Aerosol Optical Thickness Values Research Articles

Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.

Spatial analysis of particulate matter (PM10) using MODIS aerosol optical thickness observations and GIS over East Malaysia

Even though there are expert monitoring and assessment stations in large cities, air quality monitoring and measurement have a high cost and face significant issues. Data on air pollution can be acquired from remote sensing satellites for large areas and at a reasonable expense to compensate for monitoring stations on the ground. This research presented a method for retrieving PM10 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Thickness (AOT) records. The study depends on a previously established equation to retrieve PM10 over Malaysia in September 2014. In Contrast Reduction Technique, we used Aerosol Robotic Network AERONET AOT to define the reference. The conversion factors, representing the relationship between AOT and PM10 satellite columns, were determined using a mathematical approach. The size and type of aerosol, relative humidity, and boundary layer height vary globally and seasonally, thus the AOT–PM10 relationship fluctuates spatially and temporally. The conversion factor was then applied to the MODIS image to predict the surface level of PM10 concentrations in micrograms via cubic meter (μg/m3). Moreover, the achieved correlation coefficient R2 of calculated PM10 based on ground truth data was equal to 0.92. Based on the results obtained from the thematic maps, PM10 levels are significantly higher in specific cities such as Kuala Lumpur and Johor. Where PM10 ranged from (26.5 to 72) µg/m3, while AOT values were between (0.12 and 0.83). It raises concerns about the environmental health threats and their relationship to air quality in these regions as a research topic.

Regional transportation and influence of atmospheric aerosols triggered by Tonga volcanic eruption

A cataclysmic submarine volcano at Hunga Tonga-HungaHa'apai (HTHH) near Tonga, erupted violently on 15 January 2022, which injected a plume of ash cloud soaring into the upper atmosphere. In this study, we examined the regional transportation and potential influence of atmospheric aerosols triggered by HTHH volcano, based on active and passive satellite products, ground-based observations, multi-source reanalysis datasets and atmospheric radiative transfer model. The results indicated that about 0.7 Tg (1 Tg = 109 kg) sulfur dioxide (SO2) gas were emitted into stratosphere from the HTHH volcano, and were lifted to an altitude of 30 km. The regional averaged SO2 columnar content over the western Tonga increased by 10–36 Dobson Units (DU), and the mean aerosol optical thickness (AOT) retrieved from satellite products increased to 0.25–0.34. The stratospheric AOT values caused by HTHH emissions increased to 0.03, 0.20, and 0.23 on 16, 17, and 19 January, respectively, accounting for 1.5%, 21.9%, and 31.1% of total AOT. Ground-based observations also showed an AOT increase of 0.25–0.43, with the maximum daily average of 0.46–0.71 appeared on 17 January. The volcanic aerosols were remarkably dominated by fine-mode particles and posed strong light-scattering and hygroscopic abilities. Consequently, the mean downward surface net shortwave radiative flux was reduced by 2.45–11.9 Wm-2 on different regional scales, and the surface temperature decreased by 0.16–0.42 K. The maximum aerosol extinction coefficient was 0.51 km−1 appeared at 27 km, which resulted in an instantaneous shortwave heating rate of 1.80 Khour−1. These volcanic materials stayed stable in the stratosphere and completed one circle around the earth within 15 days. This would exert a profound influence on the energy budget, water vapor and ozone exchange in the stratosphere, which deserves to be further studied.

An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity.

The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.

The Impact of COVID-19 Lockdowns on Satellite-Observed Aerosol Optical Thickness over the Surrounding Coastal Oceanic Areas of Megacities in the Coastal Zone

Nearly 40 years of aerosol optical thickness (AOT) climate data record (CDR) derived from NOAA operational satellite Advanced Very High Resolution Radiometer (AVHRR) observation over the global oceans is used to study the AOT changes due to the COVID-19 lockdown over the surrounding coastal oceanic areas of 18 megacities in the coast zone (MCCZ). The AOT difference between the annual mean AOT values of 2020 with COVID-19 lockdown and 2019 without the lockdown along with the 2020 AOT annual anomaly are used to effectively identify the AOT changes that are a result of the lockdown. We found that for most of the 18 MCCZ, the COVID-19 lockdowns implemented to contain the spread of the coronavirus resulted in a decrease between 1% and 30% in AOT due to reduced anthropogenic emissions associated with the lockdowns. However, the AOT long-term trend and other aerosol interannual variations due to favorable or unfavorable meteorological conditions may mask AOT changes due to the lockdown effect in some MCCZ. Different seasonal variations of aerosol amount in 2020 relative to 2019 due to other natural aerosol emission sources not influenced by the lockdown, such as dust storms and natural biomass burning and smoke, may also conceal a limited reduction in the annual mean AOT due to the lockdown in MCCZ with relatively loose lockdown. This study indicates that the use of long-term satellite observation is helpful for studying and monitoring the aerosol changes due to the emission reduction associated with the COVID-19 lockdown in the surrounding coastal oceanic areas of MCCZ, which will benefit the future development of the mitigation strategy for air pollution and emissions in megacities.

