This PDF file contains the front matter associated with SPIE Proceedings Volume 11152, including the Title Page, Copyright Information, Table of Contents, Author and Conference Committee lists.

An artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels, the retrieved total cloud visible optical depth, and vertical humidity profiles is trained to detect multilayer (ML) ice-over-water cloud systems as identified by matched CloudSat and CALIPSO (CC) data. The multilayer ANN, or MLANN, algorithm is also trained to retrieve the optical depth and the top and base heights of the upper-layer ice clouds in ML systems. The trained MLANN was applied to independent MODIS data resulting in a combined ML and single layer hit rate of 80% (77%) for nonpolar regions during the day (night). The results are more accurate than currently available methods and the previous version of the MLANN. Upper-layer cloud top and base heights are accurate to ±1.2 km and ±1.6 km, respectively, while the uncertainty in optical depth is ±0.457 and ±0.556 during day and night, respectively. Areas of further improvement and development are identified and will be addressed in future versions of the MLANN.

The focus of this paper is to introduce a novel strategy for comparison of unfiltered radiances in remote sensing devised for CERES scanners. The strategy is referred to as “matched sites targeting”, in which CERES instruments scan at nadir along their respective collocated ground-tracks. This strategy is enabled by similarities in the Suomi-NPP (FM5)/JPSS1 (FM6) and Aqua (FM3) satellite orbits, and a special scan profile available for the CERES scanners. Comparison of collected data in this strategy is done at a footprint level between the instruments (FM5 and FM3 or FM6 and FM3) for specific scene types, determined by their “almost” coincidental ground-tracks. A far more stringent test of the measurement consistency is achieved as averages of 330 collocated nadir samples are compared. A comparison of comprehensive “all-sky” measurements is also included as a reference. Results of the unfiltered radiance comparison are based on ES8 or ERBE-like data product using Edition1 for FM5, Edition1-CV for FM6 and Edition4 for FM3; cloud coverage is verified using MODIS data available in SSFs product.

A Principal Component-based Radiative Transfer Model (PCRTM) has been developed to simulate hyper-spectral remote sensing data in the cloudy atmosphere from far IR to visible and UV spectral regions quickly and accurately. Multi-scatterings of multiple layers of clouds/aerosols are included in the model. The PCRTM model is capable of simulating top of atmospheric radiance or reflectance spectral from 50 wavenumber to 30000 wavenumber. We will describe applications of the PCRTM model for solving various atmospheric remote sensing problems such as atmospheric temperature, moisture, and trace gas profiles retrievals, spectral fingerprinting, inter-satellite calibration, and instrument trade studies.

The irregular shape of mineral dust provides a strong signature on active and passive polarimetric remote sensing observations. Nowadays, advanced lidar systems operating in the framework of ACTRIS are capable of providing quality assured, calibrated multi-wavelength linear particle depolarization ratio measurements, while new developments will provide us more polarimetric measurements in the near future. Passive polarimeters are already part of ACTRIS and their integration in operational algorithms is expected in the near future. This wealth of new information combined with updated scattering databases and sophisticated inversion schemes provide the means towards an improved characterization of desert dust in the future.

We present here some examples from the ACTRIS journey on dust research during the last decade, aiming to demonstrate the progress on issues such as: (a) the discrimination of desert dust in external mixtures, (b) the separation and estimation of the fine and coarse particle modes, (c) the synergy of passive and active remote sensing for the derivation of dust concentration profiles, (d) the provision of dust-related CCN and IN particle concentrations for aerosol-cloud interaction studies, (e) the development of new scattering databases based on realistic particle shapes, (e) the application of these techniques on spaceborne lidar retrievals for the provision of global and regional climatological datasets. Future plans within ACTRIS for the evaluation and advancement of the methodologies and retrievals are also discussed, combined with new developments within the framework of the D-TECT ERC Grant.

Cloud detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking can be translated directly into significant uncertainty in the retrieved downstream geophysical products. The problem is particularly challenging when only of a limited number of spectral bands is available, and thermal infrared bands are lacking. This is the case of Proba-V instrument, for which the European Space Agency (ESA) carried out a dedicated Round Robin exercise, aimed at intercomparing several cloud detection algorithms to better understand their advantages and drawbacks for various clouds and surface conditions, and to learn lessons on cloud detection in the VNIR and SWIR domain for land and coastal water remote sensing. The present contribution is aimed at a thorough quality assessment of the results of the cloud detection approach we proposed, based on Cumulative Discriminant Analysis. Such a statistical method relies on the empirical cumulative distribution function of the measured reflectance in clear and cloudy conditions to produce a decision rule. It can be adapted to the user's requirements in terms of preferred levels for both type I and type II errors. In order to obtain a fully automatic procedure, we choose as a training dataset a subset of the full Proba-V scenes for which a cloud mask is estimated by a consolidated algorithm (silver standard), that is from either SEVIRI, MODIS or both sensors. Within this training set, different subsets have been setup according to the different types of surface underlying scenes (water, vegetation, bare land, urban, and snow/ice). We present the analysis of the cloud classification errors for a range of such test scenes to yield important inferences on the efficiency and accuracy of the proposed methodology when applied to different types of surfaces.

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. Results from the algorithm, besides filling a gap in precipitation and virga detection by radars, are of particular interest for the scientific community because will help to better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

