Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models

Abstract

Abstract. Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices.

On the role of local and large-scale atmospheric variability in snow cover duration: a case study of Montevergine Observatory (Southern Italy)

“…López-Moreno et al 2020, Bonsoms et al 2021). Instead, very few studies have been devoted to the Italian Apennines (Petriccione and Bricca 2019, Raparelli et al 2021, Diodato et al 2022. From these works, it emerged an overall negative trendency in SCD in almost all examined sites, in line with worldwide trends.…”

Using Artificial Neural Networks to Couple Satellite C-Band Synthetic Aperture Radar Interferometry and Alpine3D Numerical Model for the Estimation of Snow Cover Extent, Height, and Density

“…The area of interest is the central Italy Apennines range between 41.5°-43.5° north latitude and 12.5°-14.5° east longitude. This region, bounded by the Tyrrhenian Sea on the west side and the Adriatic Sea on the east side, is characterized by a complex orography with deep valleys, medium-short rivers, small lakes, and relatively steep mountains [44]. The highest peak is the Corno Grande at 2912 m above sea level in the Gran Sasso Mountain range which hosts the Calderone glacier, the southernmost glacier (indeed, in the last decades transformed into a glacieret) of the European continent.…”

“…The simulation of fundamental snow cover properties is based on the Alpine3D model simulations. Alpine3D is a Lagrangian numerical model used to simulate high-resolution surface processes, and in particular snow processes in mountainous regions [47] [44]. The Alpine3D model is driven by external data such as measurements at stations or outputs from meteorological models.…”

“…The Alpine3D model, driven by forecasted weather data, has been validated in the AOI [44]. Results showed that Alpine3D is able to reproduce the observed snow height and snow water equivalent with a bias of 6 cm and 35 mm respectively, with a correlation coefficient of 0.87 for snow height and 0.74 for snow water equivalent.…”

“…Most of snow remote sensing applications from space have been focused on major mountain systems, such as Sierra Nevada, Alps, and Himalayan regions (e.g., [14], [15], [29], [31]). The Italian Apennines, have never been considered for this kind of studies by the scientific community, probably due to the high spatial and temporal variability of their snow cover [44]. Nevertheless, the central Apennines region holds a key role for the meteorological dynamics in the Mediterranean area and, moreover, it hosts the southernmost European glacier, the Calderone.…”