Development of an improved aerosol product over the Indian subcontinent: Blending model, satellite, and ground‐based estimates

Singh, Randhir; Singh, Charu; Ojha, Satya P.; Kumar, Annamalai Senthil; Kumar, Akhilesh

doi:10.1002/2016jd025335

Cited by 15 publications

(11 citation statements)

References 47 publications

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“…In [39], the authors found a correlation coefficient of 0.73, an average error around 46.15%, and an MBE of 0.045. It was also found that only 52.12% of CAMS MACC AOD values were within ±0.05 to ±0.15 of the Aeronet AOD.…”

Section: Discussionmentioning

confidence: 94%

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Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

et al. 2020

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Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions.

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Section: Discussionmentioning

confidence: 94%

“…Aeronet locations were selected on the basis of proximity to BSRN locations and available data were extracted on the basis of the time period of INSAT-3D data. The AOD data from Aeronet was available at 500 nm, which was converted to 550 nm using Ångstrom law [39,40].…”

Section: Ground Measurementsmentioning

confidence: 99%

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

et al. 2020

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“…This is mainly because, the underlying correlation structure and the actual variance values not only dictate the pattern in which observations get merged with the background data but also decides the weights given to each of the parent datasets during the merging 20 process. In the present study, the observation error covariance matrix (O) is considered to be diagonal implying that errors in GR AAODs from different ground-based stations are uncorrelated, which is generally true and is followed earlier also (Niu et al, 2008;Zhang et al, 2008;Singh et al, 2017). As the diagonal terms of the covariance matrix refer to variance of the corresponding data, the diagonal terms of O are formed by the taking square of uncertainties in the GR AAODs which are estimated as explained below.…”

mentioning

confidence: 85%

“…Due to this, the final assimilated AODs (for Chung et al (2005) and Adhikary et al (2008)) over most parts of Indian region are largely represented by satellite retrieved AODs with their inherent large uncertainties as discussed earlier. 5 More recently, Singh et al (2017) have combined AODs simulated by ECMWF with those retrieved by MODIS, Multiangle Imaging SpectroRadiometer (MISR) as well as in situ measured AODs by total 35 AERONET stations spread over Indian as well as Arabian region. However, even in this case employing about 17 AEORNET stations over Indian region, most of these stations were in the monsoon trough region and north-east India with no representation of other parts of the domain.…”

Section: Introductionmentioning

confidence: 99%

“…As such, Mitra et al (2003Mitra et al ( , 2009) have employed Cressman method (Cressman, 1959), a variant of SCM, for forming 5 daily analyses of rainfall over Indian region by merging rain gauge measurements with satellite retrieved precipitation. For AOD assimilation, the similar method has been employed by Chung et al (2005), while Optimal Interpolation (OI) has been used by Collins et al (2001); Adhikary et al (2008) and Singh et al (2017). Further, 3D-VAR has been employed by Niu et al (2008) and Zhang et al (2008) for assimilation of dust aerosol properties and AODs respectively.…”

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confidence: 99%

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Assessment of Regional Aerosol Radiative Effects under SWAAMI Campaign – Part 1: Quality-enhanced Estimation of Columnar Aerosol Extinction and Absorption Over the Indian Subcontinent

