Abstract Aerosol Optical Depth Research Articles

Impacts of the Aerosol Representation in WRF-Solar Clear-Sky Irradiance Forecasts over CONUS

Abstract Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) the default Tegen climatology, 2) imposing AOD forecasts from the GEOS-5 model, 3) imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model, and 4) the Thompson–Eidhammer aerosol-aware water/ice-friendly aerosol climatology. More than 8 months of these 15-min output forecasts are compared with high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected because of the lesser sensitivity of GHI to aerosol loading. Hourly average clear-sky DNI and GHI were also analyzed, and they were additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is. Significance Statement Particles in the atmosphere called aerosols, which can include dust, smoke, sea salt, sulfates, black carbon, and organic carbon, absorb and scatter incoming sunlight. Improving the representation of aerosols in numerical weather prediction models reduces forecast errors in solar irradiance at ground level, particularly direct normal irradiance, during cloud-free conditions. This in turn should result in improved accuracy of solar power forecasts, especially for concentrated solar power (CSP) plants. CSP plants tend to be built in more arid, less cloudy regions that are also prone to dust loading, so accurate aerosol forecasts are particularly relevant. Comparing four representations of aerosols in the WRF-Solar model over eight months of forecasts across the United States reveals substantial differences in clear-sky irradiance forecast skill.

A Spatio-Temporal Weighted Filling Method for Missing AOD Values

Aerosol Optical Depth (AOD) is a key parameter in defining the characteristics of atmospheric aerosols, evaluating atmospheric pollution, and studying aerosol radiative climate effects. However, a large amount of the AOD data obtained by satellite remote sensing are missing due to cloud cover and other factors. To obtain AOD data with continuous distribution in space, this study considers the spatial and temporal correlation of AOD and proposes a spatio-temporal weighted filling method based on a sliding window to supply the missing AOD data blocks. The method uses the semivariogram and autocorrelation function to judge the spatial and temporal correlation of AOD and uses the AOD spatial autocorrelation threshold as the sliding window size, and then it builds a spatio-temporal weighted model for each window to fill in the missing values. We selected the area with full values for simulation. The results show that the accuracy of this method has been significantly improved compared with the mean filling method. The R2 reaches 0.751, the RMSE is 0.021, and the filling effect is smoother. Finally, this method was used to fill in the missing values of the MultiAngle Implementation of Atmospheric Correction (MAIAC) AOD in the Beijing–Tianjin–Hebei region in 2019, and AErosol RObotic NETwork (AERONET) AOD was used as the true value for testing. The results show that the filled AOD has a high correlation with AERONET AOD, the R2 is 0.785, and the RMSE is 0.120. A summary of the AOD values of the 13 cities in the Beijing–Tianjin–Hebei region shows that the values in the first and third quarters are higher than those in the second and fourth quarters, with the highest AOD value in March and the second highest in August; among the 13 cities, the AOD values in Chengde and Zhangjiakou are lower than those in the other cities.

Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks

Aerosol Optical Depth (AOD) is a crucial parameter for various environmental and climate studies. Merging multi-sensor AOD products is an effective way to produce AOD products with more spatiotemporal integrity and accuracy. This study proposed a conditional generative adversarial network architecture (AeroCGAN) to improve the estimation of AOD. It first adopted MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) AOD data to training the initial model, and then transferred the trained model to Himawari data and obtained the estimation of 1-km-resolution, hourly Himawari AOD products. Specifically, the generator adopted an encoder–decoder network for preliminary resolution enhancement. In addition, a three-dimensional convolutional neural network (3D-CNN) was used for environment features extraction and connected to a residual network for improving accuracy. Meanwhile, the sampled data and environment data were designed as conditions of the generator. The spatial distribution feature comparison and quantitative evaluation over an area of the North China Plain during the year 2017 have shown that this approach can better model the distribution of spatial features of AOD data and improve the accuracy of estimation with the help of local environment patterns.

Harmonized and high-quality datasets of aerosol optical depth at a US continental site, 1997\u20132018

Aerosol optical depth (AOD) characterizes the aerosol burden in the atmosphere, while its wavelength dependence is a sign of particle size. Long-term records of wavelength-resolved AOD with high quality and suitable continuity are required for climate change assessment. Typically, climate-related studies use AOD products provided by several, and perhaps different, ground-based instruments. The measurements from these instruments often have different accuracy and temporal resolution. To preserve the advantages of these products (high quality) and to reduce their disadvantages (patchy records), we generate a merged dataset obtained from four instruments deployed at a US continental site in which a nearly-continuous AOD record is found at two wavelengths (500 and 870 nm) with high quality and high temporal resolution (1-min) for a 21-yr period (1997–2018). The combined dataset addresses: (1) varying data quality and resolution mismatch of the individual AOD records, and (2) the uncertainty of the merged AOD and its relevance for user-specified needs. The generated dataset will be beneficial for a wide range of applications including aerosol-radiation interactions.

