data: root mean sq anom detrended-bfl [ Hydromet Seasonal_Forecast Precipitation Forecast DomTerProb ]

root mean sq anom detrended-bfl [ Hydromet Seasonal_Forecast Precipitation Forecast DomTerProb ] : Dominant Tercile Probabilities data

DomTerProb adif Dominant Tercile Probabilities from Hydromet Seasonal_Forecast Precipitation Forecast: Forecast, Error and Skill.

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered [ (2.5)] :grid
Forecast Issue Date (forecast_reference_time): grid: /S (months since 1960-01-01) ordered (0000 1 Apr 2019) to (0000 1 Aug 2021) by 1.0 N= 29 pts :grid
Longitude (longitude): grid: /X (degree_east) ordered (61.525W) to (56.475W) by 0.05000003 N= 102 pts :grid

Other Info

bufferwordsize

CE

null

colorscalename

tercileclassesscale

CS

null

datatype

doublearraytype

file_missing_value

fnname

maskle

maxncolor

254

missing_value

NaN

pointwidth

units

ids

history

[ masklt ( { [ dominant_class ( Hydromet Seasonal_Forecast Precipitation Forecast TercProb ) - 1. ] * 11. } , 22 ) + dominant_class ( Hydromet Seasonal_Forecast Precipitation Forecast TercProb Above_Normal ) ] + [ masknotrange ( { [ dominant_class ( Hydromet Seasonal_Forecast Precipitation Forecast TercProb ) - 1. ] * 11. } , 10 , 12 ) + dominant_class ( Hydromet Seasonal_Forecast Precipitation Forecast TercProb Near_Normal ) ]

masklt [ ( { dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb ] - 1. } * 11. ) , 22 ] + dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb Above_Normal ]

masklt [ ( { dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb ] - 1. } * 11. ) , 22 ]
dominant_class over C[Below_Normal, Above_Normal]

dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb Above_Normal ]
dominant_class over TercProb[<35, >80]

masknotrange [ ( { dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb ] - 1. } * 11. ) , 10 , 12 ] + dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb Near_Normal ]

masknotrange [ ( { dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Below_Normal, Above_Normal]

dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb Near_Normal ]
dominant_class over TercProb[<35, >80]

maskgt [ ( { dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb ] - 1. } * 11. ) , 0 ] + [ ( dominant_class { Hydromet Seasonal_Forecast Precipitation Forecast TercProb Below_Normal } * -1. ) + 12. ]

maskgt [ ( { dominant_class [ Hydromet Seasonal_Forecast Precipitation Forecast TercProb ] - 1. } * 11. ) , 0 ]
dominant_class over C[Below_Normal, Above_Normal]

[ dominant_class ( Hydromet Seasonal_Forecast Precipitation Forecast TercProb Below_Normal ) * -1. ] + 12.
dominant_class over TercProb[<35, >80]

root mean sq anom detrended-bfl [ Hydromet Seasonal_Forecast Precipitation Forecast DomTerProb ]

Averaged over Y[1.025N, 8.525N] minimum 0.0% data present

Last updated: Fri, 12 Jan 2024 14:16:35 GMT
Expires: Fri, 09 Feb 2024 00:00:00 GMT

Data Views

	L	S	X
root mean sq anom detrended-bfl [ Hydromet Seasonal_Forecast Precipitation Forecast DomTerProb ] 2.5 months[ X \| L S]	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 S
Differentiate along X S
Take differences along X S

Average over X S | X S |
RMS (root mean square with mean *not* removed) over X S | X S |
RMSA (root mean square with mean removed) over X S | X S |
Maximum over X S | X S |
Minimum over X S | X S |
Detrend (best-fit-line) over X S | X S |

Note on units