∂province [ NiMet WASP queriesENACTS statequery exp1_year ]: ∂province data
∂province [ NiMet WASP queriesENACTS statequery exp1_year ].
Independent Variables (Grids)
province
grid: /province (ids) unordered [ (0±Á'0±Á') (0±Á'0±Á') (m) (0±Á'0±Á') (0±Á'0±Á') (0±Á'0±Á') (0±Á'0±Á') (0±Á'0±Á') (ver) () (à-) (0±Á'0±Á') (Т@() (رÁ'رÁ'ØPC) (0±Á'0±Á'0±Á') (0±Á'0±Á'0±Á') (0±Á'0±Á'0±Á') (0±Á'0±Á'0±Á'!) (бÁ'бÁ'ØPC) (0±Á'0±Á') (0±Á'0±Á'0±Á') (0±Á'0±Á'0±Á') (0±Á'0±Á'0±Á') (0±Á'0±Á'0±Á') (a) (À±Á'À±Á'pe) (0±Á'0±Á') () (form) ( ½m) (`) (oriz) (ê¼m) (0) (er_v) (r1_year,exp1_year,status,disp_area,adm0_code,adm0_name,cast(shape_leng as float8) as shape_leng,cast(shape_area as float8) as shape_area,asbinary(the_geom,'ndr') as the_geom,asbinary(coarse_geom,'ndr') as coarse_geom from (select adm1_name as province,* from g2015_2014_1 where adm1_name is not null and adm0_name~'Nigeria' order by adm1_name)as mybquery) as ivarq;)] :grid
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.
Average over province
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RMS (root mean square with mean *not* removed) over province
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RMSA (root mean square with mean removed) over province
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Maximum over province
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Minimum over province
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Detrend (best-fit-line) over province
|