The Cerdanya valley in northeast Spain experiences intense cold air pooling which decouples the valley atmosphere from the regional circulation, especially in winter. This makes air temperature prediction a challenge. A network of 40 temperature sensors was installed in 2012 along seven elevational transects to collect hourly temperatures throughout the cold pool, enabling measurement of the detailed cold pool structure for the first time. Sensors were also installed in the upper Conflent valley to the northeast for comparison, where previous research has shown that there is reduced cold air pooling. Sensor data were validated against automatic weather station observations at two locations. Through calculation of hourly lapse rates in various elevation bands for 2 years, frequent inversions developing up to 1450 m are shown, and sometimes extending much higher than this, concentrating in winter. Case studies of two intense episodes in December 2012 and January 2013 show that model simulations, despite being able to simulate broad mechanisms of cold air pooling formation, underestimate the amount of cooling. This is due to over-enthusiastic simulation of a low level jet in the first case and mountain waves in the second. Solving these model problems has important consequences for the future ability to predict episodes of extreme low temperatures and associated hazards (frost, fog) in Cerdanya and mountain valleys elsewhere.

Different types of land use (LU) have different physical properties which can change local energy balance and hence vertical fluxes of moisture, heat and momentum. This in turn leads to changes in near-surface temperature and moisture fields. Simulating atmospheric flow over complex terrain requires accurate local-scale energy balance and therefore model grid spacing must be sufficient to represent both topography and land-use. In this study we use both the Corine Land Cover (CLC) and United States Geological Survey (USGS) land use databases for use with the Weather Research and Forecasting (WRF) model and evaluate the importance of both land-use classification and horizontal resolution in contributing to successful modelling of surface temperatures and humidities observed from a network of 39 sensors over a 9 day period in summer 2013. We examine case studies of the effects of thermal inertia and soil moisture availability at individual locations. The scale at which the LU classification is observed influences the success of the model in reproducing observed patterns of temperature and moisture. Statistical validation of model output demonstrates model sensitivity to both the choice of LU database used and the horizontal resolution. In general, results show that on average, by a) using CLC instead of USGS and/or b) increasing horizontal resolution, model performance is improved. We also show that the sensitivity to these changes in the model performance shows a daily cycle.

This study took place in the Pyrenees Range, in the northeastern Iberian Peninsula. The Pyrenees extend longitudinally, separating the Iberian Peninsula from the rest of Europe, and high peaks around 3000 m arise from deep valleys. As a mountain range it creates a barrier to advection, in this case from the north and south, and typical meteorological phenomena of mountainous areas occur within it (inversions, Foehn effect, extreme wind-chill, snow storms). Thus, two specific valleys in Catalonia were considered, Val d'Aran and Cerdanya. In both valleys automatic weather stations (AWSs) are available at similar heights. Although these valleys are only 100 km apart, they have different climates. However, the main reason for developing the study was that Numerical Weather Prediction (NWP) has problems when forecasting temperatures in complex terrain areas, mainly in the valley floor in winter season.Firstly, different equations based on a multilinear regression were obtained for each weather station. Multilinear regression was considered in this case as the most suitable downscaling method and data used were provided by the AWSs and MM5 (PSU/NCAR mesoscale model) numerical weather prediction model outputs.These equations were obtained to set up a Geographically Weighted Regression (GWR) method, although this one was modified and changed to a Vertically Weighted Regression (VWR) in order to create vertical temperature profiles.

The aim of this research was to detect cold‐air pool (CAP) days during winter and quantify the synoptic situations that produce decoupling between free air and valley bottom temperatures in the Cerdanya basin in the eastern Pyrenees (Spain). Patterns prone to CAP (see figure) are defined by an anticyclonic ridge over the Iberian Peninsula and absence of surface wind in Cerdanya, especially in December and January. Warm air advection from the south is also associated with some of the patterns.

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