Verification of dust forecast over the Indian region with satellite and ground based observations

This article presents the verification results of the dust forecast by a numerical model over India and neighbouring regions. National Centre for Medium Range Weather Forecasting Unified Model (NCUM) is a global numerical weather prediction (NWP) model with a prognostic dust scheme. Evaluation of the performance of dust forecast by NCUM is carried out in this study. Model forecast of dust optical depth (DOD) at 550 nm is validated against ground-based and satellite observations since optical depth measurements in mid-visible wavelength are easily available. Daily 5-day forecast based on 00 UTC initial condition during dust dominated pre-monsoon season (April–May) of 2014 is used in this study. Location specific and geographical distribution of dust forecast is validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite retrieved DOD observation at 532 nm, Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), Ozone Monitoring Instrument (OMI), aerosol index, and Aerosol Robotic Network (AERONET) station data of total and coarse mode AOD. The verification results indicate that NCUM dust forecast generally gives good representation of large scale geographical distribution of dust over the western region of India. DOD forecasts show good correlation with co-located CALIPSO DOD over the western part (0.71) compared to central (0.58) and eastern (0.61) part of India in April while it show similar trend in May with slightly improved correlation (0.68) over the eastern part of India. Results also show that DOD forecasts are better correlated to AERONET coarse mode AOD observations over Jaipur in April and over Kanpur in May. Vertical distribution of dust concentrations in the forecast show reasonably good agreement with attenuated backscatter and depolarization ratio from CALIPSO observations. The model is also able to simulate spatiotemporal distribution of dust during a major dust event as observed by CALIPSO, MODIS, and OMI.     

Lidar and spectroradiometer measurements of atmospheric aerosol optical characteristics over an urban area in Sofia,Bulgaria

A series of campaigns involving a systematic investigation of the atmosphere over an urban area of Sofia city were carried out. A European Aerosol Research Lidar Network (EARLINET) scanning aerosol lidar, a spectroradiometer, a standard sun photometer and a ground meteorological station were used in the observations. Multiple aerosol layers of variable thickness (200–600 m) were observed systematically in the planetary boundary layer (PBL) over the study area and the experimental data were compared with theoretical data. A study of the optical characteristics of the atmospheric aerosol, including the extinction coefficient, aerosol optical depth (AOD) and Angstrom parameters α and β, was performed and their variations followed during the convective boundary layer (CBL) formation. Values of the AOD obtained using the different instruments during simultaneous measurements were compared. Preliminary results show that the AOD values recorded by the sun photometer and those calculated on the basis of the spectroradiometer data are higher than those retrieved from the lidar data. Determination of the atmospheric optical depth and extinction coefficient using a ground-based spectral instrument is a relatively simple and inexpensive method of monitoring the total aerosol content in the atmosphere as well as the air quality over the region.     

双通道算法与MODIS算法反演中国近海气溶胶光学特性的对比研究

利用双通道和IMAPP气溶胶反演算法处理TERRA/MODIS L1B数据得出中国近海气溶胶的光学厚度,与AERONET太阳光度计的反演结果作对比分析,验证了反演方法的可行性。同时,对各海域的反演结果及表征粒子谱宽度的Angstrom指数(α)的变化情况进行了分析,结果表明：在东海和日本以南等广阔海域,两种反演算法的结果同AERONET太阳光度计的观测结果基本一致,相关性较好;在渤海和黄海近海岸一带两者气溶胶光学厚度的反演值均偏高,其原因主要是由这些海域的二类水体的影响导致的。探讨分析了这些海域的水域特征及光学特性,为研究发展适合中国近海气溶胶特性的反演算法提供了依据。     

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.     