Observation of clouds and precipitations with radars in millimeter waves is one of the most fruitful method to investigate those interior structures because the sensitivities for small particles and droplets are much better than those in longer wavelengths. We have developed the cloud profiling FMCA (Frequency Modulated Continuous Wave) 95 GHz Doppler Radar, named “FALCON-I” (Fig.1; FALCON=FMCW Radar for Cloud Observations), at Chiba University. FALCON-I consists of two 1-m diameter antennas and has a spatial resolution of 0.18 degree (which corresponds to 15m at the height of 5km) and ranging resolution of typically 50m. We make regular observations at zenith with the temporal interval of 10 seconds and make scanning observations with +/- 5-degree in one direction from the zenith with the interval of 15 seconds when we want to observe spatial extent of clouds. Fig.2 shows clouds and precipitations observed with FALCON-I on 15 August 2017 at Chiba University. Time-height intensity map from 00:00-02:00 UT (09:00-11:00 JST; Fig.2a) and Doppler profile map at 00:50 UT (Fig.2b) are presented. The intensity map shows that precipitations started around 00:35 UT at the height of 5 km. The Doppler profile map shows the cloud at 5-7km in height has Doppler velocities about 0 to -2 m/s, which negative velocity means downward motion. At the bottom of the cloud i.e. 5km in height, rain droplets with the Doppler velocities of up to -6 m/s are abruptly produced within a few hundred meters in height. Terminal velocity of 6 m/s corresponds to about 2 mm diameter droplets. Rain droplets were produced intermittently in this case and one of the gropes of droplets is marked with a solid line which just started falling below the cloud bottom as shown in Fig.2b. We can recognize several groups of droplets whose inclines in Fig.2b are getting steeper with falling down. We can derive droplet number distribution N(D) from the Doppler spectra by assuming the observed velocities are terminal velocities. Successive Doppler profile maps and movies from 00:30 to 01:00 UT show dynamic phenomena in the beginning phase of precipitations such as production and acceleration of rain droplets at the bottom of the cloud, evaporation of droplets, changes of number distributions during the falling way, and so on. These results of analysis show that observations of millimeter-wave Doppler radar FALCON-I are powerful methods to investigate micro processes in clouds and precipitations. Fig.1. Cloud Profiling Doppler Radar FALCON-I consists two 1-m antennas and observes clouds and precipitations at 95GHz with high spatial resolution of 0.18 degree. Fig.2. Clouds and precipitations observed with FALCON-I on 15 August 2017 at Chiba University. Time-height intensity map from 00:00-02:00 UT (a) and Doppler profile map at 00:50 UT (b) are presented. The intensity map shows that precipitations occurred around 00:35 UT at the height of 5 km. The Doppler profile map shows the cloud at 5-7km in height has Doppler velocities about 0 to -2 m/s, which negative velocity means downward motion. At the bottom of the cloud i.e. 5km in height, rain droplets with the Doppler velocities of up to -6 m/s are abruptly produced within a few hundred meters. Terminal velocity of 6 m/s corresponds to about 2 mm diameter droplets. Rain droplets were produced intermittently in this case and falling as shown in (b).

We develop a new climatology of the macrophysical properties of single-layer shallow cumuli (ShCu), such as cloud amount and cloud base/top heights, observed during 19 summers (2000-2018) at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) Central Facility in northern Oklahoma, USA. Similar to the established datasets, the climatology incorporates the well-known advantages of the narrow field-of-view (FOV) lidar-radar measurements to resolve the vertical structure of clouds along the wind direction. In contrast to these datasets, the climatology combines the well-known advantages of the wide-FOV sky images to describe the horizontal changes of cloud amount across the wind direction. The recent update includes (1) a new tool for visualization of these across-wind changes with user-selected spatial and temporal resolutions, (2) an additional macrophysical property, the so-called cloud equivalent diameter (CED), estimated over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) and (3) environmental parameters. Our development of the extended climatology is aimed to enhance understanding of the environmental impact on the diurnal evolution of the cloud macrophysical properties and thus to improve performance of ShCu parameterizations.

Satellite provides good observational data for the weather and climate analysis of the Tibetan Plateau. Three kinds of cloud fraction data that comes from PATMOS-x/NOAA, CLARA-A2/NOAA and MODIS / Aqua were evaluated over Tibetan Plateau. Compared with ground observation, PATMOS-x had the highest correlation and the mean correlation was higher than 0.8. Three kinds of long term cloud fraction data showed similar spatial and temporal distribution pattern, namely that eastern Plateau has more cloud than western, northern Plateau has more cloud than southern and day time has more cloud than night. All three kinds of cloud data made mistake with snow along the ridge of mountain. In the daytime, CLARA-A2 had the highest monthly average cloud fraction. At night, the monthly average cloud fraction of Aqua/MODIS was more than that of PATMOS-x and CLARA-A2 except in summer. All three kinds of cloud fraction had similar annual mean value. CLARA-A2 had minimum cloud fraction in summer and maximum cloud fraction in winter. The linear regression and accumulate bias analysis showed that the annual mean cloud fraction of both PATMOS-x and CLARA-A2 displayed a decrease trend from 1982 to 2015. The trend of night time cloud fraction was more obvious than that of daytime. CLARA-A2 displayed obvious trend than PATMOS-x, especially at night.

In this paper, we present a 2-Dimensional (2D) Optimal Interpolation (OI) technique for spatially scattered infrared satellite observations, from which level 2 products have been obtained, in order to yield level 3, regularly gridded, data. The scheme derives from a Bayesian predictor-corrector scheme used in data assimilation and is based on the Kalman filter estimation. It has been applied to 15-minutes temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and temperature products and to Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (NH3) retrievals, a gas affecting the air quality. Results have been exemplified for target areas over Italy. In particular temperature retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. Our findings show that the proposed strategy is quite effective to fill gaps because of data voids due, e.g., to clouds, gains more efficiency in capturing the daily cycle for surface parameters and provides valuable information on NH₃ concentration and variability in regions not yet covered by ground-based instruments.

Bi-directional Reflection Distribution Function (BRDF) defines anisotropy of the surface reflection. It is required to specify the boundary condition for radiative transfer (RT) modeling. Measurements of reflected radiance by satellite- and air-borne sensors provide information about anisotropy of surface reflection. Atmospheric correction needs to be performed to derive BRDF from the reflected radiance. Common approach for BRDF retrievals consists of the use of kernel-based BRDF and RT modeling that needs to be done anew at every step of the iterative process. The kernels’ weights are obtained by minimization of the difference between measured and modeled radiance. This study develops a new method of retrieving kernel-based BRDF that requires RT calculations to be done only once. The method employs the exact analytical expression of radiance at any atmospheric level through the solutions to two auxiliary atmosphere-only RT problems and the surface-reflected radiance at the surface level. The latter is related to BRDF and solutions to the auxiliary RT problems by a Fredholm integral equation of the second kind. The approach requires to perform RT calculations one time before the iterations. It can use observations taken at different atmospheric conditions assuming that surface conditions remain unchanged during the time span of observations. The algorithm accurately catches zero weights of the kernels that may be a concern if the number of kernels is greater than 3 in current mainstream approaches. The study presents numerical tests of the BRDF retrieval algorithm for various surface and atmospheric conditions.

