This PDF file contains the front matter associated with SPIE Proceedings Volume 10424, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

The paper reports on the current status of the Maritime Aerosol Network (MAN) which is a component of the Aerosol Robotic Network (AERONET). A public domain web-based data archive dedicated to MAN activity can be found at https://aeronet.gsfc.nasa.gov/new_web/maritime_aerosol_network.html . Since 2006 over 450 cruises were completed and the data archive consists of more than 6000 measurement days. In this work, we present MAN observations collocated with MODIS Terra, MODIS Aqua, MISR, POLDER, SeaWIFS, OMI, and CALIOP spaceborne aerosol products using a modified version of the Multi-Sensor Aerosol Products Sampling System (MAPSS) framework. Because of different spatio-temporal characteristics of the analyzed products, the number of MAN data points collocated with spaceborne retrievals varied between ~1500 matchups for MODIS to 39 for CALIOP (as of August 2016). Despite these unavoidable sampling biases, latitudinal dependencies of AOD differences for all satellite sensors, except for SeaWIFS and POLDER, showed positive biases against ground truth (i.e. MAN) in the southern latitudes (<50° S), and substantial scatter in the Northern Atlantic “dust belt” (5°-15° N). Our analysis did not intend to determine whether satellite retrievals are within claimed uncertainty boundaries, but rather show where bias exists and corrections are needed.

This work describes an algorithm for aerosol retrieval focusing on the second global imager (SGLI) mounted on the forthcoming global change observation mission - 1st climate satellite (GCOM-C1). The SGLI has a function to measure semi-Stokes components (I, Q, and U) of the Earth's reflectances at red (678 nm) and near infrared (868 nm) with large tilting angle (±45 deg.) along track direction. The SGLI also measures total reflectance from nadir looking position from near UV (380 nm) to far infrared (12 μm). The aerosol retrieval algorithm for the SGLI uses two-channel polarization information from slant path direction as well as nadir looking reflectance at blue channel (443 nm) to estimate aerosol optical thickness, Ångström exponent, and single scattering albedo of particles. The algorithm is tested by the polarization and directionality of the Earth's reflectances (POLDER) measurements.

Uncertainties of aerosol parameters are the limiting factor for atmospheric correction over inland and coastal waters. For validating remote sensing products from these optically complex and spatially inhomogeneous waters the spatial resolution of automated sun photometer networks like AERONET is too coarse and additional measurements on the test site are required. We have developed a method which allows the derivation of aerosol parameters from measurements with any spectrometer with suitable spectral range and resolution. This method uses a pair of downwelling irradiance and sky radiance measurements for the extraction of the turbidity coefficient and aerosol Ångström exponent. The data can be acquired fast and reliable at almost any place during a wide range of weather conditions. A comparison to aerosol parameters measured with a Cimel sun photometer provided by AERONET shows a reasonable agreement for the Ångström exponent. The turbidity coefficient did not agree well with AERONET values due to fit ambiguities, indicating that future research should focus on methods to handle parameter correlations within the underlying model.

Aerosol, together with cirrus clouds, play a fundamental role in the earth-atmosphere system radiation budget, especially at tropical latitudes, where the Earth surface coverage by cirrus cloud can easily reach 70%. In this study we evaluate the combined aerosol and cirrus cloud net radiative effects in a wild and barren region like South East Asia. This part of the world is extremely vulnerable to climate change and it is source of important anthropogenic and natural aerosol emissions. The analysis has been carried out by computing cirrus cloud and aerosol net radiative effects through the Fu-Liou-Gu atmospheric radiative transfer model, adequately adapted to input lidar measurements, at surface and top-of-the atmosphere. The aerosol radiative effects were computed respectively using the retrieved lidar extinction from Cloud-Aerosol Lidar with Orthogonal Polarization in 2011 and 2012 and the lidar on-board of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations for the South East Asia Region (27N-12S, 77E-132E) with 5° x 5° spatial resolution. To assess the cirrus cloud radiative effect, we used the ground-based Micro Pulse Lidar Network measurements at Singapore permanent observational site. Results put in evidence that strong aerosol emission areas are related on average to a net surface cooling. On the contrary, cirrus cloud radiative effect shows a net daytime positive warming of the system earth-atmosphere. This effect is weak over the ocean where the albedo is lower and never counter-balances the net cooling produced by aerosols. The net cooling is stronger in 2011, with an associated reduction in precipitations by the four of the five rain-gauges stations deployed in three regions as Sumatra, Kalimantan and Java with respect to 2012. We can speculate that aerosol emissions may be associated with lower rainfall, however some very important phenomena as El Nino Southern Oscillation , Madden-Julian Oscillation, Monsoon and Indian Dipole are not considered in the analysis.

