Evaluation of diabatic initialization improvements in the numerical weather prediction model Hirlam, focusing on the effect this may have on precipitation and dispersion forecasts

Amongst the key issues concerning mesoscale modeling capability for air pollution and dispersion applications are precipitation and cloud cover forecasts. The long-standing problem of the spin-up of clouds and precipitation in numerical weather prediction models limits the accuracy of the prediction of short-range dispersion and deposition from local sources. Customary the spin-up problem is avoided by only using NWP forecasts with a lead time greater than the spin-up time of the model. Due to the increase of uncertainty with forecast range this reduces the quality of the associated forecasts of the atmospheric flow. ements through diabatic initialization in the spin-up of large-scale precipitation in the Hirlam NWP model are discussed. In a synthetic example the effects of these improvements on a dispersion forecast are explored specifically for wet deposition. Using a case study of several weeks the optimal lead time for precipitation is discussed. alysis presented in this paper leads to the conclusion that, at least for situations where large-scale precipitation dominates, proper diabatic initialization of a weather model may limit spin-up so that its full forecast range can be used. The implication for dispersion modeling is that such an improved model is particularly useful for short-range forecasts and the calculation of local deposition. The sensitivity of the hydrological process to proper initialization implies that the spin-up problem may reoccur with changes in the model and increased model resolution. This is demonstrated using a recent version of Hirlam. Spin-up should therefore not only be an ongoing concern for atmospheric modelers, but a reason for close cooperation with dispersion modelers.