NASA’s future Earth System Observatory (ESO) will provide key information related to understanding climate change processes, mitigating natural hazards, fighting forest fires, and improving real-time agricultural processes. The Atmosphere Observing System (AOS) constellation is a key component of the ESO, providing the atmospheric part of the ESO and focusing on two of the five designated observables from the 2017 NASA Earth Science Decadal Survey: aerosols and clouds, convection, and precipitation (CCP). AOS is made up of two projects, one in an inclined orbit (referred to as AOS-I) and the other in a polar, sun synchronous orbit (AOS-P), with both projects addressing synergistic aerosol and CCP science. The constellation is expected to deliver a comprehensive suite of observations to address coupled aerosol-cloud-precipitation interactions, with science objectives focused on low and high cloud feedbacks; the dynamics and structure of convective systems and properties of the aerosol environment; phase partitioning and precipitation formation in frozen and mixed-phase clouds; aerosol microphysical and optical properties, aerosol sources, and relationships to air quality; aerosol vertical redistribution and processing by clouds and precipitation; and aerosol direct and indirect effects. AOS-I and AOS-P are expected to launch no earlier than July 2028 and December 2030, respectively. This talk will describe the science objectives of AOS and the mission architecture and measurement capabilities.

How to cite: Braun, S., Yorks, J., Thorsen, T., and Cecil, D.: The NASA Atmosphere Observing System (AOS): Future Space-Based and Suborbital Observations for the Study of Coupled Aerosol-Cloud-Precipitation Interactions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5092, https://doi.org/10.5194/egusphere-egu23-5092, 2023.

It is well established that the Aerosol-Cloud Interaction (ACI) processes play a key-role in global precipitation and are a strong modulator of cloud radiative forcing and climate, and yet remain poorly understood despite decades of research. Aerosol-cloud interactions are one of the most uncertain aspects of anthropogenic climate change (Seinfeld et al., 2016a; IPCC, 2021).

Global datasets on cloud microphysical state – especially droplet number concentration and size distribution – provide important constraints that are required for reducing the ACI uncertainty. Recently, Quaas et al. (2020) showed that satellite remote sensing is the only approach that offers the potential of obtaining global datasets with frequent coverage; current retrieval algorithms, however, carry many uncertainties and require constraints that can only be addressed with in situ and/or ground-based remote sensing observations.

Our study aims to evaluate retrievals of cloud droplet number (Nd), effective radius (r_eff) and optical thickness provided by the CLoud property dAtAset using SEVIRI - Edition 3 (CLAAS-3) cloud products of Satellite Application Facility on Climate Monitoring (CM SAF).  For this reason, we used co-located in-situ measurements of aerosols and cloud dynamical properties in conjunction with remote sensing observations at the high-altitude regional background station Hellenic Atmospheric Aerosol and Climate Change (HAC²) during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign, which took place from Fall 2021 to Spring 2022 at Mount Helmos in Peloponnese, Greece (https://calishto.panacea-ri.gr/).

In this study, we adopt an approach first applied to droplet retrievals in an urban environment (Foskinis et al. 2022). Ground-based remote sensing instrumentation involved includes a Doppler depolarization lidar (HALO) at 1550 nm to provide the vertical velocity (w) of the air masses, a Doppler cloud radar at 94 GHz (RPG) to provide the equivalent reflectivity factor (Z), and the mean Doppler velocity (VD), and a radiometer at 89 GHz provides the liquid water path (LWP). Furthermore, the in-situ instrumentations employed a co-located scanning Mobility Particle Size (SMPS) measuring the size distribution of submicron aerosol, and a Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) to provide the aerosol chemical composition of the aerosols. The in-situ dataset together with the airmass vertical velocity distributions are used as input to a state-of-the art parameterization to predict the droplet number (N_d) in clouds formed in the vicinity of the HAC² station. Retrievals with the the CLAAS-3 cloud properties product from CMSAF are then evaluated with in-situ observations carried out with a cloud probe instrument (PVM-100) and the droplet number calculations.

Compared to our previous study (Foskinis et al. 2022), this study is implemented in a different physical system, where we examined again the dependence of the Spectral Dispersion of Droplets (SDD) on N_d and we found a new optimized expression between SDD-N_d which can be used on the established droplet number retrieval algorithm (Bennartz et al., 2007) for non-precipitating planetary boundary layer clouds in order to mitigate the bias.