Influence of atmospheric modeling on spectral target detection through forward modeling approach in multi-platform remote sensing data

Identifying objects or pixels of interest that are few in numbers and sparsely populated in imagery is referred to as target detection. Traditionally, the inverse modeling (IM) approach, usually a slow and computationally intensive process, is used for detecting targets using surface reflectance spectra. For the emerging online methods in remote sensing, modeling the at-sensor radiance of target material, i.e., a forward modeling (FM) approach, can be used. Compared to the IM approach, FM is better suited to online methods due to its potential for adaptation to regional atmospheric modeling. Spectral knowledge transfer of a target from a known to an unknown atmospheric condition is the primary outcome of an efficient target detection framework. However, such an endeavor requires an exhaustive assessment of the target detection process under different atmospheric models and associated uncertainties. The objective of this work is to assess the quantitative impact of atmospheric parameters on the detectability of engineered targets. Specifically, the impact of critical atmospheric parameters such as aerosol optical thickness (AOT), standard atmospheric profiles, and aerosol models are considered. For this effect, we designed a multi-platform image acquisition setup that acquired targets concurrently using a ground-based terrestrial hyperspectral imager (THI), an airborne hyperspectral imager (AVIRIS-NG), and a space-borne multispectral imager (Sentinel-2). We used a point-based spectroradiometer and pixel-based THI to collect the in-situ reference target reflectance spectra and generated a radiance spectral library by simulating TOA radiance spectra using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model. We have considered two cases of target radiance simulations, i.e., (i) corresponding to a grid of different AOT values for a predefined atmospheric and aerosol profile, and (ii) corresponding to varying combinations of atmospheric and aerosol profiles at a given AOT. The detection has been carried out using multiple target detection algorithms. Results indicate that the spectral knowledge-based targets can be detected in remote sensing data under different atmospheric model scenarios using the FM approach. A detection rate of about 75% and 50% have been consistently obtained for remote sensing data from airborne and space-borne platforms with a false alarm (FA) rate of 10-2 to 10-3 respectively. Change in the AOT across atmospheric models has resulted in decision-changing implications in the target detection modeling. The selection of the wrong atmospheric profile can potentially aggravate the number of FAs produced by a particular detection algorithm.

ИЗМЕНЧИВОСТЬ АЭРОЗОЛЬНОЙ ОПТИЧЕСКОЙ ТОЛЩИНЫ В ГОРНЫХ И ПРЕДГОРНЫХ РАЙОНАХ СЕВЕРНОЙ ОСЕТИИ ПО ДАННЫМ СПУТНИКОВЫХ ИЗМЕРЕНИЙ

Atmospheric aerosol is one of the indicators of air quality that affects the environmental situation. The aerosol optical thickness (AOT) of the atmosphere is studied in the mountainous, foothill, and plain regions of Republic of North Ossetia-Alania (Russian Federation) based on satellite data EOS Terra, Aqua. Several geographical locations at different heights were selected for the analysis. Daily series of AOT, air temperature and precipitation data were obtained on period over 20 years for each point. The results of statistical analysis of long-term values of AOT, temperature and precipitation are shown. There is a statistically significant relationship between the averaged values, and there is also a dependence on the height of the location and the proximity of mountains. Long-term average dependencies provide a basis for predicting the AOT value based on the measured temperature at height of 2 m for territories located in a complex landscape at different altitudes.

Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

Retrospective assessment of pregnancy exposure to particulate matter from desert dust on a Caribbean island: could satellite-based aerosol optical thickness be used as an alternative to ground PM10 concentration?

