Assimilation of ground-based remote sensing profiler data at MeteoSwiss

Conventional atmospheric measurement systems fail to provide observations of the essential variables characterizing the planetary boundary layer (PBL) with satisfactory spatial and temporal resolutions. Moreover, the available profile observations are very sparsely distributed. Due to this observational gap the thermodynamic structure of the PBL in the initial conditions for NWP is prone to errors, just as the representation of winds in the lower atmosphere. This affects the accuracy of forecasts of high-impact phenomena such as convective storms or winter fog and low stratus, and is relevant for the quality of downstream applications, warnings, and emergency responses.

Humidity and wind exhibit a very high variability in space and time and, together with temperature, determines the atmospheric stability. It is therefore of major interest to investigate the potential benefit of assimilating additional profile observations of humidity, temperature, and wind into the NWP system. In this contribution, we give an overview of our efforts to include novel, ground-based remote sensing profiler observations into the 1km mesh-size, LETKF-based, ensemble data assimilation system COSMO/KENDA-1.

We present the assimilation of brightness temperatures from three microwave radiometers installed on the Swiss Plateau using the RTTOV-gb forward operator, as well as experiments assimilating water vapor mixing ratio and temperature profiles from a Raman lidar located in Payerne. Additionally, we show an assimilation case study with wind lidars focusing on a local wind system in Basel.
The assimilation of Raman lidar observations leads to improved humidity analyses and precipitation forecasts, particularly for high intensities. It is further shown that state-dependent observation errors lead to more skillful results than constant observation errors.