Displays

The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA-SEOM CIRCAS (ConsIstent Retrieval of Cloud Aerosol Surface) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations.  It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space.

Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This “twilight zone” can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap.

The CIRCAS project ultimately aims at overcoming the need of an external cloud mask, letting the CISAR algorithm discriminate between aerosol and cloud properties. This would also help reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations.

Results from the processing of S3A/SLSTR observations will be shown and evaluated against independent datasets.

How to cite: Luffarelli, M., Govaerts, Y., Sotiriadis, S., Brockmann, C., Kirches, G., Storm, T., and Pinnock, S.: Towards a consistent retrieval of cloud/aerosol single scattering properties and surface reflectance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16090, https://doi.org/10.5194/egusphere-egu2020-16090, 2020.

Cirrus clouds are the highest altitude clouds in the troposphere and play an important role in the climate system. They can either have a cooling or heating effect in radiation balance around of the planet, depending on which altitude and temperature they appear. Despite the importance of this type of clouds for the radiation budget there are still big gaps of knowledge regarding their micro and macro physical properties (e.g. particle sizes, ice water content, occurrence and coverage at the upper troposphere and lower stratosphere), especially for optically very thin cirrus in the tropopause region, which are difficult to detect even for active lidar measurements. Due to the long path length through the atmosphere and good vertical resolution passive infrared limb measurements are especially well suited to observe this type of clouds. The presentation will highlight the current status in infrared limb sounding and corresponding particle parameter retrievals with respect to recent and future space and airborne sensors (e.g. CRISTA, MIPAS, and IR limb-imaging instruments).

How to cite: Spang, R., Bartolome, I., Ungermann, J., Griessbach, S., Hoffmann, L., Krämer, M., Höpfner, M., Dinelli, B., Maestri, T., Siddans, R., Müller, R., and Riese, M.: Infrared limb sounding of cirrus clouds: state of knowledge, recent progress, and future prospects, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4554, https://doi.org/10.5194/egusphere-egu2020-4554, 2020.

This work aims at determining the best performing mid and far-infrared (MIR and FIR) joint spectral interval to identify and classify clouds in the Antarctic region by mean of a machine learning algorithm.

About 1700 spectral-resolved radiances, collected during 2013 by the ground based Radiation Explorer in the Far InfraRed-Prototype for Applications and Development, REFIR-PAD (Palchetti et al., 2015) at Dome C, Antarctic Plateau, are selected in coincidence with the co-located with backscatter and depolarization profiles derived from a tropospheric lidar system (Ricaud et al., 2017) to pre-classify clear sky, ice clouds, or mixed phase clouds.

A machine learning cloud identification and classification algorithm named CIC (Maestri et al., 2019), trained with a pre-selected set of REFIR-PAD spectra, is applied to this dataset by assuming that no other information than the spectrum itself is known.

The CIC algorithm is applied by considering different spectral intervals, in order to maximize the classification results for each class (clear sky, ice clouds, mixed phase clouds). A CIC "threat score" is defined as the classification true positives divided by the sum of true positives, false positives, and false negatives. The maximization of the threat score is used to assess the algorithm performances that span from 58% to 96% in accordance with the selected interval. The best performing spectral range is the 380-1000 cm^-1. The result, besides suggesting the importance of a proper algorithm calibration in accordance with the used sensor, highlights the fundamental role of the FIR part of the spectrum.

The calibrated CIC algorithm is then applied to a larger REFIR-PAD dataset of about 90000 spectra collected from 2012 to 2015. Some results of the full dataset cloud classification are also presented.

The present work contributes to the preparatory studies for the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission that has recently been selected as ESA’s 9^th Earth Explorer mission, scheduled for launch in 2026. 

References:

Maestri, T., Cossich, W., and Sbrolli, I., 2019: Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared, Atmos. Meas. Tech., 12, pp. 3521 - 3540

Palchetti, L., Bianchini, G., Di Natale, G., and Del Guasta, M., 2015: Far infrared radiative properties of water vapor and clouds in Antarctica. Bull. Amer. Meteor. Soc., 96, 1505–1518, doi: http://dx.doi.org/10.1175/BAMS-D-13-00286.1.

Ricaud, P., Bazile, E., del Guasta, M., Lanconelli, C., Grigioni, P., and Mahjoub, A., 2017: Genesis of diamond dust, ice fog and thick cloud episodes observed and modelled above Dome C, Antarctica, Atmos. Chem. Phys., 17, 5221–5237, https://doi.org/10.5194/acp-17-5221-2017.

How to cite: Magurno, D., Maestri, T., Cossich, W., Di Natale, G., Palchetti, L., Bianchini, G., and Del Guasta, M.: Antarctic cloud detection and classification from far and mid infrared downwelling radiance spectra: performances optimization and results, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9036, https://doi.org/10.5194/egusphere-egu2020-9036, 2020.

The measurement of atmospheric composition from space requires also a precise knowledge regarding the appearance of clouds within the observed scene, and if present, a quantification of cloud properties such as cloud fraction, cloud height and cloud optical thickness/cloud albedo. The Copernicus mission Sentinel-5 Precursor, being operational since 2 years, covers the UV/VIS/NIR/SWIR spectral region. By covering the spectral region of the Oxygen A-band in the NIR, it provides an excellent prerequisite to retrieve the cloud parameters mentioned above. The same holds true for the anticipated Sentinel-4 mission (foreseen launch in 2023). In this contribution we present the most recent advances in the algorithms for retrieving the operational cloud products from TROPOMI onboard Sentinel-5 Precursor and from the future Sentinel-4/UVN onboard MTG-S. The applied cloud retrieval algorithms OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) have their heritage with GOME/ERS-2 and GOME-2 on MetOp-A/B/C, where they have already been successfully implemented in an operational environment. OCRA uses a broad band color space approach in the UV/VIS in combination with a set of cloud-free reflectance background composite maps to determine a radiometric cloud fraction while the ROCINN algorithm retrieves the cloud top height, cloud optical thickness and cloud albedo from NIR measurements in and around the oxygen A-band, taking as a priori input the cloud fraction computed by OCRA. ROCINN includes two different cloud models. One which treats clouds more realistically as layers of scattering water droplets (clouds-as-layers, CAL) and another one which treats clouds as simple Lambertian reflectors (clouds-as-reflecting boundaries, CRB). Substantial improvements to the algorithms have been implemented recently, some of which will be presented here, e.g. improved background maps, inclusion of cloud phase, retrieval of surface properties using machine learning. Further validation efforts via satellite-to-satellite comparisons with VIIRS on Suomi-NPP have been carried out and consolidate the product quality.