This is a report on the spatial variation of atmospheric carbon dioxide (CO₂) concentration with urban congestion estimated from Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) hyperspectral images procured for the first time in the tropical urban atmosphere of India. It combines hyperspectral field measurements and airborne image analysis and puts forward a modified version of differential optical absorption spectroscopic technique, termed as a-DOAS that defines the absorption depths through weighted average of radiance at absorbing and non-absorbing wavebands. Field spectroradiometry was carried out for part of two adjacent cities, namely Howrah and Kolkata, over sites of varied population density and vehicle abundance at both narrow and wide angle field-of-view. The local concentrations of atmospheric CO₂ at the ground surface and the solar illumination at the measuring spot were noted simultaneously. As an alternative to the general validation method of comparing the image-derived absorption depth with that simulated with radiative transfer model, the present work is based on the image-derived and the ground-based data. The relative differences of the surface reflectance for the pixels of different features were reduced by normalizing the pixel values with suitable constants. While calculating the CO₂ absorption depths, a correction for the adjacent water vapor absorption band was made with MODTRAN simulation. Since the pixel-to-pixel variation of radiance was too fine, the CO₂ map was generated by computing average through convolution and filtering with kernel of 11×11 pixels. The overall observation was lower concentration for vegetated regions in comparison with concreted urban areas.

An experiment for the retrieval of the high-detailed spatial NO2 distribution in the troposphere using measurements of the GSA instrument onboard the Resurs-P satellite was performed in 2016. 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. The paper presents preliminary results of validation of the GSA high-detailed NO2 field obtained on September 29, 2016 for Hebei province, the North China Plain, which is the most NO2 polluted area in the world. To validate the coarse structures in the obtained NO2 field we performed comparisons with OMI NO2 observations having the resolution of 13 km x 24 km. The comparison confirmed the reliability of the GSA NO2 fields in general. For the validation of fine structures detected in the NO2 fields of GSA/Resurs-P, we are developing methods based on comparisons with chemical transport models. The paper presents preliminary comparison of the Resurs-P tropospheric NO2 field with simulation based on HYSPLIT dispersion model. For the solution of the problem, a high-detailed chemical transport model is under the development.

In a series of recent papers we have developed and tested a full physical retrieval scheme for the Infrared Atmospheric Sounder Interferometer or IASI, which is capable of simultaneously retrieving surface temperature, emissivity and atmospheric profiles of temperature, H₂O, O₃, HDO, CO₂, N₂O, CO, CH₄, SO₂, HNO₃, NH₃, OCS, CF₄ atmospheric profiles. Until now, the performance of this scheme has concerned the column amount of gas species. In this paper, we will assess the capability of the methodology to retrieve the vertical profile of CO₂. In this respect, the effectiveness of the method mostly relies on the degree of freedom (dof) of the retrieval. In principle, for an unconstrained Least Square approach, dof achieves its larger value and is equal to the number of layers, N_L which is used to represent the vertical profile. Using Optimal Estimation we have dof ≤ N_L and the actual value is determined by the background covariance matrix. In this study, we use a novel approach, which directly allows us to represent the CO₂ profile with orthogonal eigenvectors or vertical modes that explain which fine-scale structures we can resolve with our retrieval. The number of vertical modes we can effectively resolve depends on the IASI information content for CO₂. To check the capability of the scheme to retrieve the CO₂ profile we have considered a retrieval exercise for a target area close to Mauna Loa and Cape Kumukahi validation stations in the Hawaii region. Five years of IASI data have been collected on such an area and processed according to the above methodology. It will be shown that IASI is capable of retrieving at most 2-3 information pieces or vertical modes for the CO₂ vertical profile, which yield an accurate estimation of the CO₂ total amount and the correct smooth shape expected for a long-lived species such as CO₂ far from intense pollution sources.

Semi-transparent clouds, the so-called cirrus, frequently contaminate satellite images. Recently, Gao and Li (2017) have developed an empirical method for thin cirrus correction with focus on data provided by Landsat-8. This correction allows one to estimate clear-sky apparent reflectance. Validated qualitatively, we propose here a quantitative validation method using Sentinel-2 data by comparing the corrected image with a clear sky reference image. Their method shows good results on dark surfaces, like water, with an apparent reflectance found close to 0.02. On the other hand, it becomes less accurate for thicker cirrus and on more reflective surfaces. In addition, the data analysis shows that pixels located in the shadow of the cirrus are over-corrected. The downward path should therefore be taken into account when correcting the signal.

We study the Earth surface polarized reflectance using data collected by a space-based lidar. Accurate modelling of the surface reflectance supports retrieval algorithm development for the current and future Earth Science missions. Strong polarization of the laser light from Cloud-Aerosol Transport System (CATS) instrument, operated in 2015-2017, and nighttime measurements yield higher signal-to-noise ratio for polarization compared to the previous analysis of reflected, initially unpolarized, solar light.

According to current huge data requirements for the global climate change assessment, DBAR Data Sharing Principles, as well as the national policymaking in response to the global agreement (Framework Convention on Climate Change (FCCC)) on combating climate change, to reform the research mode of carbon data based exploration, to integrate carbon satellite data, models and computing technologies to advance interdisciplinary study, and to implement a big earth data e-science platform for global carbon researches are very essential and necessary. Cooperation on the Analysis of carbon Satellites data (CASA), a new international scientific programme, was approved by the Chinese Academy of Sciences (CAS) in 2018, which was participated by CAS/Institute of Atmospherics Physics and National Super Computer Center in Wuxi. Massive data resources (standard, value-added carbon satellite products and auxiliary data), relevant analysis models, and the super-computing capacity (100 trillion FLOPs computing power and 1 PB of storage) has been integrating into the CASA big e-science platform. Forthcoming products, including carbon satellites standard products, higher precision CO₂ reprocessed products, and application dataset based on above two kinds of CO₂ products, are processed and analyzed online on the CASA e-science platform. The first global XCO₂ product produced from TanSat will be released at September of 2019. Research mode of carbon data-based is going to be reformed under the support of big data and supercomputing power.

Measurements of atmospheric parameters, such as wind velocity, air temperature and moisture, provide important information for a diverse set of fields, ranging from estimating energy output of wind farms to predicting extreme weather events to understanding urban climatology. Performing these measurements quickly, reliably and with high accuracy presents a yet unsolved challenge. Especially when working in or close to complex terrain, such as forests, hillsides or urban landscapes, no available system can properly perform such measurements.

The Fraunhofer Institute for Physical Measurement Techniques IPM is developing a novel multispectral scanning LiDAR system. The goal is to simultaneously and accurately measure wind speed, air temperature and moisture over complex terrain for the first time.

