scholarly journals Retrieving global aerosol sources from satellites using inverse modeling

2008 ◽  
Vol 8 (2) ◽  
pp. 209-250 ◽  
Author(s):  
O. Dubovik ◽  
T. Lapyonok ◽  
Y. J. Kaufman ◽  
M. Chin ◽  
P. Ginoux ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
A Priori ◽  
Time Variability ◽  
Aerosol Transport ◽  
Space And Time ◽  
Aerosol Emission ◽  
Coarse Mode ◽  
Aerosol Distribution ◽  
Aerosol Sources

Abstract. Understanding aerosol effects on global climate requires knowing the global distribution of tropospheric aerosols. By accounting for aerosol sources, transports, and removal processes, chemical transport models simulate the global aerosol distribution using archived meteorological fields. We develop an algorithm for retrieving global aerosol sources from satellite observations of aerosol distribution by inverting the GOCART aerosol transport model. The inversion is based on a generalized, multi-term least-squares-type fitting, allowing flexible selection and refinement of a priori algorithm constraints. For example, limitations can be placed on retrieved quantity partial derivatives, to constrain global aerosol emission space and time variability in the results. Similarities and differences between commonly used inverse modeling and remote sensing techniques are analyzed. To retain the high space and time resolution of long-period, global observational records, the algorithm is expressed using adjoint operators. Successful global aerosol emission retrievals at 2°×2.5 resolution were obtained by inverting GOCART aerosol transport model output, assuming constant emissions over the diurnal cycle, and neglecting aerosol compositional differences. In addition, fine and coarse mode aerosol emission sources were inverted separately from MODIS fine and coarse mode aerosol optical thickness data, respectively. These assumptions are justified, based on observational coverage and accuracy limitations, producing valuable aerosol source locations and emission strengths. From two weeks of daily MODIS observations during August 2000, the global placement of fine mode aerosol sources agreed with available independent knowledge, even though the inverse method did not use any a priori information about aerosol sources, and was initialized with a "zero aerosol emission" assumption. Retrieving coarse mode aerosol emissions was less successful, mainly because MODIS aerosol data over highly reflecting desert dust sources is lacking. The broader implications of applying our approach are also discussed.

scholarly journals Retrieving global sources of aerosols from MODIS observations by inverting GOCART model

2007 ◽  
Vol 7 (2) ◽  
pp. 3629-3718 ◽  
Author(s):  
O. Dubovik ◽  
T. Lapyonok ◽  
Y. J. Kaufman ◽  
M. Chin ◽  
P. Ginoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Thickness ◽  
Transport Model ◽  
A Priori ◽  
Inversion Algorithm ◽  
Numerical Tests ◽  
Aerosol Emission ◽  
Coarse Mode ◽  
Aerosol Sources ◽  
Aerosol Emissions

Abstract. Knowledge of the global distribution of tropospheric aerosols is important for studying the effects of aerosols on global climate. Chemical transport models rely on archived meteorological fields, accounting for aerosol sources, transport and removal processes can simulate the global distribution of atmospheric aerosols. However, the accuracy of global aerosol modeling is limited. Uncertainty in location and strength of aerosol emission sources is a major factor in limiting modeling accuracy. This paper describes an effort to develop an algorithm for retrieving global sources of aerosol from satellite observations by inverting the GOCART aerosol transport model. To optimize inversion algorithm performance, the inversion was formulated as a generalized multi-term least-squares-type fitting. This concept uses the principles of statistical optimization and unites diverse retrieval techniques into a single flexible inversion procedure. It is particularly useful for choosing and refining a priori constraints in the retrieval algorithm. For example, it is demonstrated that a priori limitations on the partial derivatives of retrieved characteristics, which are widely used in atmospheric remote sensing, can also be useful in inverse modeling for constraining time and space variability of the retrieved global aerosol emissions. The similarities and differences with the standard "Kalman filter" inverse modeling approach and the "Phillips-Tikhonov-Twomey" constrained inversion widely used in remote sensing are discussed. In order to retain the originally high space and time resolution of the global model in the inversion of a long record of observations, the algorithm was expressed using adjoint operators in a form convenient for practical development of the inversion from codes implementing forward model simulations. The inversion algorithm was implemented using the GOCART aerosol transport model. The numerical tests we conducted showed successful retrievals of global aerosol emissions with a 2°×2.5° resolution by inverting the GOCART output. For achieving satisfactory retrieval from satellite sensors such as MODIS, the emissions were assumed constant within the 24 h diurnal cycle and aerosol differences in chemical composition were neglected. Such additional assumptions were needed to constrain the inversion due to limitations of satellite temporal coverage and sensitivity to aerosol parameters. As a result, the algorithm was defined for the retrieval of emission sources of fine and coarse mode aerosols from the MODIS fine and coarse mode aerosol optical thickness data respectively. Numerical tests showed that such assumptions are justifiable, taking into account the accuracy of the model and observations and that it provides valuable retrievals of the location and the strength of the aerosol emissions. The algorithm was applied to MODIS observations during two weeks in August 2000. The global placement of fine mode aerosol sources retrieved from inverting MODIS observations was coherent with available independent knowledge. This was particularly encouraging since the inverse method did not use any a priori information about the sources and it was initialized under a "zero aerosol emission" assumption. The retrieval reproduced the instantaneous global MODIS observations with a standard deviation in fitting of aerosol optical thickness of ~0.04. The optical thickness during high aerosol loading events was reproduced with a standard deviation of ~48%. Applications of the algorithm for the retrieval of coarse mode aerosol emissions were less successful, mainly due to the currently existing lack of MODIS data over high reflectance desert dust sources. Possibilities for enhancing the global satellite data inversion by using diverse a priori constraints on the retrieval are demonstrated. The potential and limitations of applying our approach for the retrieval of global aerosol sources from aerosol remote sensing are discussed.