Toward aerosol optical depth retrievals over land from GOES visible radiances: determining surface reflectance

Frequent observations of aerosol over land are desirable for aviation, air pollution and health applications. Thus, a method is proposed here to correct surface effects and retrieve aerosol optical depth using visible reflectance measurements from the Geostationary Operational Environmental Satellite (GOES). The surface contribution is determined from temporal compositing of visible imagery, where darker pixels correspond to less atmospheric attenuation and surface reflectance is deduced from the composite using radiative transfer. The method is applied to GOES‐8 imagery over the eastern US. Retrieved surface reflectance is compared with separate retrievals using a priori ground‐based observations of aerosol optical depth. The results suggest that surface reflectances can be determined to within ±0.04. The composite‐derived surface reflectance is further analysed by retrieving aerosol optical depth and validating retrievals with Aerosol Robotic Network (AERONET) observations. This analysis indicates that the retrieved optical depth is least biased, hence the surface reflectance is most accurate, when the composite time period varies seasonally. Aerosol optical depth retrievals from this validation are within ±0.13 of AERONET observations and have a correlation coefficient of 0.72. While aerosol optical depth retrieval noise at low optical depths may be limiting, the retrieval accuracy is adequate for monitoring large outbreaks of aerosol events.     

基于深蓝算法的Sentinel-2数据气溶胶光学厚度反演

中高分辨率气溶胶信息对于高精度地表反射率反演以及城市空气环境质量监测具有重大意义,但在城市及稀疏植被等高亮地表区域,气溶胶光学厚度(AOD)的高精度反演一直是定量遥感领域的难点之一。以北京城市区和包头沙漠区为例,利用MODIS地表反射率产品构建先验知识约束条件,基于深蓝算法实现了13景Sentinel-2高亮地表的AOD反演。为验证算法精度,将反演结果与全球气溶胶自动观测网(AERONET)站点实测值、Sentinel-2官方插件Sen2Cor处理结果、Landsat-8反演值作对比。结果表明:①采用深蓝算法反演的AOD值与AERONET实测值具有显著的相关性(R^2>0.9,RMSE=0.056);②无论是沙漠高亮区还是植被较少的城市高亮区,Sen2Cor插件反演的AOD值整景均为固定值,无空间分布,不符合实际情况;③Sentinel-2深蓝算法反演结果与准同步过境的Landsat-8反演的AOD产品在空间分布上具有高度一致性,较好地反映了人类活动特征。相比于目前官方产品,深蓝算法适合Sentinel-2数据高亮区域的气溶胶反演,在绝对精度和空间分布趋势方面均具有明显优势。     

Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.     

基于MODIS影像的PM10反演——以北京市为例

提出了一种从MODIS影像上反演可吸入颗粒物浓度(PM10)的方法。该方法的基础为从MODIS影像上反演得到的3个可见光波段气溶胶光学厚度(AOD)计算的ngstrm-α。ngstrm-α与颗粒物粒径有关,根据ngstrm-α能够得到颗粒物有效半径,进而估算颗粒物浓度。反演的气溶胶光学厚度由AERONET北京站与香河站验证。PM10反演结果由北京市环保局发布的AQI反插得到的PM10(AQI)进行验证。结果表明:从MODIS影像上反演的3个可见光波段AOD与AERONET基站AOD具有良好的相关性,相关系数为0.923,均方根误差为0.149。该方法反演的PM10与PM10(AQI)相关系数为0.794,均方根误差为48.34(μg/m3)。     

Retrieval of AOD from Sentinel-2 Data based on Deep Blue Algorithm

Medium-to-high resolution aerosol information is of great significance for surface reflectance inversion and urban ambient air quality monitoring. However, the high-precision aerosol optical thickness (AOD) retrieval in bright areas, such as cities and sparse vegetation areas, has long plagued the quantitative remote sensing applications. Taking Beijing urban area and Baotou desert area as examples, using MODIS surface reflectance products to construct prior knowledge constraints, the AOD inversion of 13 scenes Sentinel-2 images in bright areas was realized based on the deep blue algorithm. To verify the accuracy of the algorithm, the result were compared with the Sentinel-2 official algorithm processing result, the Landsat-8 official aerosol products and the ground-measured AOD data from the Global Aerosol Automated Observing Network (AERONET). The results indicate that the retrieved AOD values from deep blue algorithm is significantly correlated with the measured value of AERONET(R² > 0.90, RMSE = 0.056 0), and the AOD spatial distributions are also well consistent with those from Landsat-8, which reflects the characteristics of human activities. But, whether in desert bright area or urban bright area with less vegetation, the AOD values retrieved by Sen2Cor plug-in are fixed, no spatial distribution and do not conform to the actual situation. In general, compared with the current official products, the deep blue algorithm is suitable for aerosol retrieval in high-brightness areas of Sentinel-2 data，and has obvious advantages in terms of estimation accuracy and spatial distribution trend.     

