# Forecasting and modelling ice layer formation on the snowpack due to freezing precipitation in the Pyrenees

Louis Quéno, Vincent Vionnet, Frédéric Cabot, Dominique Vrécourt, Ingrid Dombrowski-Etchevers

► To cite this version:

Louis Quéno, Vincent Vionnet, Frédéric Cabot, Dominique Vrécourt, Ingrid Dombrowski-Etchevers. Forecasting and modelling ice layer formation on the snowpack due to freezing precipitation in the Pyrenees. Cold Regions Science and Technology, 2018, 146, pp.19-31. 10.1016/j.coldregions.2017.11.007. hal-03684301

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# Forecasting and modelling ice layer formation on the snowpack due to freezing precipitation in the Pyrenees

Louis Quéno<sup>a, *</sup>, Vincent Vionnet<sup>a, d</sup>, Frédéric Cabot<sup>b</sup>, Dominique Vrécourt<sup>b</sup>, Ingrid Dombrowski-Etchevers<sup>c</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

### Keywords:

Freezing precipitation

Ice layer

Snowpack

Hazard forecasting

Pyrenees

## ABSTRACT

In the Pyrenees, freezing precipitation at high elevations is quite frequent in winter, leading to the formation of an ice layer on the surface of the snowpack. It may cause many accidents amongst mountaineers and skiers, with sometimes more fatalities per winter than avalanches. Such events are not predicted by current operational systems for snow and avalanche hazard forecasting. A crowd-sourced database of surface ice layer occurrences was first built up, using reports from Internet mountaineering and ski-touring communities, to mitigate the lack of observations from conventional observation networks. Simple diagnostics of freezing precipitation were then developed, based on the cloud water content and 2-m temperature forecast by the Numerical Weather Prediction model AROME, operating at 2.5-km resolution. An evaluation over five winters gave a probability of detection reaching 81% and a false alarm ratio of 23% compared to occurrences reported in the observation database. A new modelling of ice formation on the surface of the snowpack due to impinging supercooled water was added to the detailed snowpack model Crocus. It was combined with the atmospheric diagnostic of freezing precipitation. Resulting snowpack simulations over five winters captured the formation of the main observed ice layers. The performance of the diagnostic associated with the ice formation modelling was assessed for the event of 5–6 January 2012, with altitudinal and spatial distributions of the ice layer matching the observations. These simple methods enable to forecast the occurrence of surface ice layer formations and to simulate their evolution within the snowpack, even if an accurate estimation of the amount of freezing precipitation remains the main challenge.

## 1. Introduction

The most common risks encountered by mountaineers and skiers in mountains during the winter season are snow avalanches (Schweizer et al., 2003). Over the period 1980–2014 in France, ANENA (French National Association for Snow and Avalanche Studies) reported an average of 31 fatalities every winter due to avalanches (Jarry, 2015). Avalanche hazard forecasts are issued daily during the winter season in the Alps, Corsica and the Pyrenees by Météo-France forecasters. They recently reported another source of numerous accidents in the Pyrenees. Freezing precipitation, leading to the formation of a pure ice layer at the surface of the snowpack, is a quite frequent phenomenon in the Pyrenees.

An intense event of ice formation on the snowpack highlighted the necessity to better understand and predict such a phenomenon. During the night of the 5–6 January 2012, a thick ice layer formed due to freezing precipitation at high elevations, in a synoptic context of warm

front in a North-West flow with strong winds and orographic blocking on the French side of the Pyrenees. The ice layer thickness often reaching 5 cm or more was prevalent over the Pyrenean massif. Because of subsequent clear sky conditions, this ice layer remained two weeks or more on the surface of the snowpack. During this period, mountain rescue services reported an exceptionally high number of accidents by sliding of back-country skiers and mountaineers caused by this glassy and very hard surface, which made the progression in mountainous terrain hazardous (Fig. 1). 9 fatalities occurred during the two following weeks. This human toll was greater than the average number of two fatalities due to avalanches in the Pyrenees per winter over the last five years (source: ANENA). The consequences of this event were particularly marked due to the persistence of ice on the surface. However, according to avalanche forecasters such events are rather frequent in the Pyrenees. New methods to predict and model the phenomenon are sorely needed to warn mountaineers and skiers in advance.

<footnote><sup>a</sup> Météo-France/CNRS, CNRM UMR3589, CEN, Grenoble, France</footnote>

<footnote><sup>b</sup> Météo-France, Tarbes, France</footnote>

<footnote><sup>c</sup> Météo-France/CNRS, CNRM UMR3589, Toulouse, France</footnote>

<footnote><sup>d</sup> Centre for Hydrology, University of Saskatchewan, Saskatoon, Canada</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: louis.queno@gmail.com (L. Quéno).</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Mountaineer using crampons on the ice layer after the event of 5–6 January 2012. Credits: Club Alpin Français des Montagnards Ariégeois.</figcaption></figure>

Currently, operational snowpack simulations for avalanche hazard forecasting run with the detailed snowpack model SURFEX/ISBA/Crocus (Vionnet et al., 2012), mentioned as Crocus hereafter. It is used within the SAFRAN-Crocus-ME Pra model chain applied over the main French mountainous areas (Durand et al., 1999; Lafaysse et al., 2013). The meteorological analysis and forecasting system SAFRAN (Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige; Analysis System Providing Atmospheric Information to Snow; Durand et al., 1993) provides the meteorological forcing for Crocus. In particular, the precipitation phase is derived from a simple threshold of 1°C for air temperature at 2 m above the ground. It only enables the representation of snowfall and rain but not of freezing precipitation. An inaccurate estimation of the precipitation phase may change the stratigraphy simulated by Crocus and consequently the stability and avalanche hazard predicted by the expert system ME PRA (Giraud, 1992). Since no snowpack observations are assimilated in operational models, the snowpack stratigraphy may remain skewed during the whole winter season. Thus, freezing precipitation events are considered as a source of errors in numerical snowpack forecasting by French operational services in the Pyrenees.

The latest developments in mesoscale Numerical Weather Prediction (NWP) systems offer new opportunities to forecast atmospheric icing and freezing precipitation, since they include advanced cloud micro-physics schemes with a representation of mixed-phase cloud processes and a higher diversity of water species. For instance, Thompson et al. (2009) used the Weather Research and Forecasting (WRF) model at kilometric resolution with an improved microphysics scheme for winter precipitation (Thompson et al., 2004) to predict icing due to freezing rain, freezing drizzle or freezing fog. Forecasting freezing precipitation is still a challenge: in the Integrated Forecasting System of ECMWF (European Centre for Medium-Range Weather Forecasts), cloud and precipitation physics were recently modified to improve freezing rain predictions (Forbes et al., 2014). Additionally, the high horizontal resolution of mesoscale NWP systems gives a better representation of the topography, which allows to study these events in complex terrain. Drage and Hauge (2008) used MM5 to simulate atmospheric icing in a Norwegian coastal mountainous terrain, in-cloud icing was also simulated by Yang et al. (2012) on Mount Washington (U.S.A.) with the regional mesoscale model GEM-LAM at 1-km grid spacing, and WRF was used by Podolskiy et al. (2012) to simulate rime formation on Mount Zao (Japan) and by Fernández-González et al. (2015) to simulate a freezing drizzle event in the Guadarrama Mountains (Spain), both at 1-km resolution.

