A RPG-HATPRO ground-based microwave radiometer (MWR) was operated in a deep Alpine valley during the Passy-2015 field campaign. This experiment aims to investigate how stable boundary layers during wintertime conditions drive the accumulation of pollutants. In order to understand the atmospheric processes in the valley, MWRs 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 an MWR and 1 h forecasts from the French convective scale model AROME. Retrievals were compared to radiosonde data launched at least every 3 h during two intensive observation periods (IOPs). 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 errors were 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 deep near-surface temperature inversions very well. Larger errors were observed in cloudy conditions due to the difficulty of ground-based MWRs 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 conditions was observed. From this study, we can conclude that MWRs are expected to bring valuable information into numerical weather prediction models up to 3 km in 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 proven that MWRs are capable of resolving deep near-surface temperature inversions observed in complex terrain during highly stable boundary layer conditions.

Mountainous areas require appropriate measurement strategies to cover the full spectrum of details concerning the energy exchange at the Earth’s surface and to capture the spatiotemporal distribution of atmospheric dynamic and thermodynamic fields over them. This includes the range from turbulence to mesoscale processes and its interaction. The surface energy balance needs appropriate measurement strategies as well. In this paper, we present an overview of important experiments performed over mountainous terrain and summarize the available techniques for flow and energy measurements in complex terrain. The description includes ground-based and airborne in situ observations as well as ground-based and airborne remote sensing (passive and active) observations. Emphasis is placed on systems which retrieve spatiotemporal information on mesoscale and smaller scales, fitting mountainous terrain research needs. Finally, we conclude with a short list summarizing challenges and gaps one faces when dealing with measurements over complex terrain.

The Land Surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment (LIAISE) campaign examined the impact of anthropization on the water cycle in terms of land–atmosphere–hydrology interactions. The objective of this study is to assess the effects of irrigation on the atmosphere and on precipitation in Weather Research and Forecasting model simulations during the LIAISE special observation period in July 2021. Comparisons between simulations and observations show better verification scores for air temperature, humidity, and wind speed and direction when the model included the irrigation parametrization, improving the model warm and dry bias at 2 m over irrigated areas. Other changes found are the weakening of the sea breeze circulation and a more realistic surface energy partitioning representation. The boundary‐layer height is lowered in the vicinity of irrigated areas, causing a decrease in the lifting condensation level and the level of free convection, which induce increases in convective available potential energy and convective inhibition. Precipitation differences between simulations become relevant for smaller areas, close to the irrigated land. When convection is parametrized, simulations including irrigation tend to produce a decrease in rainfall (negative feedback), whereas convection‐permitting simulations produce an increase (positive feedback), although the latter underestimates substantially the observed precipitation field. In addition, irrigation activation decreases the areas exceeding moderate hourly precipitation intensities in all simulations. There is a local impact of irrigated land on model‐resolved precipitation accumulations and intensities, although including the irrigation parametrization did not improve the representation of the observed precipitation field, as probably the precipitation systems during the LIAISE special observation period in July 2021 were mostly driven by larger scale perturbations or mesoscale systems, more than by local processes. Results reported here not only contribute to enhance our understanding of irrigation effects upon precipitation but also demonstrate the need to include irrigation parametrizations in numerical forecasts to overcome the biases found.

Air quality issues are frequent in urbanized valleys, particularly in wintertime when a temperature inversion forms and the air within the valley is stably stratified over several days. In addition to pollutant sources, local winds can have a significant impact on the spatial distribution and temporal evolution of pollutant concentrations. They can be very complex and difficult to represent in numerical weather prediction models, particularly under stable conditions. Better knowledge of these local winds from observations is also a prerequisite to improving air quality prediction capability. This paper analyses local winds during the Passy-2015 field experiment that took place in a section of the Arve river valley, near Chamonix–Mont-Blanc. This location is one of the worst places in France regarding air quality. The wind analysis, which is mainly based on scanning Doppler lidar data sampling a persistent temperature inversion episode, reveals features consistent with the higher pollutant concentrations observed in this section of the valley as well as their spatial heterogeneities. In particular, an elevated down-valley jet is observed at night in the northern half of the valley, which, combined with a weak daytime up-valley wind, leads to very poor ventilation of the lowest layers. A northeast–southwest gradient in ventilation is observed on a daily-average, and is consistent with the PM10 heterogeneities observed within the valley.

Recent studies reported that precipitation and mountain waves induced low tropospheric level circulations may be decoupled or masked by greater spatial scale variability despite generally there is a connection between microphysical processes of precipitation and mountain driven air flows. In this paper we analyse two periods of a winter storm in the Eastern Pyrenees mountain range (NE Spain) with different mountain wave induced circulations and low-level turbulence as revealed by Micro Rain Radar (MRR), microwave radiometer and Parsivel disdrometer data during the Cerdanya-2017 field campaign. We find that during the event studied mountain wave wind circulations and low-level turbulence do not affect neither the snow crystal riming or aggregation along the vertical column nor the surface particle size distribution of the snow. This study illustrates that precipitation profiles and mountain induced circulations may be decoupled which can be very relevant for either ground-based or spaceborne remote sensing of precipitation.