Assimilating spaceborne lidar dust extinction can improve dust forecasts

Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.

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Assimilating spaceborne lidar dust extinction improves dust forecasts

10.5194/acp-2021-442 ◽

2021 ◽

Author(s):

Jerónimo Escribano ◽

Enza Di Tomaso ◽

Oriol Jorba ◽

Martina Klose ◽

Maria Gonçalves Ageitos ◽

...

Keyword(s):

Optical Depth ◽

Extinction Coefficient ◽

Temporal Structure ◽

Added Value ◽

Sources Of Information ◽

Dust Extinction ◽

Desert Dust ◽

Deep Blue ◽

Skill Scores ◽

Ground Based Lidar

Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from space-borne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating space-borne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the space-borne lidar profiles upon the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of two months over Northern Africa, the Middle East and Europe. We assimilate DOD derived from VIIRS/SUOMI-NPP Deep Blue, and for the first time CALIOP-based LIVAS pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AERONET Sun photometers and ground-based lidar dust extinction profiles from field campaigns (CyCARE and Pre-TECT). Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discriminating desert dust from other aerosol types.

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The impact of data assimilation on the prediction of Asian desert dust using an operational 4D-Var system

10.5194/acp-2018-78 ◽

2018 ◽

Author(s):

Angela Benedetti ◽

Francesca Di Giuseppe ◽

Luke Jones ◽

Vincent-Henri Peuch ◽

Samuel Rémy ◽

...

Keyword(s):

Air Quality ◽

Data Assimilation ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Asian Dust ◽

Added Value ◽

Desert Dust ◽

Deep Blue ◽

Dust Events ◽

The Impact

Abstract. Asian Dust is a seasonal meteorological phenomenon which affects East Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three one-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to air quality over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments. Particulate Matter (PM) data from the China Environmental Protection Agency (CEPA) were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert-dust events and to improve the forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better that the unconstrained experiment, generally showing smaller mean normalized bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts well into the forecast. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a big role in delivering improved forecasts of Asian Dust.

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Evaluation of NMMB-MONARCH dust reanalysis within the DustClim ERA4CS project

10.5194/egusphere-egu2020-21863 ◽

2020 ◽

Author(s):

Michail Mytilinaios ◽

Lucia Mona ◽

Francesca Barnaba ◽

Sergio Ciamprone ◽

Serena Trippetta ◽

...

Keyword(s):

Middle East ◽

Size Distribution ◽

Optical Depth ◽

Extinction Coefficient ◽

Dust Storms ◽

Northern Africa ◽

Dust Deposition ◽

Dust Extinction ◽

Dust Size Distribution

An advanced dust reanalysis with high spatial (at 10km x 10km) and temporal resolution is produced in the framework of DustClim project (Dust Storms Assessment for the development of user-oriented Climate Services in Northern Africa, Middle East and Europe) [1], aiming to provide reliable information on dust storms current conditions and predictions, focusing on the dust impacts on various socio-economic sectors.This regional reanalysis is based on the assimilation of dust-related satellite observations from MODIS instrument [2], in the Multiscale Online Nonhydrostatic Atmosphere Chemistry model (NMMB-MONARCH) [3], over the region of Northern Africa, Middle East and Europe. The reanalysis is now available for a seven-year period (2011-2016) providing the following dust products: Columnar and surface concentration, distributed in 8 dust particle size bins, with effective radius ranging from 0,15&#956;m to 7,1&#956;m, dust load, dry and wet dust deposition, dust optical depth (DOD) and coarse dust optical depth (radius>1&#956;m) at 550nm and profiles of dust extinction coefficient at 550nm.A thorough evaluation of the reanalysis is in progress to assess the quality and uncertainty of the dust simulations, using dust-filtered products, retrieved from different measurement techniques, both from in-situ and remote sensing observations. The datasets considered for the DustClim reanalysis evaluation, provide observations of variables that are included in the model simulations. The DOD is provided by AERONET network [4] and by IASI [5], POLDER [6], MISR [7] and MODIS space-borne sensors; Dust extinction profiles are provided by ACTRIS/EARLINET network [8] and CALIPSO/LIVAS dataset [9]; Dust PM10 surface concentrations derived from INDAAF/SDT [10] network and estimated from PM10 measurements [11] performed within EEA/EIONET [12] network; Dust deposition measurements collected by the INDAAF/SDT and the CARAGA/DEMO [13] networks; Dust size distribution from in situ observations (ground-based and airborne); And column-averaged dust size distribution at selected stations from the AERONET network.In this work, we present the results of the model evaluation for the year 2012. The first evaluation results will focus on dust extinction coefficient profiles from EARLINET and LIVAS, on DOD using AERONET, MISR and MODIS datasets, and on dust PM10 concentration from INDAAF/SDT network. Moreover, a DOD climatology covering the whole reanalysis period (2011-2016) will be compared with the results obtained from AERONET network.&#160;References[1] https://sds-was.aemet.es/projects-research/dustclim[2] https://modis.gsfc.nasa.gov/[3] Di Tomaso et al., Geosci. Model Dev., 10, 1107-1129, doi:10.5194/gmd-10-1107-2017., 2017.[4] https://aeronet.gsfc.nasa.gov/[5] Cuesta et al., J. Geophys. Res., 120, 7099-7127, 2015.[6] http://www.icare.univ-lille1.fr/parasol/overview/[7] https://misr.jpl.nasa.gov/[8] https://www.earlinet.org/[9] Marinou et al., Atmos. Chem. Phys., 17, 5893&#8211;5919, https://doi.org/10.5194/acp-17-5893-2017, 2017.[10] https://indaaf.obs-mip.fr/[11] Barnaba et al., Atmospheric environment, 161, 288-305, 2017.[12] https://www.eionet.europa.eu/[13] Laurent et al., Atmos. Meas. Tech., 8, 2801&#8211;2811, 2015.&#160;&#160;AcknowledgementDustClim project is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

