What are the temporal and spatial resolution of AgMERRA?

There are different sources of data which all of them provide or record climate and weather variables. These sources are including: In-situ data, namely synoptic stations, gridded datasets such as CRU and AgMERRA, CMIP5 outputs, regional climate models, and etc., among of them the gridded datasets are newly used in papers and researches.

AgMERRA is a gridded dataset which is prepared different weather variables, in different spatial and temporal resolutions. The AgMERRA and AgCFSR climate forcing datasets were created as an element of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to provide consistent, daily time series over the 1980-2010 period with global coverage of climate variables required for agricultural models. These datasets were designed to be useful for AgMIP's coordinated, protocol-based studies of agricultural impacts ranging from biophysical process studies to global agricultural economic models (Rosenzweig et al., 2013).

The spatial resolution of AgMERRA divide to three different scales, namely 0.25 degree, 0.5 degree, and 1 degree for seven different weather variables. Mean, maximum, and minimum temperatures (degree Celsius) are in 0.5 degree. The values of precipitation (mm) are in 0.25 degree. The amount of solar radiation (MJ/m2/day) are achieved in 1 degree.

The values Relative Humidity at Time of maximum temperature (%) are in 0.25 degree. The wind speed (m/s) that the spatial resolution of this variable in AgMERRA is equal to 0.25 degree. There is a complete table in the https://data.giss.nasa.gov/impacts/agmipcf/ that you can find further information about these resolutions. The temporal resolution of AgMERRA is in daily scale.

The seven variables are available in daily scale over 1980-2010. AgMERRA was created to be used explicitly for research purposes with uses for impacts analysis and scenario generation, but it is important to recognize that they do not represent a climate observational record. This dataset may not be suitable for other applications, including, but not limited to, detailed trend analysis or energy and water budget evaluation because they blend datasets and rely on reanalyses with changing inputs between 1980 and 2010. Rosenzweig, C., et al., 2013:

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies, Agr. Forest Meteorol., 170, 166-182, doi:dx.doi.org/10.1016/j.agrformet.2012.09.011.

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