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Snowfall in elevated areas of the mid-latitudes has a strong impact on infrastructure, freshwater availability, and the climate system. The Cantabrian Mountains of the northwestern Iberian Peninsula are very vulnerable to climate change because of their moderate altitudes, which limits their snowfall. Monitoring snow events is essential for the evaluation of weather and climate prediction models. However, measurement networks are scarce in mountainous areas and have great uncertainties because of blizzards. In this study, a multiphysics ensemble of the Weather Research and Forecasting (WRF) model was designed using three microphysics and two planetary boundary layer (PBL) schemes to simulate nine snowfall events in the Cantabrian Mountains during autumn and winter 2021–2022. The WRF was validated using several snow characteristics, such as liquid water equivalent, snow cover, and snow depth. Liquid water equivalent was evaluated using snow-gauge networks and satellite products in an assessment of snow cover. In addition, a monitoring network of webcams and snow poles was implemented, improving the low density of snow observations in the mountains. The results showed good model performance for detection of snow cover and slight overestimation of liquid water equivalent and snow thickness, which may have been caused by under-catchment that is generally an effect of wind on the measurement systems and by snow compaction, respectively. Morrison microphysics and Mellor-Yamada-Nakanishi-Niino (MYNN PBL) yielded better results for liquid water equivalent at higher altitudes and output greater snow cover. The results help determine the best configurations for snow modelling in the study area to develop future studies of the spatiotemporal patterns of snow distribution. • Monitoring system of snow events in Cantabrian Mountains has been developed. • We selected nine snowfall events during autumn and winter 2021–2022. • Three microphysics and two PBL parameterizations were tested. • Morrison microphysics and MYNN PBL yielded better results at higher altitudes. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most relevant fields in atmospheric modeling because of its environmental, social and economic implications. However, precipitation validation from weather model outputs presents substantial challenges, such as measurement uncertainties, use of gridded datasets vs. direct observations, and the selection of statistical goodness-of-fit measures. The main difficulty of working with precipitation is that it can be spatially irregular, especially in extreme events. High temporal aggregation smooths the field and reduces verification uncertainty. For this reason, validations are usually focused on a daily scale. However, many extreme events occur on shorter periods, for which a sub-daily precipitation assessment is required. In this paper, hourly precipitation verification of the Weather Research and Forecasting (WRF) model is explored for 45 extreme precipitation events (EPEs) recorded in northeastern Spain. For this, stations with recorded EPEs were classified according to the hourly distribution of precipitation. WRF simulations were established considering three microphysics and two planetary boundary layer (PBL) parameterizations. Finally, several statistical goodness-of-fit measures and spatial and temporal precipitation distributions were used for evaluating WRF performance. The results showed that microphysics were more important than PBL parameterizations. Goddard and Thompson together with Mellor-Yamada-Nakanishi and Nino PBL gave better results for most of the analyzed characteristics. However, an optimal combination of parameterizations was not obtained for all EPEs, because event characteristics had important effects on model performance. • Hourly precipitation verification of the WRF model has been explored. • We selected 965 stations where extreme precipitation was recorded. • Three microphysics and two planetary boundary layer parameterizations were tested. • Goddard and Thompson together with Mellor-Yamada-Nakanishi and Nino gave better results. [ABSTRACT FROM AUTHOR]

Abstract Aviation safety is a priority that may be compromised by adverse weather conditions. This is the case for poor visibility in the vicinity of airports, which can pose a risk during takeoff and landing. For airports that are prone to fog because of their location, an accurate forecast of poor-visibility episodes is vital. However, the forecasting of low clouds is still a challenge in numerical weather prediction, especially when an airport is near complex terrain for which the use of non-hydrostatic mesoscale models is mandatory. All these factors are present at Tenerife Norte Airport, which is commonly affected by poor visibility from low clouds related to persistent trade winds and moist flows from the Atlantic Ocean. In this paper, several methods for estimating visibility based on mesoscale model outputs are tested. Use of the HARMONIE-AROME model is encouraged because of its excellent performance in the detection of poor-visibility episodes (False Alarm Ratio = 0.34–0.38; Frequency Of Misses = 0.22–0.38, depending on the model version and method used). In addition, the use of satellite application facilities is proposed for the nowcasting of low clouds affecting the airport area. Specifically, we used products that estimate cloud type, cloud top altitude, and integrated water vapor content in the boundary layer. Finally, an application is presented for the monitoring of weather conditions in real time to estimate poor-visibility risk. Highlights • Poor visibility frequently poses a risk to aviation safety at GCXO airport. • Forecasting of these episodes remains a challenge in numerical weather prediction. • Mesoscale models and satellite application facilities are proposed for the task. [ABSTRACT FROM AUTHOR]

Aircraft icing is one of the most dangerous weather phenomena in aviation security. Therefore, avoiding areas with high probability of icing episodes along arrival and departure routes to airports is strongly recommended. Although such icing is common, forecasting and observation are far from perfect. This paper presents an analysis of an aircraft icing and turbulence event including a commercial flight near the Guadarrama Mountains, during the aircraft approach to the airport. No reference to icing or turbulence was made in the pre-flight meteorological information provided to the pilot, highlighting the need for additional tools to predict such risks. For this reason, the icing episode is simulated by means of the Weather Research and Forecasting (WRF) model and analyzed using images from the Meteosat Second Generation (MSG) satellite, with the aim of providing tools for the detection of icing and turbulence in the airport vicinity. The WRF simulation shows alternating updrafts and downdrafts (> 2 m s − 1 ) on the lee side of the mountain barrier. This is consonant with moderate to strong turbulence experienced by the aircraft on its approach path to the airport and suggests clear air turbulence above the mountain wave cloud top. At the aircraft icing altitude, supercooled liquid water associated with orographic clouds and mountain waves is simulated. Daytime and nighttime MSG images corroborated the simulated mountain waves and associated supercooled liquid water. The results encourage the use of mesoscale models and MSG nowcasting information to minimize aviation risks associated with such meteorological phenomena. [ABSTRACT FROM AUTHOR]