Received: 06 Apr 2018 – Discussion started: 06 Jun 2018
Abstract. A weather generator is introduced that has a Bartlett−Lewis rainfall generator in which storms with exponentially distributed time intervals between their starting times consist of cells of which the intervals between their starting times are exponentially distributed, and their durations and rainfall rates are both gamma–distributed. Each day is either overcast or clear, with the probability of a cloudy day depending on the daily rainfall. A temperature generator uses a sinusoidal annual signal of which the mean and the amplitude are both normally distributed. For overcast days, the amplitude is reduced. Superimposed on this signal is a first–order autoregressive model with independently identically normally distributed shocks for the daily mean temperature, which is assumed to be the average of the daily minimum and maximum temperature. The difference between the daily mean and extremes follows a lognormal distribution, the standard deviation of which is reduced for overcast days. The daily extraterrestrial radiation, mean and extreme temperatures, and, for one of the two models used, the 30–day rainfall sum, determine the daily potential evapotranspiration. To permit the generation of very long time series, leap years are taken into account. One hundred years of weather data were generated for two contrasting climates. The results show that the choice of the evapotranspiration model is consequential for temperate climates. Additional calculations demonstrate the effect of the daily temperature fluctuations on the potential evapotranspiration. Standard computational resources (laptop) suffice to run the weather generator. The Fortran90 source codes, input file formats, and user manual are provided.
This preprint has been withdrawn.
How to cite. de Rooij, G. H.: A simple weather generator for applications with limited data availability: TEmpotRain 1.0 for temperatures, extraterrestrial radiation, and potential evapotranspiration, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2018-97, 2018.
Areas that have few or no weather stations or are subject to climate change still need weather data in order to model the demand for water, the risk of floods and droughts, etc. TEmpotRain generates rainfall, daily temperature extremes, and daily potential evaporation (from the soil) / transpiration (by plants). The physical meaning of the model parameters is clear. This allows realistic values for them to be estimated, even for hypothetical (future) climates for which data are not available.
Areas that have few or no weather stations or are subject to climate change still need weather...