This PDF file contains the front matter associated with SPIE Proceedings Volume XXXXX, including the Title Page, Copyright information, and Table of Contents.

Opening remarks by conference chairs for conference Remote Sensing of Clouds and Atmosphere XXV

Fine particulate matter (PM2.5) has strong and adverse effects on the environment and human health. To estimate health risks and environmental impacts it is very important to know about current and prospective amounts of ground-level PM2.5 concentrations on regional scales. The in-situ station network in Germany is well developed but still provides only selective spatial information on air pollution. For a gapless monitoring additional data sets are required. Satellite data provides area-wide measurements of air pollutants and can depict their synoptic distribution in an adequate way. Chemical-transport models are moreover able to predict the amount and dispersion of aerosols in a very high temporal and spatial resolution, which makes them the key tool in monitoring air quality on regional and local scales. Modelling of aerosols is still very uncertain due to the complexity of accurately including aerosol properties and transfer processes, but also because of inaccurate emission data bases. In our study we use satellite data to produce detailed maps of PM2.5 distributions for Germany with the objective of using them as input for the air quality forecast system POLYPHEMUS/DLR. We want to improve the general performance of the model by adjusting the PM2.5 amounts in the model with observation data from satellites in terms of data assimilation. PM2.5 concentrations cannot be measured directly by satellites. This paper presents a semi-empirical linear regression approach to estimate ground-level PM2.5 concentrations using satellite observations of aerosol optical depth (AOD). The method was applied to different satellite sensor products, namely MODIS and SLSTR. For both sensors the resulting PM2.5 concentrations showed good correlations with in-situ station measurements with R-values of 0.83 for MODIS and 0.81 for SLSTR for the considered year 2018. Differences in the spatial coverage of the two satellite sensors induced us to combine the data sets to an ensemble product. We found major benefits using this ensemble, primarily regarding the data amount for the calculation of local mean values of PM2.5 concentrations. We could produce detailed maps of ground-level PM2.5 concentrations which can be used for the identification of high polluted areas, the monitoring of transnational pollution patterns and the localization of specific emission sources. The assimilation of the produced datasets into the air quality model POLYPHEMUS/DLR will be the next step in our study.

Levels of formaldehyde (HCHO) and nitrogen dioxide (NO2) were analyzed in Delhi (the capital of India) and its surrounding region from January 2010 to December 2014, using the Aura satellite’s OMI sensor data of spatial resolution 0.25°x0.25°. Time sequence plots show that formaldehyde concentrations were the maximum during monsoon period and minimum during the winter months. Nitrogen dioxide concentrations on the other hand were found to be the minimum during the monsoon months and the maximum during the winter months. The scatter plot between the formaldehyde and NO2 levels along with the time sequence plots suggest the presence of a significant anti-correlation between the two. Seasonal wind rose plots which showed that the wind generally blows from west to east which is due to the subtropical westerlies. Seasonal wind speed temporal plot showed that the wind blows with maximum speed in the summer season, then in monsoon followed by winter and the minimum speed of wind is in the post monsoon season. Spatial maps of nitrogen dioxide and formaldehyde in the study area using ArcGIS were plotted and hot spots were identified. For nitrogen dioxide, the east side of Delhi was found to be having higher concentrations as compared to other parts of Delhi. But hot spots for formaldehyde were not easy to demarcate as they were changing with season which is possibly due to the complex chemistry of formaldehyde in the troposphere. A possible explanation has been suggested on the basis of atmospheric photochemistry of formaldehyde and nitrogen dioxide for the observed variations. The results suggest the presence of highly complex chemistry among them.

