CALIPSO Data Research Articles

Evaluation of Aeolus feature mask and particle extinction coefficient profile products using CALIPSO data

Evaluation of Aeolus feature mask and particle extinction coefficient profile products using CALIPSO data

CACM-Net: Daytime Cloud Mask for AGRI Onboard the FY-4A Satellite

Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named “Convolutional and Attention-based Cloud Mask Net (CACM-Net)”, was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results.

Physical and optical properties of cirrus and subvisible cirrus clouds over Arabian sea and Bay of Bengal region

Cirrus clouds play a crucial role in regulating the Earth’s radiation budget, and this paper aims to contribute to a deeper understanding of their optical and geometrical properties over the Arabian Sea (AS) and Bay of Bengal (BoB) regions using CALIPSO data. Comprehensive statistics are derived, encompassing mean values of cirrus cloud top, base altitude, geometrical thickness, cloud optical depth, and temperature. Over the AS region, the mean values are 15.10 ± 1.50 km, 12.63 ± 1.75 km, 2.52 ± 1.37 km, 0.4 ± 0.58, and -62.30 ± 10.6 (°C), respectively. For the BoB region, the corresponding values are 15.43 ± 1.51 km, 12.72 ± 1.74 km, 2.71 ± 1.46 km, 0.49 ± 0.67, and -64.35 ± 10.7 (°C). A larger spread in optical depth and a higher frequency of occurrence for both cirrus and subvisible cirrus (SVC) clouds were observed over BoB compared to AS. Additionally, the study delves into SVC cloud characteristics, emphasizing their thinness and higher base altitudes compared to cirrus clouds. This comprehensive investigation contributes valuable insights into the distinctive properties of cirrus and SVC clouds in these regions, enhancing our knowledge of atmospheric processes and their implications for climate modelling and predictions.

Physical characteristics of convective and non-convective cirrus clouds from CALIPSO data over the South China Sea

Studying the characteristics and mechanisms of convective and non-convective cirrus clouds over the South China Sea is vital for their impact on regional climate dynamics, and enhancing predictive models for weather and climate forecasts. This study utilizes eight years of CALIPSO data (from March 2007 to February 2015) to investigate convective and non-convective cirrus clouds. Explicit new insights include the observation that convective cirrus cloud samples are three times more numerous than non-convective cirrus clouds. Convective cirrus clouds are associated with humid conditions and demonstrate higher ice water content (IWC) values ranging from 10−3 to 10−1 g m−3, whereas non-convective cirrus clouds tend to be drier, exhibiting IWC values ranging from 10−4 to 10−3 g m−3. Both cirrus cloud types exhibit a maximum cloud fraction at 10°N. Convective cirrus reach their peak cloud fraction at an altitude of 14 km, while non-convective cirrus typically occur at altitudes between 15 and 16 km. The seasonal variability of the convective cirrus cloud fraction primarily reflects bottom-up positive specific humidity anomalies originating from convective activity, whereas the non-convective cirrus cloud fraction is influenced by top-down negative temperature anomalies.

Reasons for Low Fraction of Arctic Stratospheric Cloud in 2014/2015 Winter

AbstractPolar stratospheric clouds (PSCs) play a key role in Arctic amplification and stratospheric ozone destruction in polar regions. In this paper, we used the CALIPSO data to analyze the spatiotemporal distribution of Arctic PSCs from 2006 to 2021. We found that Arctic PSCs mainly appear in December, peak in late December and early January, disappearing in late February and early March. PSCs can extend from heights near the tropopause to over 25 km. However, there is the lowest fraction of PSCs in the 2014/2015 winter. This study found that the temperature in the 2014/2015 winter was warmer than the 15‐year average temperature, with the lowest temperature slightly below the PSCs formation temperature of about 5 K. The formation of the Ural blocking high accompanied by the poleward propagation of the planetary wave caused a sudden stratospheric warming (SSW) event on 3 January 2015, during which the warm air entered the polar vortex and divided it into two lobes. Additionally, a reduction in SO2 column mass density before the SSW event resulted PSCs occurring with a frequency of only 0.148 and dissipating rapidly in December. Moreover, the concentration of H2O and HNO3 in the gravitational settling process of PSCs decreased by 20–50%, the reduction of condensation nuclei made PSCs with the highest frequency of 0.074 in February appear briefly and then disappear. The chemical and dynamic analysis of PSCs formation is needed to further understand the spatiotemporal distribution of Arctic PSCs and to better predict future Arctic amplification and ozone destruction.

