Nowcasting precipitation using variational data assimilation

Abstract

Nowcasting, synonymous for short term (0-6h) forecasting of precipitation with high detail in terms of location, timing and intensity, is a dynamic field of research in weather forecasting that is relevant in mitigating the impact of severe weather events. Along with the development of remote sensing instruments, the first methods that were developed to nowcast precipitation are based on the extrapolation of radar echoes, which are still widely employed in mobile and web applications today. However, the forecasting skill of these extrapolation based methods degrades rapidly with time. Especially in the case of convective rainfall extrapolation methods are not able to forecast the development of these precipitation events that have a more turbulent nature, primarily because they lack the description of the physical processes that govern the formation and dissipation of convective precipitation. For that reason, efforts in nowcasting are shifting towards numerical weather prediction (NWP) based methods. “High resolution” (<4km) NWP models covering a limited area are employed for their ability to describe large scale convection. Still, especially for accurately forecasting precipitation, these high resolution models require a detailed initial state which is generally not achieved by conventional initialization from global models having a much coarser resolution (>25km). To obtain a more accurate and detailed description of the initial state so called data assimilation methods have been developed, which use detailed information from recent observations to update the model.

In this study it is investigated whether the precipitation forecasting skill of a regional Weather Research and Forecasting (WRF) model covering the Benelux can be improved by means of variational data assimilation. To that end, a variety of atmospheric observations is assimilated into a 4-km resolution WRF model using the accompanying WRFDA system with a 3DVAR rapid update cycling strategy. Besides conventional observations from surface weather stations, airports and soundings, observations from remote sensing platforms like radar (reflectivity and radial velocity) and zenith total delay (ZTD) from GPS are also assimilated. In a case study featuring a large squall line it is shown that assimilation of every single type of observation results in a notable improvement of forecasting skill, except for the radar assimilation of hydrometeors whose impact on the forecast is negligible after having precipitated to the surface. Especially assimilation of estimated humidity based on radar reflectivity yields a significant improvement during the full 6 hours of the forecast. The ability of the other types of observations to positively impact forecasting skill is attributed the large scale forcing by wind convergence along the cold front that is driving the formation of precipitation. Overestimation of precipitation quantity in the scenario without assimilation however is aggravated when radar derived humidity is assimilated, most likely as a consequence of the rapid update cycling strategy.