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

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

precipitacion partial_lon partial_lat partial_time partial_time partial_time partial_time partial_time partial_lon ∂_lon ∂_time ∂_lat ∂_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)

lon: grid: /lon (degree_east) ordered (58.56579W) to (53.93421W) by 0.2894735 N= 17 pts :grid

Other Info

bufferwordsize

CE

null

CS

null

datatype

doublearraytype

file_missing_value

-999.0

missing_value

NaN

units

27085.0966292764 meter radian^-3 east year^-2

history

root mean sq anom $partialdiff sub lon$ mean $partialdiff sub time$ $partialdiff sub lat$ $partialdiff sub lon$ [ UY precipitacion ]

Averaged over time[Feb 1925, Nov 2009] minimum 0.0% data present
Averaged over lat[29.78261S, 35.71739S] 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
root mean sq anom $partialdiff sub lon$ mean $partialdiff sub time$ $partialdiff sub lat$ $partialdiff sub lon$ [ UY precipitacion ] [ lon \|]	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
Differentiate along lon
Take differences along lon