs and emphasizes the ability of ERA5 to essentially capture the observed pattern but also reveals many small- and mesoscale structures observed by the remote sensing instruments but not captured by ERA5.

Emissions of toxic gases and aerosols in southern Africa observed during the 2019 JJASO period

There are several different types of sources responsible for the emissions of carbon monoxide (CO), sulphur dioxide (SO2), nitrogen dioxide (NO2) and aerosols in Africa. These emissions make the African continent vulnerable to the impacts on climate change, human health and the environment. It has been shown that the southern African emissions are dominant in the winter season (JJA). However, few studies have been done to study the winter and spring season in unison to understand these emissions. Therefore, in this study, both satellite data, such as Sentinel-5P and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), and reanalysis data from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), were used to study emissions in the 2019 JJASO period. Furthermore, the sequential Mann-Kendall (SQMK) test was used to study the trends of SO2, CO, Angstrom exponent and UV aerosol index data during the 2019 JJASO period. The results from sentinel-5P showed the dominance of CO and NO2 in the Democratic Republic of the Congo (DRC) and dominance of SO2 and NO2 in the Republic of South Africa (RSA). These emissions were largely from wildfires and coal-fired power stations, respectively. However, an SO2 hot spot was also observed over the DRC region which was found to be from an active volcano. CALIPSO data agreed with these observations and further showed the maximum vertical distribution of these aerosols and gases to be at ~ 2 km. The SQMK test of Angstrom exponent and UV aerosol index showed that aerosol dominance from emissions change during the JJASO period. Therefore, studying the JJASO period does give a better understanding of emissions in southern Africa.

Comparison of MODIS- and CALIPSO-Derived Temporal Aerosol Optical Depth over Yellow River Basin (China) from 2007 to 2015

In this study, Collection 6.1 (C6.1) of different aerosol optical depth (AOD) products of different spatial resolutions were used from the aqua moderate resolution imaging spectroradiometer (MODIS) including dark target (DT), deep blue (DB), deep blue (DB), and DT-DB (DTB). These products were compared with cloud-aerosol lidar, and infrared pathfinder satellite observation (CALIPSO) AOD retrievals over the Yellow River Basin (YERB), China from 2003 to 2017. The YERB was divided into three sub-regions, namely YERB1 (the mountainous terrain in the upper reaches of the YERB), YERB2 (the Loess Plateau region in the middle reaches of the YERB), and YERB3 (the plain region downstream of the YERB). Errors and agreement between MODIS and CALIPSO data were reported using Pearson’s correlation (R) and relative mean bias (RMB). Results showed that the CALIPSO whole layers AOD (AODS) were better matched with MODIS AOD than the CALIPSO lowest layer AOD (AOD1). The time series of AOD shows higher values in spring and summer, and a small difference in AOD products was observed in autumn. The overall average value of CALIPSO AOD and MODIS AOD both fitted the order: YERB3 > YERB2 > YERB1. The CALIPSO AOD retrievals have the best consistency with the DTB10K and the lowest consistency with DT3K. Overall, the regional distributions of the CALIPSO AOD and MODIS AOD are significantly different over the YERB, and the difference is closely related to the season, region, and topography. This study can help researchers understand the difference of aerosol temporal and spatial distribution utilizing different satellite products over YERB, and also can provide data and technical support for the government in atmospheric environmental governance over YERB.

Revisiting global satellite observations of stratospheric cirrus clouds

Abstract. As knowledge about the cirrus clouds in the lower stratosphere is limited, reliable long-term measurements are needed to assess their characteristics, radiative impact and important role in upper troposphere and lower stratosphere (UTLS) chemistry. We used 6 years (2006–2012) of Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measurements to investigate the global and seasonal distribution of stratospheric cirrus clouds and compared the MIPAS results with results derived from the latest version (V4.x) of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. For the identification of stratospheric cirrus clouds, precise information on both the cloud top height (CTH) and the tropopause height is crucial. Here, we used lapse rate tropopause heights estimated from the ERA-Interim global reanalysis. Considering the uncertainties of the tropopause heights and the vertical sampling grid, we define CTHs more than 0.5 km above the tropopause as stratospheric for CALIPSO data. For MIPAS data, we took into account the coarser vertical sampling grid and the broad field of view so that we considered cirrus CTHs detected more than 0.75 km above the tropopause as stratospheric. Further sensitivity tests were conducted to rule out sampling artefacts in MIPAS data. The global distribution of stratospheric cirrus clouds was derived from night-time measurements because of the higher detection sensitivity of CALIPSO. In both data sets, MIPAS and CALIPSO, the stratospheric cirrus cloud occurrence frequencies are significantly higher in the tropics than in the extra-tropics. Tropical hotspots of stratospheric cirrus clouds associated with deep convection are located over equatorial Africa, South and Southeast Asia, the western Pacific, and South America. Stratospheric cirrus clouds were more often detected in December–February (15 %) than June–August (8 %) in the tropics (±20∘). At northern and southern middle latitudes (40–60∘), MIPAS observed about twice as many stratospheric cirrus clouds (occurrence frequencies of 4 %–5 % for MIPAS rather than about 2 % for CALIPSO). We attribute more frequent observations of stratospheric cirrus clouds with MIPAS to the higher detection sensitivity of the instrument to optically thin clouds. In contrast to the difference between daytime and night-time occurrence frequencies of stratospheric cirrus clouds by a factor of about 2 in zonal means in the tropics (4 % and 10 %, respectively) and at middle latitudes for CALIPSO data, there is little diurnal cycle in MIPAS data, in which the difference of occurrence frequencies in the tropics is about 1 percentage point in zonal mean and about 0.5 percentage point at middle latitudes. The difference between CALIPSO day and night measurements can also be attributed to their differences in detection sensitivity. Future work should focus on better understanding the origin of the stratospheric cirrus clouds and their impact on radiative forcing and climate.

Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms

The cloud-top height (CTH) product derived from passive satellite instrument measurements is often used to make climate data records (CDR). CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) provides CTH parameters with high accuracy, but with limited temporal-spatial resolution. Recently, the Advanced Himawari Imager (AHI) onboard Japanese Himawari-8/-9, provides high temporal (every 10 min) and high spatial (2 km at nadir) resolution measurements with 16 spectral bands. This paper reports on a study to derive the CTH from combined AHI and CALIPSO using advanced machine learning (ML) algorithms with better accuracy than that from the traditional physical (TRA) algorithms. We find significant CTH improvements (1.54–2.72 km for mean absolute error, MAE) from four different machine learning algorithms (original MAE from TRA method is about 3.24 km based on CALIPSO data validation), particularly in high and optically thin clouds. In addition, we also develop a joint algorithm to combine optimal machine learning and traditional physical (TRA) algorithms of CTH to further reduce MAE to 1.53 km and enhance the layered accuracy (CTH < 18 km). While the ML-based algorithm improves CTH retrieval over the TRA algorithm, the lower or higher clouds still exhibit relatively large uncertainty. Combining both methods provides the better CTH than either alone. The combined approach could be used to process data from advanced geostationary imagers for climate and weather applications.

CORRECTION FOR THE CIRRUS CLOUD TOP HEIGHT OF MODIS BASED ON CALIPSO IN BEIJING-TIANJIN-HEBEI REGION

Abstract. Cirrus plays an important role in atmospheric radiation. It affects weather system and climate change. Satellite remote sensing is an important kind of observation for cloud. As a passive remote sensing instrument, large bias was found for thin cirrus cloud top height retrieval from MODIS (Moderate Resolution Imaging Spectroradiometer). Comparatively, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) aboard CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) which is an active remote sensing instrument can acquire more accurate characteristics of thin cirrus cloud. In this study, CALIPSO cirrus cloud top height data was used to correct MODIS cirrus cloud top height. The data analysis area was selected in Beijing-Tianjin-Hebei region and data came from 2013 to 2017. Linear fitting method was selected based on cross-validation method between MODIS and CALIPSO data. The results shows that the difference between MODIS and CALIPSO changes from −3~2 km to −2.0~2.5 km, and the maximum difference changes from about −0.8 km to about 0.2 km. In the context of different vertical levels and cloud optical depth, MODIS cirrus cloud top height is improved after correcting, which is more obvious at lower cloud top height and optical thinner cirrus.

Temporal characteristics of aerosol optical properties over the glacier region of northern Pakistan

Glacier melting due to light-absorbing aerosol has become a growing issue in recent decades. The emphasis of this study is to examine aerosol loadings over the high mountain glacier region of northern Pakistan between 2004 and 2016, with sources including local emissions and long-range transported pollution. Optical properties of aerosols were seasonally analyzed over the glacier region (35–36.5°N; 74.5–77.5°E) along with three selected sites (Gilgit, Skardu, and Diamar) based on the Ozone Monitoring Instrument (OMI). The aerosol sub-type profile was analyzed with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to understand the origin of air masses arriving in the study region. The highest values of aerosol optical depth (AOD) and single scattering albedo (SSA) occurred during spring, whereas aerosol index (AI) and absorption AOD (AAOD) exhibited maximum values in winter and summer, respectively. The minimum values of AOD, AI, AAOD, and SSA occurred in winter, autumn, winter, and autumn, respectively. The results revealed that in spring and summer the prominent aerosols were dust, whereas, in autumn and winter, anthropogenic aerosols were prominent. Trend analysis showed that AI, AOD, and AAOD increased at the rate of 0.005, 0.006, and 0.0001 yr−1, respectively, while SSA decreased at the rate of 0.0002 yr−1. This is suggestive of the enhancement in aerosol types over the region with time that accelerates melting of ice. CALIPSO data indicate that the regional aerosol was mostly comprised of sub-types categorized as dust, polluted dust, smoke, and clean continental. The types of aerosols defined by OMI were in good agreement with CALIPSO retrievals. Analysis of the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model revealed that air parcels arriving at the glacier region stemmed from different source sites.