scholarly journals The zonal structure of tropical O<sub>3</sub> and CO as observed by the Tropospheric Emission Spectrometer in November 2004 – Part 1: Inverse modeling of CO emissions

2009 ◽  
Vol 9 (11) ◽  
pp. 3547-3562 ◽  
Author(s):  
D. B. A. Jones ◽  
K. W. Bowman ◽  
J. A. Logan ◽  
C. L. Heald ◽  
J. Liu ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
A Priori ◽  
Systematic Errors ◽  
A Posteriori ◽  
Chemical Transport Model ◽  
Australian Region ◽  
Equatorial Africa ◽  
Modeling Analysis ◽  
The Tropics

Abstract. We conduct an inverse modeling analysis of measurements of atmospheric CO from the TES and MOPITT satellite instruments using the GEOS-Chem global chemical transport model to quantify emissions of CO in the tropics in November 2004. We also assess the consistency of the information provided by TES and MOPITT on surface emissions of CO. We focus on the tropics in November 2004, during the biomass burning season, because TES observations of CO and O3 and MOPITT observations of CO reveal significantly greater abundances of these gases than simulated by the GEOS-Chem model during that period. We find that both datasets suggest substantially greater emissions of CO from sub-equatorial Africa and the Indonesian/Australian region than in the climatological emissions in the model. The a posteriori emissions from sub-equatorial Africa based on TES and MOPITT data were 173 Tg CO/yr and 184 Tg CO/yr, respectively, compared to the a priori of 95 Tg CO/yr. In the Indonesian/Australian region, the a posteriori emissions inferred from TES and MOPITT data were 155 Tg CO/yr and 185 Tg CO/yr, respectively, whereas the a priori was 69 Tg CO/yr. The differences between the a posteriori emission estimates obtained from the two datasets are generally less than 20%. The a posteriori emissions significantly improve the simulated distribution of CO, however, large regional residuals remain, and are likely due to systematic errors in the analysis. Reducing these residuals and improving the accuracy of top-down emission estimates will require better characterization of systematic errors in the observations and the model (chemistry and transport).

Time-series of GhG emissions estimation for the learning of inverse modeling

2021 ◽  
Author(s):  
Carlos Gómez-Ortiz ◽  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Time Series ◽  
Inverse Modeling ◽  
Fossil Fuel ◽  
Transport Model ◽  
A Priori ◽  
Ghg Emissions ◽  
Atmospheric Concentration ◽  
Training Tool ◽  
Sources And Sinks ◽  
Atmospheric Inversions

