data: root mean sq anom $partialdiff sub time$ min $integral dlat$ [ UY prec ]

root mean sq anom ∂_time min ∫dlat [ UY prec ] : ∂_time Precipitacion data

prec int_dlat int_dlat min partial_time partial_time ∂_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 (days since 1960-01-01) ordered [ (0600 31 Jan 1925 - 1800 1 Mar 1925) (1800 1 Mar 1925 - 1800 31 Mar 1925) (1800 31 Mar 1925 - 0600 1 May 1925) ... (1800 31 Oct 2009 - 0600 1 Dec 2009)] N= 1018 pts :grid

Other Info

CE

null

CS

null

datatype

realarraytype

file_missing_value

-999.0

missing_value

NaN

units

2.42406840554769×10^-09 meter radian north second^-1 year^-1

history

root mean sq anom $partialdiff sub time$ min $integral dlat$ [ UY prec ]

min over lat[29.3587S, 36.1413S]
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

	time
root mean sq anom $partialdiff sub time$ min $integral dlat$ [ 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