Cloud-Aerosol Lidar With Orthogonal Polarization Data Research Articles

First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).

Analysis of Long‐Term Trends in the Vertical Distribution and Transport Paths of Atmospheric Aerosols in Typical Regions of China Using 15 Years of CALIOP Data

AbstractLong‐range transport and vertical distribution of aerosols are important factors for assessing the uncertainty in aerosol radiative forcing. This paper reveals the vertical distribution and trends of aerosol optical properties in China using 15 years of Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The Hybrid Single‐Particle Lagrangian integrated trajectory model was used to analyze the transport and trends of aerosols in the layer with the highest occurrence frequencies of dust, polluted dust, polluted continental and elevated smoke aerosols. The results indicated that (a) there were significant regional and seasonal differences in the vertical distribution of aerosols. The aerosol optical depth (AOD) trend in a given region depends on the changes in the aerosol type with the highest frequency and the layer corresponding to the largest AOD in the vertical profile. The frequency of polluted dust aerosols was the highest in Beijing‐Tianjin‐Hebei (BTH) and Central China. The considerable decrease in the 0–2 km AOD led to a significant trend of the column AOD. (b) The changes of AOD and main aerosol types in a region are also affected by the changes of aerosol sources and long‐range transport pathways. In the BTH, dust aerosols originated from the Mongolian Plateau, accounting for 57.88% of the total trajectories. The Pearl River Delta was dominated by elevated smoke aerosols, with trajectories mainly originating from the Myanmar and Vietnam, accounting for 27.38% and 29.59%, respectively. The trend of 15‐year backward trajectories of dust aerosols on the Tibetan Plateau indicated that the trajectory from India is increasing.

Potential improvement of XCO2 retrieval of the OCO-2 by having aerosol information from the A-train satellites

ABSTRACT Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information.

MERRA-2 reanalysis-aided sea fog detection based on CALIOP observation over North Pacific

Sea fog can lead to a dangerous meteorological hazard over oceans and coastal areas. Cloud-aerosol Lidar with Orthogonal Polarization (CALIOP) is an effective tool to detect sea fog, however it is ineffective for dense fog detection because the lidar signal has been fully attenuated before reaching the layer base. To address this issue by improving dense fog detection, a reanalysis-aided sea fog detection method using CALIOP lidar measurements and MERRA-2 reanalysis dataset is proposed taking the Northwestern Pacific (NP) as study area. In this method, CALIOP lidar measurements are used to locate the ceiling and base of the hydrometeor layers, and extract the candidate fog layers in the Marine Atmospheric Boundary Layer (MABL). Reanalysis dataset are used to identify real fog from the candidate fog layers based on the near-surface meteorological difference under different weather conditions (sea fog, stratus and stratus precipitation). Method validation using in-situ weather records from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) and five climate stations, shows the detection accuracy and false alarm rate of 89.4% and 2.0%, respectively. Most importantly, about 87.4% of dense fogs are successfully discriminated with the aid of reanalysis data. Model application to CALIOP data from 2007 to 2019 shows pronounced spatial and seasonal characteristics of sea fog distribution over NP, with the highest frequency (>40%) found over the Kuril Islands, Sea of Okhotsk and Bering Sea from June to August. The example demonstrates that by using time series CALIOP observations and the proposed method, one may expect spatio-temporally complete sea fog information, which is important especially for remote regions with scarce in-situ measurements (e.g. polar regions). Our research proposes and proves a novel idea of sea fog detection from space by using satellite lidar and reanalysis data, which may benefit the sea fog forecasting and dynamics analysis, by providing unprecedented large-scale high-quality observations, especially over dense fog conditions.

