data: mean $partialdiff sub time$ $partialdiff sub lon$ [ UY prec ]

mean ∂_time ∂_lon [ UY prec ] : ∂_time ∂_lon Precipitacion data

prec partial_lon partial_lon partial_time partial_time ∂_time ∂_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 (29.5S) to (36S) by 0.2826087 N= 24 pts :grid
time: grid: /time (months since 1960-01-01) ordered (16 Feb 1925 - 15 Mar 1925) to (16 Oct 2009 - 15 Nov 2009) by 1.0 N= 1017 pts :grid

Other Info

CE: null
CS: null
datatype: realarraytype
file_missing_value: -999.0
missing_value: NaN
units: 8.25059224988386 meter radian^-1 east year^-2
history: Averaged over lon[59W, 53.5W] 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	time
UY[precipitacion prec ]
mean $partialdiff sub time$ $partialdiff sub lon$ [ UY prec ] [ time \| 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 time lat
Differentiate along time lat
Take differences along time lat