Desert dust transported from the Saharan-Sahel region to the Caribbean Sea is responsible for peak exposures of particulate matter (PM). This study explored the potential added value of satellite aerosol optical thickness (AOT) measurements, compared to the PM concentration at ground level, to retrospectively assess exposure during pregnancy. MAIAC MODIS AOT retrievals in blue band (AOT470) were extracted for the French Guadeloupe archipelago. AOT470 values and PM10 concentrations were averaged over pregnancy for 906 women (2005-2008). Regression modeling was used to examine the AOT470-PM10 relationship during pregnancy and test the association between dust exposure estimates and preterm birth. Moderate agreement was shown between mean AOT470 retrievals and PM10 ground-based measurements during pregnancy (R2 = 0.289). The magnitude of the association between desert dust exposure and preterm birth tended to be lower using the satellite method compared to the monitor method. The latter remains an acceptable trade-off between epidemiological relevance and exposure misclassification, in areas with few monitoring stations and complex topographical/meteorological conditions, such as tropical islands.

Variations and tendencies of cloud, aerosol and solar radiation in the past decades over East of the Asian Part of Russia based on surface and satellite data records

The results of studies of atmospheric aerosol over the territory of Russia are of great interest from the point of view of environmental and climatic problems. The article presents the results of the spatial-temporal variability of aerosol optical thickness (AOT) over the Asian Part (AP) of the Russian Federation in recent decades. The Database (DB) “Aerosol optical thickness of the atmosphere from satellite and ground-based observations” created by the authors was used. The presented trends in AOT changes (annual values, summer values) and statistical estimates of real empirical series show that there is a good agreement between the annual values of AOT for satellite and ground-based values, for monthly values the discrepancy has more value. Based on observational and reanalysis data, the interannual variability of the average annual and average summer values of the total radiation flux coming to the surface was studied. The empirical typification of interannual changes in the intra-annual course of the cloud cover values (%) over the considered time interval was carried out. The analysis was performed for the Tiksi Arctic station and for the Yakutsk, Ussuriysk, Irkutsk and Yekaterinburg stations located in the AP of the Russian Federation.

The spatio-temporal characteristics of aerosol optical thickness as well as the relationship with PM2.5 in Xiamen city, China

Long-term aerosol optical thickness (AOT) composited data (2002-2017) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft was used to evaluate the temporal and spatial variability of aerosol in Xiamen city by using wavelet analysis, and the relationship between the surface mass concentrations of particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) and the AOT was analyzed by using linear regression. The results showed that AOT increased gradually from 2002 to 2011, and then decreased. AOT displayed a significant 9-month periodicity in AOT was inferred wavelet analysis. AOT also showed significant annual variability in response to changes in weather and aerosol pollution. We observed highest AOT values in April, with a monthly mean of 1.00 ± 0.18. Lowest values were observed in December, with a mean AOT of 0.52 ± 0.11. Multi-year monthly AOT fluctuations were lowest in January with a low variation coefficient (0.14), and the largest fluctuations appeared in July with a high variation coefficient (0.29). Higher AOT values (~ 1.1) were predominantly located in the southern urban areas of Xiamen and lower AOT values (~ 0.3) were mainly located in northern rural regions. The aerosol pollution was serious in April with the smallest spatial variation coefficient of 0.25, and the highest spatial variation coefficient appeared in July. Highest intraannual variability predominantly occurred in the high-value areas in the center of Xiamen. AOT values remained high in Xiamen Island throughout the year with a multi-year mean of 0.87. There was a moderate correlation between ground-based PM2.5 and MODIS AOT. Therefore, we confirm the suitability of MODIS AOT to accurately estimate PM2.5 concentration and evaluate the temporal and spatial characteristics of air quality in Xiamen.

Retrieval of aerosol optical thickness and surface parameters based on multi-spectral and multi-viewing space-borne measurements