June to October Aerosol Optical Depth over the Arctic at Various Spatial and Temporal Scales in MODIS, MAIAC, CALIOP and GOES Data

The accuracy of the cloud-aerosol lidar with orthogonal polarization (CALIOP), moderate resolution imaging spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Geostationary Operational Environmental Satellite (GOES) aerosol optical depth (AOD) products for the Arctic north of 59.75°N was examined by means of 35 aerosol robotic network (AERONET) AOD sites. The assessment for June to October 2006 to 2020 showed MAIAC AOD agreed the best with AERONET AOD; CALIOP AOD differed the strongest from the AERONET AOD. Cross-correlations of CALIOP AOD along the satellite path indicated that AOD-values 40 km up-and-down the path often failed to represent the AERONET AOD-values within ±30 min of the overpass in this region dominated by easterly winds. Typically, CALIOP AOD was lower than AERONET AOD and MAIAC AOD at the sites, especially, at sites with mean AOD below 0.1. Generally, MODIS AOD values exceeded those of MAIAC. Comparison of CALIOP, MAIAC, and MODIS products resampled on a 0.25° × 0.25° grid revealed differences among the products caused by their temporal and spatial resolution, sample habit and size. Typically, the MODIS AOD-product showed the most details in AOD distribution. Despite differences in AOD-values, all products provided similar temporal evolution of elevated and lower AOD.

Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration

Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations (R > 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties.

Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data

Clouds play a significant role in the climate system, which affects the radiation balance and modulates the global hydrological cycle. However, the existing cloud property products have poor spatiotemporal continuity with only daytime cloud property retrieval results, which makes it challenging for us to carry out researches related to clouds at night. In this study, the effect of parallax error between the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Himawari-8 data is corrected based on parallax correction algorithm by referring to cloud parameters extracted from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud property product. Due to the different sensitivity of different channels to clouds, this study treats the CALIOP data with cloud optical depth greater than 0.2 as clouds, otherwise as clear sky. Cloud property retrieval model based on the XGBoost machine learning (ML) algorithm is developed for the advanced Himawari imager (AHI) onboard the Himawari-8 satellite. The ML model can achieve unified cloud mask, cloud top temperature (CTT), and cloud top height (CTH) retrieval for both daytime and nighttime with high spatial (0.02°) and temporal (10 min) resolutions using the AHI thermal data. The retrieval results are extensively evaluated over study regions (80° E ~ 135° E, 18° N ~ 55° N) by comparisons with cloud property products of JAXA (Japan Aerospace Exploration Agency) AHI, MODIS, and CALIOP data. The ML algorithm has higher cloud detection accuracy with cloudy sky (clear sky) hit rate (CHR) and false alarm rate (FAR) of 90.79% (88.35%) and 5.74% (4.67%) compared with the JAXA AHI (CHR = 92.81% (60.64%) and FAR = 19.66% (3.61%)) and MODIS (CHR = 88.50% (72.32%) and FAR = 13.10% (6.06%)) cloud products, and the ML algorithm also performs well for thin clouds over bright surfaces. The CTH and CTT retrieved by the ML algorithm (RMSE = 1.83 km and 12.29 K) are in good agreement with the CALIOP cloud property product, and the root mean squared error (RMSE) of CTH and CTT is reduced by 33.70% (18.67%) and 35.28% (16.45%) on average, respectively, compared with the JAXA AHI (MODIS) cloud product with RMSE of 2.76 km (2.25 km) and 18.99 K (14.71 K). In addition, the ML algorithm can obtain higher cloud property retrieval accuracy at night compared with the results during the day.

