data: spi prob pcpn_accum [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] * masklt [ flaggt ( mean flaggt { [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] , 0.0 } , 0.3333333 ) , 1.0 ]

spi prob pcpn_accum [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] * masklt [ flaggt ( mean flaggt { [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] , 0.0 } , 0.3333333 ) , 1.0 ] : Standardized Precipitation Index data

spi prob pcpn_accum [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] * masklt [ flaggt ( mean flaggt { [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] , 0.0 } , 0.3333333 ) , 1.0 ] .

Independent Variables (Grids)

Time: grid: /T (months since 1960-01-01) ordered (Jan 1951) to (Dec 2005) by 1.0 N= 660 pts :grid

Other Info

CE

colorscalename

precip_colors

CS

-3

datatype

realarraytype

file_missing_value

-9.9899998

maxncolor

254

missing_value

NaN

pointwidth

units

unitless

history

Averaged over T2[1951, 2005]
Boxes with less than 0.0% dropped

colorscale

Last updated: Tue, 09 Feb 2021 09:35:21 GMT

Data Views

	T
spi prob pcpn_accum [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] * masklt [ flaggt ( mean flaggt { [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] , 0.0 } , 0.3333333 ) , 1.0 ] [ T \|]	M
spi prob pcpn_accum [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] * masklt [ flaggt ( mean flaggt { [ ESGF-CORDEX Africa hist CLMcom-CCLM4-8-17_v1 CNRM-CERFACS-CNRM-CM5_r1i1p1 precipitation ] , 0.0 } , 0.3333333 ) , 1.0 ] [ T \|]	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 T
Differentiate along T
Take differences along T