Closes issue #34 soil carbon from soilgrids2.0 #79
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🛠 Issue reference
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Closes Issue #34
Created ISRIC directory and convert.py. The conversion
.pydownloads the data, converts it from Homolosine to WGS84, coarsens resolution from 250m (homolosine) to 0.5 degree (wgs84) using weighted averaging. Note, this weighted averaging reduced the range of data from about 0-22 to 0-16. In the future, we might consider the conservative coarsening function created by xESMF, but calculating the weights (not in parallel) takes eons and I didn't really want to waste time on it. Anyway, looks like ESM folks use this package to resample data to a coarser resolution while maintaining the original range of the higher resolution data.🧪 Testing
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xbetween the brackets to ensure CF complianceDimensions
timewith attributes/encoding:axisattribute isTunitsattribute/encoding isdays since YYYY-MM-DD HH:MM:SSlong_nameattribute istimecalendarencoding isnoleapboundsencoding istime_boundslonwith attributes:axisattribute isXunitsattribute isdegrees_eastlong_nameattribute islongitudelatwith attributes:axisattribute isYunitsattribute isdegrees_northlong_nameattribute islatitudenv, which is an array of length2that contains the start date and end date bounding the datasetData Variables and their Attributes
time,lat, andlondimensionslong_nameattribute is specifiedunitsattribute is specifiedancillary_variablesattribute is specified if an uncertainty value is providedtime,lat, andlondimensionslong_nameattribute is specified (e.g., cSoil standard_deviation)unitsattribute is specified; it is unitless, so it should be1timeandnvdimensionslong_nameattribute is specified astime_boundsunitsis encoded asdays since YYYY-MM-DD HH:MM:SSGlobal Attributes