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Can you tell me, what are daily gridded datasets?

Using interpolated data may increase uncertainty as interpolation methods can be a source of error as well (Goovaerts,1997). Many studies have demonstrated that when there are large and irregular elevation differences between the interpolated regions and its vicinal precipitation gauging stations, the error of estimation is high (Chang et al. 2005). Although there are various different interpolation methods, there is no single method suitable to be applied in every circumstance (Nalder and Wein 1998). As a result, during past decades, few data-base sources of gridded daily datasets have been developed. These can be classified into three main categories: gauge-based such as APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation) (Yatagai et al. 2009 and 2012), satellite-based like PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks) (Hong et al. 2004) and TRMM (Tropical Rainfall Measuring Mission), and merged satellite-gauge products such as AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications).

There are few database sources of gridded daily datasets that we can divide them into three main categories: satellite-based like PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks) and TRMM (Tropical Rainfall Measuring Mission), gauge-based like APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), and merged satellite-gauge products such as AgMERRA (The Modern-Era Retrospective analysis for Research and Applications).

These sources can produce different datasets that they have differences in number of variables, domain of producing data, and different domains periods.

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