AROME (Application of Research to Operations at MEsoscale; Seity et al., 2011; Brousseau et al., 2016) is the mesoscale NWP system of

Météo-France, operating at kilometric grid spacing over France (2.5 km from 2008 to 2015, 1.3 km since then). It was recently used to drive snowpack simulations with Crocus at kilometric resolution over the French Alps (Vionnet et al., 2016) and the Pyrenees (Quéno et al., 2016). Vionnet et al. (2016) highlighted AROME potential for mountain meteorology and Quéno et al. (2016) showed a good representation of the snow cover spatial distribution and benefits in terms of daily snow depth variations, despite strong positive biases in terms of snow depth. More specially, AROME includes a comprehensive cloud micro-physics scheme, and thus, simulates supercooled cloud water, which is of particular interest in regard to the issue of freezing precipitation. The aim of the present study is to assess the potential of AROME associated with Crocus for the detection of freezing precipitation events and the modelling of the formation of an ice layer on the snowpack in the Pyrenees. The original character of the study derives from the use of cloud microphysical informations from a mesoscale NWP system in mountainous terrain combined to a snowpack model for ground icing.

The paper is organized as follows. In Section 2, we briefly present the observed phenomenon and its meteorological context, as well as available observations for identified events. We introduce an observation database built up using information posted on Internet by the skitouring community. Section 3 describes the NWP model AROME and the detailed snowpack model Crocus. Section 4 presents the development of a simple method to detect freezing precipitation in the Pyrenees using AROME, and the implementation of a module to simulate the ice layer formation due to freezing precipitation in Crocus. These new methods are assessed in Section 5 following two axes: (i) sensitivity study and temporal occurrence of the detections over five winters, (ii) spatial and altitudinal distribution of the simulated freezing precipitation and ice formation for the 5–6 January icing event. Results are discussed in Section 6 with a particular emphasis on the limitations of the methods and the observation database.

## 2. Description of the observed phenomenon

### 2.1. Meteorological context in the Pyrenees

This study focuses on the Pyrenees (Fig. 2), the natural border separating France from Spain, stretching from the Atlantic Ocean to the Mediterranean Sea, a mountain range with many summits exceeding 3000 m a.s.l. The domain of study covers France, Andorra and Spain, from 41.6°N to 43.6°N and from -2.5 °E to 3.5°E.

In the Pyrenees, freezing precipitation on the snowpack occurs in very specific meteorological conditions. During common freezing rain situations in plains, snow flakes melt in an intermediate warm layer to form water drops and these drops become supercooled in a layer close to the ground below freezing point. In the situations studied here, freezing precipitation is associated with an entirely negative temperature sounding, which implies that water drops stay supercooled all along the vertical profile.

It is almost always associated with a warm front and N/NW flow with orographic blocking in the French Pyrenees. The cloud top temperature is between -5 °C and -10 °C, so that cloud droplets stay supercooled and do not turn into ice crystals and snow flakes (Rauber et al., 2000). The temperature vertical profile remains below freezing point down to the ground. Cloud droplets undergo a collision-coalescence process (Cober et al., 1996) which contributes to the formation of drops up to the size of drizzle (100 µm–500 µm) falling on the ground (the so-called “warm rain process”). The process of coalescence is favoured by strong winds and vertical updraft giving significant precipitation (Rasmussen et al., 1995; Fernández-González et al., 2015).

To avoid the formation of ice or snow, the ice nuclei must have a low number concentration. Furthermore, at temperatures warmer than -10 °C, only few ice nuclei are active (Rauber et al., 2000). The Pyrenees are mostly exposed to oceanic westerly winds, which very likely favour the formation of freezing drizzle due to the low amount of ice
<figure><img src="image_4.png" /><figcaption>Fig. 2. Domain of study: the Pyrenees, with AROME topography at 2.5 km resolution. Black lines represent the regions where avalanche bulletin are issued daily. The red line indicates the transect of Fig. 6 (0.65°E). Point A: 42.65°N, 0.65°E, 2563 m in AROME topography.</figcaption></figure>

nuclei in oceanic air masses (Rasmussen et al., 2002). The influence of the oceanic air masses on the freezing drizzle spatial distribution was highlighted by Stuart and Isaac (1999), who showed that Atlantic regions of Canada were the most frequently affected by freezing drizzle. Similarly, it seems that freezing precipitation on the snowpack is much rarer in the Alps than in the Pyrenees.

## 2.2. Observations

After the event of 5–6 January 2012, the ice layer was present on all aspects, although there was more ice on the windward sides due to the projection of drops by strong winds, while the leeward sides of ridges were locally sheltered. It covered an altitudinal range from 1800 m–2000 m (thinner ice on wet snow under 2000 m) to 2600 m–2800 m: Fig. 3 illustrates the three main types of snow surfaces found after the event, following the simplified vertical temperature profile and the elevation of clouds. As a general rule, ice layers are often found at altitude above usual daily observations made by ski patrollers in ski resorts. Thus, very limited information is available for avalanche forecasters in these situations. The temporal occurrence of freezing precipitation can be guessed from the stop of some high-altitude anemometers, jammed by accreting ice. But the standard observation network, including snow depth sensors, cannot be

used to detect these events. An alternative source of data is necessary.

There has been a recent growing interest in crowd-sourcing to address the lack of observations, particularly in atmospheric sciences (Muller et al., 2015), for example to have a large spatial coverage of precipitation type observations (Elmore et al., 2014). Crowd-sourcing data was also used through social networks for avalanche hazard forecasting (e.g. Tremper and Diegel, 2014), or through GPS-tracking to study skiers' behaviour in avalanche terrain (Hendrikx and Johnson, 2016). In the present study, we used an original source of information for snowpack studies. Internet ski-touring and mountaineering communities gather a large amount of observations on a large geographical and temporal domain. Furthermore, mountaineers and back-country skiers begin their ascent from valleys to reach summits of mountains, hence a potentially wide spatial and altitudinal coverage. Unfortunately, as their outing is mostly made when the weather is nice, observations are not exhaustive. Expert validation is necessary as reports are subjective.

