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A RPG-HATPRO ground-based microwave radiometer (MWR) was operated ina deep Alpine valley during the Passy-2015 field campaign. This experimentaims to investigate how stable boundary layers during wintertime conditionsdrive the accumulation of pollutants. In order to understand the atmosphericprocesses in the valley, MWRs continuously provide vertical profiles oftemperature and humidity at a high time frequency, providing valuableinformation to follow the evolution of the boundary layer. A one-dimensionalvariational (1DVAR) retrieval technique has been implemented during the fieldcampaign to optimally combine an MWR and 1 h forecasts from the French convectivescale model AROME. Retrievals were compared to radiosonde data launched atleast every 3 h during two intensive observation periods (IOPs). Ananalysis of the AROME forecast errors during the IOPs has shown a largeunderestimation of the surface cooling during the strongest stable episode.MWR brightness temperatures were monitored against simulations from theradiative transfer model ARTS2 (Atmospheric Radiative Transfer Simulator) andradiosonde launched during the field campaign. Large errors were observed formost transparent channels (i.e., 51–52 GHz) affected by absorption model andcalibration uncertainties while a good agreement was found for opaquechannels (i.e., 54–58 GHz). Based on this monitoring, a bias correction ofraw brightness temperature measurements was applied before the 1DVARretrievals. 1DVAR retrievals were found to significantly improve the AROMEforecasts up to 3 km but mainly below 1 km and to outperform usualstatistical regressions above 1 km. With the present implementation, aroot-mean-square error (RMSE) of 1 K through all the atmospheric profile wasobtained with values within 0.5 K below 500 m in clear-sky conditions. Theuse of lower elevation angles (up to 5°) in the MWR scanning andthe bias correction were found to improve the retrievals below 1000 m. MWRretrievals were found to catch  deep near-surface temperatureinversions very well. Larger errors were observed in cloudy conditions due tothedifficulty of ground-based MWRs to resolve high level inversions that arestill challenging. Finally, 1DVAR retrievals were optimized for the analysisof the IOPs by using radiosondes as backgrounds in the 1DVAR algorithminstead of the AROME forecasts. A significant improvement of the retrievalsin cloudy conditions and below 1000 m in clear-sky conditions was observed. From thisstudy, we can conclude that MWRs are expected to bring valuable informationinto numerical weather prediction models up to 3 km in altitude both in clear-sky and cloudy-skyconditions with the maximum improvement found around 500 m. With an accuracybetween 0.5 and 1 K in RMSE, our study has also proven that  MWRs are capable ofresolving deep near-surface temperature inversions observed in complexterrain during highly stable boundary layer conditions

atmospheric measurement techniques

Combining ground-based microwave radiometer and the AROME convective scale model through 1DVAR retrievals in complex terrain: an Alpine valley case study