Retrieving particulate matter concentrations over the contiguous United States using CALIOP observations

Using twelve years (2007–2018) of NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) near-surface 532 nm aerosol extinction retrievals, multi-year mean and trends of particulate matter (PM) concentrations are derived over the contiguous United States (CONUS). Different from past studies that use column integrated aerosol optical thickness, here only near-surface CALIOP aerosol extinction is used for deriving near-surface PM with aerodynamic diameters less than 2.5 μm (PM2.5) concentrations using an innovative, bulk-mass-modeling-based method. Compared against ground based PM2.5 measurements from the U.S. Environmental Protection Agency (EPA), an encouraging relationship between CALIOP-derived PM2.5 and EPA-observed PM2.5 (Deming slope = 0.89; RMSE = 3.42 μg/m3; mean bias = −1.00 μg/m3) is found using combined daytime/nighttime CALIOP data. Also, comparable trends in PM2.5 concentrations from the EPA and daytime and nighttime CALIOP data are found for most of the eastern CONUS and imply that air quality is generally improving over this region for the study period. Over the western CONUS, a seasonal analysis reveals that PM2.5 trends are positive during the more active wildfire season (June through November) but negative for other months. This study suggests that lidar data show promise in their use for obtaining PM2.5 estimates and provides motivation to further explore aerosol extinction-based PM concentration retrievals in anticipation of future space-based lidar missions.

Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) Version 1.0 Cloud Top Property Product From Himawari-8/AHI: Algorithm Development and Preliminary Validation

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties (cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)). The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forests method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients (R) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, wherein the CTT R value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT R value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed herein capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia-Pacific region.

Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms

Aerosol optical depth (AOD) has been widely used to estimate the near-surface PM2.5 (fine particulate matter with particle size less than 2.5 μm). However, the total-column AOD obtained by passive remote sensing instruments can neither distinguish the contribution of AOD in various altitude layers nor obtain the vertical PM2.5 concentration. In this study, we compared several AOD-PM2.5 models including Extra Trees (ET), Random Forest (RF), Deep Neural Network (DNN), and Gradient Boosting Regression Tree (GBRT), and analyzed the corresponding results using AOD of different altitudes and auxiliary data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results indicate that the ET model performs best in terms of the model effectiveness and feature interpretation on the training dataset. We conclude that the feature importance of the bottom layer AOD is higher than that of the upper and total column AOD. The results showed that regional differences existed in the optimal height of the AOD-PM2.5 correlation in study area. The results of cross-validation indicate that ET manages the most appealing overall performance with an R2 (RMSE) of 0.85 (17.77 μg/m3). Regarding the 729 sites involved in this study, 73% had R2 > 0.7, and the region or season with higher AOD feature importance achieves better model performance. The results of the AOD-PM2.5 model in each layer were corrected using the AOD weight, to obtained the PM2.5 vertical concentrations from 2015 to 2019. The results highlight that the high PM2.5 concentration area is primarily near the ground and decreases with height. Additionally, the PM2.5 vertical concentration in Beijing-Tianjin-Hebei (−1.80 μg/m3, P < 0.001), Central China (−1.62 μg/m3, P < 0.001), and Pearl River Delta (−0.66 μg/m3, P < 0.001) show an apparent downward trend. We believe that the vertical distribution analysis of PM2.5 can provide meaningful information for studying air pollution.

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

Overview of inversion methods of ocean subsurface particulate backscattering coefficient by using CALIOP data

Particulate backscatter coefficient (bbp) is the core parameter of ocean optics when applied to marine ecology and biogeochemistry. However, the current commonly used in-situ methods can not carry out large-scale detection in a short time, and passive ocean color remote sensing can not work under dark and cloudy conditions. In contrast, the active remote sensing method, spaceborne Light Detection and Ranging(lidar), can break through the above limitations, and has great advantages in ocean detection. Cloud Aerosol Lidar with Orthogonal Polarization(CALIOP), launched in 2006, became the first spaceborne lidar able to provide global seawater bbp, particularly important for polar and diurnal observations. In this paper, the principle of the CALIOP system and three levels of its major data products were introduced in detail. Paper focused on combing the method of bbp inversion with the depolarization ratio of the CALIOP 532 nm polarization channels and the subsequent improvement measures such like the deconvolution method correcting the transient response of the system. A corresponding algorithm was fully developed, which the detailed complete inversion process was summarized, and the current bbp inversion results were presented. The works were intended to provide reference for data processing and application of the China space-borne oceanic lidar in the future.