An ice layer observation database was built up, based on ski-touring and mountaineering reports from the Camptocamp website (http://www.camptocamp.org, last access: 26 January 2017) for the period 08/2010–08/2015. Reports on outing in the Pyrenees which contained words in French such as “glace” (ice), “vitrifiée”(glassy/glaze) or “verglacée”(icy) were selected. The wording of these reports and their

<figure><img src="image_5.png" /><figcaption>Fig. 3. Scheme illustrating the types of snow surfaces after the event of 5–6 January 2012 depending on the altitude and the temperature vertical profile. Credits: (top) author M. Matveieff; (middle) author F. Cabot; (bottom) author J.-M. Fontan, licence CC-by-nc-nd, URL http://www.camptocamp.org/outings/315471/fr/col-d-aoube-collet-sans-nom-par-les-cabanes-d-aoube.</figcaption></figure>
associated pictures helped to confirm or invalidate the presence of an ice layer (e.g. differentiate it from a melt-refreeze crust). Dates and locations of events were recorded. Ambiguous situations were removed. Overall, 92 observations constitute the database over the five years. 41 mountain rescue reports were added for the event of 5–6 January 2012.

# 3. Models

## 3.1. NWP model: AROME

AROME is the meso-scale NWP system of Météo-France (Seity et al., 2011). It provides operational forecasts over France since December 2008 at 2.5-km grid spacing, upgraded to 1.3 km in 2015 (Brousseau et al., 2016). It is a spectral and non-hydrostatic model. The kilometric resolution enables to represent meso-scale effects of the topography and its dynamical influence on the atmosphere in mountainous areas (Vionnet et al., 2016).

In this paper, we will underline the added benefits brought from AROME for the prediction of ice layer formation events in mountainous areas. AROME simulates cloud development and precipitation using the ICE3 cloud microphysical scheme (Pinty and Jabouille, 1998). ICE3 is a one-moment bulk scheme with five prognostic hydrometeors (cloud droplets, rain, ice crystals, snow and graupel). In particular, cloud water and cloud rain are characterized after Kessler's scheme (Kessler, 1969). It can provide valuable information for mountain meteorology and snowpack-related issues, in addition to usual precipitation and ground-level variables. The hydrometeor mixing ratio (in $g kg^{-1}$) at a given altitude, will be mentioned hereafter as: CWC (cloud water content), CRC (cloud rain content), CIC (cloud ice content), CSC (cloud snow content) and CGC (cloud graupel content).

AROME provides a 30-h forecast at the 0, 6, 12 and 18 UTC nominal analysis times over a domain covering France. In this study, we used the hourly forecasts of the 0 UTC analysis time, from +6 h to +29 h, as they can be used in the morning by operational services for immediate hazard forecast. They were extracted on a regular latitude/longitude grid at 0.025° (approximately 2.5 km) resolution over the Pyrenees domain (Fig. 2).

## 3.2. Snowpack model: Crocus

Snowpack simulations for operational avalanche hazard forecasting were performed with the detailed snow cover model Crocus (Brun et al., 1992; Vionnet et al., 2012). It is coupled with the ISBA land surface model within the SURFEX (EXternalized SURFace) simulation platform (Masson et al., 2013), which enables to fully simulate the interactions between snowpack and soil. The physical properties of the snowpack are modelled along its stratigraphy (with a user-defined maximum number of layers – 50 in this study). The model is used in offline mode and requires the following driving data: reference level temperature and specific humidity (usually 2 m above ground), wind speed (usually 10 m above ground), incoming short-wave and long-wave radiations, solid and liquid precipitation. In particular, liquid precipitation is considered as incoming fluxes in the first snow layer. The water flow through the snowpack is modelled with the bucket approach and uses a series of reservoirs at each snow layer (Vionnet et al., 2012; D'Amboise et al., 2017). Simulations were carried out with a 15 min internal time step, on flat surfaces.

# 4. Methods for freezing precipitation forecasting and ice layer modelling

## 4.1. Potential and limitations of AROME for freezing precipitation forecasting

We first studied the behaviour of AROME for the simulation of the event of 5–6 January 2012. Fig. 4 illustrates the vertical gradient of

<figure><img src="image_6.png" /><figcaption>Fig. 4. Vertical gradient of cumulated precipitation forecast by AROME from 5 January 2012, 12 UTC, to 6 January 2012, 6 UTC, averaged over Haute-Bigorre massif (Fig. 2).</figcaption></figure>

precipitation forecast by AROME between 5 January 2012, 12 UTC (13 local time), and 6 January 2012, 6 UTC (7 local time), averaged over the massif of Haute-Bigorre (Fig. 2, the area with most ice layer observations for this event). It shows that above 2000 m there is mainly a mix of snow and graupel, the little amount of rain falling at the beginning of the period when the 0°C-isotherm was high enough. AROME did not forecast any freezing precipitation for this event.

To study how the microphysics scheme simulates the episode along the flow, we focused on a North-South transect across the Pyrenees. The location of the transect (0.65°E), identified by a red line in Fig. 2, was chosen to cover a large altitudinal range. The temporal evolution of hydrometeor content as well as precipitation and temperature forecast by AROME for the afternoon and night of 5–6 January 2012 is represented in Fig. 5 at point A of the transect (42.65°N, 0.65°E, Esera massif, Fig. 2), at an altitude of 2563 m where freezing precipitation was observed. The entire temperature profile stays below freezing for the duration of this event, including the 2 m temperature, which is consistent with the meteorological context of freezing precipitation through collision-coalescence process (Section 2.1). A very high CWC is simulated, reaching up to $0.8 g kg^{-1}$ at 17 UTC. It decreases between 21 UTC and 22 UTC, together with a sudden increase in solid hydrometeors, including graupel. This suggests that AROME simulates the formation of ice crystals and snow flakes by auto-conversion and Bergeron processes and the formation of graupel by riming of supercooled droplets on snow flakes. These processes favour the depletion of supercooled liquid water and prevent the formation of freezing precipitation in the model. Instead, the model erroneously simulates snowfall where no snowfall was observed in the region, except for a very low amount at the end of the night locally.

