An original one‐dimensional (1‐D) retrieval followed by a three‐dimensional variational (1D+3DVar) assimilation technique is being developed to assimilate volumes of radar reflectivity data in the high‐resolution numerical weather prediction Arome model. The good performance of the 1‐D retrieval is shown for an isolated storm over southwestern France through an observing system simulation experiment. The full method is applied with real data to a flash‐flood event, which occurred in a mountainous area. For this complex case, the assimilation of reflectivity data improves short‐term precipitation forecasts. The assimilation of reflectivity data has a positive impact on the convective system's dynamics by feeding the cold pool under the storm, which controls the intensity and location of the updrafts. A one‐hourly update cycle of 3 h further improves these results. A sensitivity study is also presented to evaluate the assimilation method for this flash‐flood event in different conditions. The smoothing coefficient involved in the 1‐D retrieval is shown to have a very small impact on analyses and quantitative precipitation forecasts. The assimilation of reflectivity data is found to be able to cause the creation of a cold pool, which modifies favourably the precipitation quantitative forecast. Finally, results from an 8‐d‐long assimilation cycle are presented.

Abstract. Impact of GPS (Global Positioning System) data assimilation is assessed here using a high-resolution numerical weather prediction system at 2.5 km horizontal resolution. The Zenithal Tropospheric Delay (ZTD) GPS data from mesoscale networks are assimilated with the 3DVAR AROME data assimilation scheme. Data from more than 280 stations over the model domain have been assimilated during 15-day long assimilation cycles prior each of the two studied events. The results of these assimilation cycles show that the assimilation of GPS ZTD with the AROME system performs well in producing analyses closer to the ZTD observations in average.Then the impacts of assimilating GPS data on the precipitation forecast have been evaluated. For the first case, only the AROME runs starting a few hours prior the triggering of the convective system are able to simulate the convective precipitation. The assimilation of GPS ZTD observations improves the simulation of the spatial extent of the precipitation, but slightly underestimates the heaviest precipitation in that case compared with the experiment without GPS. The accuracy of the precipitation forecast for the second case is much better. The analyses from the control assimilation cycle provide already a good description of the atmosphere state that cannot be further improved by the assimilation of GPS observations. Only for the latest day (22 November 2007), significant differences have been found between the two parallel cycles. In that case, the assimilation of GPS ZTD allows to improve the first 6 to 12 h of the precipitation forecast.

[1] The numerical weather prediction forecast skill of heavy precipitation events in the Mediterranean regions is currently limited, partly because of the paucity of water vapor observations assimilated today. An attempt to fill this observational gap is provided by Global Positioning System (GPS) ground station data over Europe that are now routinely processed into observations of Zenith Total Delay (ZTD), which is closely related to the tropospheric water vapor content. We evaluate here the impact of assimilating the GPS ZTD on the high-resolution (2.4-km) nonhydrostatic prediction of rainfall for the heavy precipitation event of 5-9 September 2005 over Southern France. First, we assimilate the GPS ZTD observations in the three-dimensional variational (3DVAR) data assimilation system of the 9.5-km horizontal resolution ALADIN/France hydrostatic model with parameterized convection. This one-month-long assimilation experiment includes the heavy rainfall period. Prior to the assimilation, a GPS ZTD observation preprocessing is carried out for quality control and bias correction. We find that the GPS ZTD observations impact mainly the representation of the humidity in the low to middle troposphere. We then conduct forecast trials with the Meso-NH model, which explicitly resolves the deep convection, using the analyses of the 3DVAR ALADIN/France assimilation experiments as initial and boundary conditions. Our results indicate a benefit of GPS ZTD data assimilation for improving the Meso-NH precipitation forecasts of the heavy rainfall event.