Radiatively Active Hydrometeor Frequencies From CloudSat–CALIPSO Data for Evaluating Cloud Fraction in Global Climate Models

AbstractThis study derives radiatively active hydrometeor frequencies (HFs) from CloudSat–CALIPSO satellite data to evaluate modeled cloud fraction in recent historical period simulations by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models. Most CMIP5 models do not consider precipitating and convective core hydrometeors in radiative transfer calculation except for CESM1‐CAM5 with precipitating ice (snow). They do not provide radiatively active cloud fractions for all precipitating hydrometeor types such as snow fraction while the satellite‐retrieved HFs include mixed profiles of stratiform clouds and precipitation as well as convective cores. A filtering method is applied to produce estimates of nonprecipitating and nonconvective core HF (NPCHF) from the total HF (THF) excluding rain, which is defined here as the sum of NPCHF, precipitating ice (snow, graupel, and hail), and convective core HF. We determine the optimal reference HF data for model comparison that include estimates of liquid‐phase NPCHF from CloudSat radar‐only data (2B‐CWC) and ice‐phase THF from CloudSat–CALIPSO 2C‐ICE combined radar/lidar data. The zonal‐mean at selected heights and regionally averaged profiles of CMIP5 multi‐model‐mean cloud fraction show good agreements in comparison with NPCHF estimates with biases within 5%, whereas cloud fraction is significantly underestimated (up to 30%) against the THF estimates. The biases appear below the midtroposphere over the extratropics, in the upper troposphere over the midlatitude lands and a few tropical convective regions, where precipitating ice and convective core hydrometeors contribute greatly. This comparison reveals some underestimates of thick cloud HFs resulting from measurement limitations due probably to attenuation of radar/lidar signals, which cannot be qualified in this study.

Spatiotemporal and Vertical Distribution of Asian Tropopause Aerosol Layer Using Long-Term Multi-Source Data

The Asian tropopause aerosol layer (ATAL) is an enhanced aerosol concentration layer in the upper troposphere and lower stratosphere over Asia, and it has important effects on radiation balance, atmospheric circulation, regional climate, and atmospheric chemical processes. However, despite its importance, the specific structure and long-term variation trend of the ATAL have been rarely analyzed, which is critical for assessing the impact of ATAL on climate change and evaluating the performance of climate models. This study compared and analyzed the three-dimensional spatial distribution characteristics and temporal variability using CALIPSO, SAGEII, and MERRA-2 data and discussed the possible causes of the variation. The results showed that the ATAL began to appear in the mid-to-late 1990s and then strengthened rapidly until 2010, after which this trend was no longer observed. Moreover, significant heterogeneity existed in the distribution of aerosol concentration in the ATAL, showing north–south differences (NSDs) in both time and space. In addition, it was found that besides surface emissions, atmospheric circulation, the strength of convective transport, and stratosphere–troposphere exchange processes also contribute to this pattern. This study has important implications for quantifying the climate consequences of the ATAL.

Long-term three-dimensional distribution and transport of Saharan dust: Observation from CALIPSO, MODIS, and reanalysis data