How to cite: Lutz, R., Argyrouli, A., Romahn, F., Loyola, D., and Siddans, R.: Recent advances and new features in the operational cloud products of Sentinel-5 Precursor and prospects for Sentinel-4, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10117, https://doi.org/10.5194/egusphere-egu2020-10117, 2020.

NASA’s Orbiting Carbon Observatory-2 (OCO-2) includes a hyperspectral (Dl~0.02 nm) oxygen A-band sensor, and the depth of its absorption features is related to the photon path length. Photon path length increases above a cloud if it is lower (i.e. higher P_top), and within a cloud if its droplets are farther apart (i.e. lower N­_d). This is a novel approach for retrieving N_d that is independent of MODIS-like retrievals, which take an a priori vertical cloud structure and assume that non-adiabatic processes such as precipitation or entrainment affect clouds uniformly. Our last product, OCO2CLD-LIDAR-AUX, used CALIPSO P_top to help separate the above- and within-cloud path length. Here we show progress in an updated OCO-2 only retrieval of marine boundary layer clouds, including using neural networks for cloud identification and phase classification, additional retrieval of r_e, and how cloud vertical structure can bias retrieved P_top and N_d. Successfully addressing this bias would provide a new and independent N_d retrieval that should capture changes due to non-adiabatic processes, and therefore provide a new test of aerosol cloud effects.

How to cite: Richardson, M., Lebsock, M. D., and Stephens, G. L.: Hyperspectral A-band retrievals of cloud droplet number concentration from OCO-2, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22389, https://doi.org/10.5194/egusphere-egu2020-22389, 2020.

The Suomi National Polar-orbiting Partnership (SNPP) satellite carrying the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) instruments can provide high quality hyperspectral infrared (IR) data and microwave (MW) measurements. It is very important to ensure the accuracy of cloud detection in the infrared hyperspectral measurements before they are used for geophysical retrievals or data assimilation. Therefore, a cloud detection method using the CrIS hyperspectral radiances at longwave (709.5-746.0 cm^-1) and shortwave (2190-2250 cm^-1) bands and the ATMS measurements is introduced in this paper. Four steps are included in this algorithm: identifying clear FOV, estimating the number of cloud formations, thermal contrast, and cloud mask classification. Specifically, each CrIS field-of-view (FOV) is preliminarily assigned as clear or cloudy by comparing the measured IR radiances and simulated IR clear radiances which are generated from the MW-retrieved geophysical state vector based on a physical inversion method. Secondly, the number of cloud formations within one CrIS field-of-regard (FOR) is estimated using the principal component analysis (PCA). Next, CrIS radiances at longwave channels and shortwave bands are used to evaluate the thermal contrast within the FOR. Based on the above informations each FOR will finally be assigned a cloud mask classification. The cloud mask results derived from this technique are also analyzed.

How to cite: Guan, L. and Xue, Q.: A CrIS Cloud Detection Method Based on CrIS and ATMS Measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3397, https://doi.org/10.5194/egusphere-egu2020-3397, 2020.

Typically, DCCs are identified by 11 µm band brightness temperature (BT11) lower than a fixed BT threshold. Another method of combining the brightness temperature difference (BTD) between a water vapor absorption channel and a window channel to its measurement noise ratio (BNR) is adopted and applied to DCC identification. This BNR method improves the DCC detections over the legacy method because it is less contaminated with high clouds not thick and bright enough. BNR detects fewer DCCs than BT11, but with more confidence. 

Using observations of the collocated Cross-track Infrared Sounder (CrIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) from 2017 to 2018, the results show BNR has better performances than BT11 for identifying the DCC and monitoring reflective solar bands. When comparing to BT11, BNR has more robust and invariant time series of monthly reflectance for all RSBs. Because BNR affects more on the left tails (less reflective) of the histograms than the mode reflectance, the improvement is more significant on the mean values than the modes. This method can be applied to other imagers with collocated advanced infrared sounders for detecting DCCs and monitoring the calibration stabilities of RSBs. 

Recently, the hyperspectral infrared atmospheric sounders onboard China’s next-generation FengYun satellites, i.e. the Geosynchronous Interferometric InfraRed Sounder (GIIRS) on the FengYun-4 geostationary satellite series and the Hyperspectral Infrared Atmospheric Sounder (HIRAS) on the FengYun-3 polar orbiting meteorological satellite series, are in operation. Flown onboard the same platforms, the collocated (consistent in time and space) infrared sounders and imagers, provide mount of match-up measurements for the study of methodology and process for synergistic use of both infrared sounder and imager for multiple applications. The findings will provide scientific evidences for further enhancements and applications of future FengYun satellites and its observing system.

How to cite: Gong, X., Li, J., Li, Z., and Moeller, C. C.: Monitoring precipitation convective clouds with collocated hyperspectral infrared sounder and imager measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10381, https://doi.org/10.5194/egusphere-egu2020-10381, 2020.

Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5-15 years; thus, for climate studies, the use of multiple satellite sensors should be considered.

We introduce a gridded monthly AOD merged product for the period 1995-2017 obtained by combining 12 major available monthly AOD products, which provides a long-term perspective on AOD changes over different regions of the world. Different approaches for merging the individual AOD products (median, weighted according to the evaluation results) are tested. We show that the quality of the merged product is as least as good as that of individual products.

We also introduce an approach to combine the merged AOD product with the AOD time series available over land (TOMS) and ocean (AVHRR) from early 1980th.

The evaluation of the modelled AOD products with the satellite AOD product shows that the agreement between modelled and merged AOD product is closer than one between modelled and individual satellite AOD products.

How to cite: Sogacheva, L. and the AEROSAT team: Merging regional and global AOD records from major available satellite products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13685, https://doi.org/10.5194/egusphere-egu2020-13685, 2020.