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Evaluating nighttime CALIOP 0.532 μm aerosol optical depth and extinction coefficient retrievals

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-5-2747-2012 ◽

2012 ◽

Vol 5 (2) ◽

pp. 2747-2794 ◽

Cited By ~ 4

Author(s):

J. R. Campbell ◽

J. L. Tackett ◽

J. S. Reid ◽

J. Zhang ◽

C. A. Curtis ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Extinction Coefficient ◽

A Priori ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Vertical Profiles ◽

Diurnal Variability ◽

Data Points ◽

Ground Based Lidar

Abstract. NASA Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) Version 3.01 5-km nighttime 0.532 μm aerosol optical depth (AOD) datasets from 2007 are screened, averaged and evaluated at 1° × 1° resolution versus corresponding/co-incident 0.550 μm AOD derived using the US Navy Aerosol Analysis and Prediction System (NAAPS), featuring two-dimensional variational assimilation of quality-assured NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) AOD. Daytime datasets are investigated similarly for context. Regional-mean CALIOP vertical profiles of night/day 0.532 μm extinction coefficient are compared with 0.523/0.532 μm ground-based lidar measurements to investigate representativeness and diurnal variability. In this analysis, mean nighttime CALIOP AOD are mostly lower than daytime (0.121 vs. 0.126 for all aggregated data points, and 0.099 vs. 0.102 when averaged globally per normalized 1° × 1° bin), though the relationship is reversed over land and coastal regions when the data are averaged per normalized bin (0.134/0.108 vs. 0140/0.112, respectively). Offsets assessed within single bins alone approach ±20%. CALIOP AOD, both day and night, are higher than NAAPS over land (0.137 vs. 0.124) and equal over water (0.082 vs. 0.083) when averaged globally per normalized bin. However, for all data points inclusive, NAAPS exceeds CALIOP over land, coast and ocean, both day and night. Again, differences assessed within single bins approach 50% in extreme cases. Correlation between CALIOP and NAAPS AOD is comparable during both day and night. Higher correlation is found nearest the equator, both as a function of sample size and relative signal magnitudes inherent at these latitudes. Root mean square deviation between CALIOP and NAAPS varies between 0.1 and 0.3 globally during both day/night. Averaging of CALIOP along-track AOD data points within a single NAAPS grid bin improves correlation and RMSD, though day/night and land/ocean biases persist and are believed systematic. Vertical profiles of extinction coefficient derived in the Caribbean compare well with ground-based lidar observations, though potentially anomalous selection of a-priori lidar ratios for CALIOP retrievals is likely inducing some discrepancies. Mean effective aerosol layer top heights are stable between day and night, indicating consistent layer-identification diurnally, which is noteworthy considering the potential limiting effects of ambient solar noise during day.

