Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra measures global aerosol optical depth and optical properties since 2000. MODIS aerosol products are freely available and are being used for numerous studies. In this paper, we present a comparison of aerosol optical depth (AOD) retrieved from MODIS with Aerosol Robotic Network (AERONET) data for the year 2004 over Kanpur, an industrial city lying in the Ganga Basin in the northern part of India. AOD retrieved from MODIS (τ aMODI S ) at 0.55µm wavelength has been compared with the AERONET derived AOD (τ aAERON ET ), within an optimum space-time window. Although the correlation between τ aMODI S and τ aAERONET during the post-monsoon and winter seasons (R 2 ∼ 0.71) is almost equal to that during the pre-monsoon and monsoon seasons (R 2 ∼0.72), MODIS is found to overestimate AOD during the pre-monsoon and monsoon period (characterized by severe dust loading) and underestimate during the post-monsoon and winter seasons. The absolute difference between τ aMODI S and τ aAERON ET is found to be low (0.12±0.11) during the non-dust loading season and much higher (0.4±0.2) during dust-loading seasons. The absolute error in τ aMODI S is found to be about ∼25% of the absolute values of τ aMODI S . Our comparison shows the importance of modifying the existing MODIS algorithm during the dustloading seasons, especially in the Ganga Basin in northern part of India.

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Declarative data quality has been an active research topic. The fundamental principle behind a declarative approach to data quality is the use of declarative statements to realize data quality primitives on top of any relational data source. A primary advantage of such an approach is the ease of use and integration with existing applications. Over the last few years several similarity predicates have been proposed for common quality primitives (approximate selections, joins, etc) and have been fully expressed using declarative SQL statements. In this paper we propose new similarity predicates along with their declarative realization, based on notions of probabilistic information retrieval. In particular we show how language models and hidden Markov models can be utilized as similarity predicates for data quality and present their full declarative instantiation. We also show how other scoring methods from information retrieval, can be utilized in a similar setting. We then present full declarative specifications of previously proposed similarity predicates in the literature, grouping them into classes according to their primary characteristics. Finally, we present a thorough performance and accuracy study comparing a large number of similarity predicates for data cleaning operations. We quantify both their runtime performance as well as their accuracy for several types of common quality problems encountered in operational databases.

The first detailed validation of maximum temperature of Modern-Era Retrospective analysis for Research and Application Version 2 (T MERRA-2 ) against Indian Meteorological Department (T IMD ) has been carried out for 35 years over India. For this purpose, India has been divided into seven different zones, i.e Western Himalaya (WH), Northwest, North Central, Northeast (NE), West Peninsula India, East Peninsula India, and South Peninsula India. The descriptive statistics and correlation between T MERRA-2 and T IMD have been determined for monthly, seasonal, and annual basis. A significant correlation (>0.9) has been found for monthly T MERRA-2 and T IMD with a root-mean-square error value closer to 1 except for WH where a high root-mean-square error value of 18.2 is obtained. Seasonal analysis also indicates a significant correlation for all the zones except for WH and NE with a correlation value of <0.3 during monsoon season; this may be due to sparse network, cold climate, and heterogeneity due to topography. Percent bias indicates that T MERRA-2 generally overestimates the T IMD monthly observations for all the zones, that is, Northwest, North Central, NE, West Peninsula India, East Peninsula India, and South Peninsula India by 4.1%, 2.4%, 1.6%, 0.5%, 0.2%, and 0.8%, respectively, except WH where an underestimation (−82.5%) is determined. Thus, after calibration, MERRA-2 Reanalysis maximum temperature may be used for further study of extreme weather events.