A statistical analysis method to quantify dust aerosol interactions with ice cloud properties using IASI satellite data has been developed and published by L. Klüser et al. 2017. Key components of analyzing cloud properties are their classification by aerosol load and their normalization in respect to the meteorological state using a Bayes-approach. Comparing histograms of cloud properties for different aerosol classes gives then insight in statistical changes of their distribution. Using the same method twice on IASI-IMARS satellite retrieval and EMAC-MADE3 global circulation model data yields valuable insights on changes in cloud forming and lifecycle behavior inflicted by dust aerosol pollution. Overcoming scale differences between observation and simulation data sets has been a major obstacle as they have evident impact on the analysis results. Therefore, a statistical downscaling method has been customized to EMAC-MADE3 model data that focuses on preservation of critical processes while still approximating fine-scale patterns below model resolution. Both statistical analysis results for model and satellite data show clear aerosol impact on cloud property distributions with varying magnitudes and demonstrate the necessity of downscaling. More detailed analysis conducted with an increased number of aerosol classes shows quantifiable trends in aerosol impact on cloud properties.

How to cite: Offenwanger, T., Beck, C., Popp, T., Hendricks, J., and Righi, M.: A statistical analysis method estimating dust aerosol-ice cloud interactions using global circulation model and satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9141, https://doi.org/10.5194/egusphere-egu2020-9141, 2020.

One-year of ground-based Raman lidar observations have been conducted in order to characterize the aerosol properties over United Arab Emirates (UAE). In total, over 1000 aerosol layers were detected during the one-year campaign period which was carried between March 2018 and February 2019. We found that the measurement site is a receptor of frequent dust events but predominantly the dust is mixed with anthropogenic and/or aerosol of marine origin. With our multiwavelength PollyXT Raman lidar we are able to retrieve the backscatter coefficients (at 355, 532 and 1064 nm), extinction coefficients (at 387 and 607nm), particle depolarization ratios (at 355 and 532 nm), water vapour concentration (at 407 nm), and further on lidar ratios and Ångström exponents to characterize the aerosols properties in detail. In general, the average lidar ratios and linear particle depolarization ratios already showed strong presence of dust aerosols. Since the region is both a source and a receptor of mineral dust, we have also explored the pure mineral dust properties in the region. The findings suggest that the mineral dust properties over the Middle East and western Asia, including the observation site, are comparable to those of the African mineral dust regarding the particle depolarization ratios but not the lidar ratios. The lower lidar ratio values are attributed to different geochemical characteristics of the under-study region compared to soil originating from Northern Africa.

How to cite: Komppula, M., Filioglou, M., Shang, X., Giannakaki, E., Hirsikko, A., and Romakkaniemi, S.: Characterization of dust aerosol over United Arab Emirates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17479, https://doi.org/10.5194/egusphere-egu2020-17479, 2020.

Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. However, the spatial resolution of AOD, based on Moderate-resolution Imaging Spectroradiometer (MODIS), is low, and hard to meet the needs of regional air quality fine monitoring. In 2018, China launched Gaofen-6 satellite, which set up a network with Gaofen-1 enabling two-day revisited observations in China's land area, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. Along with advancement of the Earth Observation System and the launch of high-resolution satellites, it is of profound significance to give full play to the active role of high-scoring satellites, in monitoring atmospheric environmental elements such as atmospheric aerosols and particulate matter concentrations, and achieve high-resolution retrieval of AOD through Gaofen satellites.

In this paper the data of Gaofen-6 and Gaofen-1 was used to retrieve the AOD. based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm. This algorithm can retrieve the surface reflectance and AOD synchronously through constructing a closed equation based on double star observations. It can be applied to various types of surface reflectance which extends the coverage of the retrieval of AOD inversion effectively. Experimental data includes the satellite data of New South Wales and eastern Queensland on November 21, 2019, which have been suffered from unprecedented large-scale forest fires for over 2 months. The retrieval of AOD during the time with the satellite data is benefit for the prevention and monitoring of forest fire. The experimental results are compared with the AERONET ground observation data for preliminary validation. The correlation coefficient is about 0.7. The experimental results show that the method have higher accuracy, and further validation work is continuing.

How to cite: Xue, Y.: Retrieval of High Spatial Resolution Aerosol Optical Depth from Chinese gAofen Data for Australian bushfire, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3986, https://doi.org/10.5194/egusphere-egu2020-3986, 2020.

Two episodes with heavy air pollution in Nanjing, China, one in the summer and another one in the winter of 2017, were selected to study aerosol properties using sun photometer and ground-based measurements, together with source region analysis. The aerosol properties, the meteorological conditions, and the source regions during these two episodes were very different. The episodes were selected based on the air quality index (AQI), which reached a maximum value of 193 during the summer episode (26 May–3 June) and 304 during the winter episode (21–31 December). The particulate matter (PM) concentrations during the winter episode reached maximum values for PM_2.5/10 of 254 μg m⁻³ and 345 μg m⁻³, much higher than those during the summer (73 and 185 μg m⁻³). In contrast, the value of aerosol optical depth (AOD) at 500 nm was higher during the summer episode (2.52 ± 0.19) than during that in the winter (1.38 ± 0.18). A high AOD value does not necessarily correspond to a high PM concentration but is also affected by factors, such as wind, Planetary Boundary Layer Height (PBLH), and relative humidity. The mean value of the Ångström Exponent (AE) varied from 0.91–1.42, suggesting that the aerosol is a mixture of invaded dust and black carbon. The absorption was stronger during the summer than during the winter, with a minimum value of the single scattering albedo (SSA) at 440 nm of 0.86 on 28 May. Low values of asymmetry factor (ASY) (0.65 at 440 nm and 0.58 at 1020 nm) suggest a large number of anthropogenic aerosols, which are absorbing fine-mode particles. The Imaginary part of the Refractive Index (IRI) was higher during the summer than during the winter, indicating there was absorbing aerosol during the summer. These differences in aerosol properties during the summer and winter episodes are discussed in terms of meteorological conditions and transport. The extreme values of PM and AOD were reached during both episodes in conditions with stable atmospheric stratification and low surface wind speed, which are conducive for the accumulation of pollutants. Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analysis show that fine mode absorbing aerosols dominate during the summer season, mainly due to emissions of local and near-by sources. In the winter, part of the air masses was arriving from arid/semi-arid regions (Shaanxi, Ningxia, Gansu, and Inner Mongolia provinces) covering long distances and transporting coarse particles to the study area, which increased the scattering characteristics of aerosols.

