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

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

prec prec partial_lon partial_lat int_dtime int_dtime int_dtime ∂_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 (58.71053W) to (53.78947W) by 0.2894736 N= 18 pts :grid

Other Info

bufferwordsize

CE

null

CS

null

datatype

doublearraytype

file_missing_value

-999.0

missing_value

NaN

units

0.273567195834312 meter radian^-2 east year

history

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

Averaged over lat[36S, 29.5S] minimum 0.0% data present
Averaged over time[0000 16 Dec 1924, 0000 16 Jan 2010] 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
mean $integral dtime$ mean $integral dtime$ $partialdiff sub lat$ $partialdiff sub lon$ [ UY prec ] [ 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