Estimation of Relationship Between Aerosol Optical Depth, PM10 and Visibility in Separation of Synoptic Codes, As Important Parameters in Researches Connected to Aerosols; Using Genetic Algorithm in Yazd

Aerosol Optical Depth (AOD) is closely related to PM10 (mass concentration of particulate matter with aero dynamical diameter less than 10 I¼m) and visibility; and all of these three parameters are so important and useful to studies connected to aerosols, troposphere dust, air pollution and atmospheric radiation budget. This study analyzed the mathematic relations between AOD, PM10 and visibility whit separation of 05, 06 and 07 synoptic conditions; whit using evolutional Genetic Algorithm. The area’s case study has been Yazd city as representative of central of Iran for 5 years (2011-2015). The aim of this analysis has been to reach relations that can estimate lack quantities of mentions data parameters from another existence data whit the least error.

Ensemble Estimation of Aerosol Optical Depth by Feature Selections from Remote Sensing Data

Aerosol optical depth (AOD) is an important quantity parameter to study the Earth’s radiation balance, climate change and environment protection. For estimating AOD by a data mining method, the synchronized records by combing satellite observed information from MOderate Resolution Imaging Spectroradiometer (MODIS) equipment with the ground-based accurate measurements of AOD from Aerosol Robotic NETwork (AERONET) work as driving attributes and prediction targets, respectively. However, compared with the number of high-dimensional remote sensing attributes, the total number of spatial-temporal collocated MODIS-AERONET observations during a couple of years is relatively not large enough for estimation modeling. It leads to unstable feature selection subsets and drops the AOD estimation accuracy. In this paper, we propose a novel ensemble approach by aggregating multiple AOD estimators. Each estimator is modeled based on features selected from remote sensing attributes by using a subsampling strategy with instance perturbation. The ensemble approach provides aggregated retrievals of AOD with higher accuracy, while also providing an estimation of retrieval uncertainty. We conducted experiments to evaluate the empirical performance of the proposed approach on two years (2009-2011) of MODIS data over 197 global AERONET sites. The encouraging results clearly showed that aggregation of estimators modeled by multiple feature selection subsets leads to accuracy improvements and uncertainty reduction in AOD retrievals.

HIGH RESOLUTION AEROSOL OPTICAL DEPTH MAPPING OF BEIJING USING LANSAT8 IMAGERY

Aerosol Optical Depth (AOD) is one of the most important parameters in the atmospheric correction of remote sensing images. We present a new method of per pixel AOD retrieval using the imagery of Landsat8. It is based on Second Simulation of the Satellite Signal in the Solar Spectrum (6S). General dark target method takes dense vegetation pixels as dark targets and derives their 550nm AODs directly from the LUT, and interpolates the AODs of other pixels according to spatial neighbourhood using those of dark target pixels. This method will down estimate the AOD levels for urban areas. We propose an innovative method to retrieval the AODs using multiple temporal data. For a pixel which has nothing change between the associated time, there must exists an intersection of surface albedo. When there are enough data to find the intersection it ought to be a value that meet the error tolerance. In this paper, we present an example of using three temporal Landsat ETM+ image to retrieve AOD taking Beijing as the testing area. The result is compared to the commonly employed dark target algorithm to show the effectiveness of the methods.

Spatio-temporal synoptic variability of aerosol optical depth and cloud properties over the Central North region of India through MODIS collection V satellite sensors