Pathak¹,

Satheesh²,

Nanjundiah³

et al. 2019

Preprint

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Abstract. Improving the accuracy of regional aerosol climate impact assessment calls for an improvement in the accuracy of regional aerosol radiative effects (ARE) estimation. One of the most important means of achieving this is to use spatially homogeneous and temporally continuous datasets of critical aerosol properties, such as spectral aerosol optical depth (AOD) and single scattering albedo (SSA), which are the most important parameters for estimating aerosol radiative effects. However, observations do not provide the above; the space-borne observations though provide wide spatial coverage, are temporally snapshots and suffer from possible sensor degradation over extended periods. On the other hand, the ground-based measurements provide more accurate and temporally continuous data, but are spatially near-point observations. Realizing the need for spatially homogeneous and temporally continuous datasets on one hand and the near-non-existence of such data over the south Asian region (which is one of the regions where aerosols show large heterogeneity in most of their properties), construction of accurate gridded aerosol products by synthesizing the long-term space-borne and ground-based data, has been taken up as an important objective of the South West Asian Aerosol Monsoon Interactions (SWAAMI), a joint Indo-UK field campaign, aiming at characterizing aerosol-monsoon links and their variabilities over the Indian region. In the Part-1 of this two-part paper, we present spatially homogeneous gridded datasets of AOD and absorption AOD (AAOD), generated for the first time over this region. These data products are developed by merging the highly accurate aerosol measurements from the dense networks of 44 (for AOD) and 34 (for AAOD) ground-based observatories of Aerosol Radiative Forcing NETwork (ARFINET) and AErosol RObotic NETwork (AERONET) spread across the Indian region, with satellite-retrieved AOD and AAOD, following statistical assimilation schemes. The satellite data used for AOD assimilation includes AODs retrieved from MODerate Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) over the same domain. For AAOD, the ground-based Black Carbon (BC) mass concentration measurements from the network of 34 ARFINET observatories and satellite-based (Kalpana-1, INSAT-3A) infrared (IR) radiance measurements, are blended with gridded AAODs (500&#8201;nm, monthly mean) derived from Ozone Monitoring Instrument (OMI)-retrieved AAODs (at 354&#8201;nm and 388&#8201;nm). The details of the assimilation methods and the gridded datasets generated are presented in this paper. The merged, gridded AOD and AAOD products thus generated, are validated against the data from independent ground-based observatories, which were not used for the assimilation process, but are representative of different subregions of the complex domain. This validation exercise revealed that the independent ground-based measurements are better confirmed by merged datasets than the respective satellite products. As ensured by assimilation techniques employed, the uncertainties in merged AODs and AAODs are significantly less than those in corresponding satellite products. These merged products also exhibit all important, large-scale spatial and temporal features which are already reported for this region. Nonetheless, the merged AODs and AAODs are significantly different in magnitude, from the respective satellite products. On the background of above mentioned quality enhancements demonstrated by merged products, we have employed them for deriving the columnar SSA and analysed its spatio-temporal characteristics. The columnar SSA thus derived has demonstrated distinct seasonal variation, over various representative subregions of the study domain. The uncertainties in the derived SSA are observed to be substantially less than those in OMI SSA. On the backdrop of these benefits, the merged datasets are employed for the estimation of regional aerosol radiative effects (direct), the results of which would be presented in a companion paper; Part-2 of this two-part paper.

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Discerning the pre-monsoon urban atmosphere aerosol characteristic and its potential source type remotely sensed by AERONET over the Bengal Gangetic plain

Priyadharshini

Verma

Giles

et al. 2018

Environ Sci Pollut Res

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In the present study, we evaluated the pre-monsoon urban atmosphere (UA) aerosol characteristics remotely sensed by Aerosol Robotic Network (AERONET) over the Bengal Gangetic plain (BGP) at Kolkata (KOL) and their implication in potential source types and spatiotemporal features. About 70% of the AERONET-sensed aerosol optical depth at 0.50 μ m, AOD (Angstrom exponent, α at 0.44-0.87 μ m) during the pre-monsoon period (February to June) was greater than 0.50 (≤ 1); the pre-monsoon mean of AOD (α) was 0.73 (0.83) which was found being slightly higher (lower) than nearby AERONET stations (Dhaka/Bhola) located over the eastern Ganges basin. The volume geometric mean radius for the fine mode (FM) (coarse mode, CM) UA aerosol from AERONET retrievals was estimated to be 0.14-0.17 (2.24-2.75) μ m. The spectral distribution of the monthly mean of UA aerosol single-scattering albedo (SSA) exhibited an increasing trend with an increase in wavelength throughout all wavelengths during April, unlike the rest of the pre-monsoon months. Investigation of aerosol types indicated the pre-dominance of dust during April and a mixture of urban/open burning with mixed desert dust during the rest of the pre-monsoon months. Potential aerosol source fields were identified over the Indo-Gangetic Plain (IGP), east coast, northwestern India, and oceanic regions; these were estimated at elevated layers of atmosphere during April and May but that at surface layers during February and June. Comparison of aerosol characteristics over the BGP (at Kolkata, KOL) with that at six other coincident AERONET sites over India revealed mean AOD at KOL being 11 to 91% higher than the rest of the AERONET stations, with the relative increase at KOL being the highest during March; this was attributed to persistent high values of both FM and CM AOD unlike the rest of the stations. The monthly mean of SSA was the lowest at KOL among AERONET stations, during February and March. Comparison of the AOD from the AERONET aerosol retrievals over the BGP UA with the coincident Moderate Resolution Imaging Spectroradiometer (MODIS) latest retrievals (C005 and C006) indicated a moderate correlation between the two retrievals; discrepancy in MODIS-retrieved relative distribution of FM and CM AOD was inferred compared to AERONET in the UA.

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Development of an improved aerosol product over the Indian subcontinent: Blending model, satellite, and ground‐based estimates

Cited by 15 publications

References 47 publications

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

Assessment of Regional Aerosol Radiative Effects under SWAAMI Campaign – Part 1: Quality-enhanced Estimation of Columnar Aerosol Extinction and Absorption Over the Indian Subcontinent

Discerning the pre-monsoon urban atmosphere aerosol characteristic and its potential source type remotely sensed by AERONET over the Bengal Gangetic plain

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