data: $partialdiff sub time$ root mean sq [ UY prec ]

∂_time root mean sq [ UY prec ] : ∂_time Precipitacion data

prec prec prec prec 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)

lon: grid: /lon (degree_east) ordered (58.85526W) to (53.64474W) by 0.2894737 N= 19 pts :grid
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: 1.38888888888889×10^-07 meter second^-1 year^-1
history: Averaged over lat[36.1413S, 29.3587S] 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
$partialdiff sub time$ root mean sq [ 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