Comparison of Backscatter Coefficient at 1064 nm from CALIPSO and Ground–Based Ceilometers over Coastal and Non–Coastal Regions

This study investigates the direct comparison of backscatter coefficient profiles at 1064 nm which were measured by CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) and by ground–based ceilometers located in coastal and non–coastal regions. The study uses data recorded between 2013 and 2016 to investigate the challenges involved in performing such a comparison in different environments. The standard Level 2 CALIOP Aerosol Profile version 4 product is evaluated against data from two ground–based Jenoptik CHM15K ceilometers: One at Mace Head (western Ireland) and the other at Harzgerode (central Germany). A statistical analysis from a series of CALIOP overpasses within 100 km distance from the ground–stations is presented considering different along–track averages in CALIOP data (5 km, 15 km, 25 km, 35 km, and 100 km) at the closest approach. The mean bias calculated from the correlative measurements between CALIOP and the ground–based ceilometers shows negative bias for 80% of the cases analyzed at Mace Head and positive bias for 68% of the cases investigated at Harzgerode, considering both daytime and nighttime measurements in cloud–free scenarios. The correlation of these results with HYSPLIT shows that different air samples play a role in the comparison. To our knowledge, this is the first study that addresses the limitations and capabilities in comparing CALIOP data with ground–based ceilometers at 1064 nm wavelength in different environments.

An Algorithm for Daytime Sea Fog Detection over the Greenland Sea Based on MODIS and CALIOP Data

Xiao, Y.F.; Zhang, J., and Qin, P., 2019. An algorithm for daytime sea fog detection over the Greenland Sea based on MODIS and CALIOP data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 95-103. Coconut Creek (Florida), ISSN 0749-0208.Sea fog is one of the dangerous weather disasters affecting scientific investigation and maritime transportation on the Arctic Ocean. In this paper, a detection algorithm for daytime sea fog over the Arctic Ocean is proposed taking the Greenland Sea as an example. With the assistance of satellite lidar data from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations)/CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization), a large number of sample points of sea fog, low level clouds, mid-high level clouds, the sea surface, ice, and snow were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) images. The radiance (reflectance and emissivity) and texture (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) characteristics of each MODIS channel for sea fog and other features were analyzed. Thereafter, a new algorithm for sea fog detection is proposed. The step-by-step algorithm first masks the sea surface, sea ice, and snow using the reflectance of MODIS channels 2 and 7. Then, the sea fog is separated from low and medium-high level clouds using the homogeneity of channel 18. The validation compared with CALIOP data shows encouraging accuracy for sea fog detection with probabilities of detection (POD) ∼80 %, and probabilities of falsity (POF) ∼8.5 %.

Large-and-Sparse-particle Clouds (LSC): Clouds which are subvisible for space-borne lidar and observable for space-borne cloud radar

Large-and-Sparse-particle Clouds (LSC), characterized by large particle size (radius > 50 μm) and small number concentration (<104 m−3), were observed with the space-borne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and with the space-borne 94-GHz cloud profiling radar (CPR). CALIOP was found to be less sensitive to the LSC as compared to CPR because of the particle size distribution of LSC; hence, the cloud type is different from usual ones because CALIOP is generally more sensitive to clouds as compared to CPR when there is no lidar attenuation. An empirical criterion was introduced to identify the LSC from CALIOP and CPR data. The data analysis showed that the LSC tend to appear at high latitudes. The lifetime of LSC would be in the order of hours, because the terminal velocity of LSC particles exceeded 1 km h−1. LSC would not be fallstreak because no cloud existed above. LSC appeared to destroy supercooled clouds (SC) because their cloud top heights were higher as compared to SC, and because LSC and SC did not tend to coexist. Because of their thin optical depth, LSC did not directly impact radiative forcing; however, LSC may indirectly influence radiative forcing through changes in the distribution of SC.