Fig. 6 represents the CWC, CIC and CSC forecasts by AROME along the transect on 5 January, 20 UTC (21 local time). It gives a spatial overview of the simulation: high CWC are modelled on the French side, with maximum values above 2000 m, reaching $0.8 g kg^{-1}$, associated with temperatures below 0 °C. The cloud top temperature is warmer than -8 °C. The orographic shield and Foehn effect due to strong NW wind is visible on the Spanish side with no clouds and a strong increase in temperatures: the iso-0 °C is at around 2100 m on the French side while it quickly reaches around 3500 m on the Spanish side. The CSC and CIC are much lower than the CWC: they reach a maximum of around $0.3 g kg^{-1}$ at the highest peak. CRC and CGC are both close to zero (not shown). The simulated cloud microphysics thus correspond to the conditions necessary for the formation of freezing precipitation through collision-coalescence process, described in Section 2.1. Additionally, Fig. 7 represents a map of the average CWC at 100 m above
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Evolution of cloud hydrometeor content at 100 m level (top, lines), hourly precipitation (top, bars) and temperature at 100 m and 2 m levels (bottom), forecast by AROME at point A (42.65°N, 0.65°E, 2563 m, Fig. 2) from 5 January 2012, 12 UTC, to 6 January 2012, 6 UTC.</figcaption></figure>

the ground over the Pyrenees, between 14 UTC and 6 UTC, with locations of observed ice layers and associated accidents. The good spatial correlation of these two data indicates the potential of CWC at 100 m for freezing precipitation detection.

## 4.2. Freezing precipitation forecasting

### 4.2.1. Occurrence

The study of the event of 5–6 January 2012 shows that AROME simulates the wrong precipitation phase despite the simulation of high CWC in the cloud microphysics scheme. This finding highlights the need for a new diagnostic of freezing precipitation that is not based on precipitation forecasts. Similarly, Fernández-González et al. (2015) showed that WRF did not simulate supercooled precipitation for a freezing drizzle event in mountains, and had to develop an algorithm based on the cloud microphysics scheme to forecast it.

The collision-coalescence process leading to the growth and precipitation of supercooled cloud water is not correctly simulated by AROME. Consequently, we decided to introduce an atmospheric diagnostic of freezing precipitation occurrence, based on the forecast of supercooled cloud water. The first condition is a subfreezing 2 m air temperature. Secondly, a threshold of liquid water at 100 m has to be determined. This method is consistent with the findings of Rasmussen et al. (2002) showing that a CWC of 0.35 $g kg^{-1}$ is a threshold for drizzle formation onset. The supercooled cloud rain also has to be included in this threshold to account for the potential cases where AROME simulates freezing precipitation. Thus, we defined the liquid water content $LWC = CWC + CRC$.

To assess the choice of the thresholds, AROME daily forecast archives were analyzed over the entire Pyrenean mountain range from 1 August 2010 to 1 August 2015. The freezing precipitation diagnostics based on different thresholds were computed every hour, and compared to the ice layer observation database (Section 2.2), from November to

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** AROME forecast of (a) CWC, (b) CIC and (c) CSC on 5 January 2012, 20 UTC, along a North-South transect, 0.65°E, identified by a red line in Fig. 2.</figcaption></figure>

May. Several scores were computed to identify the most useful diagnostic to forecasters: POD (probability of detection, proportion of observed events correctly forecast), FAR (false alarm ratio, proportion of false alarms amongst forecast events), ETS (equitable threat score, a performance measure adapted to rare events; Nurmi, 2003) and frequency bias (number of forecasts divided by the number of observations). For convenience, these scores were summarized in a modified ROC diagram (Relative Operating Characteristic; e.g. Mason and Graham, 2002), where the x-axis is the FAR, more adapted to rare events (Lalaurette, 2003). Two diagnostics were defined: the Freezing
<figure><img src="image_9.png" /><figcaption>Fig. 7. Average CWC at 100 m forecast by AROME between 5 January 2012, 14 UTC, and 6 January 2012, 6 UTC. Red dots: accident reports. Orange dots: mountaineers' observations.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8. Simplified heat balance for a supercooled water drop impinging the snowpack surface.</figcaption></figure>

Precipitation Alert (FPA) to predict the occurrence of the main freezing events and the Freezing Precipitation Diagnostic (FPD) to cover the full duration of these events and thus enable to estimate the amount of precipitation.

### 4.2.2. Amount of precipitation

After having determined an occurrence of freezing precipitation, we then need to determine the amount of freezing precipitation. A simple solution was considered, using AROME conventional precipitation outputs. When the diagnostic occurs, AROME solid and liquid amount of precipitation is entirely taken as supercooled liquid precipitation. This is also the method used by Fernández-González et al. (2015) to estimate the amount of freezing drizzle when it is diagnosed. This method assumes purely liquid precipitation, which can be justified by the fact that freezing rain and freezing drizzle occur most frequently without any other type of precipitation (Cortinas et al., 2004). Its limitations are discussed in Section 6.1.

## 4.3. Modelling the ice layer formation in the snowpack

### 4.3.1. Limitations of the current Crocus version

In its current version, the snowpack model Crocus only considers warm rain or snowfall from the meteorological forcing, so it does not handle freezing precipitation. Incoming supercooled water is considered as liquid at 0 °C in heat exchange calculations, which implies percolation and a deep moistening of the snowpack. Liquid water may refreeze later on but it cannot be compared with the physics of the formation of a pure ice layer when supercooled drops reach the surface of the snowpack. Wever et al. (2016) noted that the detailed snowpack model SNOWPACK (Bartelt and Lehning, 2002) has the same limitations for modelling ice layer formation due to freezing precipitation. They proposed a new modelling approach for the formation of ice layers due to water flows inside the snowpack, but this approach cannot treat freezing precipitation. Conversely, the method we introduce hereafter does not treat ice layer formation inside the snowpack.

### 4.3.2. Modelling of ice formation

New developments were added to Crocus in order to take into account supercooled water precipitation from the atmospheric forcing. The heat and mass budgets of supercooled drops impacting a cold surface were often modelled for aircraft icing (e.g. Fortin et al., 2006) or for icing on structures (wires, cylinders; e.g. Jones, 1996; Makkonen, 2000). The modelling described hereafter is simplified compared to aircraft icing, where wind plays a crucial role in ice accretion. Furthermore, the surface considered here is flat. A major difference with icing on structures is the porosity of the snow surface to water flows.

It is assumed that large supercooled water drops (drizzle or rain) impact the snow surface, forming glaze ice on the surface, contrary to rime ice formed by smaller droplets (Myers et al., 2002). Indeed, small droplets (cloud droplets smaller than 100 µm) entrap air while freezing, which gives a white colour to rime ice (dry growth process). Larger drops have time to spread to form glaze ice (wet growth process). Fig. 8 summarizes the new module implemented in Crocus for the freezing of impacting supercooled water. Each flux is counted positively following the orientation of the arrow. At time $t_0$, a water drop (mass $m$, temperature $T < 0$ °C) falls on the snow surface. $T$ is assumed to be the air temperature at 2-m level, even though the largest raindrops may not have time to fully adjust their temperature to the surface layer temperature while falling. At time $t_1$, the drop impinges the surface, which breaks its metastable state with an instantaneous increase of temperature to the thermodynamic ice-water equilibrium at a temperature $T_0 = 0$ °C (less than 20 ms; Tavakoli et al., 2015). During this first freezing stage, a fraction $\phi_S$ of the water solidifies and releases latent heat $\Delta H_0$:

