1. IBIS Weather generator
March 12th 2002

2. Why do we need one?
IBIS runs at hourly to 30-minute time-step to better represent physical processes, but most of available climate inputs have monthly time-step.
=> We need to get climate inputs at IBIS time-step
In the past, we were using 30-year averaged climate record, which did not include extreme events.
=> Most surface processes being nonlinear, we need to prescribe extreme events (as a decadal drought) in the input, even to get a realistic averaged output.

3. The basics The WG is a stochastic model, which
determines the probability of weather events in the month
(based on Richardson 1981, Richardson and Wright 1984, Geng et al. 1985)
includes diurnal variability
(Friends A. unpublished, Spitters et al. 1986; Nikolov and Zeller, 1992; Campbell and Norman)
To keep dependence in time, internal correlation and seasonal characteristics, the WG follows two assumptions:
Temperature, radiation and relative humidity fluctuations from day to day are linked to the occurrence of rain.
For any variable, the value of day d depends on the value of day d-1.

4. Monthly -> daily (subroutine: daily in weather.f)
Precipitation
The occurrence of wet and dry days (P(W/D) and P(W/W)) is defined using Markov chain, the Markov chain being implemented using monthly rainfall and number of wet days.
The amount of rain is defined using gamma probability, and readjusted to conserve the monthly input amount.
Temperatures (min., max., mean.), radiation, relative humidity
Any daily value is related to the previous day value using auto-correlation matrixes.
These different climate variables are linked to precipitation variation through the wet/dry conditions (there is an extra multiplying factor depending on wet/dry conditions)
Wind speed
Daily values are defined using a two-parameter gamma distribution.

5. Daily -> hourly (subroutine: diurnal in weather.f)
Precipitation
The length (between 4 and 24h) and the starting time of rainfall are defined once per day for the whole globe, using random series.
The partition between rain and snow depends on the temperature (compared to 0ºC).
Temperatures (Using Fourier series, maximum at 2pm)
Relative humidity
Hourly values are derived from sine function adjusted using the minimum temperature (within the value range of 0,100).
Radiation (direct and indirect incoming solar radiations, downward infra red)
Hourly SR are calculated according to orbital parameters.
Hourly IR is calculated using temperature and relative humidity.
Wind speed (random with minimum and maximum thresholds: 2.5 and 10m/s)

6. The main caveats
Careful: the WG doesn’t exactly conserve monthly climate inputs but for the precipitation.
Spatial correlation between grid-cells is not accounted for on hourly and daily timescales.
IBIS uses the same set of parameters for the whole globe
fixed parameters: the matrix of auto-correlation of T, R and RH
computed parameters: length of rainfall
The fixed parameters have been tuned for the current climate, which introduces a bias when used under different climate as future or paleo-climate

7. Recommendations
Keep in mind that the WG can produce some unexpected results (number of wet days inconsistent with observation, too long rainfall events, etc…).
Use daily inputs when they are available (e.g., NCEP-NCAR data set over the US).
Finally, you can potentially adjust the WG set of parameters to your site or region (Enjoy!).