We present the current state of a scanner system for synchronized steering of multiple laser beams from different LiDAR units towards positions in the commonly visible intersection volume, subtending up to 7/8th of the full solid angle. We also present the state of an in-house developed Doppler Wind LiDAR and our current proposal for a combined wind, air temperature and water vapor LiDAR.

Ground-based remote sensing by three ceilometers for mixing layer height detection over Augsburg as well as a Radio- Acoustic Sounding System (RASS) for temperature and wind profile measurements at the campus of Augsburg University are applied together with UAV height profiling with low-weight meteorological sensors and particle counter to monitor the three-dimensional dynamics of the lower atmosphere. Results about meteorological influences upon spatial variation of air pollution exposure are presented on this data basis which is more than one year long. Special focus is on the information about atmospheric layering as well as mixing and transport conditions for emitted particulate matter. Better understanding of these complex processes support knowledge about quality of air, which we breath, and especially high air pollution episodes and hot spot pollution regions.

Pollination is a biologically-relevant process that affects the structure of ecosystems since pollen contributes to determine the spatial distribution of plant species. It is thus of interest for mapping ecosystem services for policy support and decision making to increase our knowledge of pollen grain behavior in the atmosphere (source, emission, processes involved during their transport, etc.) at fine temporal and spatial scales. First simulations with the Barcelona Supercomputing Center MONARCH dispersion model (known before as NMMB/BSC-CTM) of Pinus pollen in the atmosphere were performed during a 5-day pollination event observed in Barcelona, Spain, between 27 – 31 March, 2015. MONARCH is an online atmospheric composition model that solves the life cycle of water vapor, gases and aerosols within a meteorological model. A new aerosol emission scheme for pollen grains has been implemented in the system. The emission scheme considers wind speed at 10 m, friction velocity, and temperature and specific humidity at 2 m as main drivers of the mobilization of Pinus pollen grains. The meteorological information is available for the emission scheme at each meteorological integration time step. The spatial distribution of the pine species (P. halepensis, P. pinea) that pollinate from February to April in Catalonia has been derived from the Cartography of habitats of Catalonia and the tree density was obtained from the Forest Inventory of Catalonia. A domain over north-east Spain at 9 km x 9 km horizontal resolution covering Catalonia is designed with 48 vertical layers. The initial and boundary meteorological conditions are derived from the fifth major global ECMWF ReAnalysis (ERA-5). To evaluate the model performances, the simulations are compared (i) to groundbased concentration measurements performed with a Hirst collector in Barcelona downtown, and (ii) to vertically-resolved measurements performed 4 km west of Barcelona downtown with a Micro Pulse Lidar (MPL). A method based on the lidar polarization capabilities was used to retrieve the contribution of the pollen to the total signal. The conversion from optical lidar-retrieved properties to concentration was optimized by minimizing the sum of the squared deviations between the lidar-retrieved concentration at the first height and the true (Hirst) concentration measured at the ground. In terms of surface concentration, the simulation performs well during the center of the event with major underestimation at the beginning. As far as the vertical distribution of airborne Pinus pollen is concerned, simulations reproduce well the shape of the profiles but the intensity tends to be underestimated. Three major limitations are identified with the model runs: (1) the poorly known phenology emission function, (2) the temporal development of the convective planetary boundary layer in coastal areas, which directly affects the vertical structure of the pollen dispersion; (3) the development of the sea breeze and a proper representation of the sea coast line, that play a significant role on the skills of the meteorological mesoscale model.

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, and an important task continue to be agreement of parameters used in chemical transport models (CTMs) with experimental results. We present preliminary results of the comparison of the measured by DOAS technique and simulated by SILAM NO₂ integral contents (IC) at Zvenigorod Scientific Station (ZSS) located in 38 km west from Moscow. The comparison covers January and July of 2014 when background and polluted by Moscow air mass observed at ZSS. 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 5.4×10¹⁶ molec×cm^-2 when polluted air masses come from Moscow megacity. Simulated NO₂ IC has similar behavior. As a whole, a good agreement between measured and simulated datasets is observed. Some underestimation of the NO₂ emission presents for sources located to the west and south-west from ZSS and overestimation ones for sources located to the north and north-west from ZSS.

Data of night-time ground-based measurements of the atmospheric ozone spectral line 142.175 GHz over Moscow were used for detection of variations in the ozone mixing ratio (OMR) at altitudes of the secondary (near 90 km) and tertiary (near 65 km) night maxima in the OMR profile. The ozone spectra were recorded by low-noise microwave spectrometer MOS-4 with frequency resolution of 0.1 MHz and time resolution of 110 or 90 s, not quite evenly spaced in time. The spectra were averaged over groups of 6…60 ones. The OMR values at altitudes of 90 and 65 km were determined by the improved least-squares method applied to differences of brightness temperatures within ±0.5 MHz frequency offsets from the ozone line centre. Then special algorithm based on the Lomb periodograms with sliding data window was used to determine spectral power and frequencies of the ozone variations. Estimates of the OMR errors depending on the instrumental noise and number of averaged spectra were obtained by computer simulations and used to calculate detection thresholds of the Lomb algorithm. Wave-type variations in the OMR values with periods of 3…5 h were detected with probability of 85-90% at altitude of 90 km and 95-97% at 65 km. Spans of the variations were up to 9 ppm at 90 km and up to 2 ppm at 65 km. The paper presents descriptions of the instrumentation, observation procedure, data processing methods, and some results of the data analysis.

In spaceborne LIDAR, the measurement of both intensity and time of flight of a luminous signal is widely used to investigate the atmosphere and the earth surface. In this scenario, a laser flash is sent from a satellite towards the target and a receiver records the intensity versus time: the recorded time correlates with the distance of the scatterer from the source while the intensity of the signal carries information on scatterer type, number density and intermediate extinction. Starting from an 8x8 array of high-performance Single Photon Avalanche Diodes (SPADs) fabricated with a fully planar custom-technology, we developed a module prototype for spaceborne LIDAR. An alignment board is able to provide the alignment of the trigger signal coming from the laser with the start of the acquisition time with an accuracy better than 1ns. Data coming from the SPAD are then summed and a digital word corresponding to the number of counts in time bins as short as 8.3ns.