Desert dust and sand storms from the Sahara can sometimes be transported thousands of kilometers, causing social and economic damages throughout their path, deteriorating the air quality, and affecting human health. The seasonal climatology and transport pathways of dust aerosols are, therefore, essential to a deeper understanding of the impact of dust on the affected region. This study investigated the three-dimensional climatology of dust aerosol distribution and its transportation characteristics over the Sahara, Mediterranean, and Europe using multiple satellites and reanalysis data. The results showed a strong spatial variation of dust aerosols, with the highest loading over the Sahara Desert and a gradual latitudinal decrease toward the north with the lowest dust load over Europe. Temporally, higher dust activities were observed during summer and spring in terms of dust occurrence frequency and contribution to the total aerosol loading. Dust aerosols significantly increased over Central Sahara and decreased over Central Europe. Saharan dust was efficiently transported toward Europe during summer and spring, mainly at higher altitudes of 2–6 km. It could reach as north as the latitude of ∼42° N in the transect of 5° – 15° E in the layer 2–4 km during summer, while in winter, the occurrence of dust was at lower altitudes (< 2 km) in some areas in Europe, north of 45° N. The dust transport pathway toward Europe showed a long-standing dust layer mainly appearing over the Mediterranean from the near-surface up to 3 to 4 km during summer and spring, contributing to the meridional transport of dust. The dust layer characteristics and the planetary boundary layer (PBL) height above Europe showed the likelihood of dust intrusion into Europe's PBL during summer. This work also highlighted the main elements affecting dust distribution and transport toward Europe: wind, topography, and the PBL height.

Hypothetical Cirrus Band Generation for Advanced Himawari Imager Sensor Using Data-to-Data Translation With Advanced Meteorological Imager Observations

Cirrus cloud contributes significantly to earth's radiation budget and the greenhouse effect. The Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite lacks a 1.37 μm band, sensitive to monitoring cirrus clouds. This article proposed a conditional generative adversarial network-based data-to-data translation (D2D) model to generate a hypothetical AHI 1.37 μm band. For training and testing the D2D model, the Geo-Kompsat-2A Advanced Meteorological Imager (AMI) 1.37 μm bands and other highly correlated bands to cirrus from July 24, 2019 to July 31, 2020, were used. The D2D model exhibited a high level of agreement (mean of statistics: correlation coefficient (CC) = 0.9827, bias = 0.0004, and root-mean-square error (RMSE) = 0.0086 in albedo units) between the observed and D2D-generated AMI 1.37 μm bands from validation datasets. The application of the D2D model to the AHI sensor showed that the D2D-generated AHI 1.37 μm band was qualitatively analogous to the observed AMI 1.37 μm band (average of statistics: bias = 0.0026, RMSE = 0.0191 in albedo units, and CC = 0.9158) on the 1st, 15th, and 28th of each month of 2020 in the common observing regions between Korea and Japan. The validation results with the CALIPSO data also showed that the D2D-generated AHI 1.37 μm band performed similarly to the observed AMI 1.37 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">µ</i> m band. Consequently, this article can significantly contribute to cirrus detection and its application to climatology.

Analysis of satellite-derived data for the study of fouling in aircraft engines

Atmospheric particulate is one of the main causes of performance degradation in gas turbine engines, especially in the aeronautical field where filter barriers are absent. The ingested particles can stick to the blade surfaces of the engine, varying their shape and roughness. As a consequence, engine performance degradation takes place. The type and the amount of the particles ingested depend on the flight zones and altitude. During their missions, aircrafts follow a prescribed path defined in terms of altitude, longitude, and latitude. During its route, the aircraft engine encounters different environments characterized by different temperature, pressure, and air composition. Regarding the latter issue, the knowledge of this characteristic can be key information when these statistics are needed for obtaining data useful for engine degradation assessment or prediction. Many satellites, such as the environmental satellite CALIPSO, are employed to study the terrestrial aerosol and clouds profile by using a LIDAR (Laser Detection and Ranging). This technology is commonly used to determine the distance between a light emitter and an object and it is based on the light refraction phenomenon. Backscatter coefficients profiles data, which characterize the distribution of particles and aerosols in the atmosphere, are available in the open literature from the findings of CALIPSO. In this work, a new methodology to estimate the aerosol type and concentration encountered by an aircraft during a mission is proposed. To test the feasibility of this method, two aircraft missions for different length scales (medium and long haul) are analyzed and an estimate of the particulate encountered by the engines is provided. The mission analysis has been conducted by discretizing the altitude profile, longitude, and latitude coordinates of each flight and then cross-referencing them with the particulate concentration obtained from CALIPSO data.