How to cite: Foskinis, R., Papayannis, A., Nenes, A., Eleftheriadis, K., Vratolis, S., Fetfatzis, P., Gini, M., Diapouli, E., Zografou, O., Granakis, K., Berne, A., Billault-Roux, A.-C. M., Komppula, M., and Vakkari, V.: Evaluating and improving the retrieval of cloud droplet number: case studies in an urban region and orographic environments in the E. Mediterranean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16043, https://doi.org/10.5194/egusphere-egu23-16043, 2023.

The next generation Meteosat Third Generation sounding satellite (MTG-S) is expected to be launched at the end of 2024. MTG-S will carry a hyperspectral interferometer, namely InfraRed Sounder (IRS), to provide a four-dimensional look at the atmosphere from a geostationary orbit. IRS will measure the radiance emission from the earth at 1960 infrared spectral channels at two bands (2250 ~ 1600 cm^-1 and 1210 ~ 680 cm^-1), with a spectral resolution of around 0.60 cm^-1.

Satellite-based cloud information is essential for the subsequent retrievals of atmospheric and surface products or for data assimilation in numerical weather prediction (NWP) models. Our project aims at studying the feasibility of retrieving cloud properties from MTG/IRS simulated data, notably cloud mask, cloud classification, and cloud top pressure/temperature/height following NWC SAF threshold methodologies. In this poster, the radiative transfer modelling by RTTOV is carried out at various atmospheric and cloud conditions to study the IRS spectral behaviours, especially at window channels. We will also discuss our preliminary retrieval of a cloud mask by brightness temperature thresholds using the selected IRS channels.

How to cite: Chen, X., Kerdraon, G., Péré, S., Vidot, J., and Fontaine, E.: Retrieving cloud properties based on MTG/IRS (Meteosat Third Generation/InfraRed Sounder), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17366, https://doi.org/10.5194/egusphere-egu23-17366, 2023.

Aerosol lidar is widely used in the planetary boundary layer (PBL) height calculation due to its high spatiotemporal resolution. Most of the PBL height (PBLH) algorithms for aerosol lidar are valid for single aerosol layer structure, but overestimate the PBLH under multilayer aerosol/cloud structures. To fill the gap, a new algorithm of PBLH calculation based on multilayer recognition and idealized-profile (MR-IP) is developed. In this algorithm, residual layer and/or suspended aerosol/cloud layer are first recognized based on the signal to noise ratio (SNR) of lidar, the range squared correction signal (RCS) and its gradient (∇RCS). In residual and/or suspended aerosol/cloud layer, positive and negative ∇RCS exist simultaneously, while inside the PBL only a single negative ∇RCS exists. These characteristics are used to discern residual layer and/or suspended aerosol/cloud layers. Aerosol and cloud layers are further distinguished by the ratio of RCS in the objective layer (RCS(rs)) to the mean RCS in the background layer. After multilayer recognition, the PBLH is calculated based on idealized-profile (IP) method. A yearlong (642 samples) comparison of the PBLH calculated by lidar and radiosonde verified the new algorithm in PBLH calculation under complicated structures (R=0.81). The mean PBLH calculated by the MR-IP method is 635.4 m, consistent with the PBLH defined by radiosonde (665.3 m). Furthermore, the residual layer, suspended aerosol layer and cloud layer can also be discerned by the new algorithm.

How to cite: Pan, Y. and Wang, Y.: A new algorithm for planetary boundary layer height calculation based on multilayer recognition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10843, https://doi.org/10.5194/egusphere-egu23-10843, 2023.

LIDAR is an optical profiler that generally works during clear sky periods.  Lidars operation during disturbed weather conditions is rare [1]. An Infrared lidar sensor was developed indigenously at NARL site to profile clouds and rain during thunderstorm periods. The lidar operates in slant mode through a window and make measurements continuously during cloudburst periods. The developed lidar employs a Nd:YAG laser that operates at its fundamental spectrum in the Infrared band and works in pulsed mode. A spare optical tube assembly (OTA) is employed in the experimental work for collecting the backscattered infrared photons. A high degree of alignment made between laser and OTA units to collect light photons from far ranges. An adjustable conical pin-hole system is employed in the present work, which permitted lidar to function in daylight period. A silicon avalanche photodiode (APD) is used in the demonstration work for optical sensing and signal conditioning. Thin-film interference (IF) filter doublet and a peltier cooled APD supported the lidar measurements at room temperature conditions. An Ethernet interfaced single channel transient recorder unit employed in the receiver measurements, which digitizes signal at 40 MHz rate. The experimental data were recorded at one-second sampling with 7.5 m range resolution. The pump laser uses an in-line optical attenuator that switches at 20 pulses per second. The laser radar probes the atmosphere at a slant angle through a window of the lidar room. The lidar first measurement during thunder clouds and rain is shown in Figure 1. Figure 1 contains two panels. The first panel illustrates the range time intensity map generated using the lidar data that collected between 1200 and 1300 Hrs LT on November 2018 at NARL site. The data plotted in Figure 1 correspond to 7.5 m range resolution at one second time sampling. The other panel of Figure 1 indicates the height profile of lidar range corrected signal (RCS) that obtained at 1203 Hrs LT, which has been shown indicate the lidar signal strength during cloud conditions.  One can notice from Figure 1 the downward movement of thunderstorm cloud deck with time, which further leads fall of rain over land.  One can see rainfall as varying streaks of intensity with range. Different color bands shown in Figure 1 indicate the variations in the intensity of lidar RCS. The red color band indicates the peak value that represents the thunderstorm cloud base. The yellow-orange represents heavy rain events, whereas the shades of green and blue color indicate light rain. The lidar signal overlap occurs at a range of around 100 m, which is 50 m above ground level.