Aerosol optical depth (AOD) values at 550 nm derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensors have been analyzed in the present study. The retrieved MODIS satellite data have been used to investigate the temporal heterogeneity in columnar aerosol characteristics over the Central North region of India with a resolution of 1° × 1° grid in magnitude for the 10-year period of 2003–2012. Seasonal and inter-annual variations of AOD values are examined in light of regional synoptic climatic meteorology. On an annual basis, high annual average AOD values (>0.5) are found at Delhi (~0.75 ± 0.44), Kanpur (~0.69 ± 0.7) and Chandigarh (~0.55 ± 0.16), whereas low annual average AOD values (<0.5) are found at Raipur (~0.38 ± 0.046), Jaipur (~0.38 ± 0.096), Bhopal (~0.33 ± 0.087), Nainital (~0.19 ± 0.097) and Shimla (~0.19 ± 0.11). Highest annual mean increase in AOD (~75 %) has been found over Jaipur, whereas lowest over Chandigarh (~21 %) across Central North region of India during the study period of 2003–2012. We have investigated the relationship between Terra AOD values at 550 nm and other cloud parameters, namely cloud fraction, water vapor, cloud top temperature, cloud top pressure, cloud optical depth and cloud effective radius over this urban tropical zone of India so as to have a better understanding of aerosol–cloud interaction. A one-tailed distribution single paired Student’s t test has also been applied to the Terra AOD values and cloud parameters.

Aerosol optical depth variability over the southeastern Arabian Sea

Aerosol optical depth (AOD), an index of aerosol concentration, is used to study atmospheric features. Accordingly, spatiotemporal variability of AOD in the atmospheric column over the southeastern Arabian Sea (SEAS) is investigated utilizing monthly data obtained from both the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), from September 1997 to December 2010, and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua, from July 2002 to December 2010. A comparison between the data from both sensors showed similar averages (~0.13), but with different standard deviations (0.03 and 0.02) over the SEAS. The AOD distribution was found to be dominated by an annual cycle controlled by the monsoon climate, with maximum aerosol concentration during July/August (~0.2) and minimum during November/December (<0.1). Empirical mode decomposition (EMD) analysis revealed the influence of the Quasi-Biennial Oscillation (QBO) and El Niño Southern Oscillation (ENSO) in producing inter-annual variability of 2% and 1%, respectively. Simulated backwards trajectories of aerosols, using Hybrid Single-Particle Lagrangian Integrated Trajectory models, indicated two main remote sources, i.e. sea salt from the Arabian Sea and dust particles from the Arabian Peninsula are the key factors contributing to an increase in the concentration of aerosols over the study area during the southwest monsoon period, irrespective of the opposing phases of QBO.

An Improved Method for Measuring AOD Using Field Spectrometer

Aerosol optical depth (AOD) was an important parameter of the aerosol monitoring. This study had proposed an improved method for measuring AOD using field spectrometer, which included a basic algorithm and a correction model together. By using this method, the AOD observed by field spectrometer kept quite consistent with those observed by sun photometer. The RMS of AOD observed by field spectrometer was less than 0.05 which met the precision requirement of the WMO. Besides, this method did not need long time to operate the instrument, and had no strict requirement for platform stability. So the improved method could be used to measure marine aerosols more accurately and handily, and it belonged to contribution for marine remote sensing technology.

Study on spatial-temporal variation of aerosol optical depth over the Yangtze Delta and the impact of land-use/cover

Aerosol optical depth (AOD) is a principal indicator used to describe the atmospheric condition and effect of aerosols on radiative transfer in the Earth’s atmosphere. In this study, the spatial-temporal variation of AOD of the Yangtze Delta during 2000–2011 and the impact of land cover were studied. The results show that Moderate Resolution Imaging Spectroradiometer (MODIS) AOD was in good agreement with data obtained from six ground-based Aerosol Robotic Network (AERONET) sun photometers (R > 0.75). The AOD over the Yangtze Delta presented an obvious one year cycle variation from 2000 to 2011. The largest value often appeared in May or June, and the smallest often appeared in December or January. The AOD over main cities has increased gradually over the years. The AOD over urban areas increased faster than that in rural areas. A change of land-use/cover leads to a change of AOD. Areas of land-use/cover with increased intense human activity leads to changes in higher AOD whereas an increase in forested areas leads to an AOD change to a lower value. Forest land has a purification effect against atmospheric aerosols.

