served from IRI/LDEO Climate Data Library
IRI Analyses ICPAC Models Montreal CMC2-CanCM4 FORECAST MONTHLY prec: Total Precipitation data
Analyses ICPAC Models Montreal CMC2-CanCM4 FORECAST MONTHLY prec Total Precipitation from IRI: International Research Institute for Climate and Society.Independent Variables (Grids)
- Lead (forecast_period)
- 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 (10.0) by 1.0 N= 10 pts :grid
- S
- grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2011) to (0000 1 Jul 2019) by 1.0 N= 103 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) periodic (0.5E) to (0.5W) 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
- 4
- CE
- null
- center
- Montreal (RSMC)
- CS
- null
- datatype
- realarraytype
- file_missing_value
- 9.99900026E20
- grib_name
- PRATE
- gribcenter
- 54
- gribfield
- 1
- gribleveltype
- 1
- gribNumBits
- 16
- gribparam
- 59
- missing_value
- NaN
- PDS_TimeRange
- 10
- pointwidth
- 0
- standard_name
- lwe_precipitation_rate
- units
- mm /day
- standard units*
- 1.15740740740741×10-08 meter second-1
- history
- S: 0000 1 Dec 2015 to 0000 1 Jul 2019 appended from Models NMME Cansips FORECAST MONTHLY prec
References
Kirtman_etal2014Last updated: Tue, 08 Dec 2020 21:55:17 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| L | M | S | X | Y | |
 IRI Analyses ICPAC Models Montreal CMC2-CanCM4 FORECAST MONTHLY prec[ X Y | L M S] | M | M | M | M | M |
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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 S
- Differentiate along X Y L M S
- Take differences along X Y L M S
RMS (root mean square with mean *not* removed) over X Y L M S | X Y X L X M X S Y L Y M Y S L M L S M S | X Y L X Y M X Y S X L M X L S X M S Y L M Y L S Y M S L M S | X Y L M X Y L S X Y M S X L M S Y L M S | X Y L M S |
RMSA (root mean square with mean removed) over X Y L M S | X Y X L X M X S Y L Y M Y S L M L S M S | X Y L X Y M X Y S X L M X L S X M S Y L M Y L S Y M S L M S | X Y L M X Y L S X Y M S X L M S Y L M S | X Y L M S |
Maximum over X Y L M S | X Y X L X M X S Y L Y M Y S L M L S M S | X Y L X Y M X Y S X L M X L S X M S Y L M Y L S Y M S L M S | X Y L M X Y L S X Y M S X L M S Y L M S | X Y L M S |
Minimum over X Y L M S | X Y X L X M X S Y L Y M Y S L M L S M S | X Y L X Y M X Y S X L M X L S X M S Y L M Y L S Y M S L M S | X Y L M X Y L S X Y M S X L M S Y L M S | X Y L M S |
Detrend (best-fit-line) over X Y L M S | X Y X L X M X S Y L Y M Y S L M L S M S | X Y L X Y M X Y S X L M X L S X M S Y L M Y L S Y M S L M S | X Y L M X Y L S X Y M S X L M S Y L M S | X Y L M S |
Note on units