Figure 1. Infrared lidar measurements of cloud and rain during thunderstorm period over NARL site through a window.

References

[1]. R. Vishnu, Y. Bhavani Kumar, T. Narayana Rao, Anish Kumar M. Nair, A. Jayaraman , “Development of lidar sensor for cloud-based measurements during convective conditions,”, Proc. SPIE. 9876, Remote Sensing of the Atmosphere, Clouds, and Precipitation VI

How to cite: Yellapragada, B. K.: Sensing of rainy clouds using an IR lidar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7341, https://doi.org/10.5194/egusphere-egu23-7341, 2023.

Spaceborne Doppler profiling radars (SDPR) are among the leading instruments considered by space agencies to study atmospheric dynamics. For instance, the European and Japan space agencies are developing the Earth Cloud Aerosol Radiation Explorer (EarthCARE) mission, which will carry the first spaceborne Doppler profiling radar [1,2], while NASA is currently developing the Atmosphere Observing System mission, with a constellation of Doppler radars [3]. 

However, operating an SDPR from low-Earth Orbit (LEO) is challenging due to the large instantaneous speed of the spacecraft (V_SAT ~7200 m/s), which affects velocity measurements by broadening the Doppler spectrum that is being measured. Three major error sources that are caused by this spectral broadening are 1) Non-Uniform BeamFilling (NUBF) biases, 2) prohibitive broadening of the measured spectral widths, and 3) a noisiness of the velocity and width measurements [4,5].

In this presentation we will discuss a novel method that we have developed to overcome NUBF and spectral broadening errors that affect SDPR measurements. This method, coined the ExpliSyT approach, is based on the explicit hierarchical representation of the various Doppler moments. For instance, it allows to correct for the broadening of the measured spectral width (second-order Doppler moment), using the measured mean velocity (first-order Doppler moment) and reflectivity factor (zeroth-order Doppler moment). The resulting corrections enable accuracte retrievals of the full spectrum, which in turns enables a higher-order Doppler characterization of atmospheric dynamics. 

The method will be illustrated with simulations of EarthCARE’s radar, and of a notional Displaced Phase Center Antenna (DPCA) configuration developed at JPL [6]. The DPCA configuration uses a pair of collimated antennas to reduce the severity of the spacecraft-induced fading. 

REFERENCES:
[1] A.J. Illingworth et al., “The EarthCARE satellite: The next step forward in global measurements of clouds, aerosols, precipitation, and radiation,” Bull. Amer. Meteorol. Soc., vol. 96, no. 8, pp. 1311–1332, 2015.
[2] H. Kumagai, H. Kuroiwa, S. Kobayashi, and T. Orikasa, “Cloud profiling radar for EarthCARE mission,” Proc. SPIE, vol. 4894, pp. 118–125, Apr. 2003.
[3] https://aos.gsfc.nasa.gov/
[4] R. Meneghini and T. Kozu, Spaceborne Weather Radar. Boston, MA, USA: Artech House, 1990.
[5] P. Kollias, S. Tanelli, A. Battaglia, and A. Tatarevic, “Evaluation of EarthCARE cloud profiling radar Doppler velocity measurements in particle sedimentation regimes,” J. Atmos. Ocean. Technol., vol. 31, no. 2, pp. 366–386, Feb. 2014.
[6] S. L. Durden, P. R. Siqueira, and S. Tanelli, “On the use of multi-antenna radars for spaceborne Doppler precipitation measurements,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 1, pp. 181–183, 2007.

