data: $partialdiff sub lon$ mean $integral dtime$ $partialdiff sub lat$ [ UY prec ]

∂_lon mean ∫dtime ∂_lat [ UY prec ] : ∂_lon ∂_lat Precipitacion data

prec prec partial_lat int_dtime int_dtime ∂_lon ∂_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)

lon: grid: /lon (degree_east) ordered (58.71053W) to (53.78947W) by 0.2894736 N= 18 pts :grid
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

CE: null
CS: null
datatype: realarraytype
file_missing_value: -999.0
missing_value: NaN
units: 3.28280635001174 meter radian^-2 north
history: 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

	lon	time
UY[precipitacion prec ]
$partialdiff sub lon$ mean $integral dtime$ $partialdiff sub lat$ [ 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