data: root mean sq anom $integral dlat$ $partialdiff sub lat$ max root mean sq $partialdiff sub lon$ [ UY prec ]

root mean sq anom ∫dlat ∂_lat max root mean sq ∂_lon [ UY prec ] : ∂_lat ∂_lon Precipitacion data

prec prec partial_lon partial_lon max partial_lat int_dlat ∂_lat ∂_lon Precipitacion from UY: PRECM_UY_v1p1: Gridded precipitation dataset at 30 km. It corresponds to Experiment No. 3 in the documentation. is

Independent Variables (Grids)

Other Info

bufferwordsize

CE

null

CS

null

datatype

doublearraytype

file_missing_value

-999.0

missing_value

NaN

units

0.687549354156987 meter radian^-1 north year^-1

history

$integral dlat$ $partialdiff sub lat$ max root mean sq $partialdiff sub lon$ [ UY prec ] 36S

Averaged over time[Jan 1925, Dec 2009] minimum 0.0% data present
max over lon[58.71053W, 53.78947W]

root mean sq anom $integral dlat$ $partialdiff sub lat$ max root mean sq $partialdiff sub lon$ [ UY prec ]

Averaged over lat[29.5S, 36S] 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

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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
Differentiate along
Take differences along

Average over
RMS (root mean square with mean *not* removed) over
RMSA (root mean square with mean removed) over
Maximum over
Minimum over
Detrend (best-fit-line) over

Note on units