$$ \Delta H_0 = -\phi_S ml_{fus} \quad (1) $$

where $l_{fus}$ is the latent heat of fusion for water (kJ kg$^{-1}$). Consequently, the thermodynamical equilibrium is:

$$ mc_w(T_0 - T) - \phi_S ml_{fus} = 0 \quad (2) $$

where $c_w$ is the specific heat capacity of liquid water (kJ kg$^{-1}$ K$^{-1}$). So the fraction of water instantaneously solidified is:
$$ \phi_S = \frac{c_w}{l_{fus}}(T_0 - T) \qquad (3) $$

i.e. 1.25% per degree of supercooling. For instance, for a drop at -5 °C, 6.3% of water is frozen during the first stage. The remaining water stays liquid at T₀ and may freeze only if the energy sinks (surface fluxes and conductive flux with the snow surface) counterbalance the energy sources. This second freezing stage lasts longer, which enables the water to spread and form dense glaze ice or to percolate in the snow-pack. The quantity of the remaining water freezing in the second stage is determined by the energy budget:

$$ \Delta H = Q_{cond} + Q_{surf} \qquad (4) $$

where ΔH is the latent heat released for the solidification of the remaining water. Qcond is the conductive heat flux from the ice to the first snow layer and is always positive in this case (energy sink) because the ice layer temperature is 0 °C and the snow temperature is negative. Qsurf is the surface heat flux from the ice layer to the atmosphere (sink or source of energy depending on atmospheric conditions).

$$ Q_{surf} = H_{F} + LE - R_{net} \qquad (5) $$

It includes the net radiative flux Rnet, with a long-wave radiative cooling (air colder than the ice surface at 0 °C) and a short-wave radiative heating (zero at night and small in cloudy conditions). It also includes the sensible heat flux from the surface to the atmosphere Hf (energy sink in this case because the surface temperature is at 0 °C and the air temperature is strictly negative) and the latent heat flux of evaporation and sublimation to the atmosphere LE (energy sink). The air frictional heating and the kinetic heating of impinging drops are neglected because they are small compared to the other terms of the energy budget (Jones, 1996; Makkonen, 2000).

### 4.3.3. Implementation into Crocus

In practice, during one simulation time-step (15 min here), the surface energy budget (exposed previously) is computed and compared to the energy required to solidify all impinging supercooled water. If the energy sinks are high enough to compensate the latent heat released by the solidification of the whole water (added to the other energy sources), all the water forms an ice layer, whose temperature may then decrease. Conversely, if the energy sinks are smaller than the energy sources, only the corresponding amount of water forms ice, while the remaining liquid water can percolate into the underlying snow layers. The percolation of liquid water is taken into account in the current version of Crocus through a bucket model (see Section 3.2). However, the impermeability of ice to water percolation is not represented in the current version. The liquid water holding capacity of the two first centimeters at the surface of the snowpack is increased when there is a freezing precipitation diagnostic, to mimic the presence of an impermeable ice layer at the surface of the snowpack. The lateral run-off of liquid water along slopes and the possible persistence of a liquid water film over the ice during more than 15 min (Crocus time step) are not taken into account here.

The ensuing physical properties of the new ice layer are defined using the usual Crocus schemes (Vionnet et al., 2012). The new layer grain properties are implemented within the B92 snow metamorphism scheme (Brun et al., 1992): the ice is represented as large melt forms, i.e. non-dendritic case, maximum grain diameter (2 mm) and maximum sphericity. Its density is assumed to be the density of pure ice (917 kg m⁻³, Myers et al., 2002). The snow layers aggregation scheme is modified to prevent the instantaneous aggregation of a thin ice layer with the underlying snow layer, and thus enable the formation and growth of a new ice layer. We keep the original albedo parameterization of Crocus, split in three spectral bands and depending on the optical diameter and the age of grains (Vionnet et al., 2012). This parameterization gives ice layer albedos consistent with pure ice spectral albedo (e.g. Andrieu et al., 2016). For clarity sake, the new version of

Crocus described in the present section is called Crocus-ice thereafter.

## 5. Results

The methods introduced in Section 4 are applied in two stages. In Section 5.1, we determine the threshold on LWC for the freezing precipitation diagnostic through a sensitivity study and a comparison to the observation database over 5 winters (from 2010 to 2015). We evaluate the ability of this diagnostic to detect temporal occurrence of freezing precipitation and draw a first climatology of freezing precipitation events in the Pyrenees. AROME diagnostic is then combined with Crocus-ice to simulate the ice layer formation and its evolution. In Section 5.2, the new freezing precipitation diagnostic is assessed for the major event of 5–6 January 2012, in terms of spatial and altitudinal distribution. Crocus-ice simulations driven by AROME diagnostic are also compared to the observations for this event.

### 5.1. Temporal occurrence of freezing precipitation: a five-year study

#### 5.1.1. Sensitivity study

Diagnostics implying different LWC₁₀₀m thresholds at subfreezing 2-m temperatures were compared to the observation database in terms of temporal occurrence over the whole Pyrenees. Observations were associated with a diagnosed freezing precipitation event if they closely followed it, or if indications in the report enabled to associate it with a given diagnostic. For instance, observations for February 2012 match the diagnostic of 5–6 January 2012, since the reports described an “old ice layer”, at the surface in wind-exposed areas. Fig. 9 is a ROC diagram that summarizes the most relevant diagnostics tested. A detection is given when the LWC₁₀₀m threshold is overpassed during at least two consecutive hours. It was associated with observations only if the detection covered at least 6 pixels of the domain, to account for the lack of spatial exhaustivity in the observations.

* Freezing precipitation alert over the whole Pyrenees

First, we want to determine a diagnostic to be used by forecasters as an alert of large freezing precipitation events at the scale of the whole Pyrenees. To this end, the POD has to be high with a reasonably low FAR. The ETS synthesizes both scores: no detection or 100% of false alarms would give ETS = 0, while the best performance (ETS = 1) would be reached for 100% of detections without any false alarms. The blue curve (Fig. 9) represents these scores for a range of LWC₁₀₀m thresholds from 0.4 g kg⁻¹ to 0.7 g kg⁻¹. A threshold of 0.7 g kg⁻¹ gives no false alarms but less than 30% of detections. The POD is increased to 81% and the FAR to 23% with a threshold of 0.6 g kg⁻¹. A threshold of 0.5 g kg⁻¹ increases the FAR to more than 60% with no more detections. A threshold of 0.4 g kg⁻¹ enables to detect all events (POD = 100%), with a high FAR of 75%. Overall, the highest ETS of 0.65 is reached given a LWC₁₀₀m threshold of 0.6 g kg⁻¹. Hereafter, we use this threshold to define the first diagnostic as “freezing precipitation alert” (FPA):

$$ \text{FPA: } LWC_{100m} \geq 0.6g \ kg^{-1} \\ \text{and } T_{2m} < 0^{\circ}\text{C during at least } 2h $$