To date, a limited understanding of the source and sink action for CO₂ by vegetation persists because it is impossible to distinguish between the CO₂ captured by photosynthesis processes and that released for operations related to plant respiration. In this context, carbonyl sulfide (OCS) is receiving increasing interest. The same as CO₂, OCS is captured by plants in the chlorophyll photosynthesis phase through catalysis of the same enzyme AC (carbonic anhydrase), but, unlike CO₂ , the reaction is irreversible, i.e., the captured OCS is no longer released into the atmosphere. In this way, OCS could be a powerful potential proxy for photosynthetic capture of CO₂ and, therefore, directly related to Gross Primary Productivity or GPP. The objective of this work consists of a feasibility study for the simultaneous estimation of CO₂ and OCS with hyperspectral infrared observations provided by IASI (Infrared Atmospheric Sounder Interferometer) instrument. The OCS has a relatively intense band at 2060 cm^-1, which is well within the coverage of the IASI. This band has been analyzed with the aim of carrying out a complete assessment of the feasibility of estimating the columnar content of the OCS from satellite, simultaneously with CO₂. The analysis of sensitivity and error of the estimate has been carried out with the level 2 processor for IASI, developed by the Applied Spectroscopy group of the School of Engineering of the University of Basilicata. For the analysis, different (parametric and non-parametric) retrieval strategies were considered. It will be shown that the non-parametric strategy shows better performance. Finally, the results have been validated with in situ observations from the Mauna Loa station in Hawaii. The validation study has covered a period from 2014 to 2019, and we conclude that both OCS and CO₂ can be simultaneously retrieved from IASI band 3 with a very high degree of accuracy. A significant, unexpected, result from our analysis is that OCS concentration in the atmosphere is going down since 2017. We have shown that CO parallels this reduction as well, which could be the effect of world-wide actions to reduce the combustion and use of coal (a source of both OCS and CO) in industry and residential sectors.

Moscow megapolis is among the world's 20 largest megacities. Intensive air emissions of pollutants affect not only the state of the atmosphere above the megapolis, but also far beyond its limits. To obtain diagnostic and predictive assessments of this impact intensive measurements of air pollutants as well as chemical transport simulations are developed. Agreement of parameters used in chemical transport models (CTMs) with experimental results continues to be an important task. We present preliminary results of a comparison of the measured by DOAS technique and simulated by COSMO-Ru7-Art NO₂ integral contents (IC) in the atmospheric boundary layer (ABL) at Zvenigorod Scientific Station (ZSS) located in 38 km west from Moscow. The comparison covers summer seasons of 2015 and 2016. The measured NO₂ IC in the ABL observed at ZSS does not exceed 0.5×10¹⁶ molec×cm^-2 in background conditions of the atmosphere when non-east wind direction dominated. It grows up to 4×10¹⁶ molec×cm^-2 when polluted air masses come from Moscow megacity. Simulated NO₂ IC has similar behavior. In general, a good agreement between measured and simulated datasets is observed. Usage of improved algorithm for NO₂ IC measurements allows to compare measured and simulated diurnal variations of NO₂ IC in the ABL. Increasing of the sample of analyzed data allows to reveal and analyze measured and simulated weekly cycle of NO₂ IC in the ABL. Observed disagreements between measured and simulated characteristics of NO₂ IC variations can be a source for improving of NO₂ emission inventory.

Using measurements of the GSA instrument onboard the Resurs-P satellite, the authors performed an experiment for the retrieval of the high-detailed spatial NO2 distribution in the troposphere. The authors developed an algorithm to obtain the tropospheric NO2 2D distribution with the horizontal spatial resolution reaching 2.4 km for the first time at the world level and provided on a grid with a step of 120 m. The high spatial resolution of the NO2 space measurements for the first time allowed the identification of local sources of NO2 pollution and their plumes. Earlier, we compared our large-scale NO2 distribution structures with measurements from another satellite instrument, OMI, and obtained a reasonable agreement between the NO2 fields taken by the two systems on September 29, 2016 for Hebei province, the North China Plain, which is the most NO2 polluted area in the world. For the validation of fine structures detected in the NO2 fields of GSA/Resurs-P, we are developing methods based on comparisons with numerical models. The paper presents preliminary results of comparisons of the GSA/Resurs-P tropospheric NO2 measurements with simulations performed by models describing the transport of impurities in the atmosphere with different accuracy.

