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

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

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

Independent Variables (Grids)

lon: grid: /lon (degree_east) ordered (58.71053W) to (53.78947W) by 0.2894736 N= 18 pts :grid
time: grid: /time (months since 1960-01-01) ordered (16 Jan 1925 - 15 Feb 1925) to (16 Nov 2009 - 15 Dec 2009) by 1.0 N= 1019 pts :grid

Other Info

bufferwordsize: 8
CE: null
CS: null
datatype: doublearraytype
file_missing_value: -999.0
missing_value: NaN
units: 39.3936762001409 meter radian^-2 north year^-1
history: Averaged over lat[36S, 29.5S] 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

	lon	time
UY[precipitacion prec ]
mean $partialdiff sub lon$ $partialdiff sub lat$ [ UY prec ] [ time lon \|]	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 lon
Differentiate along time lon
Take differences along time lon