data: mean flaggt [ ( { Rwanda Daily precip } * dT ) , 40.0 ]

mean flaggt [ ( { Rwanda Daily precip } * dT ) , 40.0 ] : PoE 40.0 mm in (Jul 1 - Sep 30) data

Rwanda Daily precip aprod flag flag flag flag PoE 40.0 mm in (Jul 1 - Sep 30) from SOURCES: the IRI/LDEO collection of climate data.

Independent Variables (Grids)

Longitude (longitude): grid: /X (degree_east) ordered (28.5375E) to (30.975E) by 0.0375 N= 66 pts :grid
Latitude (latitude): grid: /Y (degree_north) ordered (2.9625S) to (1.05S) by 0.0375 N= 52 pts :grid

Other Info

CE

100

colorscalename

correlationcolorscale

CS

datatype

realarraytype

fnname

flaggt

maxncolor

254

missing_value

NaN

pointwidth

1.0

scale_max

100.0

scale_min

0.0

units

percent

standard units*

0.01

history

mean flaggt [ ( { Rwanda Daily precip } * dT ) , 40.0 ]

Boxes with less than 0.0% dropped
Averaged over T[Jul-Sep 1983, 2400 30 Sep 2021] minimum 0.0% data present

colorscale

Last updated: Mon, 09 May 2022 12:12:55 GMT

Data Views

	X	Y
mean flaggt [ ( { Rwanda Daily precip } * dT ) , 40.0 ] [ X Y \|]	M	M

SOURCES .Rwanda .Daily .precip
  (TotRain)
   /seasonalStat parameter
   (PerDA) eq
   {dataflag}if
  (1)
   /DayStart parameter
   ( ) append
   (Jul)
    /seasonStart parameter
    append
   ( - ) append
   (30)
    /DayEnd parameter
    append
   ( ) append
   (Sep)
    /seasonEnd parameter
    append
   5
    /spellThreshold parameter
    1
     /wetThreshold parameter
     0.0 /seasonalStat get_parameter
       (TotRain) eq
       {nip nip flexseastotAvgFill /units /mm def 40.0 /probExcThresh1 parameter}if
   /seasonalStat get_parameter
    (NumWD) eq
    {3 -1 roll pop flexseasonalfreqGT 30.0 /probExcThresh2 parameter}if
   /seasonalStat get_parameter
    (NumDD) eq
    {3 -1 roll pop flexseasonalfreqLT 30.0 /probExcThresh3 parameter}if
   /seasonalStat get_parameter
    (RainInt) eq
    {3 -1 roll pop flexseasonalmeandailyvalueGT /units (mm/day) def 10.0 /probExcThresh4 parameter}if
   /seasonalStat get_parameter
    (NumDS) eq
    {flexseasonalnonoverlapDSfreq /units /spells def 3.0 /probExcThresh5 parameter}if
   /seasonalStat get_parameter
    (NumWS) eq
    {flexseasonalnonoverlapWSfreq /units /spells def 3.0 /probExcThresh6 parameter}if
   (PoE)
    /yearlyStat parameter
    (CoV) eq
    {pop dup[T]rmsaover exch[T]average div /percent unitconvert ( ) exch}if
   startcolormap DATA
     0 100 RANGE
     transparent black navy
        0 VALUE
        navy navy
          2 bandmax
          blue blue
            6 bandmax
            DeepSkyBlue DeepSkyBlue
              12 bandmax
              aquamarine aquamarine
                20 bandmax
                PaleGreen PaleGreen
                  25 bandmax
                  moccasin moccasin
                    30 bandmax
                    yellow yellow 40
                     bandmax
                     DarkOrange DarkOrange 50
                     bandmax
                     red red 60
                     bandmax
                     DarkRed DarkRed 80
                     bandmax
                     brown brown 100
                     bandmax
                     brown endcolormap
   /yearlyStat get_parameter
    (CoV) eq
    {DATA 0 50 RANGE}if
   /yearlyStat get_parameter
    (Var) eq
    {pop units exch /units undef[T]stddev Infinity maskge dup mul DATA 0 AUTO RANGE exch /units exch cvntos (2) append cvn def ( ) exch}if
   /yearlyStat get_parameter
    dup
     (PoE) eq
     exch
     (PonE) eq
     or
    { /probExcThresh parameter exch units 3 -2 roll exch flaggt[T]average /yearlyStat get_parameter (PoE) ne {-1 mul 1 add}if /percent unitconvert correlationcolorscale DATA 0 100 RANGE exch cvntos ( ) exch /probExcThresh get_parameter s== exch ( ) exch ( ) 5 array astore concat exch}if
   exch
   /yearlyStat get_parameter exch
    (in ) lpar /seasonStart get_parameter
       ( ) /DayStart get_parameter
         ( - ) /seasonEnd get_parameter
           ( ) /DayEnd get_parameter
             rpar 12 array astore
   concat
   /long_name exch def
  /name /proba def

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
Differentiate along X Y
Take differences along X Y

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

Note on units