According to the latest satellite observations, clouds cover about 67% of the earth's surface and affect the radiation budget of the earth-atmosphere system by scattering and absorbing short-wave radiation from incident sunlight and longwave radiation from the ground. The characteristics of cloud particles at different altitudes are complex and changeable in space and time, the microphysical properties of real clouds are still unknown. As a kind of discrete random medium, cloud inevitably causes background interference to satellite-to-earth link laser communication, satellite remote sensing, etc. Therefore, the research on light scattering and radiation characteristics of clouds is not only helpful for predicting climate change and understanding the radiation budget of the global atmospheric system, but also has important significance for other fields in atmospheric physics. This paper starts with the modeling of the spatial distribution of the cloud, studies the spatial distribution patterns and internal microstructures of clouds, uses the multi-scale superposition algorithm in fractal theory to establish a three-dimensional spatial distribution model of clouds, and controls the parameters of the different types of clouds in the algorithm. The cloud layer can be regarded as an irregular structure composed of water droplets or ice crystal particles of various sizes in a three-dimensional space. The modeling of the spatial distribution of clouds includes two main parts: the modeling of clouds in spatial morphology and the modeling of the clouds in microscopic physical structure. For modeling of the spatial distribution of clouds, a multi-scale overlay algorithm is used. This algorithm has many controllable parameters and flexible control. It can generate various types of clouds according to needs, and can simulate dynamics by increasing the fractal dimension cloud structure. The multiscale superposition algorithm is used to establish the spatial distribution models of different types of clouds.

Globally, remotely sensed agricultural monitoring is impeded by cloudy conditions which render the acquired images useless. In semi-arid landscapes, rainfed croplands dominate agricultural production; thus, the majority of planting occurs in the rainy season, characterized by erratic cloud cover. The clear-sky pixels in partly-cloudy images can be used to increase the number of useful observations for quantitative analysis. This is achieved by cloud screening and atmospheric correction (AC) processes. However, the effectiveness of various AC approaches under partly-cloudy conditions is still unknown. Many studies validate surface reflectance (SR) under clear-sky conditions, with only a few focusing on the validation of SR under partly-cloudy conditions. This study sought to validate Sentinel-2 SR products derived from various AC approaches, i.e., MAJA, Sen2Cor, iCor, and FORCE, using in-situ spectral measurements. A partly-cloudy image with >60% cloud cover, acquired over a semi-arid agricultural landscape and ±3 days of the field measurements, was used as a test case. The results showed R² of <0.2 with in-situ SR in the VIS and SWIR, and R² of ~0.6 in the Red-edge and NIR regions, attributable to low aerosol scattering effect in the NIR. However, RMSE, MAE, and BIAS error metrics showed consistently higher errors across all AC approaches and crop types, with varying magnitude per spectral band. This finding can be attributed to the high presence of clouds in the image, which enhanced the apparent path radiance, causing an overestimation of the aerosol optical thickness, hence an underestimation of SR. The need for further validation of SR under various cloud conditions emanates from this study, to ascertain the utility of AC approaches under such conditions. Overall, the results have implications for the utility of remotely sensed images for agricultural crop monitoring in partly-cloudy conditions.

In this work, an investigation of the capability of a statistical cloud detection scheme, implemented through the use of a multilayer feed-forward neural network, is assessed. The whole methodology is applied to a set of IASI L1C spectral radiances, covering the period January 2016-November 2016 and related to Eastern Europe and tropical areas. From a subsampled training dataset where the sky conditions are “certainly” known, we have performed the supervised learning of statistical features of the cloudy- and clear- sky conditions, where truth data have been taken from a cloud mask product of the Advanced Very High-Resolution Radiometer (AVHRR). Also, to improve the neural network classification performances: i) Principal Component Analysis (PCA) of IASI spectra and ii) neural network learning regularization techniques, have been used. Finally, the neural network classification analysis, evaluated during the training with a validation dataset and then with a test dataset, shows very good performance in detecting clouds, with an accuracy of about 93%.

Cloud detection is a fundamental pre-processing task for high resolution satellite images, where the presence or the absence of the cloud plays an important role in making a decision for further processing. Existing techniques are based on per-pixel classification for region segmentation. However, due to the similarity of the features with other patterns like ice or snow, it may lead to misclassification. Moreover, cloud detection imposes the detection of cloud shadow as well since it also covers land areas. In order to come up with an efficient technique to tackle the complexity of pattern diversity, we exploit the recent advances in machine learning by designing and training a deep convolutional neural network model (ConvNet) based on multi-scale feature learning. Our proposed technique claims that different types of features can be learned at different scales to discriminate between image patterns. We chose two publicly available datasets for training. First, the 38-Cloud dataset was annotated as cloudy and non-cloudy classes. Second, the SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) dataset with seven classes including cloud, ice/snow, and shadow. Both datasets contain images with four bands (R: Red, G: Green, B: Blue, Nir: Near Infrared), which we use as inputs of the ConvNet model for training and testing. The experimental results show that our proposed method can effectively detect clouds in complex scenes.

