scholarly journals Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10542
Author(s):  
Jinglong Li ◽  
Xiangyu Ge ◽  
Qing He ◽  
Alim Abbas
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Detection Methods ◽  
Taklimakan Desert ◽  
The Taklimakan Desert ◽  
Modis Aod

Aerosol optical depth (AOD) is a key parameter that reflects aerosol characteristics. However, research on the AOD of dust aerosols and various environmental variables is scarce. Therefore, we conducted in-depth studies on the distributions and variations of AOD in the Taklimakan Desert and its margins, China. We examined the correlation characteristics between AOD and meteorological factors combined with satellite remote sensing detection methods using MCD19A2-MODIS AOD products (from 2000, 2005, 2010, and 2015), MOD13Q1-MODIS normalized difference vegetation index products, and meteorological data. We analyzed the temporal and spatial distributions of AOD, periodic change trends, and important impacts of meteorological factors on AOD in the Taklimakan Desert and its margins. To explore the relationships between desert aerosols and meteorological factors, a random forest model was used along with environmental variables to predict AOD and rank factor contributions. Results indicated that the monthly average AOD exhibited a clear unimodal curve that reached its maximum in April. The AOD values followed the order spring (0.28) > summer (0.27) > autumn (0.18) > winter (0.17). This seasonality is clear and can be related to the frequent sandstorms occurring in spring and early summer. Interannual AOD showed a gradually increasing trend to 2010 then large changes to 2015. AOD tends to increase from south to north. Based on the general trend, the maximum value of AOD is more dispersed and its low-value area is always stable. The climatic index that has the most significant effect on AOD is relative humidity.

Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Sanja Grgurić ◽  
Josip Križan ◽  
Goran Gašparac ◽  
Oleg Antonić ◽  
Zdravko Špirić ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mass Concentration ◽  
Meteorological Data ◽  
Neural Network Approach ◽  
Data Set ◽  
Independent Variables ◽  
Data Variability ◽  
Modis Aod ◽  
The Relationship

AbstractThis study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship.The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model.It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.

scholarly journals Dynamics of dust storm and its response to meteorological conditions and anthropogenic impact in South edge of Taklimakan desert, China

MAUSAM ◽  
2021 ◽  
Vol 72 (3) ◽  
pp. 619-626
Author(s):  
AISHAJIANG AILI ◽  
XU HAILIANG ◽  
LIU XINGHONG ◽  
ZEESHAN AHMED ◽  
LI LI
Keyword(s):  
Wind Speed ◽  
Dust Storm ◽  
Human Activities ◽  
Vegetation Index ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Source Region ◽  
Dust Storms ◽  
Taklimakan Desert ◽  
Storm Frequency

In this study, the varying trends of dust storm frequency in a typical oasis located at the South edge of Taklimakan desert, China were analyzed by using time series analysis and regression models. The LUCC (land use/cover change) data, NDVI (Normalized Difference Vegetation Index) remote sensing data, meteorological data and dust storm frequency data for the period of 2004-2018 were collected from local station and ERDAS (Earth Resources Data Analysis System) software, the multivariate relationships between human activities, natural factor and dust storm frequencies were analyzed by using Principal Component Analysis (PCA). Results indicated that the annual dust storm frequency in the study period increased with fluctuation. The monthly dust storm frequency shows higher values between the months of March and June, which accounts for 72.3% of the annual dust storm frequency. Precipitation and wind speed are two meteorological factors which can impact the dust storm formation and its frequency. The correlation between dust storm frequency and temperature was insignificant. Moreover, human activities indirectly affected the dynamics of dust storms by changing the vegetation cover and direct dust emissions. Furthermore, multivariate analysis highlighted a clear relationship among dust storm frequency, meteorological factors and NDVI. The high loadings of dust storm frequency, precipitation, wind speed and NDVI on a PC indicated that increase in precipitation and NDVI will decline dust storm frequency, whereas higher wind speed will enhance dust storm frequency. The findings of this study could be useful to understand the possible causes of dust storms, which can provide the basis for controlling the dust storm source region and also mitigation of the negative effects dust storm on the environment.

scholarly journals Merging aerosol optical depth data from multiple satellite missions to view agricultural biomass burning in Central and East China

2012 ◽  
Vol 12 (4) ◽  
pp. 10461-10492 ◽  
Author(s):  
Y. Xue ◽  
H. Xu ◽  
L. Mei ◽  
J. Guang ◽  
J. Guo ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Meteorological Data ◽  
Temporal Distribution ◽  
East China ◽  
Agricultural Biomass ◽  
Spatial Coverage ◽  
Modis Aod ◽  
Single Sensor

