عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Numerical weather prediction (NWP) models without initialization techniques may be result in unreal and inaccurate data. Many initialization methods, such as linear and nonlinear normal mode initialization, have been developed and applied in the field of NWP by atmospheric researchers and modelers. In application, such techniques are very complex and expensive. One of the most efficient and simple techniques which can be used in operational forecasting is digital filter initialization. Digital filter initialization methods are applied to eliminate non-physical and high frequency waves from NWP models. These unwanted waves can affect the results of the models and cause its results to depart from real world and observed conditions.
In this paper, different filters (1- uniform filter, 2- Lanczos filter, 3- Hamming filter, 4- Blackman filter, 5- Kaiser filter, 6- Potter filter, 7- Dolph [Dolph-Chebyshev] window, 8- Dolph filter and 9- Recursive High-Order filter) are theoretically investigated.
The theoretical study of these filters shows that the Dolf filter works better than the other filters. This superiority can be verified using a digital filter initialization technique associated with the Dolf filter in the weather research and forecasting (WRF) model and investigating its results. Subsequently, the digital filter initialization methods provided in the WRF model are tested for the region of Iran. Three different digital filter initialization techniques, namely the digital filter launch, diabatic digital filter and twice digital filter initialization, with nine aforementioned filters were prepared in the WRF model. The WRF model was set with a 45-kilometer grid size for the region at 12-50 oN and 12-87 oS. The WRF model was run over this region with and without a digital filter initialization technique. In general, the initialization of the NWP models influences the first hours of prediction of the meteorological parameters. In this study, two parameters, including surface pressure and rainfall, were considered as indicators of the effects of digital filter initialization methods on the results of the WRF model. Therefore, the obtained results are investigated and compared for surface pressure fluctuation and rainfall.
All results indicate that applying the digital filter initialization effectively liminates nonphysical waves from surface pressure fields, especially in the first hours of prediction. This was determined by studying three parameters, including surface pressure fluctuation in some points, derivative of surface pressure fluctuation in some points, and integrated derivative of surface pressure. It was found that the twice digital filter initialization associated with the Dolph filter works better than the other techniques and filters.
For rainfall, three- and six-hours predictions of cumulative rainfall were investigated. The results of rainfall prediction with WRF model using digital filter initialization were compared with the results of WRF model without digital filter initialization and observed station data. This comparison showed that the twice digital filter initialization associated with the Dolph filter has its maximum effect during first three hours and in the second three hours has a minimum effect among other techniques. This means that unwanted fluctuations are eliminated properly during the first three hours. Also, a comparison of rainfall prediction results with observed station data indicates that the diabatic digital filter initialization associated with the Dolph filter has the minimum root mean square error. Among digital filter initialization techniques studied, the digital filter launch has sudden effects on the amount of rainfall predicted during the first three hours of prediction time, so this can induce significant errors in results of the model.