How to cite: Wang, J. and de Leeuw, G.: Contrasting Aerosol Optical Characteristics and Source Regions During Summer and Winter Pollution Episodes in Nanjing, China , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1862, https://doi.org/10.5194/egusphere-egu2020-1862, 2020.

The MEdium Resolution Spectral Imager (MERSI) onboard Chinese Fengyun-3 (FY-3) satellite is designed similar to MODIS and VIIRS, which would be an important supplement for multi-sensor measuring aerosol temporal and spatial distribution. But, there is no reliable aerosol product from MERSI by now. The plan of FY-3 missions is a sequence of eight satellites. Four have been launched, FY-3A in 2008, FY-3B in 2010, FY-3C in 2013, and recent FY-3D in the end of 2017. As the sensor MERSI becomes more mature, the demand of quantitative product is very urgent. Here, we apply MODIS land dark target (DT) algorithm to MERSI to test the quantitative ability of aerosol retrieval. Considering the sensor difference between MODIS and MERSI, we modified the process of gas absorption, cloud/snow/inland-water mask, pixel aggregation, and the most important part of band ratio for surface reflectance estimation. The global MERSI/FY-3C data of a whole year has been tested for retrieval. And the AEROENT data are used for ground validation. The scattering plot shows that MERSI AOD (aerosol optical depth) agrees well with AERONET observation that 70.7% collocations fall within expected error EE=±(0.05+0.15τ), which is even better than MODIS/TERRA AOD that 66.6% fall within EE. The global maps of monthly mean AOD are also consistent as well as MODIS. Finally, we made test to MERSI-II/FY-3D of 2 years data, the preliminary results also have a good validation and agree well with MODIS. The results of this talk indicates that the MERSI sensor has the quantitative ability for aerosol retrieval, and would be an important member of multi-sensor for aerosol measurements.

How to cite: Yang, L., Hu, X., Wang, H., Liu, P., He, X., Du, W., and Deng, A.: Land aerosol retrieval from MERSI onboard Chinese Fengyun-3, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8797, https://doi.org/10.5194/egusphere-egu2020-8797, 2020.

Aerosol single scattering albedo is a critical optical parameter that determines aerosol radiative effect. However, most existing passive satellite sensors such as MODIS and VIIRS only measures the intensity of reflected solar radiation and can only retrieve aerosol optical depth, while aerosol single scattering albedo needs to be assumed in the retrieval algorithm. On the other hand, if aerosol optical depth is known, it would be possible to retrieve aerosol single scattering albedo using satellite sensors.  In this study, we develop a machine learning based algorithm that retrieves aerosol single scattering albedo using joint visibility and satellite measurements. Combined with meteorology variables including relative humidity and boundary layer height, surface visibility can be converted to column aerosol optical depth. Then combining this converted aerosol optical depth with VIIRS measured TOA apparent reflectance, we retrieve aerosol single scattering albedo at over 2000 stations worldwide. The results compare well with AERONET retrieved SSA. However, compared with AERONET, visibility is recorded at every WMO meteorology station and has much more extensive coverage. We also applied our method to surface PM2.5 measurements obtained satisfactory results. Our work provides an aerosol single scattering albedo dataset with extensive coverage over land, which can be used for aerosol radiative forcing calculations and model validation.

How to cite: Li, J. and Dong, Y.: Retrieval of aerosol single scattering albedo using joint satellite and surface visibility measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11075, https://doi.org/10.5194/egusphere-egu2020-11075, 2020.

Recently, China successfully launched the new-generation geostationary (GEO) meteorological satellites, Fengyun-4A (FY-4A) in the year of 2016. In general, many mature and useful level-2 science product of Advanced Geostationary Radiation Imager (AGRI) onboard FY-4A were well developed in advance for satellite data users. Cloud top properties (CTP, including cloud top height, temperature, and pressure) product as an important science product is always used to monitor the rapid changes of typhoon and weather systems in the operational application. Unfortunately, we still find some invalid retrievals in CTP product of FY-4A/AGRI, which seriously impact on the use of this product (get some complaints from users). After comparing the invalid pixels with valid pixels and the same CTP product of Japanese Himawari-8 satellite and radiative transfer simulations, we find that the radiometric calibration bias of infrared band at 13.5μm is the primary impact factor on this fault. The pixels with brightness temperature lower than 200K show a bias larger than 7-8K, which directly induce the invalid retrieval process in the CTP product of FY-4A/AGRI. However, this fault in CTP also inspire us to use the invalid CTP pixel to monitor the on-orbit radiometric calibration bias of infrared band in the future.

How to cite: Min, M., Wang, F., and Xu, N.: Invalid cloud top properties product of Fengyun-4A satellite imager induced by infrared calibration bias, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1312, https://doi.org/10.5194/egusphere-egu2020-1312, 2020.

Low-level warm clouds are a major component in multilayered cloud systems and are generally hidden from the top-down view of satellites with passive measurements. By using spaceborne radar data with fine vertical resolution, this study conducts an investigation on oceanic warm clouds embedded in multilayered structures. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness around 1 km, the cloudless gap reaches its maximum exceeding 600 m. As the two cloud layers become even thinner or thicker, the cloudless gap decreases in thickness. It is believed that such knowledge on cloud overlapping is critical for fully understanding the distribution of warm clouds in three-dimensional space. The results derived in this study could help validating cloud results of numerical models, which are indeed three-dimensional in nature. They could also be used to improve the estimation of cloud radiative forcing, since it is affected by cloud occurrences and especially their vertical structures. It should be pointed out that solid explanations for the above cloud features cannot be presented by only using these satellite data themselves. 

How to cite: Liu, Q., Ding, Y., and Lao, P.: Oceanic warm cloud layers within multilevel cloud systems observed by satellite measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6311, https://doi.org/10.5194/egusphere-egu2020-6311, 2020.