Fig. 10 shows the occurrences of observed surface ice layer together with FPA over 5 winters from 2010 to 2015. An average of 4.2 events per year was observed. Most observed events occurred in the heart of winter: from 2010 to 2015, a total of 6 events were reported from November to December, 12 events from January to March and 3 events from April to May. Note that ice layers may be more difficult to identify in spring, when the snowpack is already very dense. The results of the comparison of FPA with observations are summarized in Table 1, with POD = 81%, FAR = 23%, ETS = 0.65 and a frequency bias of 1.05. These scores confirm that the FPA enables to detect the main events and
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Modified ROC diagram (POD vs FAR) for AROME diagnostics with different LWC<sub>100m</sub> thresholds (blue line), filtered by a minimum precipitation rate (red line), during at least two consecutive hours at subfreezing T<sub>2m</sub>, covering at least 6 pixels. FPA: blue triangle. Simple FPD: blue square. FPD with minimum amount of precipitation: red square. Surface ice detection by Crocus-ice driven by AROME FPD: green star. Dashed black lines represent iso-ETS.</figcaption></figure>

can be used by forecasters to detect the occurrence of these events with
reasonable confidence.

* Freezing precipitation diagnostic to estimate the amount of precipitation

During freezing precipitation events, LWC higher than 0.6 g kg⁻¹
are only reached during short time periods and not during the whole
duration of the freezing precipitation. This is illustrated in Fig. 5 for the
event of 5–6 January 2012. Therefore, a different threshold is required
to estimate the amount of freezing precipitation falling during these
events. Such information is important to estimate the thickness of the
ice layer and to drive Crocus-ice.

Fig. 9 shows that the first LWC threshold which enables 100% of detections is 0.4 g kg⁻¹, with a high FAR compared to the events reported by skiers (Fig. 9, blue square). This value is close to the CWC of 0.35 g kg⁻¹ found as a threshold for drizzle formation onset by Rasmussen et al. (2002). Hereafter, we call this second diagnostic “freezing precipitation diagnostic” (FPD):

$$
\text{FPD: } LWC_{100m} \ge 0.4\text{g kg}^{-1} \\
\text{and } T_{2m} < 0^{\circ}\text{C} \text{ during at least } 2\text{h}
$$

To verify the relevance of the 0.4 g kg⁻¹ threshold to detect suffi-
cient amounts of precipitation, a condition on minimum simultaneous
precipitation rate (at least 1 mm h⁻¹) is added to filter the diagnosed
situations. For this purpose, three LWC values (0.35 g kg⁻¹, 0.4 g kg⁻¹,
0.5 g kg⁻¹) are tested (Fig. 9, red curve). The highest ETS (0.51) is
reached for 0.4 g kg⁻¹ (Fig. 9, red square). This confirms the potential
of this threshold value for the FPD to determine the amount of freezing
precipitation.

5.1.2. *Crocus-ice driven by AROME FPD*

We present here an additional approach combining the atmospheric
diagnostic by AROME (FPD) to Crocus-ice (Section 4.3). The FPD

indicates the occurrence of freezing precipitation, which enables to
derive its amount as defined in Section 4.2.2, while Crocus-ice solves
the surface fluxes and simulates ice formation. Similarly to Quéno et al.
(2016), a continuous atmospheric forcing for the snowpack model
Crocus was built from 1 August 2010 to 31 July 2015, using AROME
consecutive hourly forecasts (Section 3.1). The FPD was applied to the
forcing, with a conversion of solid and liquid precipitation into freezing
precipitation at diagnosed hours. The modified atmospheric forcing was
used to drive Crocus-ice simulations of the snowpack, spatially dis-
tributed at 2.5-km resolution over the Pyrenees domain and over the
five-year period: if freezing precipitation occurs, the new module in
Crocus-ice is activated and ice layer formation is simulated by the
model.

* Five-year validation

The simulated snowpack was analyzed over the same period. The
occurrence of surface ice layer formation was also compared to the
observation database. We identified surface ice if the total ice thickness
(with a density higher than 700 kg m⁻³) is higher than 2 mm in the
first 5 cm of the snowpack surface. The scores are represented in Fig. 9
with a green star: the POD is 95%, with 65% of false alarms. These
scores are improved compared to the simple FPD (blue square, Fig. 9):
in particular, the number of false alarms is reduced (the frequency bias
drops from 3.95 to 2.71) and the overall performance measured by the
ETS is increased from 0.24 to 0.33. They are also improved compared to
a simple conversion of the amount of freezing precipitation derived
from the FPD into ice thickness assuming a density of 917 kg m⁻³
(green dot, Fig. 9: ETS = 0.31, frequency bias = 3.04), which highlights
the benefits of solving surface fluxes. However, the number of false
alarms remains high. This issue is discussed in Section 6.

* Focus on winter 2011–2012 at point A
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Occurrences of freezing precipitation alerts (FPA) derived from AROME (blue bars) and observations (red bars). Green rectangles indicate correspondence between FPA and observations, black rectangles indicate wrong FPA or observations without FPA.</figcaption></figure>

<caption>**Table 1**</caption>

Two-by-two contingency table for AROME FPA, for five winters (2010–2011 to 2014–2015). OY = Observed Yes; ON = Observed No; FY = Forecast Yes; FN = Forecast No; HI = Hits; FA = False Alarms; MI = Misses; CR = Correct Rejections.

<table><thead><tr><th></th><th>OY</th><th>ON</th></tr></thead><tbody><tr><td>FY</td><td>HI = 17</td><td>FA = 5</td></tr><tr><td>FN</td><td>MI = 4</td><td>CR &gg; 1</td></tr></tbody></table>

We focused on the snowpack simulation at point A (42.65°N, 0.65°E, 2563 m, Fig. 2) during winter 2011–2012, as two major and well identified ice layer formation events occurred and they were both clearly reported in the database. The evolution of the simulated snowpack density is shown in Fig. 11. Two high density ice layers of 13 mm and 6 mm were formed on 21 December 2011 and 5 January 2012 respectively, which were the two main observed ice events that winter. The thickness of the second ice layer is underestimated, due to the underestimation of the amount of freezing precipitation. Moreover, several FPD do not lead to the formation of an ice layer but rather develop into lower density crusts (identified by a black circle in Fig. 11), or only into wet snow. It can be due to two factors: low amount of freezing precipitation or too low energy sinks at the snow surface, preventing the freezing of the impinging supercooled water which mainly percolates. Therefore, this study suggests the potential added value of combining the atmospheric diagnostic with Crocus-ice: the benefits from the cloud microphysics of AROME are combined with the benefits of a physical modelling of the snowpack, relevant for predicting the formation of an ice layer.

