data: root mean sq root mean sq anom $partialdiff sub lat$ $integral dtime$ [ UY prec ]

root mean sq root mean sq anom ∂_lat ∫dtime [ UY prec ] : ∂_lat Precipitacion data

prec int_dtime partial_lat partial_lat ∂_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)

time: grid: /time (months since 1960-01-01) ordered (0000 1 Jan 1925) to (0000 1 Jan 2010) by 1.0 N= 1021 pts :grid

Other Info

bufferwordsize

CE

null

CS

null

datatype

doublearraytype

file_missing_value

-999.0

missing_value

NaN

units

0.0572957795130823 meter radian^-1 north

history

root mean sq root mean sq anom $partialdiff sub lat$ $integral dtime$ [ UY prec ]

Averaged over lon[59W, 53.5W] minimum 0.0% data present
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

	time
UY[prec precipitacion ]
root mean sq root mean sq anom $partialdiff sub lat$ $integral dtime$ [ UY prec ] [ 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