At present, most works delved into convective weather nowcasting, only a few works discussed the nowcasting uncertainty for variables in surface level. Hence, we proposed a method to estimate the uncertainty. Generating appropriate noises associated with the characteristic of the error within analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.

This study shows that in most years when haze pollution (HP) over the North China Plain (NCP) is more (less) serious in winter, air conditions in the following spring are also worse (better) than normal. Conversely, there are some years when HP in the following spring is opposed to that in winter. It is found that North Atlantic sea surface temperature (SST) anomalies play important roles in HP evolution over the NCP. Thus North Atlantic SST is an important preceding signal for NCP HP evolution.

Meteorological observations are essential for the initialization of numerical weather prediction (NWP) forecast. A common observation preprocessing system (OPLACE) has been built up to facilitate the use of meteorological observations and deliver them in an appropriate format for data assimilation in the NWP system ALADIN. The OPLACE data sources, preprocessing steps and means to make preprocessed observations available are described.

Ensemble forecasting at the convection-permitting scale (< 3 km) requires new methodologies in representing model uncertainties. In this paper a new stochastic scheme is proposed and tested in the complex terrain of the Alps. In this scheme the tendencies of the physical parametrizations are perturbed separately, which sustains a physically consistent relationship between the processes. This scheme increases the stability of the model and leads to improvements in the probabilistic performance.