Physical and chemical properties of surface and column aerosols at a rural New England site during MODIS overpass

Surface-based measurements of aerosol optical depth at a rural site in southern New Hampshire (43.11°N, 70.95°W) are compared to retrievals of the same parameter by the Moderate Resolution Imaging Spectrometer (MODIS) during April-August, 2001. Hourly averages of aerosol optical depth (AOD) were derived using a multi-filter rotating shadowband radiometer (MFRSR) at the time of NASA's Terra satellite overpass. The MODIS Level 2 aerosol product at a wavelength of 550 nm was directly compared to the MFRSR interpolated AOD at 550 nm. We were able to compare the two AOD measurement platforms on 46 days (out of a possible 128 days) and observed a good agreement between the two methods (R=0.81; slope=0.95±0.10). However, there were 11 days during this study period when MODIS measured AOD at the site, but the MFRSR did not due to excessive cloud cover. There were also 7 days when clear skies prevailed at the site during the time of MODIS overpass, but there was no AOD retrieved by MODIS. Surface measurements of fine particle (PM_2.5) mass, chemical composition, and optical properties were also performed during summer 2001. A good correlation (R=0.87) between fine particle mass and AOD measured by the MFRSR was observed. A comparison between fine particle light extinction at the surface and MFRSR AOD (at the same wavelength) also showed good agreement (R=0.80). Aerosol chemical analysis revealed that ammonium sulfate was the main aerosol component during times of very high turbidity, while organic carbon dominated during times of below-average turbidity.     

Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager

An aerosol retrieval algorithm for the first Geostationary Ocean Color Imager (GOCI) to be launched in March 2010 onboard the Communication, Ocean, and Meteorological Satellite (COMS) is presented. The algorithm retrieves aerosol optical depth (AOD), fine-mode fraction (FMF), and aerosol type in 500 m × 500 m resolution. All the products are retrieved over clear water which is defined by surface reflectance ratio between 640 nm and 860 nm (SRR) less or equal to 2.5, while only AOD is retrieved over turbid water (SRR > 2.5) due to high surface reflectance. To develop optimized algorithm for the target area of GOCI, optical properties of aerosol are analyzed from extensive observation of AERONET sunphotometers to generate lookup table. Surface reflectance of turbid water is determined from 30-day composite of Rayleigh- and gas corrected reflectance. By applying the present algorithm to MODIS top-of-the atmosphere reflectance, three different aerosol cases dominated by anthropogenic aerosol contains black carbon (BC), dust, and non-absorbing aerosol are analyzed to test the algorithm. The algorithm retrieves AOD, and size information together with aerosol type which are consistent with results inferred by RGB image in a qualitative way. The comparison of the retrieved AOD with those of MODIS collection 5 and AERONET sunphotometer observations shows reliable results. Especially, the application of turbid water algorithm significantly increases the accuracy in retrieving AOD at Anmyon station. The sensitivity study between MODIS and GOCI instruments in terms of relative sensitivity and scattering angle shows promising applicability of the present algorithm to future GOCI measurements.     