## 5.2. Spatial and altitudinal distribution of ice layers: study of 5–6 January 2012

The episode of 5–6 January 2012 is the only one with enough observations to perform a spatial validation of the FPD and associated ice layer formed in Crocus-ice.

The FPD introduced in Section 5.1.1 was applied to hourly AROME forecasts from 5 January 2012, 12 UTC, to 6 January 2012, 6 UTC. Fig. 12 illustrates the altitudinal distribution of AROME grid points where a FPD occurs during that period, together with the elevations of point observations and ranges of elevations with observed ice layer in the skiers’ reports in January 2012. For the FPD, the average altitude is 2328 m, the average minus standard deviation is 2154 m, the average plus standard deviation is 2502 m, the minimum is 1812 m and the maximum is 2667 m, while the ice observations stretch from 1850 m to 2700 m (Fig. 12). Thus, the FPD shows good agreement with the observations in terms of altitudinal extent.

The spatial extent of the amount of freezing precipitation derived from the FPD is illustrated in Fig. 13a, together with the ice thickness resulting from Crocus-ice simulation driven by AROME with FPD (Fig. 13b). All available observations of the ice layer (accident reports by rescue services, mountaineers’ observations and ice thickness measurements) are also indicated in Fig. 13a and 13b. Most of the observations are close to an area where ice layer formation is forecast, but the spatial extent of simulated ice layer formation is underestimated. It can be partly explained by the differences between the real topography and the model topography (at a 2.5 km resolution), which smooths the highest peaks, hence several isolated mountains above 2000 m are not represented. For instance, there is no diagnostic around Pic Saint-
<figure><img src="image_13.png" /></figure>

Fig. 11. Snow depth and snowpack density simulated at point A (42.65°N, 0.65°E, 2563 m, Fig. 2) during winter 2011/2012 by Crocus-ice, driven by AROME meteorological fields with freezing precipitation diagnostic (FPD). Ice layers are identified by a dark blue colour (more than 600 kg m⁻³). The black circle indicates the formation of lower density crusts.

<figure><img src="image_14.png" /></figure>

Fig. 12. Top: elevations of point observations and ranges of elevations with observed ice layer, given in the skiers' reports in January 2012. Bottom: distribution of elevations of AROME grid points with freezing precipitation diagnostic (FPD) from 5 January 2012, 12 UTC, to 6 January 2012, 6 UTC.

Barthélémy (Fig. 13a). Despite a high simulated CWC around the peak (Fig. 7), the simulated 2 m temperature is higher than 0 °C. Indeed, the peak's altitude given by AROME is 1683 m (Fig. 2) while the real altitude is 2348 m. Thus, the diagnostic is not wrong at the modelled altitude (no freezing precipitation under 1800 m).

In terms of ice thickness, a strong underestimation can be noted on Fig. 13b: the simulated ice layer is several millimetres thick (locally 2 cm), while the observed ice layer was most often several centimetres thick. It can be related to the low amount of freezing precipitation forecast by AROME for this event (Fig. 13a). The observed ice thicknesses (locally 8 cm) resulted from a stronger amount of precipitation. The precipitation and hydrometeor contents shown in Fig. 5 suggest that the freezing precipitation event is diagnosed from 15 UTC to 21 UTC, with the strongest precipitation rates occurring later in the form of snow and graupel. However, during this event, no significant snowfall was reported and only a thin layer of snow was locally observed on top of the ice layer. This suggests that a large part of snowfall predicted by AROME for this event was actually freezing precipitation.

# 6. Discussion

## 6.1. Limitations of the methods

The methods introduced in this paper are a first step to improve the prediction of ice layer formation due to freezing precipitation in the Pyrenees, which remains poorly known and simulated. They rely on simplifications, and thus imply limitations, which are discussed here.

### 6.1.1. Limitations of the diagnostics

The atmospheric diagnostic was introduced because AROME does not simulate freezing precipitation events in mountains. A similar behaviour was identified for the WRF atmospheric model when simulating freezing drizzle formation in mountainous terrain (Fernández-González et al., 2015). In our study, the diagnostic is based on usual atmospheric conditions close to the ground during freezing precipitation formed through the process of collision-coalescence of supercooled cloud droplets. The occurrence of these events is detected with good scores. Nevertheless, the precipitation rate deduced from this diagnostic is subject to a large uncertainty. In this study, we made the choice to precipitate both solid and liquid precipitation in the form of freezing precipitation when the diagnostic occurs. This assumption has limitations since the amount of total precipitation during the occurrence of the diagnostic may not correspond to the real rate of freezing rain produced by collision-coalescence which is not accurately simulated by AROME. Instead, AROME tends to simulate solid precipitation, as shown for the event of January 2012.

Two different diagnostics were proposed using AROME cloud water content and 2-m temperature: the freezing precipitation alert (FPA) and the freezing precipitation diagnostic (FPD). The FPA was proposed to be used by forecasters to detect the main freezing events whereas the FPD aims at providing an estimation of the amount of freezing precipitation. The better scores of the FPA with a high LWC threshold may be due to the fact that very high LWC are reached punctually during freezing precipitation events, but so high LWC do not include the whole episode. Therefore, the FPA is an indication that an intense and large-scale (i.e. important enough to be reported by skiers) event is going to occur, but it gives no information on the amount of precipitation and spatial extent of the episode. For these purposes, the FPD is a better indication, but it gives many false alarms at the seasonal scale. It needs to be combined with an estimation of ice layer thickness to provide relevant results. We showed these false alarms are reduced by the use of Crocus-ice, but the FAR remains high. It can be partly explained by the fact that the observation database underestimates the occurrences by construction: all events are not necessarily reported by skiers, particularly if the
<figure><img src="image_15.png" /><figcaption>**Fig. 13.** (a) Freezing precipitation between 5 January 2012, 12 UTC, and 6 January 2012, 6 UTC, simulated by AROME with FPD over the Pyrenees. Light green indicates pixels with FPD but amount of precipitation lower than 1 mm. (b) Ice thickness in the first 5 cm of the snowpack, same period and domain, simulated by Crocus-ice driven by AROME with FPD. Red dots: accident reports. Orange dots: mountaineers' observations. Arrows: measurements of ice layer thickness.</figcaption></figure>

weather is bad or if the ice formation is local.

Finally, the diagnostics could integrate other variables. However, adding a threshold on solid hydrometeor content (related to snow formation, Section 2.1) to the diagnostics, as in Fernández-González et al. (2015), did not show clear benefits in our case and was not presented in Section 5.1.1. Considering the whole vertical profile of hydrometeor contents may be more informative, and could help to confirm the FPA for forecasters.

