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

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

prec partial_lon partial_time int_dlat partial_lat ∂_lat ∂_time ∂_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 (58.85526W) to (53.64474W) by 0.2894737 N= 19 pts :grid

Other Info

bufferwordsize: 8
CE: null
CS: null
datatype: doublearraytype
file_missing_value: -999.0
missing_value: NaN
units: 8.25059224988385 meter radian^-1 north year^-2
history: Averaged over time[16 Jan 1925 - 15 Feb 1925, 16 Nov 2009 - 15 Dec 2009] 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
UY[prec precipitacion ]
mean $partialdiff sub lat$ $integral dlat$ $partialdiff sub time$ $partialdiff sub lon$ [ 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