&lt;p&gt;Inverse modeling is a commonly used method to infer greenhouse gases (GhGs) sources and sinks based on their observed concentrations in the atmosphere. It is a Bayesian framework and requires a priori fluxes of all the evaluated sources and sinks, atmospheric observations, an atmospheric transport model that relates the observations to the a priori fluxes and the uncertainties of both fluxes and observations. Various techniques exist to solve the inversion.&lt;/p&gt;&lt;p&gt;Atmospheric inverse modeling is and will become even more important in the future quantification of GhGs to monitor the compliance of the Nationally Determined Contributions (NDCs) under the Paris Agreement. Therefore, the scientific and educational communities are becoming more interested in using atmospheric inversions and this has risen a necessity of creating tools that facilitate understanding as well as training in these techniques.&lt;/p&gt;&lt;p&gt;Quantifying anthropogenic GhG emissions, such as CO&lt;sub&gt;2&lt;/sub&gt; from fossil fuel burning or CH&lt;sub&gt;4 &lt;/sub&gt;from human activities, from atmospheric concentration observations is difficult since the carbon from all sources, both natural and anthropogenic, is mixed in the atmosphere, making it necessary to use other signals or tracers to separate anthropogenic emissions from natural sources. For fossil fuel CO&lt;sub&gt;2&lt;/sub&gt; emissions radiocarbon (&lt;sup&gt;14&lt;/sup&gt;CO&lt;sub&gt;2&lt;/sub&gt;) is an excellent emission tracer because, due to its radioactive decay (~ 5000 years), it cannot be found in fossil fuels, which have been deposited millions of years ago as organic material. We have developed a Jupyter Notebook based on Python for the quantification of multi-tracer GhGs fossil fuel emissions and its isotopes. The notebook solves for the emissions by applying atmospheric inversions within a practical two-box model. The inverse modeling notebook is based on the analytical maximum a posteriori (MAP) solution of the Bayes&amp;#8217; theorem and allows to assess the error in the state vector and its uncertainty.&lt;/p&gt;&lt;p&gt;This basic but powerful notebook is meant to be an educational and training tool for university students and new researchers in the field as well as for researchers interested in the estimation of long-term (&gt;centennial) time-series of GhG emissions since it is built as a modular algorithm to be easily modified, coupled or expanded to other approaches or models depending on the application. The notebook was initially developed for the inverse modeling of CO&lt;sub&gt;2&lt;/sub&gt; and &lt;sup&gt;14&lt;/sup&gt;CO&lt;sub&gt;2&lt;/sub&gt; simultaneously and it is being expanded for additional GHG such as CH&lt;sub&gt;4&lt;/sub&gt; and &lt;sup&gt;13&lt;/sup&gt;CH&lt;sub&gt;4&lt;/sub&gt;.&lt;/p&gt;

scholarly journals Inversion analysis of carbon monoxide emissions using data from the TES and MOPITT satellite instruments

2007 ◽  
Vol 7 (6) ◽  
pp. 17625-17662 ◽  
Author(s):  
D. B. A. Jones ◽  
K. W. Bowman ◽  
J. A. Logan ◽  
C. L. Heald ◽  
J. Liu ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
A Priori ◽  
Systematic Errors ◽  
A Posteriori ◽  
Top Down ◽  
Australian Region ◽  
Temporal Sampling ◽  
Equatorial Africa ◽  
Satellite Instruments

Abstract. We conduct an inverse modeling analysis of measurements of atmospheric CO from the TES and MOPITT satellite instruments using the GEOS-Chem global chemical transport model. This is the first quantitative analysis of the consistency of the information provided by these two instruments on surface emissions of CO in an inverse modeling context. We focus on observations of CO for November 2004, when the climatological emission inventory in the GEOS-Chem model significantly underestimated the atmospheric abundance of CO as observed by TES and MOPITT. We find that both datasets suggest significantly greater emissions of CO from sub-equatorial Africa and the Indonesian/Australian region. The a posteriori emissions from sub-equatorial Africa based on TES and MOPITT data were 173 Tg CO/yr and 184 Tg CO/yr, respectively, compared to the a priori of 95 Tg CO/yr. In the Indonesian/Australian region, the a posteriori emissions inferred from TES and MOPITT data were 155 Tg CO/yr and 185 Tg CO/yr, respectively, whereas the a priori was 69 Tg CO/yr. The differences between the a posteriori emission estimates obtained from the two datasets are generally less than 20%, and are likely due to the different spatio-temporal sampling of the measurements. The a posteriori emissions significantly improve the simulated distribution of CO, however, large regional residuals remain, reflecting systematic errors in the analysis. For example, the a posteriori emissions obtained from both datasets do not completely reduce the underestimate in the model of CO column abundances over the southern tropical Atlantic, southern Africa, and over the Indian Ocean, where biases of 3–7% remain. Over eastern Asia the a posteriori emissions overestimate the CO column abundances by about 3–6%. These residuals reflect the sensitivity of the top-down source estimates to systematic errors in the analysis. Our results indicate that improving the accuracy of top-down emission estimates will require further characterization of model biases (chemical and transport) and the use of spatial-temporal inversion resolutions consistent with the information content of the observations.

