data: detrended-bfl [ Models NMME Cansips FORECAST MONTHLY vgrd ]

served from IRI/LDEO Climate Data Library

detrended-bfl [ Models NMME Cansips FORECAST MONTHLY vgrd ]: Meridional Wind data

Cansips FORECAST MONTHLY vgrd Meridional Wind from Models NMME: North American Multi-Model Ensemble (NMME).

Independent Variables (Grids)

Lead: grid: /L (months) ordered (0.5 months) to (11.5 months) by 1.0 N= 12 pts :grid
Ensemble Member (realization): grid: /M (unitless) ordered (1.0) to (20.0) by 1.0 N= 20 pts :grid
Pressure Level (air_pressure): grid: /P (hPa) ordered (200 hPa) to (850 hPa) N= 2 pts :grid
Forecast Start Time (forecast_reference_time): grid: /S (months since 1960-01-01) ordered (0000 1 Sep 2015) to (0000 1 Jul 2019) by 1.0 N= 47 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
CS: null
datatype: doublearraytype
file_missing_value: 9.99900026E20
grib_name: VGRD
gribPDSpattern: 04XXXX
missing_value: NaN
pointwidth: 650.0
standard_name: northward_wind
units: m s^-1
standard units*: meter second^-1

References

Kirtman, Ben P., and Coauthors, 2014: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; Phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585–601. doi: http://dx.doi.org/10.1175/BAMS-D-12-00050.1

Last updated: Tue, 08 Dec 2020 21:56:07 GMT

Data Views

	L	M	P	S	X	Y
detrended-bfl [ Models NMME Cansips FORECAST MONTHLY vgrd ][ X Y \| L M P S]	M	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 L M P S
Differentiate along X Y L M P S
Take differences along X Y L M P S