Considering the increase of atmospheric pollution levels in our cities, due to emissions from vehicles and domestic heating, and the growing threat of terrorism, it is necessary to develop instrumentation and gather know-how for the automatic detection and measurement of dangerous substances as quickly and far away as possible. The Multi- Wavelength DIAL, an extension of the conventional DIAL technique, is one of the most powerful remote sensing methods for the identification of multiple substances and seems to be a promising solution compared to existing alternatives. In this paper, first in-field tests of a smart and fully automated Multi-Wavelength mini-DIAL will be presented and discussed in details. The recently developed system, based on a long-wavelength infrared (IR-C) CO₂ laser source, has the potential of giving an early warning, whenever something strange is found in the atmosphere, followed by identification and simultaneous concentration measurements of many chemical species, ranging from the most important Greenhouse Gases (GHG) to other harmful Volatile Organic Compounds (VOCs). Preliminary studies, regarding the fingerprint of the investigated substances, have been carried out by cross-referencing database of infrared (IR) spectra, obtained using in-cell measurements, and typical Mixing Ratios in the examined region, extrapolated from the literature. First experiments in atmosphere have been performed into a suburban and moderately-busy area of Rome. Moreover, to optimize the automatic identification of the harmful species to be recognized on the basis of in cell measurements of the absorption coefficient spectra, an advanced multivariate statistical method for classification has been developed and tested.

Fast and accurate radiative transfer model is the key for satellite data analysis, numerical weather prediction, and observation system simulation experiments for climate study applications. We have developed a Principal Component-based radiative transfer model (PCRTM) which can simulate radiative transfer in the cloudy atmosphere from far IR to visible and UV spectral regions quickly and accurately. Multi-scattering of multiple layers of clouds/aerosols is included in the model. A hybrid stream discrete multiple scattering scheme is used to minimizing the number calculation need. The computation speed is 3 to 4 orders of magnitude faster than the medium speed correlated-k option MODTRAN5 and LBLRTM. The PCRTM calculated radiance spectra agree with the Modtran and LBLRTM within 0.02%. Application of this model to various hyperspectral instrument will be shown and discuss. 

Determining whether a scene observed with a satellite imager is composed of a thin cirrus over a water cloud or thick cirrus contiguous with underlying layers of ice and water clouds is often difficult because of similarities in the observed radiance values. In this paper an artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels and the retrieved total cloud visible optical depth, is trained to detect multilayer ice-over-water cloud systems as identified by matched April 2009 CloudSat and CALIPSO (CC) data. The CC lidar and radar profiles provide the vertical structure that serves as output truth for a multilayer ANN, or MLANN, algorithm. Applying the trained MLANN to independent July 2008 MODIS data resulted in a combined ML and single layer hit rate of 75% (72%) for nonpolar regions during the day (night). The results are comparable to or more accurate than currently available methods. Areas of improvement are identified and will be addressed in future versions of the MLANN.

Cloud amount is an essential and extensively used macrophysical parameter of cumulus clouds. It is commonly defined as a cloud fraction (CF) from zenith-pointing ground-based active and passive remote sensing. However, conventional retrievals of CF from the remote sensing data with very narrow field-of-view (FOV) may not be representative of the surrounding area. Here we assess its representativeness using an integrated dataset collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site in Oklahoma, USA. For our assessment with focus on selected days with single-layer cumulus clouds (2005-2016), we include the narrow-FOV ARM Active Remotely Sensed Clouds Locations (ARSCL) and large-FOV Total Sky Imager (TSI) cloud products, the 915-MHz Radar Wind Profiler (RWP) measurements of wind speed and direction, and also high-resolution satellite images from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that the root-mean-square difference (RMSD) between the 15-min averaged ARSCL cloud fraction (CF) and the 15-min averaged TSI fractional sky cover (FSC) is large (up to 0.3). We also discuss how the horizontal distribution of clouds can modify the obtained large RMSD using a new uniformity metric. The latter utilizes the spatial distribution of the FSC over the 100° FOV TSI images obtained with high temporal resolution (30 sec sampling). We demonstrate that cases with more uniform spatial distribution of FSC show better agreement between the narrow-FOV CF and large-FOV FSC, reducing the RMSD by up to a factor of 2.

