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

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

prec partial_lon int_dtime int_dtime partial_lat partial_lat partial_lat ∂_lat ∂_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)

lat: grid: /lat (degree_north) ordered (29.78261S) to (35.71739S) by 0.2826087 N= 22 pts :grid

Other Info

CE

null

CS

null

datatype

realarraytype

file_missing_value

-999.0

missing_value

NaN

units

188.090948814419 meter radian^-3 east

history

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

Averaged over lon[58.85526W, 53.64474W] minimum 0.0% data present
max over time[0000 1 Jan 1925, 0000 1 Jan 2010]

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

	lat
grid: /time (months since 1960-01-01) ordered (0000 1 Jan 1925) to (0000 1 Jan 2010) by 1.0 N= 1021 pts :grid
grid: /lat (degree_north) ordered (29.78261S) to (35.71739S) by 0.2826087 N= 22 pts :grid	M
max $partialdiff sub lat$ mean $partialdiff sub lat$ $integral dtime$ $partialdiff sub lon$ [ UY prec ] [ \| lat]	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 lat
Differentiate along lat
Take differences along lat