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Evaluating nighttime CALIOP 0.532 μm aerosol optical depth and extinction coefficient retrievals

Atmospheric Measurement Techniques ◽

10.5194/amt-5-2143-2012 ◽

2012 ◽

Vol 5 (9) ◽

pp. 2143-2160 ◽

Cited By ~ 34

Author(s):

J. R. Campbell ◽

J. L. Tackett ◽

J. S. Reid ◽

J. Zhang ◽

C. A. Curtis ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Extinction Coefficient ◽

A Priori ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Vertical Profiles ◽

Diurnal Variability ◽

Data Points ◽

Ground Based Lidar

Abstract. NASA Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) Version 3.01 5-km nighttime 0.532 μm aerosol optical depth (AOD) datasets from 2007 are screened, averaged and evaluated at 1° × 1° resolution versus corresponding/co-incident 0.550 μm AOD derived using the US Navy Aerosol Analysis and Prediction System (NAAPS), featuring two-dimensional variational assimilation of quality-assured NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) AOD. In the absence of sunlight, since passive radiometric AOD retrievals rely overwhelmingly on scattered radiances, the model represents one of the few practical global estimates available from which to attempt such a validation. Daytime comparisons, though, provide useful context. Regional-mean CALIOP vertical profiles of night/day 0.532 μm extinction coefficient are compared with 0.523/0.532 μm ground-based lidar measurements to investigate representativeness and diurnal variability. In this analysis, mean nighttime CALIOP AOD are mostly lower than daytime (0.121 vs. 0.126 for all aggregated data points, and 0.099 vs. 0.102 when averaged globally per normalised 1° × 1° bin), though the relationship is reversed over land and coastal regions when the data are averaged per normalised bin (0.134/0.108 vs. 0140/0.112, respectively). Offsets assessed within single bins alone approach ±20%. CALIOP AOD, both day and night, are higher than NAAPS over land (0.137 vs. 0.124) and equal over water (0.082 vs. 0.083) when averaged globally per normalised bin. However, for all data points inclusive, NAAPS exceeds CALIOP over land, coast and ocean, both day and night. Again, differences assessed within single bins approach 50% in extreme cases. Correlation between CALIOP and NAAPS AOD is comparable during both day and night. Higher correlation is found nearest the equator, both as a function of sample size and relative signal magnitudes inherent at these latitudes. Root mean square deviation between CALIOP and NAAPS varies between 0.1 and 0.3 globally during both day/night. Averaging of CALIOP along-track AOD data points within a single NAAPS grid bin improves correlation and RMSD, though day/night and land/ocean biases persist and are believed systematic. Vertical profiles of extinction coefficient derived in the Caribbean compare well with ground-based lidar observations, though potentially anomalous selection of a priori lidar ratios for CALIOP retrievals is likely inducing some discrepancies. Mean effective aerosol layer top heights are stable between day and night, indicating consistent layer-identification diurnally, which is noteworthy considering the potential limiting effects of ambient solar noise during day.

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Assessment of seasonal forecast skills of temperature and precipitation: a comparison of 5 different models over the Mediterranean region

10.5194/ems2021-168 ◽

2021 ◽

Author(s):

Filippo Calì Quaglia ◽

Silvia Terzago ◽

Jost von Hardenberg

Keyword(s):

Characteristic Curve ◽

Seasonal Forecast ◽

Added Value ◽

Sources Of Information ◽

Seasonal Forecasts ◽

Anomaly Correlation ◽

Probability Score ◽

Temperature And Precipitation ◽

Skill Scores ◽

The Mediterranean

Seasonal forecasts are increasingly employed as sources of information on the expected evolution of climate in the few months ahead by various end-users. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly time scale. The focus area is the Mediterranean, a densely populated region identified as a hotspot for climate change, where seasonal forecasts could be useful to a variety of economic sectors, including water management, hydropower production, agriculture.In this study, seasonal forecast systems issued by 5 European institutions (ECMWF, M&#233;t&#233;o-France, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them, have been analysed. The added value of these forecast systems with respect to simpler forecast approaches based on climatology and persistence has been investigated.Different deterministic (Anomaly Correlation Coefficient) and probabilistic scores (Ranked Probability Score, Continuous Ranked Probability Score and Receiver Operating Characteristic Curve) have been employed to obtain an overall assessment of the quality of the forecasts (as of Murphy, 1993 and WMO, 2018), using ERA5 dataset as a reference. We performed the analysis using 6-month forecasts starting in May and November to reproduce the following summer and the winter seasons.In general, temperature patterns and respective skill scores are better reproduced than those regarding precipitation. The anomaly correlation coefficients for MME reach the best agreement values for each season and variable except for winter temperature. Different behaviours are found for the different skill scores; their high spatial variability suggests that smaller regions could perform better for a single variable or starting date. Seasonal forecast systems, despite some limitations, show an added value with respect to simple forecast approaches based on the climatology or persistence.