Recent developments on Single Photon Avalanche Diodes (SPADs) have opened the way to the design of single-photon time of flight systems based on very large arrays of detectors. In particular, the exploitation of 3D stacking now allows the use of different technologies to optimize both the detector and the electronics. Very high performance in terms of Photon Detection Efficiency, Dark Count Noise and Afterpulsing probability can be achieved with a dedicated custom technology fabrication process, as the one developed by Politecnico di Milano. Custom SPADs require external high-performance electronics to be properly operated. In 2019, an active quenching circuit able to operate an external custom SPADs with a dead time as short as 6ns has been developed. These results open the way to the exploitation of these detectors in many applications as spaceborne remote sensing. The very short dead time, indeed, means having a quick recovery, that is paramount to investigate the layers below a very bright surface, e.g. to measure the backscatter from plankton immediately below the ocean surface. Targeting the exploitation of a 256x256 SPAD array, we designed a fully integrated front end and processing circuit able to provide the number of impinging photons during time windows as short as 8ns.

The purpose of this work is to measure by differential absorption lidar (DIAL) the ozone vertical distribution (OVD) in the upper troposphere – stratosphere at wavelengths 299/341 and 308/353 nm and to compare the results with satellite data. А lidar complex for measuring the OVD in the altitude range ~ (5–45) km has been created. Here we analyze the results of ozone lidar measurements at wavelengths of 299/341 nm and 308/353 nm in March 2017 – January 2018 at Siberian Lidar Station (SLS) and compare them with satellite (AURA/MLS and IASI/MetOp) measurements of OVD. The retrieved lidar OVD profiles in the upper troposphere – stratosphere in comparison with AURA/MLS and IASI/MetOp profiles, as well as the stitched OVD profile in comparison with the mid-latitude Kruger model confirm the prospects of using the pairs of ozone sounding wavelengths 299/341 and 308/353 nm.

A differential absorption lidar system was designed on the basis of parametric light generation with nonlinear KTA and KTP crystals, which allow turning laser radiation in the near- and mid-IR spectral regions. Lidar echoes were experimentally detected in the 1.8–2.5 and 3–4 μm wavelengths ranges. The results of first in situ measurements of the methane concentration in the mid-IR are given.

Q-switched laser sources with exceptional frequency stability are required for many remote sensing applications. Airborne systems often require operation under significant vibration which can pose a challenge for injection seeded bulk lasers. An attractive option is the utilisation of a passively Q-switched Non-Planar Ring Oscillator (NPRO), which has the potential to offer highly stable single longitudinal mode output due in part to its monolithic construction. This paper describes measurement of the frequency stability of a passively Q-switched NPRO under simulated aircraft vibration, which was achieved using a custom designed Fabry-Perot etalon to interrogate the optical frequency of every laser pulse. With no vibration, the long term drift was found to be approximately 4MHz/min. With the drift removed, the laser frequency jitter demonstrated a standard deviation of better than 200kHz over 15 minutes, which was the measurement system noise limit. Under vibration, the worst case measurement had a drift of 8MHz/min and the jitter had a standard deviation of 2.09MHz.

The Polarization and Directionality of Earth Reflectances (POLDER) instrument provides unique cloud droplet radius (CDR) and effective variance (EV) observations for the analysis of clouds on the global scale. However, the cloud droplet size distribution estimated using the conventional POLDER algorithm (Bréon et al, 2005) is limited by its coarse spatial resolution (150 km) and insufficient information for large droplets (CDR>15 µm). In this study, we proposed an improved primary cloudbow retrieval (PCR) algorithm to estimate CDR and EV from POLDER. Simulated retrievals based on a radiative transfer model indicate that primary cloudbow measurements are sensitive to large droplets (CDR>15 µm) and enable the retrieval to be applied at a higher spatial resolution; therefore, we employ POLDER polarized measurements from both primary and supernumerary cloudbow regions in the PCR algorithm. Retrieval cases using POLDER measurements reveal that the PCR algorithm is robust when the cloud fields are homogeneous. When the cloud field is heterogeneous, the estimation of CDR is sensitive to the scattering angle ranges as well as the grid size, with uncertainty less than 1 µm. In addition, a spatial resolution of 40-60 km is suitable for the PCR algorithm based on the relationship between the retrieval grid size and the total successful retrievals. Further comparisons between the PCR retrievals and operational products are conducted on the global scale using POLDER measurements for February, May, August and November 2008, revealing that PCR retrievals agree well with operational products on the global scale as CDR<15 μm. Our analysis indicates that most of the large droplets estimated using the conventional procedure are overestimated due to the absence of primary cloudbow measurements. The PCR algorithm permits an extended range of CDR (3-25 μm) and EV (0.01-0.29) estimates and a higher resolution (40-60 km) in the retrieval. Reference Bréon F M, Doutriaux-Boucher M. A comparison of cloud droplet radii measured from space[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1796-1805.

Rainfall dynamics has been being one of the least understood topics and has been affecting human civilization since time immemorial. Because of its all-pervading effect on all aspects of the society, starting from the day-to-day living of mankind to agriculture, industry, aviation, weather monitoring, and weather forecast, etc., probably it stands out to be the most significant factor that needs attention. Moreover, change in rainfall pattern all over the world necessitates an investigation of this parameter in-depth. The tropics play an essential role in regulating the atmospheric heat engine. So, the cloud characteristics in this region demand careful attention and understanding. In this paper the authors have investigated rainfall and upper air meteorological elements, viz. the cloud liquid water (CLW), the precipitation water (PW) and the latent heat (LH) derived from the data product 2A12 of the Tropical Microwave Imager (TMI) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite over four tropical locations in India, namely Bangalore (12.97N, 77.59E), Bhubaneswar (20.29N, 85.82E), Calcutta (22.57N, 88.36E), and Gadanki (13.45N, 9.16E). The study shows that the rainfall can be predicted with excellent accuracy based on the cloud liquid water (CLW), the precipitation water (PW), and the latent heat (LH). It is further found out from the investigation that though all these parameters can predict rainfall independently, all the elements put together can predict rainfall with greater accuracy. The paper presents functional relationships between rainfall and these parameters. These relationships can be used for quantitative estimation of rainfall in the data-sparse region. The article also highlights the vertical profile of these parameters starting from the Earth's surface up to 18 km above. The article describes the characterization technique for convective/stratiform dominance on surface rainfall.