How to cite: Sy, O. O. and Tanelli, S.: Novel ExpliSyT method to Correct Dynamic Measurements from Spaceborne Doppler Cloud Profiling Radars, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2975, https://doi.org/10.5194/egusphere-egu23-2975, 2023.

Satellite data is becoming a progressively accurate source of weather information. An increasing resolution and the number of spectral channels allows to determine the location and recognition of clouds due to their optical and thermal properties.

Predicting the movement of correctly defined deep convection clouds (DCC) is of great importance in forecasting the course of dangerous phenomena for households as well as human health and life. The analysis of trends and occurrence of DCC will make it possible to verify the current climate models, which estimate that the aforementioned phenomena will intensify in the future.

The most common method of detecting DCC is based on the cloud tops temperature. The lower the temperature, the higher and more extensive the cloud is.

The aim of the study is to determine the influence of parallax shift on the frequency of deep convection clouds (DCC).

A geostationary satellite is located at a fixed point in the orbit, which means that the angle of view at higher latitudes is smaller. The higher the object, the greater the parallax shift of the object in relation to its true location. In order to verify the shift and its significance in the analysis of the trend of DCC occurrence, it is necessary to check what values the shift takes and whether it causes significant losses in the number of detected DCC.

The data used for the analysis come from the geostationary satellite Meteosat Second Generation 1 (MSG1) - Meteosat-8, whose sub-satellite point has coordinates: 0^oN and 3.4° W. And from the circumpolar satellite Moderate Resolution Imaging Spectroradiometer (MODIS), which it flies at an altitude of 705 km and its cycle lasts 16 days. The cloud tops temperature was obtained from both satellites and used to estimate the cloud tops height.

The analyzes were carried out for the case study of July 4, 2005.

To perform the parallax correction of the geostationary satellite, the existing parallax correction methods were used. For the first time, an attempt was also made to perform parallax correction for a circumpolar satellite.

This research was funded by the National Science Institute of Poland. Grand no. UMO-2020/39/B/ST10/00850.

How to cite: Babicka, Z., Kotarba, A. Z., and Wojciechowska, I.: Impact of parallax correction on Deep Convection Clouds detection frequency., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12228, https://doi.org/10.5194/egusphere-egu23-12228, 2023.

As a result of progressive global warming, in some regions the frequency of Deep Convective Clouds (DCCs) is expected to increase. However, reliable information about DCCs presence still remains one of the greatest challenges in modern atmospheric sciences, so the misconclusions about existing trends can be avoid. The most accurate data seems to be radar and lidar profiles; unfortunately, the limited spatial resolution of those data, as well as the few years’ time of the operation of radar&lidar missions, makes them not sufficient in terms of providing long-term climatological research. On the other hand, traditional ground-based synoptic observations are mostly limited to land surfaces and are becoming to be provided increasingly rarely. The most promising datasets for present and future DCCs climatologies are those retrieved from geostationary satellite imagers, among which are the METEOSAT First (MFG) and Second Generation (MSG) instruments.

The most commonly used methods for detecting DCCs from satellites are the ones based on brightness temperature (BT), where the specified threshold indicate the presence of convective clouds on satellite images. The simplest of BT-based methods uses the radiances from only one spectral channel: 11 µm (BT11 method). More advances approaches additionally need radiances at 6 µm water vapor absorption channel (BT6-BT11 method) and 9.7 µm oxygen absorption channel (BT11-BT6-BT9.7 method). An utterly different method is to analyze cloud properties, such as cloud top pressure (CTP) and cloud optical thickness (COT), and to determine DCCs presence due to International Satellite Cloud Climatology Project (ISCCP) classification (COT-CTP method). Convective clouds detection can be also supported by meteorological reanalyses.

METEOSAT instruments operates since early 1980s. However, the first generation of METEOSAT imagers were retrieving radiances at only three spectral channels and thus MFG datasets would allow to use only two of the above-mentioned methods: BT11 and BT6-BT11. The rest of approaches could be applied only for MSG datasets (2000s+ time period). This study aims to investigate how many information about DCCs presence are being missed while using only the BT11 or BT6-BT11 methods (which are possible to employ for METEOSAT both First and Second Generation Imagers data) in comparison to the other methods (available for MSG, but not for MFG instruments).