Polarization lidar has been widely used in recent decades to observe the vertical structures of aerosols and clouds in the atmosphere. To obtain more information from polarization lidar measurements, we developed a dual-polarization lidar system that can detect polarization measurements simultaneously at both 355 nm and 532 nm. The vertical distributions of atmospheric aerosols and clouds over northern China were successfully observed by the developed lidar. Observational data during two typical cases (dust events and haze episodes) were used for the analysis in this study. The results showed that for dust-dominated aerosols, the depolarization ratio (DR) at 532 nm was larger than that at 355 nm, but that for air pollutants was smaller. Our results also show that dual-polarization measurements can be used to largely improve aerosol classification. Moreover, we found that there is a good relationship between the absorption coefficient of aerosols and the ratio of DRs at 532 nm and 355 nm for dust aerosols. These results confirm that the absorption characteristics of dust aerosols cause a difference in DR at the UV and VIS wavelengths, and implying that aerosol absorption may be determined by polarization lidar at the ultraviolet and visible wavelengths.

How to cite: Huang, Z., Qi, S., Zhou, T., Dong, Q., Ma, X., Zhang, S., Bi, J., and Shi, J.: Investigation of aerosol absorption with dual-polarization lidar observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6364, https://doi.org/10.5194/egusphere-egu2020-6364, 2020.

In this study, the improved algorithm of thin cloud detection for geostationary ocean color imager (GOCI) satellite was developed to classify the thin cloud area over land area. The new cloud mask approach of GOCI satellite is required to expand its ocean dedicated application to other applications such for vegetation in land or aerosol optical properties (AOPs) in atmosphere due to its attractive shortwave wavelength bands of ocean color sensors. However, when trying to apply the advantages of the ocean color bands to the land area, only visible spectral bands of GOCI make it difficult to expand the land application the other way due to its limitation of cloud detection for relatively bright land surface. Furthermore, the geostationary of GOCI satellite has highly sensitive to geometry location of sun, meaning that high effective (Bidirectional Reflectance Distribution Function) BRDF effects make it also difficult to detect cloud mask in land surface due to its anisotropically scattered surface reflectance. In this paper, cloud mask algorithm of GOCI is proposed to consider those limitations by mainly using background surface reflectance from BRDF model. Therefore, minimum difference in reflectance between TOA and land as baseline of clear atmosphere and background surface reflectance underneath cloud were estimated from BRDF model. In conclusion, our new thin cloud detection is effectively detect the thin cloud over land surface area under limited ocean color bands of GOCI. The improved thin cloud detection algorithm of GOCI will be not only useful for following on instruments such as GOCI-II of Geo-KOMPSAT-2B and Sentinel 3 Ocean and Land Color Instrument (OLCL), but also applicable for existing geostationary satellites such as Geo-KOMPSAT-2A AMI, Himawari, and GOES-R as alternative cloud masking approach.

How to cite: Yeom, J.-M., Kim, H.-W., Lee, J., Park, S., and Lee, S.: The improved thin cloud detection using BRDF model based background reflectance from GOCI geostationary satellite imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6408, https://doi.org/10.5194/egusphere-egu2020-6408, 2020.

SeaWIFS4Meris is retrieving AOD at 550nm by fitting reflectance spectra at TOA to those measured by the MERIS instrument (ENVISAT). The OLCI instrument on Sentinel 3 is the successor of MERIS. The SeaWiFS4Meris Algorithm for AOD retrieval has been subsequently adapted to process OLCI data. Compared to AATSR/SLSTR OLCI and MERIS offer a better coverage due to a wider swath (1150km for OLCI and MERIS compared to 512km for AATSR).

We present aerosol retrieval results for the second half of 2019 together with their validation.

Based on the validation, next steps for improving the algorithm are defined. First, the albedo will be recalculated based on OLCI data - currently the albedo data calculated from MERIS data of 2008 is used. Secondly, possibilities for updating the cloud mask algorithm will be analyzed using the additional bands of OLCI. Lastly, the treatment of aerosol types will be inspected.

How to cite: Dermann, D., Kosmale, M., and Popp, T.: SEAWIFS4MERIS adaptation to OLCI, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8679, https://doi.org/10.5194/egusphere-egu2020-8679, 2020.

            The safe operation of aviation and shipping, particularly in areas of insufficient coverage of automatic meteorological stations in the Arctic requires accurate interpretation of satellite images. Operational detection of fog and low stratus clouds and recognizing of them on the background of snow and ice cover and cloudiness of the upper layer is very important challenge. 

           The verified images obtained by Aqua and Terra satellites with a scanning radiometer MODIS, which operates in 36 spectral bands, with wavelengths from 0.4 µm to 14.4 µm, were collected.  With the Beam VISAT 5.0 software, which was designed to work with satellite data in raster format, thematic digital techniques of satellite multispectral information, based on difference in the values of the integral brightness of the images, both in optical and far-infrared ranges of the spectrum, have been developed.  These techniques, models of additive color synthesis, improve the quality of interpretation of fogs and low stratus clouds in terms of the complex structure of cloudiness and underlying surface in polar regions. Developed RGB combinations, which are based on the selected MODIS bands are:

1. RGB (1.6 µm; 0.8 µm; 0.6 µm)
2. RGB (0.8 µm; 3.9-8.7 µm; 10.8 µm)
3. RGB (0.8 µm; 1.6 µm; 3.9-8.7 µm)
4. RGB ((0-12)-(0-11) µm, (0-11)-(0-3.8) µm, (0-11) µm)

          Analysis of the obtained images has shown that the developed models of color synthesis help to distinguish the fog/low stratus clouds under different conditions of cloudiness and underlying surface accurately.

Key words: remote sensing, satellite imagery, additive color synthesis, fog, low stratus clouds, polar regions

How to cite: Tatsii, D. and Fedoseeva, N.: The fog/low stratus clouds in the Arctic: detection with multispectral satellite imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10867, https://doi.org/10.5194/egusphere-egu2020-10867, 2020.