Lagrangian simulation of ice particles and resulting dehydration in the polar winter stratosphere.

Polar stratospheric clouds (PSCs) and cold stratospheric aerosols drive heterogeneous chemistry and play a major role in polar ozone depletion. The Chemical Lagrangian Model of the Stratosphere (CLaMS) simulates the nucleation, growth, sedimentation, and evaporation of PSC particles along individual trajectories. Particles consisting of nitric acid trihydrate (NAT), which contain a substantial fraction of the stratospheric nitric acid (HNO3), were the focus of previous modeling work and are known for their potential to denitrify the polar stratosphere. Here, we carried this idea forward and introduced the formation of ice PSCs and related dehydration into the sedimentation module of CLaMS. Both processes change the simulated chemical composition of the lower stratosphere. Due to the Lagrangian transport scheme, NAT and ice particles move freely in three-dimensional space. Heterogeneous NAT and ice nucleation on foreign nuclei as well as homogeneous ice nucleation and NAT nucleation on preexisting ice particles are now implemented into CLaMS and cover major PSC formation pathways.We show results from the Arctic winter 2009/2010 and from the Antarctic winter 2011 to demonstrate the performance of the model over two entire PSC seasons. For both hemispheres, we present CLaMS results in comparison to measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), and the Microwave Limb Sounder (MLS). Observations and simulations are presented on season-long and vortex-wide scales as well as for single PSC events. The simulations reproduce well both the timing and the extent of PSC occurrence inside the entire vortex. Divided into specific PSC classes, CLaMS results show predominantly good agreement with CALIOP and MIPAS observations, even for specific days and single satellite orbits. CLaMS and CALIOP agree that NAT mixtures are the first type of PSC to be present in both winters. NAT PSC areal coverages over the entire season agree satisfactorily. However, cloud-free areas, next to or surrounded by PSCs in the CALIOP data, are often populated with NAT particles in the CLaMS simulations. Looking at the temporal and vortex-averaged evolution of HNO3, CLaMS shows an uptake of HNO3 from the gas into the particle phase which is too large and happens too early in the simulation of the Arctic winter. In turn, the permanent redistribution of HNO3 is smaller in the simulations than in the observations. The Antarctic model run shows too little denitrification at lower altitudes towards the end of the winter compared to the observations. The occurrence of synoptic-scale ice PSCs agrees satisfactorily between observations and simulations for both hemispheres and the simulated vertical redistribution of water vapor (H2O) is in very good agreement with MLS observations. In summary, a conclusive agreement between CLaMS simulations and a variety of independent measurements is presented.

Trans-pacific aerosol vertical structure revealed by spaceborne lidar CALIOP

The 5-year (2011–2015) spaceborne lidar CALIOP (cloud-aerosol lidar with orthogonal polarization) data were aggregated to obtain a seasonal three-dimensional (3-D) distribution of aerosols over the Northern Pacific (NP). Superior to the Moderate-resolution Imaging Spectroradiometer (MODIS) product, which provided only the bulk Aerosol Optical Depth (AOD), the vertical high-resolution Aerosol Extinction Coefficient (AEC) obtained from the CALIOP product enabled us to determine the AOD for the prescribed layers. By integrating the AEC at the prescribed height interval, the AOD beneath and above 2 km was obtained to characterize the aerosol loading in the surface layer (SL) and the free troposphere (FT), respectively. The AOD in the SL and FT present different seasonal variation patterns over the NP. The SL AOD exhibited the highest level in the winter, which was caused by sea salt emissions driven by the strongest winds. However, a much higher FT AOD occurred over the NP in the springtime compared with that of other seasons, indicating that the most significant transpacific transport occurred in the springtime, mainly in the FT. Fine resolution aerosol distributions in cross section along a transpacific direction were extracted from the CALIOP product as well. Along the cross section, aerosol loading was most abundant below 2 km for all the seasons. However, the detectable aerosol layer may extend to 10 km high in the springtime, which is much higher than that in the other seasons, and the aerosol layers below and above 2 km were separated over the remote NP. The CALIOP data demonstrated that dust plays a role in aloft aerosol transport. In terms of aerosol transport in the FT, an 8-fold decrease in the FT AOD occurred during the journey from East Asia to the Eastern Pacific in the summer, which was the sharpest decrease compared with the smallest one, which has 3-fold decrease occurred in the spring. Aerosol transport in the FT contributed 19% of the AOD over western North America (NA) in the spring, which was much higher than that in other seasons, and the aloft aerosol transport between 2 and 4 km high corresponded to a fine PM concentration of 1–2 μg m−3 as a contributor to air quality over western NA.