We use data High Rate SEVIRI Level 1.5 Image Data - MSG - 0 degree data, as well as the CLAAS-2.1 (MSG, 0 degree) data containing cloud properties such as CTP and COT for summer season of 2005 (full disk). We compare the frequencies of DCCs determined in accordance to the following methods: BT11 (with ranging brightness temperature thresholds), BT11-BT6, BT11-BT6-BT9.7, COT-CTP, BT11+meteorological reanalyses. The study answers the question how the climatological statistics of DCCs vary depending on the method adopted for detecting these clouds.

This research was funded by the National Science Centre of Poland. Grant no. UMO-2020/39/B/ST10/00850.

How to cite: Wojciechowska, I. and Kotarba, A.: Methods for detecting Deep Convective Clouds (DCCs) from METEOSAT Second Generation Imagers: A case study for 2005 summer season, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12608, https://doi.org/10.5194/egusphere-egu23-12608, 2023.

In this communication we present a pixel-based algorithm for detection of fog and low stratus (FLS) during the 24H day cycle over land and across Europe, based on geostationary satellite observations.

Fog and low Stratus are both a persistent aggregation of water particles in liquid and/or solid phases (cloud) close to the Earth surface. As the cloud-base-altitude is the only real difference between the two (fog: touching the ground; low stratus: above ground), they are frequently treated together as a single category from satellite perspective (FLS). This study presents a pixel-based method for detection of FLS over land across Europe based on Meteosat-11 SEVIRI (Spinning Enhanced Visible and InfraRed Imager) infrared observations. The method is based on a gradient boosting machine learning model that is trained with the observations from Meteorological Aviation Routine Weather Reports (METAR) and German Weather Service (DWD) stations. An intensive validation of the product over 356 METAR stations across Europe over five years of daytime winter data revealed that the method proposed is well capable of detecting FLS over land.  Specifically, the algorithm is found to detect FLS with probabilities of detection (POD) ranging from 0.83 to 0.88 (for different inter-comparison approaches), and false alarm ratios (FAR) between 0.34 and 0.36. As the algorithm operates based on the SEVIRI infrared observations only, it can be applied over day and night, making it feasible to continuously monitor the FLS status over large areas over the 24H day cycle.

How to cite: Jahani, B., Karalus, S., Zech, T., Fuchs, J., Cermak, J., and Zara, M.: Machine-learning algorithm for 24h Detection of Fog and Low Stratus over Europe based on MSG-SEVIRI infrared bands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13515, https://doi.org/10.5194/egusphere-egu23-13515, 2023.

The presence of aerosol and clouds constitutes one of the highest uncertainties regarding the energy budget of the Earth. Therefore, their continuous observation can help reduce these uncertainties, by providing more information about aerosol-cloud interactions and how these atmospheric components contribute to climate change.

To study the properties of aerosols and clouds, new infrastructure will soon be set up in Cyprus by the Eratosthenes Centre of Excellence, which was recently established through the ‘EXCELSIOR’ H2020 Widespread Teaming Project. Eratosthenes Centre of Excellence is a digital innovation hub for Earth Observation, Space Technology and Geospatial Information, aiming to become the reference centre in the East Mediterranean, north Africa and the Middle East (EMMENA region). The infrastructure will be installed in Limassol, on the south coast of the island. Apart from the fact that the site is less than 2 km from the island’s coastline, this location is extremely important in terms of the regional atmospheric composition, as the air masses affecting the site originate from the surrounding areas of EMMENA, as well as from south-eastern Europe.

Infrastructure to sample aerosol is already represented by a state-of-the-art PollyXT lidar, with measurements being registered continuously since October 2020. By the end of 2023, a similar LACROS multi-instrument platform will be available for the continuous monitoring of clouds and aerosols. This new ground-based remote sensing platform consists of a 35GHz cloud radar, a ceilometer, a microwave radiometer, a Doppler lidar, and a disdrometer. The infrastructure will be integrated into the Cyprus Atmospheric Remote Sensing Observatory (CARO).

An example of the value of the observations about cloud formation and the role of aerosol in the process of cloud formation that will be provided from the site in Limassol, can be reflected in the data collected during the CyCARE campaign, executed between October 2016 and March 2018, during which similar ground-based infrastructure was deployed.  

The authors acknowledge the ‘EXCELSIOR’: ERATOSTHENES: EΧcellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.

How to cite: Ene, D., Mamouri, R.-E., Nisantzi, A., Michaelides, S., Hadjimitsis, D., Ansmann, A., Bühl, J., and Seifert, P.: Research infrastructure for the observation of clouds and aerosol in Cyprus, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5819, https://doi.org/10.5194/egusphere-egu23-5819, 2023.

