A statistical approach to the spatial analysis of precipitation by combining multiple data sources

Precipitation data predicted by numerical models is used in a wide variety of applications. For civil protection, numerical weather prediction provides key information for decision making and as input to hydrological modelling. For climatology, climate bulletins based on reanalyses reconstructing past weather events are increasingly gaining ground. Nevertheless, it is often the case that observed precipitation data from several data sources are available over the domain covered by the numerical simulations. Sometimes the observational data have been used by the numerical models, however usually measurements from observational networks of weather stations or radar-derived precipitation estimates constitute an independent source of information for precipitation. The combination of numerical model output and observational data aiming at a more accurate and precise representation of precipitation has already become a classic of post-processing of numerical models.

The work we will present is based on the application of Inverse problem theory to the spatial analysis of precipitation. Numerical model precipitation is the background field. The observations may have been measured or estimated by more than one observational system, such as weather stations or remote sensing, and they are considered to be more reliable than numerical model precipitation. At the same time, observations are used to add locally more details to the precipitation field, thus increasing its effective spatial resolution. The final product, the combined field of precipitation, has to provide information on the precipitation uncertainty because this is needed for most applications.

Examples of the statistical approaches investigated at the Norwegian meteorological institute for hourly precipitation will be provided. The numerical model is a high-resolution ensemble local area model. The observations are radar-derived estimates from the Norwegian mosaic of weather radars and the rain-gauge measurements used have been collected by an heterogeneous network of traditional and private stations.