scholarly journals Changes in Power Plant NOx Emissions over Northwest Greece Using a Data Assimilation Technique

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 900
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Arjo Segers ◽  
Astrid Manders ◽  
Dimitris Balis ◽  
...  
Keyword(s):  
Power Plant ◽  
Data Assimilation ◽  
Energy Production ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Filter Method ◽  
Nox Emissions ◽  
A Posteriori ◽  
Data Assimilation Technique

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over Northwestern Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on the year 2015 is used as the a priori emissions in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. Relative to the a priori emissions, the assimilation suggests a strong decrease in concentrations for the station located near the largest power plant, by 80% in 2019 and by 67% in 2018. Concerning the estimated annual a posteriori NOx emissions, it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40–50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (−35% and −38% in 2018, −62% and −72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about −35% and−63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~−30% and −70%, respectively).

Ursprünge transzendentaler Ästhetik. Zum Wandel von Kants Raum- und Zeitargumentation von derInauguraldissertation zurKritik

Kant-Studien ◽  
2020 ◽  
Vol 111 (3) ◽  
pp. 331-385
Author(s):  
Christian Martin
Keyword(s):  
Present Article ◽  
A Priori ◽  
Space And Time ◽  
Representations Of Space ◽  
Widespread View

AbstractAccording to a widespread view, the essentials of Kant’s critical conception of space and time as set forth in the Transcendental Aesthetic can already be found in his 1770 Inaugural Dissertation. Contrary to this assumption, the present article shows that Kant’s later arguments for the a priori intuitive character of our original representations of space and time differ crucially from those contained in the Dissertation. This article highlights profound differences between Kant’s transcendental and his pre-critical conception of pure sensibility by systematically comparing the topic, method and argumentation of the First Critique with that of the Inaugural Dissertation. It thus contributes to a better understanding of the Transcendental Aesthetics itself, which allows one to distinguish its peculiar transcendental mode of argumentation from considerations made by the pre-critical Kant, with which it can easily be conflated.

scholarly journals Accelerating simulations using REDCHEM_v0.0 for atmospheric chemistry mechanism reduction

2018 ◽  
Vol 11 (8) ◽  
pp. 3391-3407 ◽  
Author(s):  
Zacharias Marinou Nikolaou ◽  
Jyh-Yuan Chen ◽  
Yiannis Proestos ◽  
Jos Lelieveld ◽  
Rolf Sander
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
A Priori ◽  
Chemical Mechanism ◽  
Climate Modelling ◽  
Mechanism Reduction ◽  
Computational Speed ◽  
Speed Up ◽  
Spatio Temporal ◽  
Skeletal Mechanism

Abstract. Chemical mechanism reduction is common practice in combustion research for accelerating numerical simulations; however, there have been limited applications of this practice in atmospheric chemistry. In this study, we employ a powerful reduction method in order to produce a skeletal mechanism of an atmospheric chemistry code that is commonly used in air quality and climate modelling. The skeletal mechanism is developed using input data from a model scenario. Its performance is then evaluated both a priori against the model scenario results and a posteriori by implementing the skeletal mechanism in a chemistry transport model, namely the Weather Research and Forecasting code with Chemistry. Preliminary results, indicate a substantial increase in computational speed-up for both cases, with a minimal loss of accuracy with regards to the simulated spatio-temporal mixing ratio of the target species, which was selected to be ozone.

scholarly journals Validation of nine years of MOPITT V5 NIR using MOZAIC/IAGOS measurements: biases and long-term stability

2014 ◽  
Vol 7 (11) ◽  
pp. 3783-3799 ◽  
Author(s):  
A. T. J. de Laat ◽  
I. Aben ◽  
M. Deeter ◽  
P. Nédélec ◽  
H. Eskes ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
A Priori ◽  
Validation Data ◽  
Chemistry Transport Model ◽  
Large Variability ◽  
Long Term Stability ◽  
Spatio Temporal ◽  
Good Agreement

