data: mean mean $partialdiff sub lat$ $partialdiff sub lon$ $partialdiff sub time$ [ UY precipitacion ]

mean mean ∂_lat ∂_lon ∂_time [ UY precipitacion ] : ∂_lat ∂_lon ∂_time Precipitacion data

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

Independent Variables (Grids)

time: grid: /time (months since 1960-01-01) ordered (Feb 1925) to (Nov 2009) by 1.0 N= 1018 pts :grid

Other Info

bufferwordsize

CE

null

CS

null

datatype

doublearraytype

file_missing_value

-999.0

missing_value

NaN

units

472.724114401691 meter radian^-2 east year^-2

history

mean mean $partialdiff sub lat$ $partialdiff sub lon$ $partialdiff sub time$ [ UY precipitacion ]

Averaged over lon[58.85526W, 53.64474W] minimum 0.0% data present
Averaged over lat[35.71739S, 29.78261S] 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

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