Added value of assimilating surface pressure observations from personal weather stations in AROME-France

The rise of connected objects, such as personal weather stations (PWSs), and the availability of their data, have opened up a new field of possibilities for real-time weather observation. For instance, the Netatmo PWS network measures temperature, relative humidity and pressure near the surface at high spatial density over France with around 50,000 stations. Nevertheless, such stations have measurement errors, e.g. pressure sensor has vertical calibration and drifting issues, thus they need to be processed. The objective here is to assess the relevance of assimilating these pressure observations into an operational kilometre-scale numerical weather prediction system such as AROME-France, which uses a three-dimensional variational (3D-Var) data assimilation scheme. And, despite the rich literature describing efficient correction methods, two specific criteria have to be taken into account in our study. First the data need to be coherent with standard weather station (SWS) observations actually assimilated and secondly the processing method has to respect near-real time limitations.

In a first phase, a pre-existing correction and quality control method was adapted.  The correction is based on the interpolation of the SWS observations at PWS's locations (called reference interpolation) in order to subtract an average temporal bias. Then, a data quality control is applied in order to remove the PWS time series whose values deviate too far from the reference interpolation time series, with a non-stationary threshold. In this way, we ensured the quality of the data and the scale of their representativeness. In a second phase, two experiments assimilating the PWS observations were carried out. In a first experiment, PWS biases with respect to the model's background during a selected time window are corrected before PWS observations are quality controlled by the operational screening. In a second experiment, the pre-processing method previously described is used. A thinning at 1.3 km scale is added in order to reduce the high density in the cities which could deteriorate the analysis.
Those configurations serves as a benchmark to beat, as it is clear that some adjustments must be optimized both on the quantification of the observation error, and on the kind and scale of the thinning.

Results show that, on average over the month of August 2020, the PWSs help to bring closer the analysis to SWSs. In addition, some convective events have been studied, showing the limits of the current assimilation methods and the progress to be made.