Abstract. Validation results from a comparison between Measurement Of Pollution In The Troposphere (MOPITT) V5 Near InfraRed (NIR) carbon monoxide (CO) total column measurements and Measurement of Ozone and Water Vapour on Airbus in-service Aircraft (MOZAIC)/In-Service Aircraft for a Global Observing System (IAGOS) aircraft measurements are presented. A good agreement is found between MOPITT and MOZAIC/IAGOS measurements, consistent with results from earlier studies using different validation data and despite large variability in MOPITT CO total columns along the spatial footprint of the MOZAIC/IAGOS measurements. Validation results improve when taking the large spatial footprint of the MOZAIC/IAGOS data into account. No statistically significant drift was detected in the validation results over the period 2002–2010 at global, continental and local (airport) scales. Furthermore, for those situations where MOZAIC/IAGOS measurements differed from the MOPITT a priori, the MOPITT measurements clearly outperformed the MOPITT a priori data, indicating that MOPITT NIR retrievals add value to the MOPITT a priori. Results from a high spatial resolution simulation of the chemistry-transport model MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) showed that the most likely explanation for the large MOPITT variability along the MOZAIC-IAGOS profile flight path is related to spatio-temporal CO variability, which should be kept in mind when using MOZAIC/IAGOS profile measurements for validating satellite nadir observations.

scholarly journals Extending methane profiles from aircraft into the stratosphere for satellite total column validation using the ECMWF C-IFS and TOMCAT/SLIMCAT 3-D model

2017 ◽  
Vol 17 (11) ◽  
pp. 6663-6678 ◽  
Author(s):  
Shreeya Verma ◽  
Julia Marshall ◽  
Mark Parrington ◽  
Anna Agustí-Panareda ◽  
Sebastien Massart ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Transport Model ◽  
A Priori ◽  
Atmospheric Composition ◽  
Random Errors ◽  
Chemical Transport Model ◽  
Spatial Coverage ◽  
Forecasting System ◽  
The Impact ◽  
Total Column

Abstract. Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 and 34 ppb, respectively). In comparison, the a priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

scholarly journals Seven year satellite observations of the mean structures and variabilities in the regional aerosol distribution over the oceanic areas around the Indian subcontinent

2005 ◽  
Vol 23 (6) ◽  
pp. 2011-2030 ◽  
Author(s):  
S. K. Nair ◽  
K. Parameswaran ◽  
K. Rajeev
Keyword(s):  
Indian Ocean ◽  
Southern Hemisphere ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Indian Subcontinent ◽  
Monsoon Season ◽  
Dry Season ◽  
Aerosol Transport ◽  
Summer Monsoon Season ◽  
Aerosol Distribution

Abstract. Aerosol distribution over the oceanic regions around the Indian subcontinent and its seasonal and interannual variabilities are studied using the aerosol optical depth (AOD) derived from NOAA-14 and NOAA-16 AVHRR data for the period of November 1995–December 2003. The air-mass types over this region during the Asian summer monsoon season (June–September) are significantly different from those during the Asian dry season (November–April). Hence, the aerosol loading and its properties over these oceanic regions are also distinctly different in these two periods. During the Asian dry season, the Arabian Sea and Bay of Bengal are dominated by the transport of aerosols from Northern Hemispheric landmasses, mainly the Indian subcontinent, Southeast Asia and Arabia. This aerosol transport is rather weak in the early part of the dry season (November–January) compared to that in the later period (February–April). Large-scale transport of mineral dust from Arabia and the production of sea-salt aerosols, due to high surface wind speeds, contribute to the high aerosol loading over the Arabian Sea region during the summer monsoon season. As a result, the monthly mean AOD over the Arabian Sea shows a clear annual cycle with the highest values occurring in July. The AOD over the Bay of Bengal and the Southern Hemisphere Indian Ocean also displays an annual cycle with maxima during March and October, respectively. The amplitude of the annual variation is the largest in coastal Arabia and the least in the Southern Hemisphere Indian Ocean. The interannual variability in AOD is the largest over the Southeast Arabian Sea (seasonal mean AOD varies from 0.19 to 0.42) and the northern Bay of Bengal (seasonal mean AOD varies from 0.24 to 0.39) during the February–April period and is the least over the Southern Hemisphere Indian Ocean. This study also investigates the altitude regions and pathways of dominant aerosol transport by combining the AOD distribution with the atmospheric circulation. Keywords. Atmospheric composition and structure (Aerosols and particles) – Meteorology and atmospheric dynamics (Climatology) – Oceanography: physical (Ocean fog and aerosols)

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