We present the current development of the Carbon Balance Observatory (CARBO). CARBO is a wide-swath mapping, low Earth orbit (LEO) new generation of instruments that expands on the ground-breaking CO₂ and Solar Induced Fluorescence (SIF) measurements pioneered by the Orbiting Carbon Observatory (OCO-2/3) by adding CH₄ and CO detection. The instrument’s spatial coverage is delivered at 2 km by 2 km resolution with a field-of-view of 10° to 15° from LEO for a ~200 km wide swath. It achieves roughly 20x better spatial coverage than the OCO-2 instrument, and 3x better Solar Induced Chlorophyll Fluorescence (SIF) detection sensitivity, in a smaller package. CARBO will measure CO₂ at <1.5 ppm, CH₄ at <7 ppb, CO at <5 ppb and SIF < 20%. The measurement of CO₂/CH4_/CO/SIF at these concentrations will significantly increase our ability to disentangle carbon fluxes into their constituent components. CARBO utilizes innovative immersion grating technology and enables high resolving power spectroscopy (roughly 20,000) in a smaller and lighter package that is more cost effective than current space-based CO₂ remote sensing instruments. CARBO modules cover 4 different spectral ranges (from 740 nm to 2.3μm), where two channels will be built and field tested. CARBO’s modular architecture reduces implementation risk, accelerates access to space, and extends opportunities to a more diverse set of platforms and launch vehicles. CARBO significantly improves our understanding of the global carbon cycle. Here we discuss an overview of the design elements and focus on the expected radiometric performance of channels 1 (~760 nm) and 2 (~1600 nm).

A series of laboratory experiments out to measure the absorption of the OPO laser radiation by methane as part of a calibrated CH₄: N₂ molecular mixture in a gas cell has been carried. The experiments were carried out in the spectral range of 3.30–3.43 μm, in which there is a fairly strong absorption band of methane and there is no interfering absorption by water vapor and carbon dioxide. The results of measuring the absorption of laser radiation at selected sensing wavelengths and their comparison with the calculated data are presented. Using the developed OPO-lidar system, backscattered signals were received and processed in the range of the spectrum of 3.30–3.43 μm for the horizontal route of atmospheric sensing. Based on the experimental data obtained, the absorption coefficients were estimated and the CH₄ concentrations were restored in the spectral range under study at a 800 m atmospheric path with a spatial resolution of 100 m.

The threshold wind speed is a useful criterion in determining whether strong turbulence is generated within the Stable Boundary Layer (SBL), the layer where all surface emissions remain confined during the night. Confidence turbulence estimates are extremely important for atmospheric transport and dispersion simulations, although due to its complex dinamics many aspects of the SBL are neglected by numerical models that, in turn, are the inputs and boundary conditions for the transport and dispersion simulations. Turbulence is especially important during severe episodies like hazardous material accidental releases, for example. Turbulence intensity can affect the dispersion speed, released material concentration, and its reach. For many decades, remote sensing has been an important tool in filling the gap of information and providing advances in the atmospheric sciences. The doppler lidar is increasingly being used for micrometeoroly and Planetary Boundary Layer (PBL) studies because of its autonomy and long range capability, in contrast with traditional techniques as radioprobes and captive balloons. After 1 year of continuous measurements with a doppler lidar, it was possible to determine the threshold wind speed for Ipero, Sao Paulo, Brazil. Besides threshold wind speed, it was observed that the SBL turbulence has a straight relationship with the Low-Level Jets (LLJs) that frequently occur over the region. The vertical turbulence distribution depends greatly on the LLJ characteristics, which in turn is highly variable during its life cycle. The strong turbulence regime is associated to the stronger LLJs, that presents a more defined pattern. In contrast, weak LLJs (that generate weaker SBL turbulence) present more dispersive characteristics in respect to the entire dataset. These differences are seen both for the LLJ height as for the turbulence vertical profile. These results will contribute for the atmospheric modeling and dispersion simulations, as well for the environmental studies at Ipero.

Aerosols are an important component in the Earth's atmosphere. They cause atmospheric pollution which influence negatively on human health and affect the radiation balance of the atmosphere, resulting in climate change. Aerosol distribution in the Earth's atmosphere is studied using measurements from many satellite and ground-based instruments. One of the valuable sources of atmospheric aerosol data is measurements by a ground-based network of sun and sky radiometers AERONET. The AERONET retrieval algorithm provides aerosol volume concentration. However, AERONET observations are sparse in space and time. To obtain information on aerosol volume concentrations with complete spatial and temporal coverage, model simulations can be applied. However, the agreement between model results and measurements is not good enough. To obtain the most likely true estimate on aerosol volume concentration, the optimal interpolation method is used in the present work. This approach is much less computationally expensive than other data assimilation methods. The method of optimal interpolation is based on the minimization of the mean-square error in the estimate. In the present work, the technique is used that combines observational data, statistical mean values, and results of the global chemical transport model GEOS-Chem simulation. Implementation of the optimal interpolation method makes it possible to estimate the values of the aerosol volume concentration when measurements are absent. The estimates of aerosol daily mean volume concentrations at all spatial grid points (2 x 2.5 degrees) over the East European region are determined in the present work.

Short-lived climate pollutants (SLCPs) are agents that have a short lifetime in the atmosphere, cause a warming effect on climate, and impact human health, agriculture, and ecosystems. SLCPs include black carbon (BC), methane (CH4), and tropospheric ozone (O3). BC is produced by the incomplete combustion of fossil fuels and biomass, and it is the primary component of particulate matter. Sulfate and nitrate aerosols are the major anthropogenic particulate matters and they have a cooling effect on the climate. This study focused on several SLCPs, including BC, sulfate aerosols, and nitrate aerosols. This study included SLCPs that warm and cool the climate as well as compared changes in the near-future with respect to climate due to SLCPs emissions via various pathways. The focus of this study was climate change in Asia. To estimate the effects of SLCPs on climate, an earth system model MIROC-ESM was used. Multiple experiments using MIROC-ESM were performed for the years 2010–2049, considering various greenhouse gases and SLCPs emission pathways. Results reveals that SLCP emissions can be decreased by controlling air pollution. Moreover, improvements in energy efficiency to achieve a low-carbon outcome can also greatly reduce SLCPs, especially in rapidly developing countries of Asia. The atmospheric loading of anthropogenic aerosols decreased for all experiments. However, the experiments indicated that a combination of reduction in SLCPs and carbon dioxide decreases warming but a reduction in SLCPs alone promotes warming.

