data: $integral dF$ [ IRI FD NMME_Seasonal_Forecast Temperature

served from IRI/LDEO Climate Data Library

∫dF [ IRI FD NMME_Seasonal_Forecast Temperature_ELR prob ]: Tercile Probability data

prob Tercile Probability from IRI FD NMME_Seasonal_Forecast Temperature_ELR: ELR NMME Seasonal forecasts for probabilities of below-normal, above-normal, and near-normal temperature.

Independent Variables (Grids)

Tercile Classes: grid: /C (ids) unordered [ (Below_Normal) (Normal) (Above_Normal)] :grid
F: grid: /F (months since 1960-01-01) ordered (0000 1 Feb 2017) to (0000 1 May 2024) by 1.0 N= 88 pts :grid
Forecast Lead Time in Months: grid: /L (months) ordered (1.0 months) to (4.0 months) by 1.0 N= 4 pts :grid
Longitude (longitude): grid: /X (degree_east) periodic (0) to (1W) by 1.0 N= 360 pts :grid
Latitude (latitude): grid: /Y (degree_north) ordered (90S) to (90N) by 1.0 N= 181 pts :grid

Other Info

bufferwordsize: 8
CE: null
colorscalename: tercileclassesscale
CS: null
datatype: doublearraytype
defaultvalue: 771.5
maxncolor: 254
missing_value: NaN
pointwidth: 1
units: 0.000833333333333333 year

Last updated: Mon, 15 Apr 2024 12:00:12 GMT
Expires: Tue, 14 May 2024 00:00:00 GMT

Data Views

	C	F	L	X	Y
$integral dF$ [ IRI FD NMME_Seasonal_Forecast Temperature_ELR prob ][ X Y C \| L F]	M	M	M	M	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 X Y C L F
Differentiate along X Y C L F
Take differences along X Y C L F