### 6.1.2. Limitations of Crocus-ice modelling

The ice layer formation simulated by Crocus-ice provided a realistic representation of the snowpack stratigraphy, but requires further assessment. In particular, the simple water transport scheme of Crocus does not enable to represent the possible impermeability of ice to water flows. The ice thickness might thus be underestimated in Crocus-ice, with an excess of percolation. To tackle this issue, the water percolation scheme solving Richards equation recently implemented in Crocus by D'Amboise et al. (2017) could be associated to Crocus-ice. This approach was used by Wever et al. (2014, 2016) to simulate ice formation inside the snowpack.

### 6.2. Limitations of the observation database

The lack of conventional observations like snowpack vertical profiles limits the validation of the modelled snowpack simulations by Crocus-ice. This issue highlights the need for an extensive campaign of measurements to study ice layer formation and evolution, including measurements of the ice layer density (Watts et al., 2016), its albedo (to add a specific parameterization for ice in Crocus), the subsequent snowpack mechanical properties and observations of the persistence of ice in the snowpack during the whole winter season.

The use of a crowd-sourced observation database is rare in snowpack studies (e.g. Tremper and Diegel, 2014; Hendrikx and Johnson, 2016). But the specificity of the phenomenon studied here and the lack of conventional observations made it an interesting opportunity to use the large amount of data available in the mountaineering Internet communities.

For this study, reports from mountaineers and skiers contained usable information since the presence of a pure ice layer on the surface of the snowpack is easy to identify for a skier. The key words used to filter the reports are specific enough to discriminate a pure ice layer from hard snow surface. Pictures in the reports usually confirmed the selection. The identification of ice layers in spring may be more difficult due to the usual presence of a morning melt-freeze crust, but also the
higher temperatures which make the ice disappear faster. It may ex-
plain the worse detection scores in April–May(Fig. 10).

The main limitation is the spatial distribution of the observations,
which is skewed by the difference in attendance of Pyrenean massifs:
the massif of Haute-Bigorre (Fig. 2) is highly frequented by mountai-
neers and skiers, hence a high number of reports, while very few ob-
servations are available for the Couserans massif (Fig. 2), which is much
less frequented.

7. Conclusion

Freezing precipitation on the snowpack is observed by mountain forecasters in the Pyrenees at least once per winter season. These particular meteorological events have high impacts since they may form a pure ice layer on the surface, leading to several accidents and fatalities amongst skiers and mountaineers. This paper addressed the issue of forecasting freezing precipitation on the snowpack in mountainous terrain and modelling the associated ice layer formation. To this end, we used AROME, the operational NWP system of Météo-France, and Crocus, the detailed snowpack model used for operational avalanche hazard forecasting in French mountains. To counterbalance the lack of conventional observations, the observation dataset was built on crowdsourcing, from online reports from the communities of mountaineers and back-country skiers, as well as rescue service reports.

Given that the studied freezing precipitation is formed through a
process of collision-coalescence of supercooled cloud droplets, a simple
atmospheric diagnostic of freezing precipitation was introduced, based
on a threshold of liquid water content at 100 m above ground together
with negative 2-m temperatures. These threshold were determined with
a sensitivity study, through comparisons with observations from the
crowd-sourced database from 2010 to 2015. A FPA (freezing pre-
cipitation alert) was defined with a high LWC threshold of 0.6 g kg⁻¹ to
assist forecasters identify large-scale and intense events over the
Pyrenees, with good detection scores and very few false alarms. It is a
complementary tool to the analysis of the synoptic conditions that fa-
vour freezing precipitation in the Pyrenees. A FPD (freezing precipita-
tion diagnostic) was also defined with a lower LWC threshold of
0.4 g kg⁻¹ to determine the amount of freezing precipitation. The FPD
was used to modify AROME precipitation phase in the atmospheric
forcing for snowpack simulations. The entire precipitation (solid + li-
quid) was converted into freezing precipitation when the FPD occurs.

In addition to the state of the atmosphere close to the ground, the state of the snowpack surface is crucial for the formation of ice. In particular, the energy sinks (conductive flux towards the snow, latent and sensible heat fluxes towards the atmosphere) must be high enough to counterbalance the latent heat released through solidification. The modelling of ice layer formation due to freezing precipitation was implemented in Crocus, including the energy budget associated with supercooling rupture and the parameterization of pure ice layers. Snowpack simulations were performed with this new version, driven by AROME forecasts modified by the FPD. They showed that the main ice layers were represented, while some FPD were not associated with surface conditions that enable the formation of ice. The study of the event of 5–6 January 2012 exhibited a correct match of the spatial distribution of the diagnostic and associated ice layer formed by Crocus-ice with the observations, even though the spatial extent was underestimated. The altitudinal distribution was in good adequation.

This study is a first step towards a better forecast of the formation of
the ice layer on the snowpack due to supercooled precipitation, using
existing operational tools. The successful modelling of surface ice for-
mation remains limited by the underestimation of the amount of
freezing precipitation when a diagnostic is given. Snowfalls im-
mediately following a diagnostic should also be partly included in the
freezing precipitation quantities. This issue can only be tackled by
improvements of the cloud microphysical scheme used in AROME to
better handle freezing precipitation formed by collision-coalescence,

which could be the case with the newly developed microphysical
scheme LIMA (Vié et al., 2016). The most recent version of AROME,
operating at 1.3-km resolution (Brousseau et al., 2016), may also im-
prove the spatial distribution of freezing precipitation with a more
accurate description of the topography. Finally, the new modelling of
surface ice formation within Crocus-ice suggests further explorations of
the impact of an ice layer on the simulated snowpack stratigraphy,
particularly in terms of grain types and mechanical properties. Indeed,
an ice layer may have a long-term influence on the stratigraphy after
being covered by fresh snowfalls, like a stabilizing effect or the for-
mation of faceted crystals or depth hoar in the immediate vicinity of the
ice (Moroz, 2002; Jamieson, 2006; Hammonds et al., 2015; Hammonds
and Baker, 2016). Surface ice formation modelled by Crocus-ice could
then improve the general knowledge of snowpack stratigraphy and
stability during the whole winter season, in addition to the immediate
warning of dangerous surface conditions.

Acknowledgements

The authors are grateful to the Camptocamp community
(www.camptocamp.org), and particularly to all the mountaineers
sharing their observations of the snowpack and weather in their reports.
We also thank the three anonymous reviewers who helped greatly to
improve the manuscript, M. Lafaysse, M. Dumont and S. Morin (CNRM/
CEN) for their help and discussions and Jean-Antoine Maziejewski
(Météo-France) for the English editing. CNRM/CEN is part of LabEx
OSUG@2020 (ANR10 LABX56).

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