In a relatively short time, Spire has grown from a small start-up company to the largest commercial producer of satellite-based GNSS Earth observation products. The Earth observations produced with Spire’s GNSS science receiver include atmospheric profiles performed by radio occultation (RO) and space weather observations (slant total electron content (TEC) and scintillation indices) using signals from the GPS, GLONASS, QZSS, and Galileo constellations. Spire now produces thousands of RO profiles and millions of TEC observations each day with low latency, with plans for over 100 RO-producing satellites in the full constellation. Due to its agility and rapid launch cycle, averaging launches of four to eight satellites every six weeks, Spire has the unique ability to improve performance and add capabilities on-orbit that are impossible with traditional, risk-averse satellite missions. Spire has provided RO and space weather data to the second NOAA Commercial Weather Data Pilot program, the US Air Force Commercial Weather Data Pilot program, ESA, and numerous NWP centers and research institutions. Spire is also pioneering the provision of Earth observation data to NASA and ESA researchers through unique data purchase programs. In this talk, we will overview the status and capabilities of the Spire satellites and describe the collection of RO profiles from the GPS, GLONASS, Galileo, and QZSS constellations specifically for numerical weather prediction (NWP) applications. We will also describe precise orbit determination of our low Earth orbiting satellites and inversion of the RO profiles. We will show the statistical characteristics of our products, focusing primarily on bending angle and discussing the factors affecting the quality of the profiles. We will also present third-party evaluations of Spire RO data that highlight their quality and impact through assimilation on weather forecasting. Finally, we will outline our plans for expanding the Spire RO constellation in the future.

Quasi-Biennial Oscillation is an equatorial oscillation with downward descending eastward and westward wind regime of the average period of 28 months. The present work investigates the planetary wave activity in the equatorial Mesosphere Lower Thermosphere (MLT) during anomalous QBO phase change in November 2015- July 2016. The upward displacement of eastward winds developed at 20 hPa in late 2015 accompanied by the development of westward wind centered at 40 hPa. This 40 hPa westward wind reverted back to eastward in July 2016. Spectral analysis of Kotatobang (0.20°S, 100.320°E) Meteor radar winds between November 2015 and July 2016 indicates that the presence of long-period Kelin waves of periods 23days in the equatorial mesosphere during the Anomalous QBO event. The eastward propagating 23day wave is significantly observed in the mesospheric heights at the end of April 2016 and significant till July 2016. The presence of slow phase 23day wave at mesospheric altitudes motivated us to investigate the vertical propagation characteristics of these waves. Space-time Fourier analysis of ECMWF winds and OLR show the presence of 23 day period with zonal wavenumber 1, 2 and 3 indicating that tropical convection is the potential source for these waves and westward phase of stratospheric QBO winds during anomalous phase change during February to August 2016 might have favoured these waves to reach the mesosphere.

Position estimation of the target is essential in all fields- agriculture, forestry, defense, aviation, remote sensing, communication, maritime and shipping, navigation, railways, road transport and safety, urban planning and development, land survey, and climate study, etc. While the accurate position is the most important aspect of remote sensing, the attainment of it is a challenge. In navigation systems, the error in position estimation occurs due to various factors, viz. the presence of electrons in the ionosphere, the satellite and receiver clock offset, multipath effect, the non-uniform refractive index of the troposphere, the error due to PRN code noise and noise within the receiver, and errors in the ephemeris data, to name a few. In this paper, the authors have attempted to give an account of precision of position estimation by the Indian Regional Navigation Satellite System (IRNSS), by estimating the total slant delay caused to the satellite signal. The total slant delay (TSD) in the troposphere is caused due to the change in refractive index in the troposphere. The non-uniform refractive index is caused due to variation in temperature, pressure, and water vapor present in the troposphere. The non-uniform refractive index causes the refraction of the signal causing bending and slowing. The paper presents the diurnal, daily, and monthly variations of total slant delay (TSD) over a few tropical locations in India, namely Bhubaneswar (20.14°N, 85.67°E), Gandaki (13.45°N, 79.16°E), Goa (15.45°N, 73.81°E), Kanakapura (12.54°N, 77.429°E), and Kolkata (22.53°N, 88.33°E). In an attempt to find out the factors that govern the TSD, in this paper, the authors have analyzed a few meteorological elements, viz. the surface pressure (SP), surface temperature (ST), relative humidity (RH), and integrated precipitable water (IPWV). They have also studied the dependence of TSD on the total solar irradiance (TSI). The study reveals that the TSD follows the solar cycle. Besides, the study further shows the relationship between the TSD and the meteorological elements mentioned above. The investigation shows that the total slant delay (TSD) retrieved from the different satellites in the IRNSS constellation is different. Thus, for accurate position estimation of the target, it is necessary to take an average of TSD estimated by all the satellites. The diurnal variation of TSD shows that in a month, every day its crests occur at regular intervals. The troughs of TSD also recur after a fixed interval. The periodicity of the crests and troughs is uniform throughout a particular month, i.e. the crests occur exactly at the same time every day at regular intervals; so also the troughs. This observation led the authors to investigate the solar cycle. The study reveals that both TSD and total solar irradiance replicates each other. The study also indicates that total slant delay decreases as the elevation of the satellite w.r.t. the Earth’s station increases.