Abstract. Agricultural biomass burning (ABB) in Central and East China occurs every year from May to October and peaks in June. The biomass burning event in June 2007 was very strong. During the period from 26 May to 16 June 2007, ABB occurred mainly in Anhui, Henan, Jiangsu and Shandong provinces. A comprehensive set of aerosol optical depth (AOD) data, produced by a merger of AOD product data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MIRS), is used to study the spatial and temporal distribution of agricultural biomass aerosols in Central and East China combining with ground observations from both AErosol RObotic NETwork (AERONET) and China Aerosol Remote Sensing NETwork (CARSNET) measurements. We compared merged AOD data with single-sensor single-algorithm AOD data (MODIS Dark Target AOD data, MODIS Deep Blue AOD data, SRAP-MODIS AOD data and MISR AOD data). In this comparison, we found merged AOD products can improve the quality of AOD products from single-sensor single-algorithm data sets by expanding the spatial coverage of the study area and keeping the statistical confidence in AOD parameters. There existed high correlation (0.8479) between the merged AOD data and AERONET measurements. Our merged AOD data make use of synergetic information conveyed in all of the available satellite data. The merged AOD data were used for the analysis of the biomass burning event from 26 May to 16 June 2007 together with meteorological data. The merged AOD products and the ground observations from China suggest that biomass burning in Central and East China has had great impact on AOD over China. Influenced by this ABB, the highest AOD value in Beijing on 12 June 2007 reached 5.71.

scholarly journals Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

2020 ◽  
Vol 13 (11) ◽  
pp. 5955-5975
Author(s):  
Hai Zhang ◽  
Shobha Kondragunta ◽  
Istvan Laszlo ◽  
Mi Zhou
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Bias Correction ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Retrieval Algorithm ◽  
Correction Algorithm ◽  
Time Step

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multiband algorithm similar to those of polar-orbiting satellites' sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to limitations in the land surface reflectance relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD retrieval algorithm, which vary with the Sun–satellite geometry and NDVI (normalized difference vegetation index). To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30 d period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from 0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD are comparable to those of the corrected high-quality ABI AOD. By using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the areal coverage of ABI AOD is increased by about 100 % without loss of data accuracy.

scholarly journals Using the OMI Aerosol Index and Absorption Aerosol Optical Depth to evaluate the NASA MERRA Aerosol Reanalysis

2014 ◽  
Vol 14 (23) ◽  
pp. 32177-32231 ◽  
Author(s):  
V. Buchard ◽  
A. M. da Silva ◽  
P. R. Colarco ◽  
A. Darmenov ◽  
C. A. Randles ◽  
...  
Keyword(s):  
Optical Properties ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Saharan Dust ◽  
Aerosol Layer ◽  
Anthropogenic Aerosols ◽  
Aerosol Absorption ◽  
Modis Aod ◽  
Aerosol Index

Abstract. A radiative transfer interface has been developed to simulate the UV Aerosol Index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and Aerosol Absorption Optical Depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the Aerosol Robotic Network (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the South African and South American biomass burning regions indicates that revising the spectrally-dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

scholarly journals Improved MODIS Dark Target aerosol optical depth algorithm over land: angular effect correction

2016 ◽  
Vol 9 (11) ◽  
pp. 5575-5589 ◽  
Author(s):  
Yerong Wu ◽  
Martin de Graaf ◽  
Massimo Menenti
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Vegetation Index ◽  
Surface Reflectance ◽  
Infrared Band ◽  
Surface Reflection ◽  
Global Land ◽  
Bidirectional Reflectance Factor ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Relationship

Abstract. Aerosol optical depth (AOD) product retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) measurements has greatly benefited scientific research in climate change and air quality due to its high quality and large coverage over the globe. However, the current product (e.g., Collection 6) over land needs to be further improved. The is because AOD retrieval still suffers large uncertainty from the surface reflectance (e.g., anisotropic reflection) although the impacts of the surface reflectance have been largely reduced using the Dark Target (DT) algorithm. It has been shown that the AOD retrieval over dark surface can be improved by considering surface bidirectional distribution reflectance function (BRDF) effects in previous study. However, the relationship of the surface reflectance between visible and shortwave infrared band that applied in the previous study can lead to an angular dependence of the AOD retrieval. This has at least two reasons. The relationship based on the assumption of isotropic reflection or Lambertian surface is not suitable for the surface bidirectional reflectance factor (BRF). However, although the relationship varies with the surface cover type by considering the vegetation index NDVISWIR, this index itself has a directional effect and affects the estimation of the surface reflection, and it can lead to some errors in the AOD retrieval. To improve this situation, we derived a new relationship for the spectral surface BRF in this study, using 3 years of data from AERONET-based Surface Reflectance Validation Network (ASRVN). To test the performance of the new algorithm, two case studies were used: 2 years of data from North America and 4 months of data from the global land. The results show that the angular effects of the AOD retrieval are largely reduced in most cases, including fewer occurrences of negative retrievals. Particularly, for the global land case, the AOD retrieval was improved by the new algorithm compared to the previous study and MODIS Collection 6 DT algorithm, with the increase of 2.0 and 4.5 % AOD retrievals falling within the expected accuracy envelope ±(0.05 + 15 %), respectively. This implies that the users can get more accurate data without angular bias, i.e., more meaningful AOD data.

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