What are the differences between station data and gridded datasets (in providing weather data)?

There is a major difference. When you use station data (such as synoptic station, climatology station, agricultural station, and etc.), you just use the data for a point scale (with lat and lon of the station), but when you use gridded data you can apply different meteorological data for a region that the area of the region is depend on the resolution of applied gauge.

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) (Huffman et al. 2007), and merged satellite-gauge products such as AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications)

For example AgMERRA gauge can produce weather variables with 0.5deg*0.5deg or 0.25deg*0.25deg. We can achieve the differences between station data and gridded datasets, when we don't have enough data for a region from station data, or we have lots of missing or gaps in registration of data in a specific station. In this case, gridded datasets are very proper for us to apply them in our projects. In many cases the gridded datasets have strong ability to use in studies that we don't have enough stations, you can refer to the following paper: 'Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data' DOI: 10.1007/s40333-017-0070-y

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