We describe the contribution of thermal infrared ground-based cameras in the short term Global Horizontal Irradiance (GHI) forecasting. This contribution is compared to the one of visible cameras, the most widespread technology currently used for this application. Accurate forecasts at short term horizons (5 to 30 minutes) under various and changing weather conditions represent an essential data for various applications such as optical availability and solar plant control procedures. The work presented in this paper first draw up an overview of the two cameras used in the following comparative study. The segmentation methods chosen for each of the camera and the protocol are subsequently described. Finally, the results of the study are presented and discussed. Thanks to the new opportunities it offers in terms of feature extraction and its capacities to overcome visible limitations, the thermal infrared camera shows a sizeable improvement in this comparative study.

Air quality and the associated subjective and health-related quality of life are among the important topics of urban life in our time. However, it is very difficult for many cities to take measures to accommodate today’s needs concerning e.g. mobility, housing and work, because a consistent fine-granular data and information on causal chains is largely missing. This has the potential to change, as today, both large-scale basic data as well as new promising measuring approaches are becoming available. The project “SmartAQnet”, funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), is based on a pragmatic, data driven approach, which for the first time combines existing data sets with a networked mobile measurement strategy in the urban space. By connecting open data, such as weather data or development plans, remote sensing of influencing factors, and new mobile measurement approaches, such as participatory sensing with low-cost sensor technology, “scientific scouts” (autonomous, mobile smart dust measurement device that is auto-calibrated to a high-quality reference instrument within an intelligent monitoring network) and demand-oriented measurements by light-weight UAVs, a novel measuring and analysis concept is created within the model region of Augsburg, Germany. In addition to novel analytics, a prototypical technology stack is planned which, through modern analytics methods and Big Data and IoT technologies, enables application in a scalable way.

Further developments of the comparative analysis methodology applied to capabilities of various lidar systems, from micro-lidars to systems employing the high power lasers, which use different receiving systems are conducted. Following the dimensionless parameterization approach and in order to simplify the capabilities prediction and systems comparison, to improve its clarity and expand applicability, we propose specific ways to generalize optical, energy and excess-noise parameters inherent to lidar atmospheric monitoring, taking into account their possible high-scale variability. The generalized approach is used as an example to compare lidars with PMT/APD/SiPM detectors. Features of the pro-posed approach in different conditions and applications are discussed.

The freezing level height (HFL) and the radar bright band height (BBH) are the two important parameters pertaining to cloud physics and precipitation study. The present paper investigates these two parameters over the Indian Ocean over the latitudinal range 36S-8N and the longitudinal range 45E-110E. For this purpose, the freezing level height and the radar bright band height data have been obtained from the data product 2A23 of the precipitation radar (PR) onboard the Tropical Rainfall Measuring Mission satellite (TRMM). The period of study is 1999-2002; 2007-2008. Over a location, the HFL and the BBH show monthly and yearly variations. At a particular location over the Indian Ocean, the HFLmax/min occurs mostly in the same month throughout the period of study. However, at a particular location, the maximum BBH (BBHmax) does not occur in the same month, so also the minimum BBH (BBHmin). The study shows that the BBHmin mostly occurs around 35S, while the location of the BBHmax shows strong seasonal dependence. In individual months, the longitudinally averaged HFL and the BBH show very strong correlations with the latitude, more significant correlations being with the HFL than with the BBH. The HFL also shows excellent correlations with the latitude at a fixed longitude. However, at a fixed longitude, the BBH at times, shows, but not always, correlations with the latitude. The study also shows that over the Indian Ocean, as the latitude increases, the % of times BBH<HFL also increases.

In the context of rising the frequency of natural disasters and catastrophes humanity has to develop methods and tools to ensure safe living conditions. Effectiveness of preventive measures greatly depends on quality and lead time of the forecast of disastrous natural phenomena, which is based on the amount of knowledge about natural hazards, their causes, manifestations, and impact. To prevent them it is necessary to get complete and comprehensive information about the extent of spread and severity of natural processes that can act within a defined territory. For these purposes the High Mountain Geophysical Institute developed the automated workplace for mining, analysis and archiving of radar, satellite, lightning sensors information and terrestrial (automatic weather station) weather data. The combination and aggregation of data from different sources of meteorological data provides a more informativity of the system. Satellite data shows the global cloud region in visible and infrared ranges, but have an uncertainty in terms of weather events and large time interval between the two periods of measurements, which complicates the use of this information for very short range forecasts of weather phenomena. Radar and lightning sensors data provide the detection of weather phenomena and their localization on the background of the global pattern of cloudiness in the region and have a low period measurement of atmospheric phenomena (hail, thunderstorms, showers, squalls, tornadoes). The authors have developed the improved algorithms for recognition of dangerous weather phenomena, based on the complex analysis of incoming information using the mathematical apparatus of pattern recognition.