We present a novel approach to derive Aerosol Optical Thickness (AOT) at 0.5 µm and the surface reflectance for five spectral channels at native spatial resolution from the measurements of the Polarization and Directionality of Earth’s Reflectances-3 (POLDER) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. POLDER had multi-spectral and multi-viewing capabilities. This publication describes the first step in the design of a high-quality AOT and surface property retrieval algorithm, enabling the global evaluation of future missions providing multi-spectral and multi-viewing space-borne measurements. The developed retrieval approach is based on the radiative transfer and retrieval model SCIATRAN using an analytical linearized retrieval mode. The surface is parametrized according to the Ross-Li model and the aerosol typing was using prescribed types based on the approach by Levy et al. [26]. The minimization using SCIATRAN has been done by solving the quadratic programming problem which minimizes the considered system of equations (typically) based on linear constraints. In this study we constrained the retrieval so far only by assuming that the retrieved AOT at 0.5 µm is larger than 0.01 and smaller than 1.5. Until now, the retrieval is based only upon unpolarized POLDER data and shows promisingly weak dependence on a priori information.As a first validation, the retrieved AOT values have been compared with ground based measurements over Atmospheric Radiation Measurement Climate Research Facilities (ARM) in the Southern Great Plains (SGP). The SCIATRAN retrievals are mostly homogeneous and consistent in the region around the US ARM/SGP measurement stations. The retrieval results agree well with the ARM ground based measurements of AOT. The correlation coefficient was R=0.84and part of the remaining differences between ARM measurements and SCIATRAN retrievals can be attributed to unmasked clouds. Histogram based analysis of the AOT values showed reasonable distributions using SCIATRAN. Most distributions based on SCIATRAN AOT retrievals within a radius of 25 km around the stations are significantly broader than those derived from ARM measurements during 30 minutes but are well centered around the ARM AOT distributions.

Issledovaniye izmenchivosti opticheskikh i mikrofizicheskikh kharakteristik aerozoley nad Chernym morem pod vozdeystviyem pozharov Prichernomor'ya za 2018–2019 gody

Purpose. The Black Sea region where the forest fires were recorded by the FIRMS system, as well as the atmosphere above it, namely the fire-induced variation of the atmospheric aerosol basic optical characteristics, were the main objects of the investigation. The study is aimed at examining the fires in the Black Sea region in 2018–2019 for assessing correlation between these events and variability of the basic optical characteristics over the Black Sea. Methods and Results. Based on the FIRMS system data, variations of intensity of the fire-induced radiation were studied. The results of statistical processing of the MODIS and VIIRS satellite data on the fires in 2018–2019 were represented. For the dates when the fire numbers were the highest in the Black Sea region, the basic optical and microphysical characteristics of the atmospheric aerosol were analyzed due to the SPM and AERONET data. The dates when the fire intensity was particularly high (based on the MODIS and VIIRS data) were analyzed and compared with the dates when the anomalous values of the atmospheric aerosol optical characteristics were recorded over the region under study. Conclusions. For the fire events in the Black Sea region revealed due to the MODIS and VIIRS data, complex analysis of the air mass transfer was performed by the model HYSPLIT, and the aerosol was typed by the CALIPSO algorithm. On June 22, 2019 the most intense fires were recorded. According to the aerosol typing by the CALIPSO algorithm, on this day the predominant aerosol types were the contaminated dust and smoke. Using the MODIS and VIIRS data, investigation of possible source of the aerosol transfer on this date showed that the area of intense inflammationn and smoke was located to the northeast from the Black Sea region. Since the satellite-derived data on this day showed no dust transfer either from the Sahara or the Syria deserts, it is possible to conclude that increase of the values of aerosol optical thickness АОD (500) was conditioned by transfer of the aerosol resulted from biomass burning from the north to the Black Sea region.

Primary Evaluation of the GCOM-C Aerosol Products at 380 nm Using Ground-Based Sky Radiometer Observations

The Global Change Observation Mission-Climate (GCOM-C) is currently the only satellite sensor providing aerosol optical thickness (AOT) in the ultraviolet (UV) region during the morning overpass time. The observations in the UV region are important to detect the presence of absorbing aerosols in the atmosphere. The recently available GCOM-C dataset of AOT at 380 nm for January to September 2019 were evaluated using ground-based SKYNET sky radiometer measurements at Chiba, Japan (35.62° N, 140.10° E) and Phimai, central Thailand (15.18° N, 102.56° E), representing urban and rural sites, respectively. AOT retrieved from sky radiometer observations in Chiba and Phimai was compared with coincident AERONET and multi-axis differential optical absorption spectroscopy (MAX-DOAS) AOT values, respectively. Under clear sky conditions, the datasets showed good agreement. The sky radiometer and GCOM-C AOT values showed a positive correlation (R) of ~0.73 for both sites, and agreement between the datasets was mostly within ±0.2 (the number of coincident points at both sites was less than 50 for the coincidence criterion of ≤30 km). At Chiba, greater differences in the AOT values were primarily related to cloud screening in the datasets. The mean bias error (MBE) (GCOM-C – sky radiometer) for the Chiba site was −0.02 for a coincidence criterion of ≤10 km. For a similar coincidence criterion, the MBE values were higher for observations at the Phimai site. This difference was potentially related to the strong influence of biomass burning during the dry season (Jan–Apr). The diurnal variations in AOT, inferred from the combination of GCOM-C and ozone monitoring instrument (OMI) observations, showed good agreement with the sky radiometer data, despite the differences in the absolute AOT values. Over Phimai, the AOT diurnal variations from the satellite and sky radiometer observations were different, likely due to the large differences in the AOT values during the dry season.