High-mountain protected areas are of great interest from the ecological perspective and have a strong impact on the socioeconomic system. However, protected areas such as National Parks, are scarcely investigated from the climatological point of view. Furthermore, the aerosol loading at high elevation locations is not fully characterized. Thus, the analysis of the aerosol optical depth (AOD) in these areas is crucial to assess the role of aerosols in regional climate change and in several ecosystem processes.

We have analyzed the long-term AOD ground-based and satellite remote sensing data in order to provide an accurate picture of the aerosol loading from local to regional scale, respectively. In addition, reanalysis data has been used to provide information of the aerosol typing at larger scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 global Multi-Angle Implementation of Atmospheric Correction (MAIAC) (hereafter MODIS+MAIAC) provides daily AOD data at 1 km spatial resolution. The ground-based AOD dataset is obtained from the Aerosol Robotic Network (AERONET) over Granada (South-Eastern Iberian Peninsula) and the high-mountain protected area of Sierra Nevada. Specifically, there are three AERONET stations in the region of interest: Granada at 680 m above sea level (a.s.l), Cerro Poyos at 1809 m a.s.l. and Albergue UGR at 2500 m a.s.l., which have been used for validation. Additionally, the latest reanalysis data from MERRA-2 has been employed for aerosol typing at regional scale, with a spatial resolution of 55 km x 69 km, covering the city of Granada and Sierra Nevada high-mountain protected area.

In this study, we will present the validation results of AODs from MODIS+MAIAC and MERRA-2 against AERONET stations at different altitudes. Moreover, a trend analysis of the AOD for the long-term database at the different seasons is investigated. Finally, the classification of the major aerosol types at regional scale has been performed, indicating the dominant aerosol types and the increase of Saharan dust events in the recent years over the high-mountain protected area of Sierra Nevada.

How to cite: del Águila, A., Alcaraz-Segura, D., Martínez-López, J., and Navas-Guzmán, F.: Aerosol optical depth validation and aerosol identification using satellite and ground-based data over the high-mountain protected area of Sierra Nevada (Spain), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5222, https://doi.org/10.5194/egusphere-egu23-5222, 2023.

Absorbing aerosols from dust, industrial emissions and biomass combustion have a strong impact on solar radiation in the atmosphere, and they are considered to be an important source of regional air pollution[1], especially in East Asia, where the degree of variation of absorbable aerosols is very large, so it will have a significant impact on regional climate change. Aerosol single scattering albedo (SSA) is a key variable of aerosol absorption and a key metric of climate impact. Accurate estimation of SSA is crucial to reduce uncertainties in the study of atmospheric pollution and climate effects.

The aerosol model of the current aerosol inversion algorithm is several typical regional aerosol candidate models obtained by cluster analysis of selected ground observation data[2]. These independent candidates' aerosol models result in the estimation of SSA being just a few simple constants, leading to a large bias in the results of SSA.

In the current study, an atmospheric radiative transfer model parameterized by a two-stream approximation is used to construct a genetic algorithm for application to geostationary satellite Himawari-8 / AHI data to retrieve the aerosol SSA. The inversion process is constrained by AOD and the surface bidirectional reflectance distribution function (BRDF). Using this algorithm, hourly SSA data were retrieved was retrieved during day time.

The algorithm was tested using the hourly L1 grid data of AHI from 00:00 to 07:00(UTC) from January to March 2020. Examples of the 10km satellite-retrieved SSA on March 15, 2020, are shown in Fig. 1. It can be seen that the SSA value in East China is significantly lower than in other regions. This may be due to the developed industries in southern cities that emit a large number of black carbon aerosols, while the temperature in winter is low, and the aerosol particles are not easily diffused, resulting in the SSA value of some areas maintaining a stable low value.

To evaluate the retrieval SSA results, AERONET V3 datasets were used for validation. The AERONET datasets were selected in East Asia. Fig. 2 shows the scatter plots of AHI SSA retrievals versus AERONET at 470nm, (a) all AOD, and (b) only high aerosol loading (AOD>0.4 at 470nm), respectively. This indicates that the algorithm has great advantages for SSA inversion of heavy pollution conditions.

The retrieval results from three months of AHI data were evaluated against the ground-based AERONET measurements. The AHI SSA shows good agreement with AERONET measurements, especially in heavy pollution conditions. This algorithm has been proven to be to characterize the temporal and spatial distribution of aerosol SSA.

How to cite: Xue, Y. and Jiang, X.: Retrieval of aerosol single scattering albedo over land using geostationary satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2610, https://doi.org/10.5194/egusphere-egu23-2610, 2023.