Estimating Aerosol Optical Depth Based on Regional Optimized Peterson Model

Aerosol optical depth(AOD) is one of the most important parameters in describing aerosol optical characteristics.It is widely used in researched on air quality monitoring and atmospheric correction based on radiative transfer model.A wide range of aerosol information can be fast obtained and retrieved using satellite remote sensing technology.While,satellite retrieved aerosol product has low spatial coverage because of the limitation of cloud coverage and dark target algorithm,and the time phase of the product is limited by satellite overpass time.Horizontal meteorological visibility(HMV) is another very important parameter in describing aerosol optical characteristics.It is observed 8 fixed times one day by widely distributed meteorological sites in China.It can be a good supplement of satellite retrieved aerosol optical depth data by building the convention relationship between aerosol optical depth data and horizontal meteorological visibility data.In this study,MODIS aerosol optical depth product from 2001 to 2009 and horizontal meteorological visibility data from 71 meteorological sites located in Eastern China were chosen as the research data.The research data was used to optimize Peterson model which describes the relationship between aerosol optical depth and horizontal meteorological visibility.Aerosol scale height is the main parameter that influences the conversion accuracy between aerosol optical depth and horizontal meteorological visibility.By regional Gaussian fitting method,the temporal trends of aerosol scale height were fitted.The aerosol optical depth data and horizontal meteorological visibility data in 2010 were used to validate the accuracy and the regional applicability of the optimized model.The result shows the RMSE of aerosol optical depth estimation of optimized model is 0.31,which is lower than that of Peterson model.Compared with the single site optimized model,the accuracy is similar,while the application of regional optimized model is better than the single site optimized model.

Prior knowledge-supported aerosol optical depth retrieval over land surfaces at 500 m spatial resolution with MODIS data

Aerosol optical depth (AOD) values at a spatial resolution of 500 m were retrieved over terrain areas by applying a time series of Moderate resolution Imaging Spectroradiometer (MODIS) 500 m resolution data in the Heihe region (36–42° N, 97–104° E) of Gansu Province, China; in the Pearl River Delta (18–30° N, 108–122° E), China; and in Beijing (39–41° N, 115–118° E), China. A novel prior knowledge scheme was used in the algorithm that performs cloud screening, simultaneous AOD and surface reflectance retrieval from the MODIS 500 m Level 1B data. This prior knowledge scheme produced a new Ångström exponent α, utilizing a Terra pass time α and an Aqua pass time α to better satisfy the invariant α assumption. The retrieved AOD data were compared with AOD data observed with the ground-based, automatic Sun-tracking photometer CE318 at corresponding bands in the Heihe region and with Aerosol Robotic Network (AERONET) data in the Pearl River Delta and in Beijing. Validation experiments demonstrated the potential of applying the algorithm to MODIS 500 m AOD retrieval on land; validation showed the uncertainty of Δτ = ±0.1±0.2τ over various types of underlying land surface, including cities, where τ is the aerosol optical depth. The root mean square errors (RMSEs) were around 0.1 for inland regions and up to 0.24 for cities by the sea, such as Hong Kong and Zhongshan, China.

Detailed Aerosol Optical Depth Intercomparison between Brewer and Li-Cor 1800 Spectroradiometers and a Cimel Sun Photometer

Abstract Aerosol optical depth (AOD) using different instruments during three short and intensive campaigns carried out from 1999 to 2001 at El Arenosillo in Huelva, Spain, are presented and compared. The specific aim of this study is to determine the level of agreement between three different instruments running in operational conditions. This activity, however, is part of a broader objective to recover an extended data series of AOD in the UV range obtained from a Brewer spectroradiometer. This instrument may be used to obtain AOD at the same five UV wavelengths used during normal operation for ozone content determination. As part of the validation of the Brewer AOD data, a Cimel sun photometer and another spectroradiometer, a Li-Cor 1800, were used. A detailed comparison of these three instruments is carried out by means of near-simultaneous measurements, with particular emphasis on examining diurnal AOD variability. Absolute AOD uncertainties range from 0.02 for the Cimel to 0.08 for the Brewer, with intermediate values for the Li-Cor 1800. All data during the comparison are in reasonable agreement, when taking into account the different performance characteristics of each instrument. The comparison also demonstrates current deficiencies in the Brewer data and thus the difficulty to determine AOD values with low errors.

Multi year changes of Aerosol Optical Depth in the monsoon region of the Indian Ocean since 1986 as seen in the AVHRR and TOMS data

Abstract. Aerosol Optical Depth (AOD) data based on AVHRR and TOMS was analyzed to find out the changes in the Indian Ocean from 1981 to 2004. Four regions covering Indian Ocean north of 10° S were studied in detail. The results strongly suggest that the mean value of AOD in these regions decreased from 1986–1990 to 1995–1999. The Arabian Sea and Bay of Bengal show increase thereafter whereas in the equatorial part it decreased further, during 2000–2004. The drop in AOD from the first to second period is evident in AVHRR and TOMS in the case of the Arabian Sea (North West Indian Ocean). The decrease in this case is prominent in summer. These results in general agree with the recently reported global decrease in AOD, "global brightening" and also the reversal of trend in some of the anthropogenic emissions.