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Discussion papers
https://doi.org/10.5194/acp-2019-153
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-153
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Feb 2019

Submitted as: research article | 21 Feb 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

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

Harshavardhana Sunil Pathak1, Sreedharan Krishnakumari Satheesh1,2, Ravi Shankar Nanjundiah1,2,3, Krishnaswamy Krishna Moorthy1, Sivaramakrishnan Lakshmivarahan4, and Surendran Nair Suresh Babu5 Harshavardhana Sunil Pathak et al.
  • 1Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, India
  • 2Divecha Centre for Climate Change, Indian Institute of Science, Bangalore, India
  • 3Indian Institute of Tropical Meteorology, Pune 411008, India
  • 4School of Computer Science, University of Oklohama, Norman, USA
  • 5Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India

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 nm, monthly mean) derived from Ozone Monitoring Instrument (OMI)-retrieved AAODs (at 354 nm and 388 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.

Harshavardhana Sunil Pathak et al.
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Harshavardhana Sunil Pathak et al.
Harshavardhana Sunil Pathak et al.
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Short summary
We have developed quality-enhanced, gridded datasets for Aerosol Optical Depth (AOD) and absorption AOD by assimilating highly accurate measurements from the dense network of ground-based stations, with respective satellite retrieved datasets. The assimilated datasets demonstrate improved accuracy and reduced uncertainties as compared to respective satellite products. Thus, these assimilated products emerge as important tools to improve the accuracy of climate impact assessment of aerosols.
We have developed quality-enhanced, gridded datasets for Aerosol Optical Depth (AOD) and...
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