We present a follow-up study on previous investigations of photon path lengths distributions in cloudy atmospheres using O₂ A-band measurements from the GOSAT TANSO-FTS satellite instrument (Kremmling, B., Investigation of photon path length distributions derived from oxygen A-band measurements of the GOSAT satellite instrument, PhD thesis, 2018). The original study used TANSO-FTS measurements of high spectral resolution over cloud covered ocean areas and compared them to radiative transfer simulations using the Monte Carlo model McArtim. The comparison is based on a fitting process, allowing spectral alignment as well as an adjustment of the simulated O₂ absorption. A systematic overestimation of 5-10% of the simulated O₂ absorption was found for the considered case studies. Despite the investigation of different sensitivity studies, the cause of this overestimation remained unresolved.

The consequence of these finding was the thorough investigation of clear sky measurements from TANSO-FTS between 2009 and 2015. The analysis includes the retrieval of the surface albedos and their comparison to those included in the TANSO-FTS data products as well as the subsequent fitting results of the simulated spectra. The analysis is applied to two datasets, both consisting of measurements passing different clear sky and quality criteria. Dataset 1 additionally has information from independent lidar measurements of CALIOP (CALIPSO) and is limited to the northern hemisphere due to the spatial and temporal collocation criteria. Dataset 2 has no independent collocation measurements but a more uniform distribution in space and time.

While the retrieved surface albedos compare well, an overestimation of the simulated O₂ absorption by about 5% is found for measurements over ocean. Good agreement is found for the land cases.

In order to better understand these observations, different sensitivity studies as well as fit settings are investigated. The sensitivity studies include parameters such as SZA, surface albedo, NDVI values as well as the polarization of the TANSO-FTS radiances. The presentation shows the outcome of these studies.

How to cite: Kremmling, B., Beirle, S., and Wagner, T.: Evaluation of simulated clear sky O2 A-band measurements from GOSAT over different surfaces - a sensitivity study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11416, https://doi.org/10.5194/egusphere-egu2020-11416, 2020.

Aerosols attract great attention as having critical influence on the Earth’s energy budget and human health. Geostationary satellites like Himawari-8 process advantages on temporal resolution that allows rapidly changing weather phenomena tracking and aerosol monitoring. This work aims at providing a novel error analysis for the Advanced Himawari Imager (AHI) aerosol optical depth (AOD) retrieval from the aspect of aerosol model and sun position combing with the high quality ground-based observation in Wuhan, central China. Three-year co-located AOD dataset from AHI and sun-photometer are used. AHI underestimates AOD in all the seasons. Aerosol size distributions and phase functions are discussed as parts of aerosol model to explain the underestimation of AOD. AHI sets a low fine-mode particle median radius comparing with the in-site measurement in Wuhan that increases backscattering, and finally leads to the underestimation of AOD. Sun position also affects AHI AOD retrieval, and we use solar zenith angle (SZA) and scattering angle to represent sun position. Geostationary satellites get fixed satellite position for one site that provides convenience to the discussion. SZA influences AOD retrieval mainly through the length of transfer path and higher percent of samples within expected error often appears at low SZAs. Scattering angle also has obvious influence on AOD retrieval through the simulation of phase function and causes the difference of correlation performance between AHI and sun-photometer in aspect of SZA in morning and afternoon. Finally, we applied the dark target method to retrieve AHI AOD. The comparison of AODs reveals that the retrieval method of AHI performs better in Wuhan. The better performance of AHI AOD may be due to high aerosol loading and lack of enough prior information of aerosol properties in Wuhan. Our work could also be performed on other areas or other geostationary satellites, and help us to further understand the controlling factors that affect AOD retrieval accuracy, then contribute to better AOD retrieval.

How to cite: Ma, Y., Zhang, M., Shi, Y., Gong, W., and Jin, S.: Error Analysis for the Himawari-8 Aerosol Optical Depth Basing on Parts of Aerosol Model and Sun Position over Wuhan, Central China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14410, https://doi.org/10.5194/egusphere-egu2020-14410, 2020.

Visibility and visual contrast depend on several factors such as aerosol concentration, fog attenuation and humidity as well as gas characteristics. Usually, visibility is determined by observers or by visiometers. Routine web cam photographs of Vienna  have been performed for  2 years from the meteorological measurement platform situated on the roof of one of the buildings of University of Natural resources and Life Sciences overlooking the whole city of Vienna. Photographs are taken every 30 minutes in 6 different azimuthal directions. In the following study, we used routine web cam photographs digitalization to study the correlation between the ratio of some RGB channels as well as intensity fluctuations and the aerosol optical depth and on site particulate matter measurements. We first selected only photographs taken on clear sky days

For ground truth data, we used CIMEL sun photometer data of aerosol optical depth and liquid water content, relative humidity from routine measurements from our measurement platform as well as in situ measurements of particulate matter (PM10) performed by the air quality monitoring network of the city of Vienna.

First, the correlation between the contrast in a horizontal line and the aerosol amounts in the atmosphere and particulate matter concentration as a function of time of the day and azimuthal direction was investigated. We then examined the correlation between the blue to red ratio in a vertical and horizontal line with the aerosol amounts and particulate matter concentration in the atmosphere.

Results obtained showed at some azimuth angles and time of the day correlation coefficient R squared of up to 0.85 between horizontal line contrast and in situ PM 10 and between vertical line blue to red ratio and CIMEL aerosol optical depths measurements.

How to cite: Weihs, P., Frisch-Niggemeyer, A., and Schreier, S.: Determination of aerosol optical depth and particulate matter concentrations using routine web cam measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15259, https://doi.org/10.5194/egusphere-egu2020-15259, 2020.

Three dimensional structure of cloud cover is one of the Essential Climate Variable required for accurate monitoring of the state and change of global climate. Joint CloudSat-CALIPSO space mission have provided the most reliable and comprehensive 3D information on cloud distribution worldwide to date. However, the data resulted from observations collected every 16 days – sampling interval which can be considered infrequent for most of climate-oriented applications. The reliability of the data also depends on cloud regime, and area (grid cell size) over which the data are aggregated, further complicating the uncertainty aspect of lidar-radar profiling missions. The important question related to the CloudSat-CALIPSO dataset is whether 16-day revisit period for CloudSat-CALIPSO mission is sufficient to provide a climate characteristics at high statistical significance? We address that problem evaluating the full CloudSat-CALIPSO record (2006-2011), available to the scientific community as 2B-GEOPROF-LIDAR product. The analysis focuses on two aspects. First, we perform a point estimation to determine the minimum significance level at which the lidar-radar data (mean value) is statistically significant. Second, using a bootstrap approach we calculate confidence intervals for the mean value at fixed .95 and .99 thresholds. Therefore we reveal how wide is the actual uncertainty range at 16-day revisit. The analysis accounts for grid box size over which individual lidar-laser profiles were aggregated. The study was founded by National Science of Poland under the contract no. UMO-2017/25/B/ST10/01787.