Assessment of spatiotemporal features and potential sources of atmospheric aerosols over the Tianshan Mountains in arid central Asia

Arid central Asia is characterized by a dry climate with little precipitation and frequent dust storms. At the same time, the Tianshan Mountains, a vitally important water resource supply zone and ecological barrier, are located in the heart of arid central Asia. Frequent dust storms in this region endanger snow and ice melting as well as ecological security within the Tianshan Mountains. In the present study, the CALIPSO data and HYSPLIT model were combined to assess the spatiotemporal features and potential sources of aerosols over the North Tianshan (NT), East Tianshan (ET), Central Tianshan in the East (CTE), Central Tianshan in the West (CTW), West Tianshan (WT), and South West Tianshan (SW). Results showed that the atmospheric aerosol subtypes had an apparent spatial variability. Dust and polluted dust were the two most dominant aerosol subtypes with the two accounting for over 76% of detected aerosols. The occurrence frequency of the dust within NT, ET, CTE, and CTW showed seasonal variation with two peaks in spring and autumn, while that of WT and SW peaked only in summer. The occurrence frequency of polluted dust in the ET region differed from that found in NT, CTE, CTW, WT, and SW. During all seasons, polluted dust was the dominant aerosol subtype recorded at heights from 2 km to 5 km in NT and WT. Vertically, dust and polluted dust were the most common subtypes observed between 1 and 8 km in SW. The top altitude of the dust and polluted dust layer over most of the Tianshan Mountains was greater than 5 km. The thickest dust layer over the Tianshan Mountains appeared in summer with a value larger than 2.0 km, and the thickness of the polluted dust layer was approximately between 1.0 and 1.5 km in spring. In comparison, the southwesterly transport pathways are the primary route for atmospheric aerosols to the Tianshan Mountains, which have the greater potential to carry natural and anthropogenic aerosols. Therefore, the Karakum Desert, Kyzylkun Desert, Moyunkum Desert, and Gurbantunggut Desert are the primary potential sources of dust and polluted dust aerosols over the Tianshan Mountains in arid central Asia.

Research on Lidar Network Observation of Aerosol and Pollution in Beijing 2022 Winter Olympics

During the Beijing Winter Olympics, three sets of aerosol lidar in different regions were used to conduct continuous observations to study the optical properties and vertical distribution characteristics of atmospheric aerosol. Based on the lidar data and sun photometer data, the accurate lidar ratio was determined to improve the inversion accuracy of the aerosol backscattering coefficient. The influence of meteorological conditions such as temperature, relative humidity, wind speed, and wind direction on the optical properties of aerosol were analyzed, and the type of aerosol was classified by the depolarization ratio of aerosol particles and CALIPSO data. Furthermore, the backward trajectory analysis, potential source contribution function (PSCF), and concentration weighted trajectories (CWT) were employed to explore the sources and transport mechanisms of pollutants. The analysis found that the extinction coefficient under the atmospheric boundary layer during the Winter Olympics had a spatial distribution pattern of high value in the southeast and low value in the northwest. The occurrence of aerosol pollution events is not only caused by local emissions, but is also related to regional transmission.

Identifying Seasonal and Diurnal Variations and the Most Frequently Impacted Zone of Aerosols in the Aral Sea Region.

With the desiccation of the Aral Sea, salt-alkali dust storms have increased in frequency and the surrounding environment has deteriorated. In order to increase our understanding of the characteristics and potential impact zone of atmospheric aerosols in the Aral Sea region, we evaluated seasonal and diurnal variation of aerosols and identified the zone most frequently impacted by aerosols from the Aral Sea region using CALIPSO data and the HYSPLIT model. The results showed that polluted dust and dust were the two most commonly observed aerosol subtypes in the Aral Sea region with the two accounting for over 75% of observed aerosols. Occurrence frequencies of polluted dust, clean continental, polluted continental/smoke, and elevated smoke showed obvious seasonal and diurnal variations, while occurrence frequency of dust only showed obvious seasonal variation. Vertically, the occurrence frequencies of all aerosol subtypes except dust showed significant diurnal variation at all levels. The thickness of polluted dust layers and dust layers exhibited same seasonal and diurnal variations with a value of more than 1.0 km year-round, and the layer thickness of clean continental and polluted continental/smoke shared the same seasonal and diurnal variation features. The zone most severely impacted by aerosols from the Aral Sea region, covering an area of approximately 2 million km2, was mainly distributed in the vicinity of the Aral Sea region, including western Kazakhstan, and most of Uzbekistan and Turkmenistan. The results provide direct support for positioning monitoring of aeolian dust deposition and human health protection in the Aral Sea region.