A purpose of this paper is to present verification of the consistency of unfiltered radiances measured by CERES instruments over their mission 2000-2016. The FM1 scanner on Terra, designated as the climate instrument, is used as a benchmark. The degradation modeling while the instruments on Terra and Aqua were operating in the RAPS mode is being revised, and the rate of the monthly degradation is shown to be 0.03%. The focus of this paper is on consistency between Terra CERES scanners, and it is a part of a broader investigation. Results of comparing FM2 and FM1 are reported for all-sky condition and selected scene types for shortwave and long-wave radiances based on Edition 4 ERBE-like (ES8) data product. Some scene type based results are also verified using an SSF product that contains imager (MODIS) information.

Intense precipitation phenomena occurring over the Tyrrhenian area between Tuscany, Corse Sardinia, and Liguria very often cause floods with considerable socio-economic damages. The need of monitoring such events has led to the implementation of an observing weather radar network: it firstly started with an S-band radar in Corse, three C-band radars in Liguria, Tuscany and Sardinia. Recently, the implementation of an X–band network of three radars in Tuscany and two further C-band radars in Sardinia completed the network. This work shows how this network can be used for the characterization of weather events, following their development and dynamics and providing some information about their possible evolution. Furthermore, the use of meteorological satellites observations can upscale the area of interest to the mesoscale level and provide an enlarged temporal overview. For instance, the Meteosat Second Generation satellites provide useful information about the air mass distribution, convective phenomena occurrence and microphysics in the observed scene, by combining different spectral channels. Finally, ground based observations are meaningful for assessing the observing capabilities of other instruments and for characterizing the effects on soil surface. For some selected case studies, the different observing instruments were compared and a methodology to integrate them synergically is presented and tested. Weather radars correctly detect the rainfall systems and their motion in all the case studies. Clearly, the higher spatial resolution of X-band radars allows detecting the different precipitation areas with great spatial details, while C- and S-band radars can detect phenomena at higher distances. Satellites images have lower spatial resolutions but especially thanks to the RSS (Rapid Scan Service) they can help to detect the growing or dissipating stage of the whole phenomena. Moreover the ground-based network confirms its relevance in improving the identification of the precipitation intensity and in reducing the number of false alarms.

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95% of the globe.

The hardware-software complex for detail spatial-temporal monitoring of daily and seasonal variations of atmospheric electric field was developed. It was made as a part of the integrated system of receiving, processing and providing information of simultaneous measurements of the atmospheric electric field potential gradient in four points. The seasonal features of daily variations of the electric field were investigated according to the experimental uninterrupted data received with the help of the complex. The dependence of atmospheric electric field variations on the atmospheric phenomena and meteorological parameters of the surface layer was researched in the paper.

Formaldehyde (HCHO) is involved in a lot of chemical reactions in the atmosphere. Taking into account that HCHO basically undergo by photolysis and reaction with hydroxyl radical within a few hours, short-lived VOCs and direct HCHO emissions can cause local HCHO enhancement over certain areas, and, hence, exceeding background level of HCHO can be examined as a local pollution of the atmosphere by VOCs or existence of a local HCHO source. Several retrieval algorithms applicable for DOAS measurements in cloudless were previously developed. In previous works we proposed a new algorithm applicable for the overcast conditions. The algorithm has the typical F-coefficient error of about 10% for winter season, about 5% for summer season, and varying from 15 to 45% for transition season if the atmospheric boundary layer is below the cloud base. In this paper we briefly present our results of the HCHO vertical column retrieval measured at Zvenigorod Scientific Station (ZSS) for overcast. ZSS (55°41'49''N, 36°46'29''E) is located in Moscow region in 38 km west from Moscow. Because Western winds prevail in this region, ZSS is a background station the most part of time. But in cases of Eastern wind, the air quality at ZSS is affected by Moscow megapolis, and polluted air masses formed above Moscow can reach station in a few hours. Due to the absence of alternative overcast data of HCHO, we compare our overcast data with the HCHO vertical content, which we obtained for clear sky. We investigate similarities and differences in their statistical behavior in different air mass. The average overcast HCHO data have similar to clear-sky HCHO positive temperature trends for all wind direction. We found that the average retrieved overcast HCHO contents are systematically greater than the clear-sky retrieval data. But the difference between data retrieved for the overcast and clear-sky conditions are different for Eastern and Western winds. This difference is about 0.5×10¹⁶ mol cm^-2 for Western winds and about 1.2×10¹⁶ mol cm^-2 for Eastern winds. We suppose that observed difference between the overcast and clear-sky formaldehyde data can be caused by dependence of chemical reactions leading to the HCHO destruction and the HCHO formation from Moscow anthropogenic predecessors on the cloudy conditions.

