Combining ground-based microwave radiometer and the AROME
convective scale model through 1DVAR retrievals in complex
terrain: an Alpine Valley case study
Authors: Martinet Pauline , Cimini Domenico , De Angelis Francesco , Canut Guylaine , Unger Vinciane , Guillot Remi , Tzanos Diane , Paci Alexandre 
Published: June 2017

Abstract. A RPG-HATPRO ground-based microwave radiometer (MWR) was operated in a deep Alpine valley during the Passy-2015 field campaign. This experiment aims at investigating how stable boundary layers during wintertime conditions drive the accumulation of pollutants. In order to understand the atmospheric processes in the valley, MWR continuously provide vertical profiles of temperature and humidity at a high time frequency, providing valuable information to follow the evolution of the boundary layer. A one-dimensional variational (1DVAR) retrieval technique has been implemented during the field campaign to optimally combine MWR and 1 h forecasts from the French convective scale model AROME. Retrievals were compared to radiosonde data launched at least every 3 hours during two intensive observation periods (IOP). An analysis of the AROME forecast errors during the IOPs has shown a large underestimation of the surface cooling during the strongest stable episode. MWR brightness temperatures were monitored against simulations from the radiative transfer model ARTS2 (Atmospheric Radiative Transfer Simulator) and radiosonde launched during the field campaign. Large errorswere observed for most transparent channels (i.e., 51–52 GHz) affected by absorption model and calibration uncertainties while a good agreement was found for opaque channels (i.e., 54–58 GHz). Based on this monitoring, a bias correction of raw brightness temperature measurements was applied before the 1DVAR retrievals. 1DVAR retrievals were found to significantly improve the AROME forecasts up to 3 km but mainly below 1 km and to outperform usual statistical regressions above 1 km. With the present implementation, a root-mean-square-error (RMSE) of 1 K through all the atmospheric profile was obtained with values within 0.5 K below 500 m in clear-sky conditions. The use of lower elevation angles (up to 5°) in the MWR scanning and the bias correction were found to improve the retrievals below 1000 m. MWR retrievals were found to catch very well deep nearsurface temperature inversions. Larger errors were observed in cloudy conditions due to difficulty of ground-based MWR to resolve high level inversions that are still challenging. Finally, 1DVAR retrievals were optimized for the analysis of the IOPs by using radiosondes as backgrounds in the 1DVAR algorithm instead of the AROME forecasts. A significant improvement of the retrievals in cloudy conditions and below 1000 m in clear-sky was observed. From this study, we can conclude that MWR are expected to bring valuable information into NWP models up to 3 km altitude both in clear-sky and cloudy-sky conditions with the maximum improvement found around 500 m. With an accuracy between 0.5 and 1 K in RMSE, our study has also proved MWR to be capable of resolving deep near-surface temperature inversions observed in complex terrain during highly stable boundary layer conditions.