data: mean $integral dtime$ $integral dlat$ $partialdiff sub lon$ [ UY prec ]

mean ∫dtime ∫dlat ∂_lon [ UY prec ] : ∂_lon Precipitacion data

prec prec partial_lon int_dlat int_dtime ∂_lon Precipitacion from UY: PRECM_UY_v1p1: Gridded precipitation dataset at 30 km. It corresponds to Experiment No. 3 in the documentation.

Independent Variables (Grids)

lat: grid: /lat (degree_north) ordered (29.3587S) to (36.1413S) by 0.2826087 N= 25 pts :grid
lon: grid: /lon (degree_east) ordered (58.71053W) to (53.78947W) by 0.2894736 N= 18 pts :grid

Other Info

bufferwordsize: 8
CE: null
CS: null
datatype: doublearraytype
file_missing_value: -999.0
missing_value: NaN
units: mm
standard units*: 0.001 meter
history: Averaged over time[0000 1 Jan 1925, 0000 1 Jan 2010] minimum 0.0% data present

References

Muñoz, ?~A.G., González, P., Baethgen, W.,: Gridded precipitation dataset ff or Uruguay. Version 1.1

Last updated: Sun, 02 Jun 2024 17:51:42 GMT

Data Views

	lat	lon
UY[precipitacion prec ]
mean $integral dtime$ $integral dlat$ $partialdiff sub lon$ [ UY prec ] [ lon \| lat]	M	M

Filters

Here are some filters that are useful for manipulating data. There are actually many more available, but they have to be entered manually. See Ingrid Function Documentation for more information.

Monthly Climatology calculates a monthly climatology by averaging over all years.
anomalies calculates the difference between the (above) monthly climatology and the original data.
Integrate along lon lat
Differentiate along lon lat
Take differences along lon lat