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1:3.5.2 Generated Daily Value

The triangular distribution used to generate daily relative humidity values requires four inputs: mean monthly relative humidity, maximum relative humidity value allowed in month, minimum relative humidity value allowed in month, and a random number between 0.0 and 1.0.

The maximum relative humidity value, or upper limit of the triangular distribution, is calculated from the mean monthly relative humidity with the equation:

RhUmon=Rhmon+(1βˆ’Rhmon)βˆ—exp(Rhmonβˆ’1)R_{hUmon}=R_{hmon}+(1-R_{hmon})*exp(R_{hmon}-1)RhUmon​=Rhmon​+(1βˆ’Rhmon​)βˆ—exp(Rhmonβ€‹βˆ’1) 1:3.5.4

where RhUmonR_{hUmon}RhUmon​ is the largest relative humidity value that can be generated on a given day in the month, and RhmonR_{hmon}Rhmon​ is the average relative humidity for the month.

The minimum relative humidity value, or lower limit of the triangular distribution, is calculated from the mean monthly relative humidity with the equation:

1:3.5.5

where is the smallest relative humidity value that can be generated on a given day in the month, and is the average relative humidity for the month.

The triangular distribution uses one of two sets of equations to generate a relative humidity value for the day. If then

1:3.5.6

If then

1:3.5.7

where is the average relative humidity calculated for the day, is a random number generated by the model each day, is the average relative humidity for the month, is the smallest relative humidity value that can be generated on a given day in the month, is the largest relative humidity value that can be generated on a given day in the month, and is the mean of and .

RhLmon=Rhmonβˆ—(1βˆ’exp(βˆ’Rhmon))R_{hLmon}=R_{hmon}*(1-exp(-R_{hmon}))RhLmon​=Rhmonβ€‹βˆ—(1βˆ’exp(βˆ’Rhmon​))
RhLmonR_{hLmon}RhLmon​
RhmonR_{hmon}Rhmon​
rnd1≀(Rhmonβˆ’RhLmonRhUmonβˆ’RhLmon)rnd_1 \le (\frac{R_{hmon}-R_{hLmon}}{R_{hUmon}-R_{hLmon}})rnd1​≀(RhUmonβ€‹βˆ’RhLmon​Rhmonβ€‹βˆ’RhLmon​​)
Rh=Rhmonβˆ—RhLmon+[rnd1βˆ—(RhUmonβˆ’RhLmon)βˆ—(Rhmonβˆ’RhLmon)]0.5Rhmon,meanR_h=R_{hmon}*\frac{R_{hLmon}+[rnd_1*(R_{hUmon}-R_{hLmon})*(R_{hmon}-R_{hLmon})]^{0.5}}{R_{hmon,mean}}Rh​=Rhmonβ€‹βˆ—Rhmon,mean​RhLmon​+[rnd1β€‹βˆ—(RhUmonβ€‹βˆ’RhLmon​)βˆ—(Rhmonβ€‹βˆ’RhLmon​)]0.5​
rnd1>(Rhmonβˆ’RhLmonRhUmonβˆ’RhLmon)rnd_1>(\frac{R_{hmon}-R_{hLmon}}{R_{hUmon}-R_{hLmon}})rnd1​>(RhUmonβ€‹βˆ’RhLmon​Rhmonβ€‹βˆ’RhLmon​​)
Rh=Rhmonβˆ—RhUmonβˆ’(RhUmonβˆ’Rhmon)βˆ—[RhUmon(1βˆ’rnd1)βˆ’RhLmon(1βˆ’rnd1)RhUmonβˆ’Rhmon]0.5Rhmon,meanR_h=R_{hmon}*\frac{R_{hUmon}-(R_{hUmon}-R_{hmon})*[\frac{R_{hUmon}(1-rnd_1)-R_{hLmon}(1-rnd_1)}{R_{hUmon}-R_{hmon}}]^{0.5}}{R_{hmon,mean}}Rh​=Rhmonβ€‹βˆ—Rhmon,mean​RhUmonβ€‹βˆ’(RhUmonβ€‹βˆ’Rhmon​)βˆ—[RhUmonβ€‹βˆ’Rhmon​RhUmon​(1βˆ’rnd1​)βˆ’RhLmon​(1βˆ’rnd1​)​]0.5​
RhR_hRh​
rnd1rnd_1rnd1​
RhmonR_{hmon}Rhmon​
RhLmonR_{hLmon}RhLmon​
RhUmonR_{hUmon}RhUmon​
Rhmon,meanR_{hmon,mean}Rhmon,mean​
RhLmon,Rhmon,R_{hLmon},R_{hmon}, RhLmon​,Rhmon​,
RhUmonR_{hUmon}RhUmon​