Dust storms often occur in the spring season and influence large areas of Korean peninsula. During a dust storm event, the concentration of dust particles in the atmosphere increases significantly. Satellite monitoring is a powerful tool for studying the properties of large-scale dust storms. however, amidst all uncertainties associated with aerosol properties, the inadequate information about the chemical composition of the dust also greatly affects the radiation field at the top of atmosphere (TOA). GEO-KOMPSAT-2A is a South Korean geostationary meteorological satellite for the meteorological mission and the space weather monitoring mission. It has been equipped with AMI (Advanced Meteorological Imager) and KSEM (Korean Space Environment Monitor) payloads. In this study, an algorithm will be investigated that uses four infrared channels: 8.6 μm, 10.4 μm, 11.2 μm and 12.4 μm, on the AMI. updating Asian dust components according to 25 samples collected during 14 Asian dust events occurring between 2005 and 2018 on the Korean Peninsula and compared them to 34 soil samples (<20 µm) obtained from the Mongolian Gobi Desert, which is a major source of Asian dust will be presented. We used the libRadtran radiative transfer model for simulation of the atmospheric condition, presence of the aerosols and radiance reaching TOA. according to the refractive index and size distribution dataset of new components and strong dependency of TIR wavelength bands to the optical properties of the dust we expect this method will increase the accuracy of the algorithm.

How to cite: Parsa Javid, E. and Park, S. S.: Improvement of the dust retrieval algorithm using GK-2A Geostationary satellite by updating Asian dust chemical composition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4725, https://doi.org/10.5194/egusphere-egu23-4725, 2023.

Over the past decades there has been both rapid economic growth and increase in energy use in Asia. This has led to a rapid change in the emissions of aerosols and trace gases associated with climate and air pollution, having dramatic effects on the atmosphere. Aerosol absorption optical depth (AAOD) is a measure of the optical-physical-chemical information of particles which absorb visible and UV radation, including: black carbon (BC), dust, and brown carbon (BrC). This subset of total aerosols has a significant and unique effect on air pollution and climate change, including altering the radiative balance and impacting the hydrological cycle. Different unbiased models and methods based on non-aerosol measurments are used to disaggregate urban and industrial areas from suburban and rural areas. These regions are then sampled as constrained in space and time by MISR to elucidate and quantify information about the absorbing particle size distribution, ageing, and emissions in these rapidly changing and/or heavily polluted areas.

To better understand AAOD and its impact on the atmosphere, this work uses both empirical orthogonal decomposition (EOF) and a MIE model based on a core/shell assumption, combining information from from both MISR AAOD and OMI NO2, to determine information about the geospatial and temporal distribution of absorbing aerosols, the size of these particles, regional differences, and physical and chemical properties. The AAOD is constrained by inverted NO2 emissions profiles to find the regional distribution of particulate matter. The differences in the observed values of SSA and AAOD over the four different visible bands are then used to drive the MIE model, which in turn is used to produce a probability distribution of the core size, shell size, and arosol mixing state. The inverse performance of the particle size distributions and mixing state are observed to be dramatically different over urban, industrial, and suburban areas, in specific during the times as constrained by OMI. The impacts of these changes to the atmospheric and radiative profiles over both the source regions are analyzed and used to further evaluate the atmospheric loading, transport, aging, and emissions of abosbing aerosols, with the goal of developing and quantifying the impacts on these regions undergoing the largest amount of change in the region.

How to cite: Liu, Z. and Cohen, J. B.: Characterizing Emissions from Energy Sources Using Aerosol Properties Over Multiple-Wavelengths, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4996, https://doi.org/10.5194/egusphere-egu23-4996, 2023.

Aerosols increase the down-welling infrared (IR) radiation flux in the Arctic. The activation of aerosols increases the down-welling IR flux further, depending on the type of the aerosol. A new instrument, NYAEM-FTS has been installed in Ny-Alesund, Spitsbergen to measure the down-welling IR flux. Ny-Alesund is located on the archipelago of Spitsbergen in the most Northern part of the Atlantic Ocean. It is affected by influx of air from Europe and Asia which leads to import of Aerosols from the polluted areas of Europe and Asia.

We use the measurements of NYAEM -FTS for observing (non-activated) aerosols in cloud-free conditions. We improved this algorithm that it can be used for determining the type of activated aerosol as well.

We present first analysis of the IR measurements to show the dependence of the down-welling infrared radiation on the humidity in the aerosol layer and the chemical composition of the aerosols. This helps to close the gap between the warming effect on the Arctic climate of aerosols on one hand and the warming effect of clouds on the other hand.