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Examining the contribution of near real-time data for rapid seismic loss assessment of structures

Structural Health Monitoring ◽

10.1177/1475921721996218 ◽

2021 ◽

pp. 147592172199621

Author(s):

Enrico Tubaldi ◽

Ekin Ozer ◽

John Douglas ◽

Pierre Gehl

Keyword(s):

Real Time ◽

Damage Assessment ◽

Free Field ◽

Seismic Source ◽

Seismic Damage ◽

Added Value ◽

Sensor Data ◽

Sources Of Information ◽

Bridge Model ◽

Seismic Damage Assessment

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.

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The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-8389-2014 ◽

2014 ◽

Vol 14 (16) ◽

pp. 8389-8401 ◽

Cited By ~ 7

Author(s):

J. C. Chiu ◽

J. A. Holmes ◽

R. J. Hogan ◽

E. J. O'Connor

Keyword(s):

Optical Depth ◽

Effective Radius ◽

Liquid Water Path ◽

Cloud Optical Depth ◽

Cloud Properties ◽

Warm Cloud ◽

Lidar Measurements ◽

Atmospheric Radiation Measurement ◽

Ground Based Lidar ◽

Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

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Testing an ensemble model of cirrus ice crystals using midlatitude in situ estimates of ice water content, volume extinction coefficient and the total solar optical depth

Journal of Quantitative Spectroscopy and Radiative Transfer ◽

10.1016/j.jqsrt.2009.02.021 ◽

2009 ◽

Vol 110 (14-16) ◽

pp. 1579-1598 ◽

Cited By ~ 35

Author(s):

Anthony J. Baran ◽

P.J. Connolly ◽

C. Lee

Keyword(s):

Water Content ◽

Optical Depth ◽

Extinction Coefficient ◽

Content Volume ◽

Ice Crystals ◽

Ensemble Model ◽

Ice Water Content ◽

Ice Water ◽

Volume Extinction Coefficient

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9-year spatial and temporal evolution of desert dust aerosols over South-East Asia as revealed by CALIOP

10.5194/acp-2017-797 ◽

2017 ◽

Cited By ~ 2

Author(s):

Emmanouil Proestakis ◽

Vassilis Amiridis ◽

Eleni Marinou ◽

Aristeidis K. Georgoulias ◽

Stavros Solomos ◽

...

Keyword(s):

East Asia ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Indian Subcontinent ◽

Dust Aerosol ◽

Observation Data ◽

South East Asia ◽

Desert Dust ◽

Dust Aerosols ◽

Se Asia

Abstract. We present a 3-D climatology of the desert dust distribution over South-East Asia derived using CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) data. To distinguish desert dust from total aerosol load we apply a methodology developed in the framework of EARLINET (European Aerosol Research Lidar Network), the particle linear depolarization ratio and updated lidar ratio values suitable for Asian dust, on multiyear CALIPSO observations (01/2007–12/2015). The resulting dust product provides information on the horizontal and vertical distribution of dust aerosols over SE (South-East) Asia along with the seasonal transition of dust transport pathways. Persistent high D_AOD (Dust Aerosol Optical Depth) values, of the order of 0.6, are present over the arid and semi-arid desert regions. Dust aerosol transport (range, height and intensity) is subject to high seasonality, with highest values observed during spring for northern China (Taklimakan/Gobi deserts) and during summer over the Indian subcontinent (Thar Desert). Additionally we decompose the CALIPSO AOD (Aerosol Optical Depth) into dust and non-dust aerosol components to reveal the non-dust AOD over the highly industrialized and densely populated regions of SE Asia, where the non-dust aerosols yield AOD values of the order of 0.5. Furthermore, the CALIPSO-based short-term AOD and D_AOD time series and trends between 01/2007 and 12/2015 are calculated over SE Asia and over selected sub-regions. Positive trends are observed over northwest and east China and the Indian subcontinent, whereas over southeast China are mostly negative. The calculated AOD trends agree well with the trends derived from Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer), although significant differences are observed over specific regions.

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