Aerosol optical depth retrieval over snow using AATSR data

Aerosol observations over the Arctic are important because of the effects of aerosols on Arctic climate, such as their direct and indirect effects on the Earth's radiation balance and on snow albedo. Although information on aerosol properties is available from ground-based measurements, passive remote sensing using satellite measurements would offer the advantage of large spatial coverage with good temporal resolution, even though, due to light limitations, this is only available during the Arctic summer. However, aerosol optical depth (AOD) retrieval over the Arctic region is a great challenge due to the high reflectance of snow and ice and due to the high solar zenith angle. In this article, we describe a retrieval algorithm using Advanced Along-Track Scanning Radiometer (AATSR) data, a radiometer flying on the European Space Agency (ESA) Environmental Satellite (ENVISAT), which offers two views (near nadir and at 55° forward) at seven wavelengths in the visible thermal-infrared (VIS-TIR). The main idea of the Dual-View Multi-Spectral (DVMS) approach is to use the dual view to separate contributions to reflectance measured at the top of the atmosphere (TOA) due to atmospheric aerosol and the underlying surface. The algorithm uses an analytical snow bidirectional reflectance distribution function (BRDF) model for the estimation of the ratio of snow reflectances in the nadir and forward views, as well as an estimate of the atmospheric contribution to TOA reflectance obtained using the dark pixel method over the adjacent ocean surface, assuming that this value applies over nearby land surfaces in the absence of significant sources across the coastline. An iteration involving all four AATSR wavebands in the visible near-infrared (VIS-NIR) is used to retrieve the relevant information. The method is illustrated for AATSR overpasses over Greenland with clear sky in April 2009. Comparison of the retrieved AOD with AErosol Robotic Network (AERONET) data shows a correlation coefficient of 0.75. The AODs retrieved from AATSR using the DVMS approach and those obtained from AERONET data show similar temporal trends, but the AERONET results are more variable and the highest AOD values are mostly missed by the DVMS approach. Limitations of the DVMS method are discussed. The pure-snow BRDF model needs further correction in order to obtain a better estimation for mixtures of snow and ice.     

An evaluation of satellite aerosol products against sunphotometer measurements

Because atmospheric aerosols scatter sunlight back to space, reflectance measurements from spaceborne radiometers can be used to estimate the aerosol load and its optical properties. Several aerosol products have been generated in a systematic way, and are available for further studies. In this paper, we evaluate the accuracy of such aerosol products derived from the measurements of POLDER, MODIS, MERIS, SEVIRI and CALIOP, through a statistical comparison with Aerosol Optical Depth (AOD) measurements from the AERONET sunphotometer network. Although this method is commonly used, this study is, to our knowledge, among the most extensive of its type since it compares the performance of the products from 5 different sensors using up to five years of data for each of them at global scale. The choice of these satellite aerosol datasets was based on their availability at the ICARE Data and Service Centre (www.icare.univ-lille1.fr).We distinguish between retrievals over land and ocean and between estimates of total and fine mode AOD. Over the oceans, POLDER and MODIS retrievals are of similar quality, with RMS difference lower than 0.1 and a correlation with AERONET of around 0.9. The POLDER estimates suffer from a small positive bias for clean atmospheres, which weakens its statistics. The other aerosol products are of lesser quality, although the SEVIRI products may be of interest for some applications that require a high temporal resolution. The MERIS product shows a very high bias. Over land, only the MODIS product offers a reliable estimate of the total AOD. On the other hand, the polarization-based retrieval using POLDER data allows a better fine mode estimate than that from MODIS. These results suggest the need for a product combining POLDER and MODIS products over land.The paper also analyses how the statistics change with the spatial and temporal thresholds that are used. Spatio-temporal averaging improves the statistics only slightly, which indicates that random errors are not dominant in the error budget. The paper includes various statistical indicators at global scale, and detailed results at individual ground stations can be obtained on request from the authors.     

京津冀冬季气溶胶光学厚度遥感反演

针对传统的暗像元算法难以满足植被稀疏陆表气溶胶遥感监测需求的问题,提出了冬季植被稀疏的京津冀地区气溶胶光学厚度的遥感反演方法。以2016—2018年连续3年1—2月的AQUA/MODIS L1B数据为数据源,采用暗像元算法与深蓝算法结合的方法对冬季京津冀地区的气溶胶光学厚度进行了遥感监测。使用AERONET数据对结果进行了验证,并与MODIS MYD04_L2暗像元-深蓝气溶胶产品进行了对比。结果表明,该算法在冬季京津冀地区的气溶胶监测效果远好于暗像元算法,并与MODIS气溶胶产品表现出了显著的相关性,且有效监测范围更大、空间分辨率更高。根据连续监测结果,分析了京津冀地区冬季气溶胶光学厚度空间分布特征及其影响因素。     