In the present study the results of instrument measurements of thunderstorm activity and lightning parameters on the territory of the south of the European part of the Russian Federation is received. The automated hardware-software complex for thunderstorm activity sensing is developed. It consists of lightning sensors, electric field mills, meteorological radiolocator (MRL-5) for sensing the radar characteristics of clouds, meteorological stations and software for data collection, analysis and processing. Features of spatial-temporal variations of the thunderstorm activity and lightning parameters on the territory of 650 km around the measurement center of High-Mountain Geophysical Institute (Nalchik) were found. The relations between the quantities of intracloud, cloud-to-cloud and cloud-to-ground (positive and negative) discharges were studied. Parameters of current of the lightning discharges of different polarities under the flat and mountainous terrain were received and analyzed in the paper.

Geo-KOMPSAT-2A (GK-2A), which is scheduled to be launched in 2018, is a next geostationary satellite of South Korea, following on the Communication, Ocean and Meteorological Satellite (COMS). Advanced Meteorological Imager (AMI) on the GK-2A can scan the Earth with high resolution of both time and space. Furthermore, imageprocessing system of the GK-2A ground system will produce the level 1B images in 2 minutes. Convective Initiation (CI) algorithm can get more accurate weather forecast for heavy rainfall, thunderstorm and lightning through the high resolution of time and spatial data of the GK-2A AMI. Since the convective clouds are usually caused by rapidly developing clouds in unstable atmosphere, they can be detected by the temperature differences between two consecutive images. The CI algorithm of GK-2A AMI has four steps. Convective cloud masking is the first step in the CI algorithm, which extract mature cloud area using infrared channels. The next step is grouping cloud pixels using region growing method for immature cloud area. Then the clustered area (the immature cloud area) will be tested in overlapping detection step in sequential images. The last step of the CI algorithm is “Interest field tests” for each cloud object which can cause severe weather. The CI algorithm was tested by Himawari-8 Advanced Himawari Imager (AHI) data similar to the GK-2A AMI and validated with lightning data and radar observed in ground.

Air pollution is problem of deep concern to human health. In Japan, the air pollution levels experienced during the recent period of rapid economic growth have been reduced. However, fine particulate matter (PM_2.5) has not yet reached the environmental standards at many monitoring stations. The Japanese environmental quality standard for PM_2.5 that was ratified in 2009 lags about four decades behind other air pollutants, including sulfur dioxide, nitrogen dioxide, carbon monoxide, photochemical oxidants, and suspended particulate matter. Recently, trans-national air pollutants have been observed to cause high concentrations of PM_2.5 in Japan. To obtain wide distribution of PM_2.5, the satellite based PM_2.5 products are extremely useful. We investigate PM_2.5 concentrations measured using ground samplers in Japan and the satellite based PM_2.5 products, taking into consideration various geographical and weather conditions.