The second global imager (SGLI) onboard the Japanese mission GCOM-C (2017) is a successor of ADEOS-2/GLI (2002). Both sensors have the same wavelength channels at 0.412 μm and 0.380 μm. The efficiencies of these data in detecting absorbing aerosols, such as biomass burning aerosols (BBA) or mineral dust (DUST), have been examined on a global scale in previous work¹ using GLI measurements. The purpose of this study was to examine the advantages of near-UV data collected using GLI with those of SGLI. The near-UV data not only detected absorbing aerosols but also utilized short wavelength infrared measurements to distinguish between BBA and DUST. Classification algorithms for aerosol types were suggested, as an understanding of aerosol types facilitates subsequent aerosol retrieval. Classified aerosols were then characterized based on radiation simulations with multi-spectral radiance and polarization measurements in the red and near-IR wavelength channels of SGLI.

Vegetation maps play a key role in an estimation of forest fire danger. To estimate forest fire danger, a vegetation type map of Gilbirinsky forestry situated in the Lake Baikal basin was created on basis of both the remote sensing data and field study. A Sentinel-2A satellite image was classified by the maximum likelihood method. Zones with different levels of forest fire danger have been identified: coniferous forests–extremely dangerous level, mixed forests – high level, and deciduous forests–moderate level of forest fire danger. Normalized Difference Water Index has been calculated and moisture content in vegetation has been evaluated.

China’s Fengyun-3D satellite (FY-3D), which can obtain global cloud amount data, was launched in November 2017. In this study, we qualitatively and quantitatively compare the cloud amount products from FY-3D and the moderate resolution imaging spectroradiometer (MODIS) AQUA in July and October,2018. The results show that the global spatial distribution characteristics of the FY-3D cloud amount products is consistent with that of MODIS. The distributions of the cloud amount of the two products are more similar in October, but theFY-3D cloud amount data are generally higher than the MODIS cloud amount data. After strict space-time matching, the matched samples are used for quantitative accuracy evaluation. In July 2018, the absolute error of the FY-3D cloud amount relative to MODIS is 4.5, the relative error is 0.11, the deviation is 1.68, the error standard deviation is 12.11, and the correlation coefficient reaches 84.8%. In October 2018, the absolute error of the FY-3D cloud amount relative to MODIS is 3.20, the relative error is 0.05, the deviation is 1.68, the error standard deviation is 6.38, and the correlation coefficient is 93.4%. The global error distribution shows that at mid-low latitudes, the quality of the two products is similar and the error ranges between -10 and 10%, while at high latitudes the error is relatively large. FY-3D cloud amount products can be used in studies of the global climate and climate change.

In this study, we investigated different optical properties between severe versus moderate visibility marine fog using Geostationary Ocean Color Imager (GOCI) visible band measurements. Severe and moderate visibility marine fogs are best distinguishable with the criteria of visibility as 500 m. Using this, we developed an algorithm that classifies severe visibility marine fog based on Decision Tree (DT) method. Calibration and validation data were constructed for 2016 and 2017 marine fog cases, respectively, through match-up between satellite and in-situ data. In general, marine fog region has differences of textural and optical properties with cloud. The GOCI 412 nm Rayleigh Corrected Reflectance (R_rc) reveals small spatial variability in fog than in cloud. Also, it is notable that some distinction exists in R_rc magnitude between severe and moderate visibility marine fog region. Using this feature, we have developed a satellite marine fog detection algorithm with severe/moderate visibility classification. R_rc and Normalized Local Standard Deviation (NLSD) of R_rc were determined as primary input. However, visible channel alone cannot completely distinguish marine fog from cloud because it does not provide cloud height information. Here, we used cloud top height data from Himawari-8 as a supplementary data to remove cloud that was miss-classified as fog. Hit Rate (HR) and False Alarm Rate (FAR) for moderate (severe) visibility marine fog were 0.96 (0.86) and 0.31 (0.12), respectively.

Cloudiness and precipitation are important output parameters in numerical weather prediction (NWP) models, both for their own sake and because they strongly affect other parameters, e.g., surface temperature. The spin-up problem is an important reason resulting in low accuracy of forecasts during the early prediction stage (0–6 h), so it’s necessary to introduce cloud-related information to eliminate or weaken this problem. For typhoon prediction, microwave satellite data is crucial. Profiles of cloud microphysical parameters can be retrieved from the microwave imager using certain inversion technology. The microwave imager onboard Fengyun-3B (FY-3B MWRI), the microwave imager onboard Tropical Rainfall Measuring Mission (TRMM TMI), and the Advanced Microwave Scanning Radiometer (AMSR-E) onboard AQUA are selected to do this research. Experiments of initialization of the cloud microphysical information in Global and Regional Assimilation and Prediction System (GRAPES) during typhoon MA-ON activity are carried out to investigate their impact on forecasts. The results indicate that prediction of hydrometer parameters and surface rain rate can be faster through initialization of cloud information derived from satellite microwave observations in GRAPES model and there is positive contribution during the first 6 hours of the model integration. Retrievals from MWRI, TMI and AMSR-E have a good consistency, and fusion data with three kinds of retrievals shows a more positive impact.

Launched in 2013, and 2017 respectively, Chinese Fengyun-3C and 3D meteorological satellites are equipped with two microwave sounders, Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder -2 (MWHS-2), whose observations play an important role in numerical weather prediction by data assimilation. Data quality control should be carried out before assimilation to filter out bad data, such as cloud- or rain-polluted data and questionable data. This work can’t be accomplished purely depending on MWTS or MWHS-2 themselves. MWHS-2 is taken as an example to do quality control in the paper, and the method is suitable for MWTS too. Multi-source information from other instruments onboard FY-3 is extracted to assist in the work. Cloud mask product from VIRR (Visible and InfraRed Radiometer), oceanic cloud liquid water content product from MWRI (Microwave Radiation Imager), and global rain rate product from MWRI are mapped to MWHS-2 for quality control in combination with oceanic rain detection product from MWHS-2 itself. 6 kinds of cloud and rain detection schemes are then designed to get the best choice by analyzing the characteristics of background departure. RTTOV v10 is adopted to simulate brightness temperature of MWHS-2 at all channels. The results suggested that scheme RI RC (MWRI cloud and rain information ingested) and scheme RC (all information ingested) are the two best choices for numerical assimilation application, and scheme RI RC can retain more samples. Questionable data can also be found in the way to help monitor the operational status of instruments.