Correcting MODIS aerosol optical depth products using a ridge regression model

Aerosol optical depth (AOD) is an important metric for the concentration of aerosols in the atmosphere. Dark target (DT) algorithm is a widely used physical model to retrieve AOD over land from Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, due to the limitation of surface ‘dark-target’ in some regions and over certain surface types, it does not work very well. In this paper, we propose two hybrid frameworks based on ridge regression (RR) to improve the retrieval accuracy. They are serial and parallel approaches. In both frameworks, the DT algorithm is used as a baseline to derive an initial result, and the bias between the derived AOD and the ground-truth is corrected by the RR model. To validate the effectiveness of the proposed methods, we apply them on 3093 collocated MODIS and Aerosol Robotic Network (AERONET) observations, covering 10 stations at all available time in China. The obtained results demonstrate that the proposed methods can improve retrieval performance compared to the corresponding DT algorithm and the RR model.     

基于HJ-1卫星数据反演长江三角洲地区气溶胶光学厚度

探索利用我国HJ-1卫星CCD数据,运用深蓝算法开展长江三角洲地区气溶胶光学厚度反演的可行性,并将结果与其他气溶胶光学厚度产品进行比较。针对HJ-1A和HJ-1B数据,反演结果分别与MODIS气溶胶光学厚度产品以及AERONET地基观测数据进行对比验证。结果表明:深蓝算法得到A星、B星的反演结果与MODIS气溶胶产品呈显著相关,但在数值上普遍高于MODIS产品;反演结果与AERONET站点数据之间的误差介于0.008~0.364之间,研究时段内站点数据缺乏,未对误差进行严格的统计分析。基于深蓝算法的HJ-1卫星数据反演结果虽然在数值上与MODIS气溶胶光学厚度产品存在系统性偏差,但在空间上能够较好地反映长江三角洲地区大气气溶胶分布状况,且具有空间分辨率高的优势。     

Satellite-derived UV irradiance for a region with complex morphology and meteorology: comparison against ground measurements in Santiago de Chile

Ground-based measurements of ultraviolet (UV) irradiance, carried out by a four-channel UV radiometer in Santiago de Chile from October 2004 to December 2011, have been used to estimate daily values of the UV index (UVI). These ground-based data have been compared with UVI estimates retrieved from the Ozone Measurement Instrument (OMI) on board the Aura spacecraft. Since the widely used OMI-gridded UVI data may not be suitable for the complex local morphology and meteorology, a careful screening of overpass OMI data was applied.

Nevertheless, we found that OMI-derived UVI data overestimate ground-based values; depending on cloud-cover conditions, the mean bias (MB) and the root mean square error (RMSE) range from 34.53% to 30.29% and from 35.22% to 43.50%, respectively, with the lowest MB (and the highest RMSE) values occurring under overcast conditions. Moreover, the difference between satellite-derived and ground-based UVI data exhibits a limited seasonality with somewhat larger differences in the fall season. The detected overestimation seems to be linked with the boundary layer aerosol absorption that is not accounted for by the OMI algorithm. Indeed, we found that the difference in UVI increases with the aerosol concentration (which in Santiago shows seasonal variations). Ceilometer profiles of backscatter intensities, directly related to aerosol concentrations, and PM10 concentrations correlate with UVI differences (correlation coefficient r of approximately 0.6 and 0.4, respectively) under cloud-free conditions for time scales ranging from months to years.

Additional comparisons were performed between UVI estimates retrieved from our ground-based measurements in Santiago and from the Tropospheric Emission Monitoring Internet Service (TEMIS) Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Under cloudless conditions, also TEMIS-derived data overestimate ground-based UVI estimations (by about 31%) and exhibit a small seasonality.     