Climate on Earth is determined by the Earth Radiation Budget (ERB), which quantifies the incoming and outgoing radiative energy fluxes at the top-of-atmosphere (TOA). The ERB can be monitored from space by non-scanning wide field-of-view radiometers (WFOV), or by scanning narrow field-of-view radiometers. Recently, WFOV radiometers have gained renewed interest as illustrated by the development of the RAVAN and SIMBA 3U CubeSats. RAVAN uses a Vertically Aligned Carbon Nanotubes (VACNT) coating, while the SIMBA CubeSat uses a novel cavity-based geometry with a Black Velvet coating. Both VACNT and Black Velvet are diffuse coatings, but when applied to flat sensors, the VACNT coating has a significantly lower reflectivity in comparison to classic diffuse or specular black coating materials. When used on a cavity radiometer, it is currently unclear if a VACNT coating would improve the measurement accuracy compared to other diffuse coatings, such as Black Velvet. In this paper, we therefore investigate the potential benefits of using the VACNT coating as an alternative for Black Velvet, in our in-house developed radiometer. Our analysis includes the evaluation of the influence of the cavity geometry as well as the coating absorption factor. The comparison of the VACNT with the Black Velvet coating is based on the absorption factor of the cavity that is determined using radiation view factor calculations. Scattering and stray-light analyses are carried out using commercially available ray-tracing software (ASAP R, Breault Research). We evaluated whether the coating or the geometry is the main contributing factor to the performance of the radiometer. As a conclusion, we observed that for cavity-type radiometers, the difference in the cavity absorption factor between Black Velvet and VACNT becomes negligible, favoring the use of Black Velvet, since Black Velvet has a long space heritage and appears more user friendly from a fabrication point of view, as it can be deposited in an easier and more reproducible manner on the radiometer cavity walls, including non at surfaces.

The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) is a Japanese-European collaborative earth observation satellite mission. The Cloud Profiling Radar (CPR) is one of the EarthCARE instruments and it is the world's first on-board millimeter-wave Doppler radar in space. The Japan Aerospace exploration agency (JAXA) is responsible for development of the CPR in this mission in cooperation with the National Institute of Information and Communications Technology (NICT). This paper mainly describes the development status and performance of EarthCARE CPR Proto Flight Model (PFM).

Synergistic remote sensing of the atmosphere, combined with adaptive techniques, offers unprecedented opportunities to characterise the evolution of key atmospheric features such as the Atmospheric Boundary Layer (ABL). Using long-duration, high-resolution, profiling observations from active and passive ground-based remote sensing systems during the Verification of the Origins of Rotation in Tornadoes Experiment{Southeast (VORTEX-SE) 2017 field campaign, an attempt is made to characterise ABL development over distinct regions that are well known for their relatively high tornado frequency. In this study, observing systems include an S-band radar, Vaisala CL-31 ceilometer, Doppler Wind lidar (DWL) and radiometric observations from the Collaborative Lower Atmosphere Mobile Profiling System (CLAMPS). In this work, ABL height (ABLH) tracking over the diurnal cycle, and - up to a point - its disambiguation over selected non-precipitating case examples, are attempted. Different observational sets are used, namely, radar reflectivity observations assimilated into a Kalman filter, DWL profiles of the vertical velocity, and virtual potential temperature profiles, as well as radiosoundings and cloudbase reference information collected during Intensive Observation Periods (IOP) carried out in VORTEX-SE, Alabama during 2017. Limitations and advantages of each system are discussed.