How to cite: Ji, D., Palm, M., and Notholt, J.: Infrared Radiation Effects of Aerosols in the Atmospheric Window during Wet Growth Process, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14536, https://doi.org/10.5194/egusphere-egu23-14536, 2023.

Aerosol Optical Depth (AOD) retrieval from sunphotometric measurements is sensitive to the concentration of atmospheric gases (e.g. NO₂), particularly in UV and lower visible spectral range. Current algorithms used in aerosol networks  either use  climatological NO₂ to estimate the corresponding absorption or it is totally ignored . NO₂ in the atmosphere is characterized by high spatial and temporal variations, especially in urban areas. Thus, climatological values are rarely representative of the actual NO₂ concentration, introducing non-negligible errors in AOD retrievals at specific spectral regions.

We propose a correction approach, using synchronous data from different networks/instruments. AOD is retrieved by sunphotometers (CIMEL and PREDE-POM) in AERONET and SKYNET networks. NO₂ total column is calculated by direct sun measurements of PANDORA spectroradiometers, part of PANDONIA network. Data from three stations, with colocation of these instruments are used in presented study to apply the correction and evaluate the new datasets. Two stations in Rome, Italy (Sapienza University at City Center and CNR-ISAC at Tor Vergata in suburban area) and one in Athens, Greece (National Observatory of Athens at city center). More specifically the NO₂ correction is applied on AOD at four bandwidths (340, 380, 400 and 440 nm). Propagation of the correction to the calculated Ångström Exponent is also estimated.

Highest mean relative differences are found at 440nm which are up to 1.7% for AERONET data and 5.3% at 400 nm for SKYNET (which’s algorithm does not consider NO₂). Highest absolute AOD difference found was 0.037 at 440nm. For Ångström Exponent 440-870 absolute maximum difference found was 0.31.Finally, cases of days with high NO₂ variability and the corresponding effect on AOD calculations will be presented.

How to cite: Raptis, I. P., Drosoglou, T., Valeri, M., Casadio, S., Barnaba, F., Brizzi, G., Niro, F., Campanelli, M., and Kazadzs, S.: NO2 absorption correction for enhanced AOD retrieval, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8642, https://doi.org/10.5194/egusphere-egu23-8642, 2023.

Aerosols affect climate in several ways. Aerosols together with clouds contribute the largest uncertainties to the Earth’s radiative forcing estimates, according to IPCC. Consequently, accurate retrieval of the Aerosol Optical Depth (AOD) from satellite measurements is important to improve the knowledge about aerosols in the global atmosphere and the associated influence of natural and anthropogenic events on the amount of aerosols. Since the retrieval of AOD is typically under-determined it needs assumptions concerning aerosol properties and the surface of the Earth – consequently, there are several different algorithms. We analyse data from the Copernicus Climate Change Service of retrieved AOD with Dual-View Instruments (Along Track Scanning Radiometer 2 (ATSR2), Advanced Along Track Scanning Radiometer (AATSR), Sea and Land Surface Temperature Radiometer (SLSTR)) and the Infrared Atmospheric Sounding Interferometer (IASI) for the retrieval of Dust AOD.

For reliable conclusions the results of these algorithms and of different instruments should be consistent. When looking at different regions we observe, that the consistency between different algorithms differs depending on the type of surface or the geographical location. Looking at fractions of AOD measured by different instruments, we find inconsistencies over deserts and part of the oceans with much sea salt AOD. Apart from that, the results are consistent.

Based on these results, we plan to develop a new retrieval ​​combining the different instruments in order to use their respective advantages and to reduce the errors. In a first step data from SLSTR, IASI and additionally GOME-2 will be combined.

How to cite: Stöffelmair, U. and Popp, T.: Consistency of Aerosol Optical Depth from different Aerosol retrieval algorithms and instruments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14913, https://doi.org/10.5194/egusphere-egu23-14913, 2023.

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc,  has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18,  and Himawari-8/9, and Meteosat Third Generation (MTG)   they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day.  By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale.  

While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions.  However, this poses a “Big Data” challenge. The big data challenge is not just about data storage,  but also about data discoverability,  and accessibility, and even more, about data migration/mirroring in the cloud-computing environment.   This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aAerosol rRetrieval aAlgorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources.

How to cite: Wei, J., Zhao, S., Gutkin, N., Pan, X., Gupta, P., and Levy, R.: Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7292, https://doi.org/10.5194/egusphere-egu23-7292, 2023.