Study on the Parameters of Ice Clouds Based on 1.5 µm Micropulse Polarization Lidar

Dust aerosols can participate in the heterogeneous nucleation process as effective ice nucleation particles, thus changing the physical properties of clouds. In this paper, we used an eye-safe 1550 nm micropulse polarization single photon lidar combined with meteorological stations, HYSPLIT backward trajectory analysis, ERA5 reanalysis data, CALIPSO, Himawari-8 and Terra-MODIS satellite data to compare the difference in cloud characteristics between dust and clean cirrus cases in Jinan from 26–29 March 2022. The study found that the aerosol affected the cloud effective radius, and the cloud top temperature impacted the properties of depolarization of dust ice clouds. According to the statistical results of the upper and lower quartiles, the depolarization ratio (DPR) range of dust cirrus on 26 March was 0.46–0.49, a similar range to the clean cirrus, while that of dust cirrus on 27 March was 0.54–0.59, which seemed much larger. Different height and temperature conditions lead to differences in the habits of ice crystals in clouds, thus changing the DPR. However, the range of the DPR between clean cirrus and dust cirrus showed no obvious difference, as the former was 0.43–0.53 and the latter was 0.46–0.59. Under the condition of higher aerosol loading, the lidar range-corrected signal (RCS) of cirrus clouds was stronger, and the cloud effective radius was 48 μm, larger than that of clean cirrus (32 μm). This may be the effect of dust on the microphysical properties of clouds. This study discusses the indirect effects of dust aerosols on cirrus clouds and the underlying mechanisms from the perspectives of microphysics and optics, which can provide more references for urban air pollution processes and aerosol-cloud interactions.

Spatio-Temporal Evolution of a Typical Sandstorm Event in an Arid Area of Northwest China in April 2018 Based on Remote Sensing Data

Northwest China is significantly affected by sandstorm disasters. To mitigate the negative impacts of sandstorm events, it is critical to understand the spatio-temporal variations in typical sand and dust storms and their influencing factors. In this work, using ground-based measurements of particulate matter and remote sensing data such as MODIS, OMI, and CALIPSO data, the sources of aerosol pollution and aerosol optical properties of a typical sandstorm event that occurred in Northwest China in 2018 was studied. In addition, the HYSPLIT model was used to explore the air mass trajectories in order to analyze the sand and dust migration process during the sandstorm event. Furthermore, the wind erosion sensitivity of Northwest China was analyzed via single factor analysis and multi-factor superposition of wind field intensity, soil drought index, vegetation coverage, and relief amplitude. Finally, the region of the study area having a high comprehensive wind erosion sensitivity was identified. The results showed that the PM10 concentrations exceeded 400 µg/m3 and the PM2.5/PM10 ratio did not exceeded 0.6 during the sandstorm event, indicating that natural particulate matter was dominant in the ambient air. At the epicenter of pollution, the aerosol optical depth (AOD) at 550 nm was 0.75–1. By combining AOD data with wind speed and direction data from field observation stations, it was found that the sandstorm event in 2018 mainly occurred between 1 April and 3 April, and affected all of Northwest China on 2 April and 3 April. The absorbed aerosol index (AAI) ranged between 2.5 and 4, indicating that the Taklimakan Desert was the main source of sandstorm events in Northwest China. The CALIPSO total attenuated backscatter coefficient at 532 nm indicated that the main component of tropospheric aerosol in this region was distributed in the range of 0–12.5 km. The simulated airflow track showed that it had the same dust source regions as AAI index studies. Moreover, investigation of wind erosion sensitivity in the study areas indicated that the Taklimakan Desert and other desert regions were the main ecologically sensitive areas. These conclusions can provide references and suggestions for the mitigation of damage caused by sandstorm events, in addition to the enhancement of ecological governance.

