data: $partialdiff sub Y$ $partialdiff sub P$ root mean sq [ NOAA NCEP-NCAR CDAS-1 MONTHLY Intrinsic PressureLevel CBVQ ]

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∂_Y ∂_P root mean sq [ NOAA NCEP-NCAR CDAS-1 MONTHLY Intrinsic PressureLevel CBVQ ] : ∂_Y ∂_P Covar. v and specific hum data

∂_Y ∂_P root mean sq [ NOAA NCEP-NCAR CDAS-1 MONTHLY Intrinsic PressureLevel CBVQ ] .

Independent Variables (Grids)

P: grid: /P (mb) ordered [ (962.5) (887.5) (775.0) (650.0) (550.0) (450.0) (350.0)] :grid
Time (time): grid: /T (months since 1960-01-01) ordered (Jan 1949) to (Aug 2014) by 1.0 N= 788 pts :grid
Latitude (latitude): grid: /Y (degree_north) ordered (88.75N) to (88.75S) by 2.5 N= 72 pts :grid

Other Info

calendar: standard
CE: null
center: US Weather Service - National Met. Center
CS: null
datatype: realarraytype
grib_name: CBVQ
gribfield: 1
GRIBgridcode: 2
gribleveltype: 100
gribNumBits: 16
gribparam: 193
gribvariable: 193
PDS_TimeRange: 113
pointwidth: 1.0
process: 62 wave triangular, 28 layer Spectral model from "Medium Range Forecast" run
PTVersion: 2
subcenter: NCEP Ensemble Products
units: 0.572957795130823 kilogram^-1 meter² radian^-1 north second
history: Averaged over X[1.25W, 1.25W]

References

Kalnay_etal1996

Last updated: Wed, 29 May 2024 07:29:02 GMT

Data Views

	P	T	Y
$partialdiff sub Y$ $partialdiff sub P$ root mean sq [ NOAA NCEP-NCAR CDAS-1 MONTHLY Intrinsic PressureLevel CBVQ ] [ Y \| P T]	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 Y P T
Differentiate along Y P T
Take differences along Y P T