Extinction and optical depth retrievals for CALIPSO's Version 4 data release

Abstract. The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud–Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite has been making near-global height-resolved measurements of cloud and aerosol layers since mid-June 2006. Version 4.10 (V4) of the CALIOP data products, released in November 2016, introduces extensive upgrades to the algorithms used to retrieve the spatial and optical properties of these layers, and thus there are both obvious and subtle differences between V4 and previous data releases. This paper describes the improvements made to the extinction retrieval algorithms and illustrates the impacts of these changes on the extinction and optical depth estimates reported in the CALIPSO lidar level 2 data products. The lidar ratios for both aerosols and ice clouds are generally higher than in previous data releases, resulting in generally higher extinction coefficients and optical depths in V4. A newly implemented algorithm for retrieving extinction coefficients in opaque layers is described and its impact examined. Precise lidar ratio estimates are also retrieved in these opaque layers. For semi-transparent cirrus clouds, comparisons between CALIOP V4 optical depths and the optical depths reported by MODIS collection 6 show substantial improvements relative to earlier comparisons between CALIOP version 3 and MODIS collection 5.

CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm.

Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP's other radiometric calibration procedures - i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime - depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30-34 km to 36-39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. As well, an enhanced strategy for filtering the radiation-induced noise from high energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved MERRA-2 model. An aerosol scattering ratio of 1.01 ± 0.01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2-3% lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne high spectral resolution lidar (HSRL) are reduced from 3.6% ± 2.2% in the version 3 data set to 1.6% ± 2.4 % in the version 4 release.

Performance comparison of the MODIS and the VIIRS 1.38 μm cirrus cloud channels using libRadtran and CALIOP data

The top-of-the-atmosphere (TOA) reflectances of the Visible Infrared Imaging Radiometer (VIIRS) M9 channel and the Moderate Resolution Imaging Spectroradiometer (MODIS) 26 channel have been simulated using the libRadtran radiative transfer model and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Mask data. The simulated data were analyzed to quantify the performance differences between the VIIRS M9 and the MODIS 26 channels. Analysis of simulated clear-sky TOA reflectances showed that compared MODIS channel 26, the VIIRS M9 channel always performs better in reducing background reflectance regardless of latitude, season, surface type, vapor content or surface elevation. For mid-latitude, sub-Arctic and tropical regions the VIIRS M9 channel reduce the background reflectance by approximately 66.7%, 52.6% and 41.5%, respectively over the surface type of sandstone, compared to MODIS channel 26. Simulations for cloudy skies showed that both stratus and cumulus clouds contribute less to VIIRS M9 and MODIS band 26 TOA reflectances. Analysis of observed MODIS, VIIRS and CALIOP data was consistent with the simulated results. The VIIRS M9 decreases clear-sky background reflectance by as much as 35.96% and non-cirrus cloud reflectance by 29.86% compared with the MODIS channel 26. The observed reflectances of MODIS and VIIRS cirrus channels for clear-sky, non-cirrus cloud, and cirrus cloud are 0.0133 and 0.0095, 0.020 and 0.015, 0.084 and 0.067 respectively.