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©2018 Wiley This project was funded by the Spanish government project CGL2015-65627-C3-3-R (MINECO/FEDER). Jon A. Arrillaga is supported by the Predoctoral Training Program for Non-Doctorate Researchers of the Department of Education of the Basque government (PRE_2016_2_0160, MOD = B). The first author developed part of the research during a visit to Wageningen University, supported by a EGONLABUR mobility grant from the Basque government (EP_2016_1_0048). We thank the Royal Netherlands Meteorological Institute (KNMI) for the meteorological data from Cabauw. We are also grateful to the Energy Research Centre of the Netherlands (ECN) for the CO2 and 222Rn data and we also thank OSI-SAF for the satellite full-resolution Metop data provided., We investigated sharp disruptions of local turbulence and scalar transport due to the arrival of sea-breeze fronts (SBFs). To this end, we employed a comprehensive 10-year observational database from the Cabauw Experimental Site for Atmospheric Research (CESAR, the Netherlands). Sea-breeze (SB) days were selected using a five-filter algorithm, which accounts for large-scale conditions and a clear mesoscale-frontal signal associated with the land-sea contrast. Among those days (102 in all, 8.3%), based on the value of the sensible-heat flux at the onset of SB, we identified three atmospheric boundary-layer (ABL) regimes: convective, transition and stable. In the convective regime, the thermally driven convective boundary layer is only slightly altered by a small enhancement of the shear when the SBF arrives. Regarding the transition regime, we found that the ABL afternoon transition is accelerated. This was quantified by estimating the contributions of shear and buoyancy to the turbulent kinetic energy. Other relevant disruptions are the sharp reduction in ABL depth (similar to 250 m/hr) and the sudden increase in average wind speed (> 2 m/s). In the stable regime, the arrival of the SB leads to disturbances in the wind profile at the surface layer. We observed a deviation of more than 1 m/s in the observed surface-layer wind profile compared with the profile calculated using Monin-Obukhov Similarity Theory (MOST). Our findings furthermore reveal the determinant role of the SB direction in the transport of water vapour, CO2 and Rn-222. The return of continental air masses driven by the SB circulation generates sharp CO2 increases (up to 14 ppm in half an hour) in a few SB events. We suggest that the variability in Rn-222 evolution may also be influenced by other non-local processes such as the large-scale footprint from more remote sources., Ministerio de Economía y Competitividad (MINECO)/FEDER, Department of Education of the Basque government, Basque government, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

The height of the atmospheric boundary layer or mixing layer is an important parameter for understanding the dynamics of the atmosphere and the dispersion of trace gases and air pollution. The height of the mixing layer (MLH) can be retrieved, among other methods, from lidar or ceilometer backscatter data. These instruments use the vertical backscatter lidar signal to infer MLHL, which is feasible because the main sources of aerosols are situated at the surface and vertical gradients are expected going from the aerosol loaded mixing layer close to the ground to the cleaner free atmosphere above. Various lidar/ceilometer algorithms are currently applied, but accounting for MLH temporal development is not always well taken care of. As a result, MLHL retrievals may jump between different atmospheric layers, rather than reliably track true MLH development over time. This hampers the usefulness of MLHL time series for e.g. process studies, model validation/verification and climatology. Here, we introduce a new method "Pathfinder", which applies graph theory to simultaneously evaluate timeframes consistent with scales of MLH dynamics, leading to coherent tracking of MLH. Starting from a grid of gradients in the backscatter profiles, MLH development is followed using Dijkstra's shortest path algorithm (Dijkstra, 1959). Locations of strong gradients are connected under the condition that subsequent points on the path are limited to a restricted vertical range. The search is further guided by rules based on presence of clouds and residual layers. Applied to backscatter lidar data from Cabauw, excellent agreement is found with windprofiler retrievals for a 12-day period in 2008 (R2 = 0.90) and visual judgment of lidar data during a full year in 2010 (R2 = 0.96). These values compare favourably against other MLHL methods applied to the same lidar data set and corroborate more consistent MLH tracking by Pathfinder.

Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal-resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g. variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear-sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background pro-files are extracted from the French convective-scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation, and root mean square) for water vapour channels (22.24-30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line centre (similar to 2-2.5 K) towards the high-frequency wing (similar to 0.8-1.3 K). Statistics for zenith and lower-elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show di