Aerosol remote sensing by ultraviolet (UV) wavelength is established by a Total Ozone Mapping Spectrometer (TOMS) mounted on the long-life satellite Nimbus-7 and continues to make observations using Ozone monitoring instrument (OMI) located on the Aura satellite. For example, TOMS demonstrated that UV radiation (0.331 and 0.360 μm) could easily detect absorbing particles such as mineral dust or smoke aerosols. TOMS-AI (absorbing aerosol index) has been used to identify the absorbing aerosols from space. For an upcoming mission, JAXA/GCOM-C will have the polarization sensor SGLI boarded in December 2017. The SGLI has multi (19)-channels including near UV (0.380 μm) and violet (0.412 μm) wavelengths. This work intends to examine the role of near UV data in the detection of absorbing aerosols similar to TOMS-AI played. In practice, the measurements by GLI mounted on the short Japanese mission JAXA/ADEOS-2, whose data archive period was just 8 months from April to October in 2003, are available for simulation of SGLI data because ADEOS-2/GLI installed near UV and violet channels. First of all, the ratio of data at 0.412 μm to that at 0.380 μm is examined as an indicator to detect absorbing aerosols on a global scale during ADEOS-2 era. It is noted that our research group has developed an efficient algorithm for aerosol retrieval in hazy episodes (dense concentrations of atmospheric aerosols). It can be said that at least this work is an attempt to grasp the biomass burning plumes from the satellite.

An instrument for measuring atmospheric trace gases by DOAS method using scattered solar radiation was developed in A.M.Obukhov IAP RAS. The instrument layout is based on the lab Shamrock 303i spectrograph supplemented by 2-port radiation input system employing optical fiber. Optical ports may be used with a fixed telescope or with a scanning MAX-DOAS unit. In September 2016 the IAP instrument participated in the 2nd Cabauw Intercomparison of Nitrogen Dioxide Measuring Instruments (CINDI-2) campaign, held in the Netherlands. About 40 instruments from 26 different organizations performed DOAS measurement of NO2, HCHO and other trace gases during the campaign. During the campaign the optical ports of IAP instrument had telescopes A and B with similar field of view of about 0.3°. Telescope A was always directed to the zenith. Telescope B was directed at 5° elevation angle. Two gratings were installed in the spectrometer. They provide different spectral resolution (FWHM ~0.4 and 0.8 nm respectively) and spectral window width (~70 and ~140 nm respectively). During CINDI-2 campaign we performed test measurements in UV and visible wavelength ranges to investigate instrument stability and retrieval errors of NO2 and HCHO contents. We test four operational mode of the instrument in which we used fixed grating position in UV or visible spectral region, or rotate grating between UV and visible, or change gratings using a turret. Here we present preliminary results of comparison of gas content measured by IAP instrument with the median gas content obtained on the basis of measurements of all groups participated in CINDI-2 campaign. The main goal of the study was the detailed comparison of all four modes of our instrument and understanding of probable biases and errors typical for these modes. It was found that rotation of grating turret does not significantly affected on quality of NO2 DSCD retrieval and is much more significant for HCHO DSCD retrieval.

Aerosols can play the role of cloud and ice condensation nuclei. This study shows a set of measurements and experiments analysis in remote sensing, in distinct scenarios of cloud cover during day/nightime measurements using lidar, visible all-sky camera and solar photometry. The retrieved products are studied to obtain aerosol optical and physical properties in the vicinity of clouds. In this approach cloud cover and optical properties should be retrieved as well main features used for cloud pixel identification, e. g., Red/Blue Difference, Red/Blue Ratio, Normalized Red/Blue Ratio, Saturation and Intensity. We show some case studies to illustrate this methodology.

Earlier, we developed a method for estimating the height and speed of clouds from cloud images obtained by a pair of digital cameras. The shift of a fragment of the cloud in the right frame relative to its position in the left frame is used to estimate the height of the cloud and its velocity. This shift is estimated by the method of the morphological analysis of images. However, this method requires that the axes of the cameras are parallel. Instead of real adjustment of the axes, we use virtual camera adjustment, namely, a transformation of a real frame, the result of which could be obtained if all the axes were perfectly adjusted. For such adjustment, images of stars as infinitely distant objects were used: on perfectly aligned cameras, images on both the right and left frames should be identical. In this paper, we investigate in more detail possible mathematical models of cloud image deformations caused by the misalignment of the axes of two cameras, as well as their lens aberration. The simplest model follows the paraxial approximation of lens (without lens aberrations) and reduces to an affine transformation of the coordinates of one of the frames. The other two models take into account the lens distortion of the 3rd and 3rd and 5th orders respectively. It is shown that the models differ significantly when converting coordinates near the edges of the frame. Strict statistical criteria allow choosing the most reliable model, which is as much as possible consistent with the measurement data. Further, each of these three models was used to determine parameters of the image deformations. These parameters are used to provide cloud images to mean what they would have when measured using an ideal setup, and then the distance to cloud is calculated. The results were compared with data of a laser range finder.