Intercomparison of CALIOP,MODIS, and AERONET aerosol optical depth over China during the past decade

ABSTRACT

Aerosol optical depth (AOD) data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were intercompared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as space-based Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2015. This article aims to evaluate CALIOP daytime AOD using MODIS and AERONET AODs. Comparing the AOD between CALIOP and AERONET in different regions over China using quality control flags to screen the AOD data, we find that CALIOP AOD is generally lower than AERONET AOD especially at optical depths over 0.4 likely due to differences in the cloud screening algorithms and general retrieval uncertainty. Comparison between CALIOP AOD and MODIS AOD results show that the overall spatio-temporal distribution of CALIOP AOD and MODIS AOD is basically consistent. As for the spatial distribution, both data sets show several high-value regions and low-value regions in China. CALIOP is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data sets show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. A long-term linear trend analysis based on the domain averaged monthly mean CALIOP and MODIS AOD shows agreement among CALIOP and MODIS for the trends over the 10-year period in four regions examined. The trends in AOD derived from CALIOP and MODIS indicate a decline in aerosol loading in China since 2006. It is found from frequency comparison that CALIOP and MODIS AOD generally exhibit a degree of correlation over China. Statistical frequency analysis shows that CALIOP AOD frequency distribution shows a higher peak than MODIS AOD when AOD < 0.4. For the most part, mean MODIS AOD is higher than mean CALIOP AOD. Evaluation of CALIOP AOD retrievals provides the prospect for application of CALIOP data. The intercomparison suggests that CALIOP has systematically underestimated daytime AOD retrievals, especially deteriorating with increasing AOD, and therefore, CALIOP daytime AOD retrievals should be treated with some degree of caution when the AOD is over 0.4.     

Aerosol characteristics over Delhi national capital region: a satellite view

Multi-sensor aerosol data sets are analysed to examine the aerosol characteristics over the Delhi national capital region. Both the Multiple-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) capture the seasonal cycle of aerosol optical depth (AOD) as observed by ground-based measurements. However, AOD from MISR shows a low bias relative to AOD from MODIS, which increases linearly at high AOD conditions. A large difference (by >25 W m^–2 per unit AOD) in the top-of-atmosphere direct radiative forcing efficiency derived from MODIS and MISR-retrieved AOD is observed during the winter and pre-monsoon seasons relative to the other seasons. The ubiquitous presence of dust (as indicated by non-spherical particle fraction to AOD and linear depolarization ratio values) is observed throughout the year. The aerosol layer is mostly confined to within 2 km of surface in the winter and post-monsoon seasons, while it expands beyond 6 km in the pre-monsoon and monsoon seasons. Columnar AOD is found to be highly sensitive to aerosol vertical distribution. The applicability of multi-sensor data sets and climatic implications are discussed.     

Validation of MODIS retrieval aerosol optical depth and an investigation of aerosol transport over Mohal in north western Indian Himalaya

This study deals with the optical properties of aerosols during 2007 over Mohal (31.9º N, 77.12º E) in north western Indian Himalaya, investigated using ground-based measurements and multi-satellite data. The daily average (mean ± standard deviation) aerosol optical depth (AOD) at 500 nm, Ångström exponent and turbidity coefficient values were 0.2 ± 0.1, 1.1 ± 0.3 and 0.1 ± 0.1, respectively. About 84% of AOD values retrieved from satellites were found to be within an uncertainty limit with a significant correlation coefficient around 0.70. The present study suggests that AOD retrieval using the Moderate Resolution Imaging Spectroradiometer (MODIS) is able to characterize AOD distribution over Mohal. However, to eliminate systematic errors, the existing MODIS algorithm needs to be modified in view of the changing aerosol optical properties, especially during the biomass-burning period. To investigate the influence of aerosol transport, a multi-sensor approach in conjunction with back-trajectory analysis was used. The observed higher values of AOD during dust-loading days with simultaneous study of the space-borne lidar measurements as well as back-trajectory analysis suggest the influx of desert aerosols. Transport of dust aerosols on 12 April, 27 April and 1 June caused a significant reduction in surface-reaching solar irradiance by 43, 40 and 39 W m^–2, respectively. Atmospheric forcing during these days increased by 33.8, 33.0 and 33.2 W m^–2, which translates into atmospheric heating rates of 0.95, 0.93 and 0.93 K day^–1, respectively. This indicates significant climatic implications due to arriving aerosols in north-western Indian Himalaya.