Fugitive emissions, defined as unintended or irregular leaks of gases and vapors, these are an important source of pollutants to the atmosphere, which is difficult to monitor and control. These sources are present in different sites, including megacities like São Paulo that are growing in size and economic activity. At the same time, there is a remarkable growth in concerns about the environmental issues associated with these activities. In a constantly changing world, with increasing concentrations of greenhouse gases (GHGs), among them methane (CH4) and volatile organic compounds (VOC), mitigation of atmospheric emission of these gases to contain global warming, make field campaigns in the metropolitan region of São Paulo very relevant. Optical remote sensing techniques as lidar can attend the need for real time and trustable information on fugitive emissions. The Cavity Ringdown Laser Spectroscopy (CRDS) technique was adopted because it is widely used in the detection of gas samples that absorb light at specific wavelengths and also for their ability to detect mole fractions up to the parts per trillion level. The Raman lidar system used includes a commercial laser pulsed Nd:YAG Quantel S.A., model CFR 200, with wavelengths of 355 nm, 353 nm and 396 nm, 120 mJ pulse power, with laser repetition rate of 20 Hz and pulse width of 20 s, with a spatial resolution of 7,5 m. The system includes an ethernet interface, used together with LabView software to control the measurement and readout of the acquired data. The mixing ratio of CH₄ can be observed within the planetary boundary layer. The measured methane profiles correlate with the acquisitions made with the CRDS, however, an additional contribution of control data in which the Raman lines detect with high sensitivity.

For accurate cloud ceiling information, a data fusion approach is proposed that utilizes satellite data to extend surface station information to much wider areas. Cloud base height (CBH) retrieved from satellite observations provides for much larger spatial coverage and higher resolution. The direct comparison of GOES-16 CBH with surface station ceiling yields a local bias that has to be corrected for in the initial GOES-16 cloud base information. This sparsely sampled bias correction presents an irregular 2D mesh of control points, which is then interpolated by constructing a continuous smooth field using polyharmonic splines. The influence of remote stations is restricted by grouping the control points into clusters depending on an effective distance. This cluster-based approach allows for constructing separate spline surfaces corresponding to physically different clouds. The obtained continuous bias correction function is then applied to the entire GOES-16 pixel level CBH except for areas far away from surface stations in data sparse regions such as offshore. The described method is currently being tested using daytime-only observations over the central and eastern United States. Overall, this approach has potential to provide more accurate, high spatial resolution cloud ceiling information for the aviation community.

Light scattering by cloudiness and aerosol have a significant impact on the possibility of quantitative estimation of the content of NO2, H2CO and other trace gases in the lower troposphere using the MAX-DOAS and ZDOAS techniques. Since there is a large volume of optical observations of trace gases by these techniques that are not accompanied by measurements of their characteristics, solving the problem of determining the properties of clouds and aerosol from the spectral measurements themselves could increase the accuracy of measuring trace gases. The paper considers the tasks of determining the characteristics of clouds (the bottom height, the optical depth, etc.) and aerosol (the optical depth, the vertical distribution parameters, etc.) from quantitates obtained from ZDOAS measurements (the O4 slant column, the color index, the absolute intensity, etc.). We performed numerical experiments for retrieving clouds and aerosol characteristics basing on radiative transfer simulations in cloudy atmosphere. A neural network is used as a method for solving emerging nonlinear estimation problems, the accuracy of the evaluation is determined on the training set, and a control set is used to characterize the agreement of the evaluation results (i.e., how much confidence can be given to the parameter estimation and its error).

The surface reflectance is an essential parameter for the quantitative applications using remote sensing satellite data; therefore, it is of great importance for the scientific community to produce standard surface reflectance products using an operational running algorithm and system. There have been various medium- to high-resolution satellites in China, yet there is still a lack of relevant surface reflectance products and systems. In this paper, high-resolution GF-1/GF-2 data from the year 2014 and 2017 were utilized for retrieval of surface reflectance products over land by using an operational atmospheric correction algorithm, adaptive to most multispectral satellites with visible and near-infrared bands (VNIR), namely, the VNIR approach. This method was based on the Second Simulation of a Satellite Signal in the Solar Spectrum, Vector (6SV) code and the look-up tables (LUTs). The surface reflectance products over land were validated against the ground-based atmospherically corrected reflectance over Beijing-Tianjin-Hebei regions and middle and lower regions of the Yangtze River in China. The preliminary validation results showed that the surface reflectance products agreed quiet well with the ground-based corrected reflectance, with the linear regression fitting coefficients being 1.09– 1.03, the correlation coefficients of R2 being 0.97–0.99, and the Root Mean Square Error (RMSE) being 0.01. Simultaneously, the mean reflectance normalized residuals between the surface reflectance products and the ground-based corrected reflectance were 19.7 %, 13.5 %, 8.7 %, and 6.6 %, respectively, indicating that the surface reflectance products over land derived from VNIR atmospheric correction approach had a good accuracy.

Interest in lightning research is primarily associated with the negative consequences of their direct impact, leading to fires, damage of power lines, failure of sensitive electronics and communication networks, etc. In order to prevent and protect against these consequences the detection of occurrence time and spatial position of thunderstorms, assessment of their danger degree and the direction of further development are very important. Data of lightning discharges distribution, their number and value of the current over the territory is required for lightning protection measures. Research of thunderstorm dynamics is required to understand the nature of thunderstorms. The monthly distributions of discharges were studied. This allowed to trace the trends of thunderstorms development and density of discharges per unit area. Technology of lightning sensing significantly decreases the risks of lightning damage and also takes into account the thunderstorm activity for the purpose of design and placement of buildings and structures. It is aimed at minimizing the serious violations in the power industry, mass accidents and damage to power lines, which, in turn, determines its high economic efficiency.

The parameters of lightning discharges were estimated using long-term data of the automated system of lightning detection. It was shown that the dynamic monitoring of these parameters is an effective step towards solving the problem of lightning protection. Studies of electrical parameters of discharges in the atmosphere using the data of the geophysical monitoring system, including a network of automatic lightning sensors LS8000, comprise more than five million lightning discharges for the South of Russia territory during 2009-2017. The values of lightning discharges parameters obtained during the observation period make it possible to identify the main characteristics. The regularities of changes in the parameters characterizing the electrical activity of the atmosphere in different periods of time and in different climatic zones of the southern region of the Russian Federation were investigated. Modeling with use of the statistical dependences approximation built on the distribution data reveals the main factors affecting the distributions and makes it possible to carry out the territorial zoning according to the degree of emerging risks.