AN IMPROVED RAINFALL DISAGGREGATION TECHNIQUE FOR GCMS

Meteorological models represent rainfall as a mean value for a grid sq uare so that when the latter is large, a disaggregation scheme is requ ired to represent the spatial variability of rainfall. In general circ ulation models (GCMs) this is based on an assumption of exponentiality of rainfall intensities and a fixed value of areal rainfall coverage, dependent on rainfall type. This paper examines these two assumptions on the basis of U.K. and U.S. radar data. Firstly, the coverage of an area is strongly dependent on its size, and this dependence exhibits a scaling law over a range of sizes. Secondly, the coverage is, of cou rse, dependent on the resolution at which it is measured, although thi s dependence is weak at high resolutions. Thirdly, the lime series of rainfall coverages has a long-tailed autocorrelation function which is comparable to that of the mean areal rainfalls. It is therefore possi ble to reproduce much of the temporal dependence of coverages by using a regression of the log of the mean rainfall on the log of the covera ge. The exponential assumption is satisfactory in many cases but not a ble to reproduce some of the long-tailed dependence of some intensity distributions. Gamma and lognormal distributions provide a better fit in these cases, but they have their shortcomings and require a second parameter. An improved disaggregation scheme for GCMs is proposed whic h incorporates the previous findings to allow the coverage to be obtai ned for any area and any mean rainfall intensity. The parameters requi red are given and some of their seasonal behavior is analyzed.