We present an algorithm for the rapid retrieval of the carbon dioxide total column amounts (XCO₂) using short wave infrared (SWIR) spectra of the reflected sunlight measured from space. The algorithm takes advantage of the combined processing of observational data from two different satellite missions. For the algorithm implementation we adopted the previously developed EOF (Empirical Orthogonal Functions)-based approach that exploits regression relations of the principal components of the measured spectra with target XCO₂ values. In the original algorithm version the regression coefficients were derived by using training sets of collocated satellite and ground-based observations (ground-based observations were treated as “true values”). In this paper we implemented similar approach in which training set for one satellite mission is created using collocated observations of the another “reference” space mission simultaneously on-orbit (in this case XCO₂ retrievals of the “reference” mission were treated as “true values”). This approach enables rapid data processing of the new satellite missions omitting expensive and time consuming stage of retrieval algorithm development. The feasibility of the approach was tested by joint processing of GOSAT and OCO-2 observation data. For the analysis of the algorithm precision/accuracy characteristics we used the collocated observations from the Total Carbon Column Observing Network (TCCON).

This pilot 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 a ground-based ceilometer located at the west coast of Ireland. Data recorded between 2013 to 2016 is used to investigate the challenges and limitations in performing such a comparison. The standard Level 2 CALIOP Aerosol Profile version 4 product is compared to data from a ground-based Jenoptik CHM15K ceilometer. A statistical analysis considering CALIOP overpasses within 100 km from the ceilometer is presented taking into account 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 ceilometer at Mace Head for cloud-free scenarios shows negative bias for 80% of the cases. The comparison of these results with HYSPLIT shows that air samples with higher or lesser content of marine aerosols produced different biases between the measurements. To our knowledge, this is the first study that investigates the limitations and capabilities in comparing CALIOP and ground-based ceilometer measurements using the 1064 nm wavelength.

In this paper, the authors have attempted to provide a comprehensive account of the Indian Regional Navigation Satellite System (IRNSS). The paper describes the coverage area, the space segment, ground segment, and user segment of IRNSS. The orbital parameters of all seven satellites in the IRNSS constellation have been reported. Some important definitions related to space crafts have been explained. The paper presents the method to estimate the receiver’s position and the algorithm involved. The paper also discusses various sources of error in position estimation. The paper gives a detailed description of the trajectories of all seven satellites in the constellation. The paper presents the Slant Tropospheric Delay (STD) obtained over the National Atmospheric Research Laboratory, Gadanki; National Institute of Oceanography, Goa; Regional Meteorological Centre, Kolkata; Indian Institute of Technology, Bhubaneswar; and Sona Synthetics, Kanakapura, Bangalore.

Air pollution in Japan has decreased from the elevated levels found during the period of high economic growth. However, in recent years there is increasing concern regarding air pollution caused by fine particulate matter (PM_2.5) and its effect on human health. First, we investigated the seasonal and regional characteristics of air pollution caused by PM_2.5 using ground measurements in China and Japan. Next, the cause of air pollution was classified as urban air pollution or transboundary air pollution. Air pollution in Fukuoka (in southwestern Japan) is strongly affected by transboundary air pollution. Nearly half of all air pollution in Tokyo (in eastern Japan) may be urban pollution caused by local emissions. PM_2.5 high concentration from winter to spring is often caused by transboundary air pollution. On the other hand, PM_2.5 high concentration from summer to autumn is greatly influenced by urban pollution. Air pollution caused by PM_2.5 high concentration tends to decrease in Japan. This is due to both a reduction in PM_2.5 emissions in Japan and a decrease in PM_2.5 concentrations in China.

This work demonstrates the advantages of multi-channel measurements of SGLI loaded on the Japanese mission JAXA/GCOM-C (SIKISAI in Japanese) for aerosol retrieval in severe events of biomass burning aerosol (BBA). It has been shown in the previous work that the classification algorithms for aerosol types mainly used near-UV data are available to facilitate the subsequent aerosol retrieval. These algorithms for aerosol classification are expanded to the severe biomass burning events (BBAE) in this work. The SGLI has 19 channels from near-UV to thermal IR including polarization channels in the red (0.67 μm; named P1 band) and near-IR (0.86 μm; P2 band). Here the compatibility of polarization information and non-polarization one are examined, and effective use of both is made for aerosol characterization in severe biomass burning aerosol events (BBAE).