How to cite: Kotarba, A. and Solecki, M.: Cloud amount uncertainty in merged CloudSat-CALIPSO radar-lidar observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15893, https://doi.org/10.5194/egusphere-egu2020-15893, 2020.

How to cite: Brath, M., Ekelund, R., Eriksson, P., Lemke, O., and Buehler, S. A.: Oriented particles in microwave and submillimeter radiative transfer simulations of ice clouds, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16980, https://doi.org/10.5194/egusphere-egu2020-16980, 2020.

The ability of three-dimensional dust models to accurately represent the dust life cycle is crucial for describing dust effects on radiation and clouds and for reducing the uncertainties on these processes. To improve the reliabilty of dust models, it is therefore imperative to carry out thorough evaluations of the dust properties. Dust optical and microphysical properties are accurately accessed through groundbased observations: multiwavelength lidars and sunphotometers. In this study we use the Generalized Retrieval of Atmospheric and Surface Properties (GRASP) data algorithm that combines the lidar and sunphotometer data to retrieve dust properties. GRASP is applied on a Saharan dust episode over Finokalia, Crete in Greece, on 14 May 2017. More precisely the measurements from PollyXT lidar participating in the European Aerosol Research Network (EARLINET) and the CIMEL sunphotometer participating in Aerosol Robotic Network (AERONET) are synergetically combined using the GRASP algorithm. The dust event is fully characterised through the retrieval of dust optical and microphysical properties. The retrieved properties are found to be in good agreement with the initial measurements from the AERONET sunphotometer and the lidar. Then the aforementioned tools are used to evaluate the performance of the regional dust model NMME-DREAM that has been developed to simulate and predict the atmospheric cycle of mineral dust aerosols. It is shown that the model has problems in simulating the high dust concentration values at low levels, probably due to the low spatial resolution of the model that causes difficulties in capturing the orography and the downdrafts winds.

How to cite: Konsta, D., Tsekeri, A., Solomos, S., Lopatin, A., Goloub, P., Dubovik, O., Amiridis, V., and Nastos, P.: The potential of a synergestic lidar and sunphotometer retrieval for the characterization of a dust event over Finokalia and for aerosol model evaluation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17111, https://doi.org/10.5194/egusphere-egu2020-17111, 2020.

Clouds play an important role in the radiation budget of the Earth’s atmosphere. The radiative heating and/or cooling by cloud vertical sturcture including cloud top and base, number and thickness of cloud layers, and the vertical distribution of multi-layer clouds couple strongly with the atmospheric thermodynamics, general circulation, and the hydrological cycle. Unfortunately, however, inadequate understanding of cloud properties and their vertical distributions still leads to high uncertainties in global climate models. In this study, we present the vertical distributions of multi-layer clouds derived from synergetic measurements of radiosonde and collocated ceilometer and the miraMACS Ka-band cloud radar on the roof of the Meteorological Institute Munich. Balloon-borne radiosondes penetrate the cloud layers and thus provide in-situ measurements. The profiles of temperature, relative humidity and pressure with radiosonde are used to derive cloud layers by identifying saturated levels in the atmosphere. The ceilometer is very efficient in detecting clouds and can provide a reliable estimate of the height of cloud base. The miraMACS cloud radar operating continuously in a vertical pointing mode provide radar reflectivities by hydrometeors within the radar beam. The radar observations allow for the determination of cloud layers with high temporal and vertical resolutions. Doing these exercises to the measurements of an entire year in 2018, we are able to evaluate the cloud layer retrieval methods with different instruments and to derive the statistical properties of cloud vertical structure in Munich.

How to cite: Li, Q., Ewald, F., Groß, S., Hagen, M., Tetoni, E., and Mayer, B.: Cloud vertical structure studied with synergetic measurements of Radiosonde, ceilometer and Ka-band radar in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17445, https://doi.org/10.5194/egusphere-egu2020-17445, 2020.

Aerosol measurements are carried out worldwide in order to reduce the uncertainties about the impact of aerosols on climate. Over the past two decades, different methods (ground- or satellite-based) for measuring aerosol properties have been developed, covering a variety of approaches with different temporal and spatial scales, which can be considered complementary. Aerosol optical properties are essential for assessing the effects of aerosols on radiation and climate. Aerosol single scattering albedo (SSA), along with optical depth and asymmetry parameter, is one of the three key optical properties that are necessary for radiation transfer and climate models. At the same time, SSA strongly depends on different aerosol types, thus enabling the identification of these different aerosol particles. However, despite the strong need for aerosol SSA products with global and climatological coverage, and the significant progress in retrieving SSA from satellite measurements, the satellite SSA retrievals are still subjected to uncertainties.

In this study, we perform an evaluation of the OMAERUVd (PGE Version V1.8.9.1) daily L3 (1° x 1° latitude-longitude) aerosol SSA data, which are based on the enhanced two-channel OMAERUV algorithm that essentially uses the ultraviolet radiance data from Aura/Ozone Monitoring Instrument (OMI), through comparisons against daily SSA products from 541 globally distributed Aerosol Robotic Network (AERONET) stations for a 15-year period (2005-2019). The comparison is performed between the available OMAERUVd SSA data at 354 nm, 388 nm, and 500 nm, and the AERONET SSA data at 440 nm (or 443 nm). The comparison is made on an annual and seasonal basis in order to reveal possible seasonally dependent patterns, as well as on a climatological and a year-to-year basis. The statistical metrics, such as Coefficient of Correlation (R) and Bias, are computed for individual AERONET stations as well as for all stations. The effect of availability of common OMI and AERONET data pairs on the comparison is assessed by making comparisons when at least 10, 50 and 100 common pairs are available.