Characteristics of Spatial and Temporal Evolution and Investigation of Air Pollution in Guangdong-Hong Kong-Macao Greater Bay Area Based on Ground-Space Observation Data

The temporal and spatial distribution characteristics, evolution trend and potential climatic effects of air pollution in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) were analyzed on different time scales and spatial spaces, based on ground environment observation data from June 2014 to December 2018 and satellite remote sensing inversion products from 2000 to 2018. The results show that:① From the in-situ observed daily average concentration of PM2.5, good or mild to moderate pollution occurred in January, February, October, November, and December every year, and the rest of the time was excellent. ② Based on the annual average PM2.5 concentration obtained by satellite for the past 20 years, the spatial characteristics showed that the external radiation is centered on Guangzhou and Foshan. The time evolution shows the characteristics of an Ω shape, which increases gradually from 2000 to 2009, is highest in 2008, and then gradually decreases. ③ The monthly average aerosol optical thickness (AOT) value from the Multi-angle Imaging Spectro Radiometer satellite reversion every 10 years for a period (2000-2009 for a period, 2010-2018 for a period) was used to see the monthly variation. The monthly average AOT value in the first period was larger than that in the second period of the same month, the maximum value was in March and April, and the minimum value was in November and December. It is envisaged to draw a line along the north-south direction of the Pearl River Port, which basically shows that the AOT value in the west is greater than that in the east. ④ According to the observed daily average concentration data of O3-8h, the main concentration level of O3-8h in the GBA is excellent. The cities with good ozone concentration were most numerous in 2014, with five cities, and least in 2018, with only one city. The highest ozone concentration was in September, followed by June and November, and then May and July. In the past 20 years, the spatial distribution of the average concentration of O3 monitored by satellite remote sensing showed a characteristic Ω shape, increasing initially and then decreasing. The maximum value was in May, and the north-south boundary line appeared in space. ⑤ There is a good linear relationship between the interannual variation of monthly mean temperature and radiation, whereas the relationship between AOT and radiation cannot be described by a simple linear relationship.

CAPAS DE AEROSOLES DE ORIGEN AUSTRALIANO SOBRE TERRITORIOARGENTINO DURANTE NOVIEMBRE 2019

On November 8, 2019 a dense feather of smoke was detached from the coasts of Australia due to the intense fires that affected the region. These layers of smoke were dragged by the winds to South America, arriving to the Argentine territory on November 14 and remaining in suspension for a day. In this work, the optical properties of suspended aerosols with satellite information and measurements from terrestrial are analyzed. The Angstrom coefficient is calculated and the type of aerosols present is classified. High values of aerosol optical thickness of 0,25 on average for 440 nm and Angstrom coefficients of 1,2 in the evidence the presence of smoke in the local atmosphere.

Sensitivity analysis of an XCO2 retrieval algorithm for high-resolution short-wave infrared spectra

A sensitivity analysis, based on the Greenhouse-gases Monitoring Instrument (GMI) on Chinese Gaofen 5 satellite, is described considering four input parameters that affect the retrievals of the dry air column-averaged mole fraction of CO2 (XCO2). The analysis is based on synthetic spectra (centered around 0.765 and 1.575 μm) produced by SCIATRAN. The input parameters considered include the profiles of temperature and pressure, as well as the surface albedo and aerosols. The main findings are temperature errors of 1 K and pressure errors of 2 hPa lead to XCO2 retrieval errors of less than 0.25%. The uncertainties in albedo (ranging from 0.03 to 1.0) or surface types lead to less than 2.0% retrieval error. Considering three aerosol types (urban, rural and maritime), we investigated the retrieval errors in XCO2 that were induced by incorrect assumptions on the aerosol optical thickness (AOT). For AOT values ranging from 0.02 to 1.0, the retrieval errors are less than 6.0% if aerosols are completely neglected in the retrieval. The presented results provide a basis for rational utilization of the a priori database and are conducive to increasing the inversion accuracy.