Accuracy of Cirrus Detection by Surface-Based Human Observers

Abstract The longest cirrus time series are ground-based, visual observations captured by human observers [synoptic observations (SYNOP)]. However, their reliability is impacted by an unfavorable viewing geometry (cloud overlap) and misclassification due to low cloud optical thickness, especially at night. For the very first time, this study assigns a quantitative value to uncertainty. We validate 15 years of SYNOP observations (2006–20) against data from the cloud lidar flown on board the Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) spacecraft. We develop a dedicated method to match SYNOP reports (with a hemispherical field of view) with lidar samples (along-track profiles). Our evaluation of the human eye’s sensitivity to cirrus revealed that it is moderate, at best. In perfect conditions (daytime with no mid/low-level clouds) the probability of correct detection was 44%–83% (Cohen’s kappa coefficient &lt; 0.6), and this fell to 24%–42% (kappa &lt; 0.3) at night. Lunar illumination improved detection, but only when the moon’s phase exceeded 50%. Cirrus optical depth had a clear impact on detection. When clouds at all levels were considered (i.e., real-life conditions), the reliability of the visual method was moderate to poor: it detected 47%–71% of cirrus (kappa &lt; 0.45) during the day and 28%–43% (kappa &lt; 0.2) at night and decreased with an increasing low/midlevel cloud fraction. These kappa coefficients suggest that agreement with CALIPSO data was close to random. Our findings can be directly applied to estimations of cirrus frequency/trends. Our reported probabilities of detection can serve as a benchmark for other ground-based cirrus detection methods. Significance Statement Cirrus clouds heat the atmosphere, so any increase in their frequency will contribute to climate warming. The longest cirrus time series (including the presatellite era) are surface-based detections by a human observer at a meteorological station. Our study is the first to quantitatively evaluate the reliability of these observations. Our results show that, because of the viewing geometry (cloud overlap) and human eye sensitivity, reliability ranges from moderate at best to very low. Nighttime detections are especially unreliable, as well as those in the presence of low/midlevel cloud. Cirrus frequencies and trends calculated from visual observations should, thus, be considered with caution.

Characteristics of equivalent black carbon aerosols over Doon Valley in NW Indian Himalaya during COVID-19 lockdown 2020.

Recently, black carbon (BC) has been identified as a potential transmitter for COVID-19 besides being responsible for climate change and serious health hazards. To mitigate the dreaded consequences of COVID-19 pandemic, the Government of India declared a nationwide lockdown on March 24, 2020. Accordingly, observations on equivalent black carbon (EBC) aerosols using AE 51 Aethalometer were performed during different lockdowns in Doon Valley. During April, May, June, and July, the monthly average EBC mass concentration recorded 2.12 ± 1.14 μg m−3, 2.58 ± 1.46 μg m−3, 2.74 ± 1.49 μg m−3, and 2.12 ± 1.32 μg m−3, respectively. A comparison of diurnal variation patterns with earlier studies indicates a significant reduction in EBC mass concentration levels. Bipolar NWR analysis for April and May depicts that relatively high EBC concentration was experienced with prominent south-easterly winds. The EBC concentration level during daytime was high compared to nighttime hours. Preliminary visualization of scanning electron micrographs indicates the variable morphology of aerosols. The bulk particle EDX spectral analysis indicates C, O, Na, F, Al, Si, K, Ca, and Ti elements with a dominance of C and O. Windblown dust seems to be the major contributor to the ambient aerosols. Furthermore, MODIS recorded the fire anomaly (attributed to the wheat stubble burning) starting from mid of April to early-June along the Indo-Gangetic Basin. Heavy loading of polluted aerosols was visible in CALIPSO data imageries. HYSPLIT cluster trajectories indicate that the study region is strongly influenced by the air mass transporting from the Gangetic Plain, Iran, Pakistan, Afghanistan, and Gulf region.