Cirrus clouds consisting mainly of ice crystals are important components of the atmosphere which essentially modulate the radiative budget of the Earth. Until now, the microphysical properties (i.e., size and shape) of the ice crystals, as well as their number density are poorly known because of their great variability in time and space and difficulties of field measurements. At present, cirrus clouds are widely studied by various ground-based, airborne and spaceborne instruments. Among such instruments, lidars and radars are promising devices providing active remote sensing of the clouds. In the report, we present the results of the calculations the radar-lidar ratio explicitly at a reasonable model for the size and shapes of the cirrus ice crystals using the physical-optics approximation. We show that it is the radar-lidar ratio that is mainly informative for retrieving crystal sizes. Also, we calculate the depolarization ratios for both lidar and radar. We obtain that the lidar depolarization ratio is effective for estimating crystal shapes in cirrus clouds. Such data would be useful for interpreting any data obtained simultaneously by radars and lidars.

In this work the result of calculation of backscattering matrices for non-spherical ice particles for 1.55 μm and 2 μm wavelengths is presented. The Physical Optics approximation method was used for solving this problem. The maximal dimension of particles varies from 10 μm to 1000 μm according to D. Mitchel dependency. The calculations were performed for random spatial orientation of particles. The results can be used for retrieving of microphysical properties (i.e. size and habit) of cirrus particles.

Reducing emissions of some ozone-depleting substances (ODSs) entails uneven recovery of the ozone layer as it is also affected by ODSs emissions that not restricted by the Montreal Protocol. In this regard, there remains a need to continue monitoring the total ozone content (TOC) and ultraviolet (UV) radiation, as well as to ensure the uniformity of the measurements obtained in comparison with the data for previous decades. The network of Brewer spectrophotometers, operating since the early 1980s, is one of the oldest global systems that provides high-accuracy TOC and spectral UV radiation data. A new cross-platform database application for storing measurements, internal tests and instrumental constants of Brewer spectrophotometers is under development by A.M. Obukhov Institute of Atmospheric Physics of RAS. This application is capable of running on the computers with Windows, Linux, or macOS and has one code base. The database application of the Brewer measurements is an ASP.NET Core MVC web application with a cross-platform embedded SQLite DBMS as persistent storage. The Brewer measurements database itself is implemented as a .NET Standard shared library in the C# programming language using Entity Framework (EF) Core as an object-relational mapping tool. The application represents data in a hierarchical or relational manner, with the data rationalized into discrete entity classes. All entities are mapped to their database tables with columns corresponding to the properties of the entity classes. Proposed the database application will allow maintaining the integrity and consistency of TOC and UV measurements on Brewer spectrophotometers of the global network.

Determination of the content of trace gases (such as, for example, NO2, formaldehyde) by DOAS (differential optical absorption spectroscopy) method in the lower troposphere can be difficult with significant scattering of light on clouds and aerosol. Often, the parameters of cloudiness and aerosol are unknown for specific DOAS measurements, and, therefore, the estimation of these parameters directly from the DOAS analysis data is an approach that could increase the final measurement accuracy of trace gases. In this work, we consider the problems of retrieving such characteristics as: cloud bottom height, cloud optical depth, aerosol optical depth, F-factor (a factor reciprocal to air mass factor) from the input data obtained during DOAS analysis. To do this, we trained and compared two machine learning (ML) models - neural network and random forest. Both ML algorithms solve the regression problem; data obtained by numerical computation by linearized radiative transfer model were used as a learning dataset. The dependence of the error on the test dataset depending on the regularizing parameters was investigated for the neural network. Retrieval errors of aerosol and cloud characteristics were preliminary estimated.

Natural and anthropogenic aerosol atmospheric emissions play a fundamental role in directly modulating the incoming solar radiation and affecting the air quality. Likewise, aerosols indirectly impact cloud lifetime, atmospheric column thermodynamics and precipitation patterns. For this reason, it is crucial to assess aerosol spatial and temporal variability to reduce the associated uncertainty of global climate models in correctly forecasting future scenarios and it will enable application of mitigation strategies. In this manuscript, for the first time, we developed a simple aerosol optical depth (AOD) retrieval algorithm in the blue wavelength range that does not use either look-up-tables or radiative transfer models.

The Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) provides a wealth of long-duration, high-resolution, vertically pointing observations from active and passive ground-based remote sensing systems enabling characterization of the Atmospheric Boundary Layer (ABL) development over distinct regions that are well known for their relatively high tornado frequency. Application of the Extended Kalman Filter (EKF) to BL height estimation in the convective regime (CBLH) of the diurnal cycle from S-band radar reflectivity observations¹ has shown to yield accurate results under simple CBL conditions. In this work, we revisit the radar-EKF technique and investigate its main limitations. For example, during daytime clear-sky conditions such as those prevailing in the BL morning transition, weak turbulence leads to very low reflectivity returns, limiting application of this technique. Additionally, turbulent mixing layers capped with a residual layer, and/or multi-layer scenarios can lead the filter to lose track of the BL signature over time. Doppler Wind Lidar (DWL) observations of the vertical wind velocity variance² provide complementary CBLH estimates to those of the radar-EKF combination, providing potential to disambiguate more complex convective cases. DWL estimates are, however, strongly influenced by the variance threshold selected. The complementarity of radar and DWL for CBLH estimation is studied in reference to radiosoundings.

The vertical structure of clouds significantly influences the local scale atmospheric stability and radiative forcing over any place. Over Indian region there is a notable lack of thorough and long term studies on Cloud vertical structure (CVS). Therefore, we tried to study CVS over Delhi which is one of the most important urban site having high population density and is one of the most polluted megacity of India. The local scale meteorology and atmospheric dynamics are significantly different in Delhi as compared to surrounding rural areas. Delhi has been witnessing odd precipitation trends from last two decades and has also become one of the most water scarce regions in country. The understanding of all of these crises could be improved with a long term study of CVS over Delhi. We have done seasonal comparison using long term (2000-2019) radiosonde data. We found that the frequency of multi-layer clouds is significantly higher in monsoon season as compared to other season. The frequency of high-level clouds is also significantly higher followed by low-level and middle-level clouds except in pre-monsoon where the frequency of lowlevel clouds is notably lower. We haven’t detected any deep convective and low-level convective cloud in pre-monsoon and post-monsoon season. The average cloud base and cloud top height are found to be significantly higher in premonsoon season for single, double as well as triple layer clouds whereas these clouds were thinnest in post-monsoon with cloud base lying very low for triple layer clouds.

Air pollution in Poland is a severe threat to public health and it was proven to be the main source of many lethal diseases. Across Europe, air quality in Poland is one of the worst. Thus, there is a strong demand for air quality monitoring in Poland in order to raise public awareness and to develop policies that will mitigate this huge problem. The main objective of the research was to verify measurement of the Sentinel-5 Precursor (Sentinel-5P, S-5P) satellite product - tropospheric NO2 column number density - generated by the European Space Agency (ESA) to support air pollution monitoring in Poland. In this respect, the products were validated against in-situ measurements provided by the The Chief Inspectorate for Environmental Protection (GIOS). Furthermore, there were performed analyses about cloudy to verify what are the limitations of the Sentinel-5P NO2 product. There were carried out analyses for each month in the period July-December 2018. In-situ data from 117 ground stations were used. The results of S-5P were compared with the data from all ground stations and according to area types (urban, suburban, villages and no build-up areas). The study proved a Pearson correlation coefficient of the Sentinel-5P product with the in-situ measurement as follows: S-5P vs. all station R=0.55, S-5P vs. urban station R=0.55, S-5P vs. suburban station R=0.63, S-5P vs. village and no build-up areas R=0.55. C.a. 90-100 cloudless pixels were available for each area in the analysed period. Image was usually performed twice a day, so there were 45-50 cloudless days. The fewest images were available in December (2-4 images) and July (11-12 images). Most in August, c.a. 25 images. Moreover, there was recognised the spatial distribution of NO2 in Poland. The study revealed, NO2 level was the highest in the Warsaw and its suburbs, in the area of Lodz and its suburbs, Upper Silesia Metropolis, and Cracow. Relatively higher NO2 emissions were noticed in Wroclaw and Opole. The lowest level of NO2 was revealed in the north of Poland – in Pomerania also Warmia and Masuria.