Abstract
Four-dimensional variational data assimilation (4D-Var) experiments with 6-hourly rain gauge accumulations observed at synoptic stations (SYNOP) around the globe have been run over several months, both at high resolution in an ECMWF operations-like framework and at lower resolution with the reference observational coverage reduced to surface pressure data only, as would be expected in early twentieth-century periods. The key aspects of the technical implementation of rain gauge data assimilation in 4D-Var are described, which include the specification of observation errors, bias correction procedures, screening, and quality control. Results from experiments indicate that the positive impact of rain gauges on forecast scores remains limited in the operations-like context because of their competition with all other observations already available. In contrast, when only synoptic station surface pressure observations are assimilated in the data-poor control experiment, the additional assimilation of rain gauge measurements substantially improves not only surface precipitation scores, but also analysis and forecast scores of temperature, geopotential, wind, and humidity at most atmospheric levels and for forecast ranges up to 10 days. The verification against Meteosat infrared imagery also shows a slight improvement in the spatial distribution of clouds. This suggests that assimilating rain gauge data available during data-sparse periods of the past might help to improve the quality of future reanalyses and subsequent forecasts.