The results show that about 50% (75%) of OMI-AERONET matchups agree within the absolute difference of ±0.03 (±0.05) for the 500 nm OMI SSA and the 440 nm (or 443 nm) AERONET SSA. The corresponding percentage for the 388 nm OMI SSA and the 440 nm (or 443 nm) AERONET SSA increases to 58% (81%), while the corresponding numbers for the 354 nm SSA OMI and the 440 nm (or 443 nm) AERONET are 43% (67%). It is found that in overall, OMI tends mainly to overestimate (underestimate) SSA for the 500 nm (354 nm) products in comparison to AERONET 440 nm (or 443 nm) with a total bias of 0.025 (-0.024), or 2.7% (2.6%) in relative percentage terms with respect to AERONET (mean AERONET value equal to 0.908), and an overall R value of 0.399 (0.386). At 388 nm, OMI tends to retrieve higher SSA over regions where biomass burning occurs, against lower SSA values elsewhere, with overall bias and R values equal to -0.002 (0.22%) and 0.395, respectively.

How to cite: Drakousis, P., Korras-Carraca, M.-B., Jethva, H., Torres, O., and Hatzianastassiou, N.: Global OMI Aerosol Single Scattering Albedo evaluation using ground-based AERONET, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18209, https://doi.org/10.5194/egusphere-egu2020-18209, 2020.

The performance of JAXA Himawari-8 Advanced Himawari Imager (AHI) aerosol optical depth (AOD) products over China is evaluated with ground-based AErosol RObotic NETwork (AERONET) and Sun-Sky Radiometer Observation Network (CARSNET) observations as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products. Considering the quality and quantity of valid data, the study was limited to AOD products from AHI with a Quality Assurance Flag (QA_Flag) of “good” and “very good.” The spatial distribution of the AHI AOD product is similar to that of the MODIS AOD product. The AOD correlation between AHI and MODIS is better in the morning than in the afternoon after March, however, using MODIS AOD as a reference resulted in underestimation in the morning and overestimation in the afternoon. The bias is also larger in spring and autumn than in summer and winter. Validation with sun-photometer observations indicates good correlation between AHI AOD and ground-based observations with correlation coefficients larger than 0.75 (N>1000) when barren and sparsely vegetated surfaces are excluded. At 02:30 UTC, 53% of the collocated AHI AOD observations fall in the expected error (EE) range and at 5:30 UTC, 59.3% fall above the EE. The AHI AOD overestimation was apparent at the Northern China stations in April and after October, whereas the underestimation was apparent in southern China throughout the year. The temporal variations of AHI and AERONET AOD also show that the overestimation occurred in the afternoon and underestimation occurred in the morning.

The assumption that the solar geometries were nearly identical and the surface reflectance unchanged for a month causes the surface reflectance underestimation and leads to the AOD overestimation for barren surfaces in autumn and winter. Because background aerosols were neglected, the surface reflectance was overestimated, leading to AOD underestimation in vegetated surfaces.

Overall, the JAXA AOD provides a reliable and high temporal resolution aerosol product for environmental and climate research and the aerosol retrieval algorithm requires improvement.

How to cite: Gao, L., Li, C., Chen, L., Li, J., and Che, H.: Validation of JAXA Himawari-8 Aerosol Optical Depth Products over China with AERONET and CARSNET Observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18695, https://doi.org/10.5194/egusphere-egu2020-18695, 2020.

How to cite: Kim, M., Cermak, J., Andersen, H., Fuchs, J., and Stirnberg, R.: Intercomparisons of liquid water path based on SEVIRI images and gradient boosting regression trees with in-situ observations and satellite-derived products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18806, https://doi.org/10.5194/egusphere-egu2020-18806, 2020.

Mineral dust has far reaching impact on atmospheric dynamics and the biosphere, altering both the hydrological and the carbon cycle, as well as on human health and the economy. Scattering and absorption effects of dust are enhanced in the terrestrial infrared due to the Si-O resonance bands and thus dust remote sensing with infrared sounders such as IASI (Infrared Atmospheric Sounding Interferometer) is well motivated. This work pertains to current updates on the Infrared Mineral Aerosol Retrieval Scheme (IMARS) on IASI hyperspectral data. IMARS algorithm estimates probabilistically the atmospheric state with respect to desert dust and ice clouds based on simulations of the observed signal for various dust and ice cloud properties.

A preprocessor compresses IASI radiance data in three pseudo-channels exploiting their high redundancy and accounting for the unequal distribution of their information content. From these, four distinct  brightness temperatures differences (BTD) are formed with respect to these channels, which reflect the spectral variation of dust or cloud or surface signal. By varying dust particle size distributions (s), mineralogical compositions (c), infrared optical depths (τ) and layer heights(h) we construct a simulation database constituting 6000 brightness temperature difference sets. The deviation of simulated and observed BTDs by means of a Gaussian metric yields a probability distribution function (PDF), with which the state vector as well as its probability and uncertainty are determined. The calculation of aerosol optical depth (AOD), dust layer temperature, dust effective radius, and other dust properties follows from correspondingly adding contributions (s, c, τ, h) weighted by this PDF. The ice cloud retrieval is realized in the same manner using cloud optical properties from a range of parameterizations found in literature. The distinction between dust and cloud is generally based on quality flagging in terms of the emission temperature relative to the approximated surface temperature and its expected range, and the (dust/cloud) probabilities and uncertainties with stricter criteria for the so-called dust belt. Finally, the IMARS pixel-wise product offers four levels of quality filtering in terms of probabilities, uncertainties, quality flags and information entropy.

Preliminary evaluation of IMARS AOD against AERONET coarse mode AOD obtained by the Spectral Deconvolution Algorithm, is done with data of mild level quality filtering restricted in the dustbelt and using Barnes objective analysis. Results show an overall moderate correlation (and small bias) and a stronger one for focused AERONET stations.

How to cite: Samaras, S. and Popp, T.: A probabilistic approach for the retrieval of mineral dust properties from infrared spaceborne observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19290, https://doi.org/10.5194/egusphere-egu2020-19290, 2020.