data: mean $partialdiff sub T$ min $partialdiff sub Y$ [ CIPH Rwanda CSMT

mean ∂_T min ∂_Y [ CIPH Rwanda CSMT_ENACT falciparum PcpnCondProb ] : ∂_T ∂_Y Percent Occurrence of Precipitation Condition in Historical Record data

PcpnCondProb partial_Y min partial_T ∂_T ∂_Y Percent Occurrence of Precipitation Condition in Historical Record from CIPH Rwanda CSMT_ENACT falciparum: Climatic Suitability for a Malaria Transmission.

Independent Variables (Grids)

Time: grid: /T (months since 01-Jan) periodic (16 Dec - 15 Jan) to (16 Nov - 15 Dec) by 1.0 N= 12 pts :grid

Other Info

bufferwordsize

CE

null

CI

25.0

CS

null

datatype

doublearraytype

fnname

flagge

maxncolor

254

missing_value

NaN

units

687.549354156988 radian^-1 north year^-1

history

$partialdiff sub Y$ [ CIPH Rwanda CSMT_ENACT falciparum PcpnCondProb ]

Boxes with less than 0.0% dropped
Averaged over T2[1981, 2023] minimum 0.0% data present

$partialdiff sub T$ min $partialdiff sub Y$ [ CIPH Rwanda CSMT_ENACT falciparum PcpnCondProb ]

min over X[28.5375E, 30.975E]

mean $partialdiff sub T$ min $partialdiff sub Y$ [ CIPH Rwanda CSMT_ENACT falciparum PcpnCondProb ]

Averaged over Y[2.9625S, 1.05S] minimum 0.0% data present

References

Grover-Kopec, E. K., M. B. Blumenthal, P. N. Ceccato, T. Dinku, J. Omumbo, S. J. Connor, 2006: Web-based climate information resources for malaria control in Africa. Malaria Journal, 5:38.

Last updated: Wed, 26 Jun 2024 16:53:08 GMT

Data Views

	T
mean $partialdiff sub T$ min $partialdiff sub Y$ [ CIPH Rwanda CSMT_ENACT falciparum PcpnCondProb ] [ T \|]	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 T
Differentiate along T
Take differences along T