Partially melting droplets strongly enhance lidar backscatter

There has been a long-standing problem in discrepancy between theoretical and observed backscatter by water clouds. Lidar ratios from water clouds are often much lower than what calculated out by Mie theory. In this study, we find the low lidar ratios of water clouds in CALIPSO lidar observations can be explained if we assume some large droplets with liquid spherical shells and ice cores. Using a light scattering model for layered spherical particles, we can produce lidar ratios similar to those from the CALIPSO data while assuming large droplets with ice cores in clouds. This study explains the reason of low lidar ratios for water clouds and provides a method for remotely sensing partially melting droplets.

Detecting Multilayer Clouds From the Geostationary Advanced Himawari Imager Using Machine Learning Techniques

This study develops a machine learning (ML)-based multilayer cloud detection algorithm for the passive Advanced Himawari Imager (AHI) aboard the geostationary Himawari-8 satellite. AHI measurements in the 0.64-, 1.6-, 2.3-, 3.9-, 7.3-, 8.6-, 11.2-, and 12.4- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> channels, their combinations, geolocations, and observational geometries are used as predictors, and collocated active CloudSat and CALIPSO data are used to accurately label multilayer cloud pixels as the reference/truth of the predictand. We develop an ML-based daytime model (ML-Day) that utilizes all the aforementioned predictors and an all-time one (ML-All) that excludes the solar channel-dependent variables. Among four ML algorithms, the random forest (RF) performs slightly better than the artificial neural network, K-nearest neighbor, and support vector machines. By comparing with the merged CloudSat and CALIPSO product, the ML-Day model correctly identifies ~89% single-layer clouds and ~70% multilayer clouds, outperforming the Moderate Resolution Imaging Spectroradiometer (MODIS) operational multilayer cloud product (~80% and ~40% given by Marchant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ). The success rates of ML-All for single-layer and multilayer clouds also reach ~85% and ~64%, respectively. The misclassification of our algorithm is mostly caused by missing optically thin clouds, a drawback of most radiometers without the 1.38- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> channel. Furthermore, with multilayer cloud pixels well detected by our algorithm, the AHI operational cloud top height retrievals are found to be larger biased due to multilayer cloud occurrence and might be improved by considering cloud vertical structures.

Spatial-temporal characteristics of particulate matters and different formation mechanisms of four typical haze cases in a mountain city

The mechanisms by which pollution forms and acts in mountain cities are unique and remain unclear owing to terrain and meteorological conditions in the mountains. This study investigates the pollution characteristics in Chongqing, a typical mountain city, over the period from 2013 to 2017. Routine observed surface meteorological data, CALIPSO data, ERA5 reanalysis data and the backward trajectory calculation were used for analysis and comparison among four typical pollution cases. The results can be summarized as follows: (1) Pollutants in firework and unfavorable weather condition case mainly originated from local emissions. Anthropogenic aerosols that were characterized by spherical and small particles were detected, which contributed to the higher atmospheric scattering ability. Meanwhile, high humidity was beneficial to hygroscopic growth, resulting in a mixture of haze and fog forming low-visibility conditions. The strong inversion of the upper-layer and the presence of high mountains meant that local pollutants became confined within the basin region, leading to the formation and maintenance of pollution. The important roles of the weather conditions that are associated with mountains, such as cold air damming, low-level jet, valley and gap wind, were also revealed by the variable pollution conditions. (2) Airflow originating from Guizhou, Guangxi, and Hunan, where numerous fire points were detected, transported pollutants that were released from straw burning through upper-lower layer transport to the basin during the straw burning case. The aerosols detected showed low depolarization but a high color ratio, which are the typical characteristics of smoke. The inversion layer did not cover the entire basin, indicating that smoke aerosols were transported by downdraft to mix with local pollutants. (3) During the dust case, large amounts of dust aerosols were detected reaching from the surface to the troposphere, with a special double-layer structure. A lower depolarization ratio but stronger scattering ability was observed for the dust aerosols above the study region, which was attributed to mixing with other aerosols and chemical processes. Secondary circulation occurred when the inversion layer was at around 700 hPa, bringing dust aerosols from around 850 hPa to the surface. This study provides an understanding of the optical properties of the various types of aerosols and highlights the importance of weather condition in the formation and maintenance of pollution events. The results emphasize the need for intense vertical measurement in mountain cities.