Abstract
Abstract. We investigate the appropriateness of four different methods to produce precipitation accumulation fields using radar data alone or combined with precipitation gauge data. These methods were validated for high-latitude weather conditions of Finland. The reference method uses radar reflectivity only, while three assimilation methods are used to blend radar and surface observations together, namely the linear analysis regression, the Barnes objective analysis and a new method based on a combination of the regression and Barnes techniques (RandB). The Local Analysis and Prediction System (LAPS) is used as a platform to calculate the four different hourly accumulation products over a 6-month period covering summer 2011. The performance of each method is verified against both dependent and independent observations (i.e. observations that are or are not included, respectively, into the precipitation accumulation analysis). The newly developed RandB method performs best according to our results. Applying the regression or Barnes assimilation analysis separately still yields better results for the accumulation products compared to precipitation accumulation derived from radar data alone.
Highlights
The concept of precipitation accumulation is of great importance for various applications in meteorology and hydrology
In this article we compare the results from 4 different analysis methods on how to calculate the hourly precipitation accumulation: LAPS_radar, Regression, Barnes and a new developed method regression and Barnes techniques (RandB)
The LAPS_radar serves as the reference method and since it is based on the common Z − R formula, this method is similar to what is used at many meteorological services
Summary
The concept of precipitation accumulation is of great importance for various applications in meteorology and hydrology. Climate projections under possible climate change scenarios point to likely higher frequency of storms, with intensified precipitation over Europe. This will most probably have a significant effect on the surface water balance, having a large impact on society and its economical aspects. Radar-derived precipitation products are generated at high spatial resolution but embed measurement uncertainties. More or less sophisticated assimilation methods exist, whereby surface point observations are blended together with radar data in order to establish a corrected precipitation accumulation, e.g.: co-kriging (Sun et al, 2000), the statistical objective analysis method (Pereira et al, 1998), combined bias-adjustments method (Overeem et al, 2009) and bias
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