CLASIFICACIÓN DE LOS AEROSOLES EN LA ESTACIÓN AERONET DE TUCUMÁN,ARGENTINA

On November 15th, 2017, a new sunphotometer was installed for sensing and studying aerosols at National Meteo-rological Service headquarters in the City of Tucumán (26,787oS; 65,207oO), and was integrated to the AERONET/ NASA network. The north-central region of the country is mostly affected by both local and transported biomassburning events. In this work, a statistical study is carried out with the first available measurements done betweenNovember 2017 and December 2018. This analysis shows the averaged values of the aerosol optical properties, theAngstrom coefficient and the aerosol type. In addition, an aerosol transport event during the month of September 2018is studied, highlighting high values of aerosol optical thickness of 0.45 at 440 nm and an Angstrom coefficient of 1.95,indicating the presence of smoke in the local atmosphere.

Preliminary comparison of the direct aerosol radiative forcing over Ukraine and Antarctic AERONET sites

Objectives. To analyze data on aerosol optical thickness (AOT) in the atmosphere over some Ukraine and Antarctic AERONET (AErosol RObotic NETwork) sites. To determine and compare direct aerosol radiative forcing (DRF) typical values using the data from midlatitude and Antarctic AERONET sites. Methods. Retrieval and visualization of the AERONET aerosol optical thickness and radiative forcing, data analysis and interpretation of the data. Radiative forcing evaluation using Global Atmospheric ModEl (GAME) and the AERONET operational product. Results. Aerosol optical thickness measurements are considered using observations by sun/sky photometers that are part of the AERONET sites in Ukraine (Kyiv) and at two additional sites in Antarctica (Vechernaya Hill and ARM_McMurdo sites). According to the 2015–2018 measurements at the Vechernaya Hill and ARM_McMurdo sites, the AOT values are small and are in the range of 0.05–0.1 at the 340 nm wavelength. In contrast, the corresponding AOT values from Kyiv observational site are reached 0.3–0.5 and sometimes higher. Using these AOT values from Kyiv site and the urban aerosol types, the aerosol direct radiative forcing has been evaluated by the GAME code. The top of atmosphere (TOA) DRF assessment using GAME suggests the instantaneous values of –5.7 W m–2 over vegetation surface for AOT equal 0.1. Conclusions. The AERONET derived aerosol optical thicknesses over Kyiv site show the mean value of 0.3 at 340 nm; the values over two Antarctic sites are in range of 0.03 to 0.06. Calculations using a numerical code (GAME) suggested the associated TOA instantaneous DRF of –6 W m–2 to –14 W m–2 over the Kyiv site. The values calculated as part of the AERONET Kyiv site operational product are about –20 W m–2 (BOA) and about –10 W m–2 (TOA) during 2018.

Extending XBAER Algorithm to Aerosol and Cloud Condition

The retrieval of cloud optical properties for aerosol contaminated water cloud is challenging because of the complexity of physical processes in such situations. Conventionally, cloud optical data products are typically derived, ignoring the aerosol impacts on radiative transfer in the retrieval process. This is potentially a significant source of error. In this paper, the eXtensible Bremen Aerosol Retrieval (XBAER) algorithm has been optimized for the retrieval of aerosol/cloud properties for the aerosol contaminated cloud (ACC) scenarios. This version of XBAER delivers cloud optical thickness (COT) and cloud effective radius (CER) for ACCs and simultaneously retrieves aerosol optical thickness (AOT). The surface parameterization and aerosol types used in the standard XBAER algorithm have been adapted in this retrieval to account for the ACC conditions. Aerosol types in XBAER for the retrieval of ACC scenarios have been parameterized to comprise weak and strong absorptions. The comparisons of COT, CER, and AOT retrieved using this adapted XBAER algorithm and two new NASA algorithms show good agreements, especially for biomass burning aerosol. The correlation coefficients are >0.9 for AOT, ~0.8 for CER, and ~0.7 for COT. There is also a good agreement for dust plume contaminated cloud scene. The XBAER derived AOT values for dust aerosols are systematically smaller than the NASA retrieval, but both products have the same spatial distribution patterns. The comparison of COT retrieved using the adapted XBAER algorithm and that retrieved from ground-based microwave radiometer (MWR) measurements shows much better agreement for ACC conditions with high AOT.