Convective snow bands develop in response to a cold air outbreak from the continent or the frozen sea over the open water surface of lakes or seas. The comparatively warm water body triggers shallow convection due to increased heat and moisture fluxes. Strong winds can align with this convection into wind-parallel cloud bands, which appear stationary as the wind direction remains consistent for the time period of the snow band event, delivering enduring snow precipitation at the approaching coast. The statistical analysis of a dataset from an 11-year high-resolution atmospheric regional climate model (RCA4) indicated 4 to 7 days a year of moderate to highly favourable conditions for the development of convective snow bands in the Baltic Sea region. The heaviest and most frequent lake effect snow was affecting the regions of Gävle and Västervik (along the Swedish east coast) as well as Gdansk (along the Polish coast). However, the hourly precipitation rate is often higher in Gävle than in the Västervik region. Two case studies comparing five different RCA4 model setups have shown that the Rossby Centre atmospheric regional climate model RCA4 provides a superior representation of the sea surface with more accurate sea surface temperature (SST) values when coupled to the ice–ocean model NEMO as opposed to the forcing by the ERA-40 reanalysis data. The refinement of the resolution of the atmospheric model component leads, especially in the horizontal direction, to significant improvement in the representation of the mesoscale circulation process as well as the local precipitation rate and area by the model.
The roughness and temperature differences between land and water surfaces often lead to local sub-climates such as mesoscale circulation systems or stable and unstable conditions. For instance, during autumn and winter when the water surface temperature is still warmer than the average air temperature an unstable stratification develops in low levels since the ice-free water surface appears as a source of moisture and heat to the overlying air mass. As a result, mesoscale convective precipitation events like convective snow bands may develop.
Convective snow bands are also known and studied as lake effect snow (e.g.
at the Great Lakes), cloud streets, horizontal convective rolls or vortices
with solid precipitation. At the Swedish east coast, they are often referred
to as a snow canon (
Convective snow bands lead repeatedly to severe precipitation events in the cold season of the high mid-latitudes around the world. This phenomenon occurs frequently at several regions situated in high latitudes, including the Great Lakes (Kelly, 1986). However, even the Swedish east coast and the coastal regions along the Gulf of Finland frequently experience convective snow band events. The large amounts of snow along with strong wind speeds can cause serious problems for traffic, infrastructure, and other important establishments of society. Due to global warming it is in general expected that larger areas of lakes and inland seas will stay longer ice-free (IPCC, 2015). As a result, the occurrence of those convective snow band events may become more frequent as well as more intense.
We here investigate the ability of the numerical regional climate model RCA4 (Rossby Centre regional atmospheric model) to simulate snow bands, with the purpose of identifying the sensitivity of the results to the model resolution and surface forcing conditions and to derive climate statistics of the occurrence. Convective snow bands in the Baltic Sea area have been studied widely using a variety of methods for the Gulf of Finland as well as other parts of the Baltic Sea (e.g. by Andersson and Gustafsson, 1993; Andersson and Nilsson, 1990; Vihma and Brümmer, 2002; Mazon et al., 2015; Savijärvi, 2012, 2015). The focus of our study is on the performance of a regional climate model to represent convective snow bands affecting the Swedish east coast. This emphasis allows the determination of snow band conditions based on very specific atmospheric properties associated with lake effect snow for the region.
The large-scale synoptic situation leading to convective snow band development over the Baltic Sea can be very different. However, a strong pressure gradient over the Baltic Sea is required to guide cold air masses from the north-northeast over the warm water surface. Strong prevailing northeast winds with small vertical wind shear are unusual for this latitude, but they can result from a deep low-pressure system southeast of the Baltic Sea and/or indirectly by a local high-pressure development over the cold north of Scandinavia. Therefore, convective snow bands can occur in the Baltic Sea area, and furthermore along the north- and northeast-facing coasts of Estonia, Latvia, Poland, and even Germany.
Studies by Evans and Wagenmaker (2000),
Niziol et al. (1995), Andersson and
Gustafsson (1993), and Niziol (1987) provided evidence that
convective snow bands occur under specific conditions. The most important
element for the formation of snow bands is the thermal difference between
the water surface and the overlaying air (Mazon et al.,
2015), which determines the extent of the essential heat and moisture
fluxes. Therefore, it is necessary that a large part of the water surface is
ice-free in order to ensure sufficient sensible heat release and evaporation
from the sea. A partially frozen sea changes the coast line and affects the
conditions for snow band developments considerably. Another important
condition is the presence of instability to trigger convection. For the
Great Lakes it has been observed that the minimum temperature difference
between the water surface and the 850 hPa level must be 13
The wind field throughout the boundary layer plays an essential role for the
evolution of convective snow bands. The most common and severe snow bands
are aligned parallel to a strong prevailing wind (type I snow bands defined
by Niziol et al., 1995) larger than 10 m s
The directional wind shear within the convective boundary layer is observed
to be small for the time period of the snow band event.
Niziol (1987) established a criterion for the likelihood
of lake effect snow depending on the wind shear within the steering layer of
the snow band (which determines approximately the first 50 hPa above the
ground up to 700 hPa). Thus, convective snow bands are likely to occur for a
directional wind shear of less than 30
Even the shape and the topography of the coast surrounding the water body can be essential for the snow band evolution. Andersson and Gustafsson (1993) investigated that the genesis areas for convective cloud bands are often bays at the “coast of departure”. A convergence zone develops as two opposed land breezes meet in the centre of the sea. The secondary circulation system forces convection, which continues downwind as bands of convective rolls. When reaching the “coast of arrival”, the convection can be enhanced by another land breeze raising the capping inversion. Snow bands tend to organize themselves parallel to a concave shaped shoreline. Islands that are located along the fetch disturb the heat and moisture flux from the sea to the air mass locally and can cause multiple bands to reorganize and merge or split up.
Case studies with high-resolution numerical weather prediction (NWP) models have been carried out successfully to represent convective snow bands. The present study, however, evaluates the potential of applying a regional climate model with relatively coarse resolution to conduct climatological studies for this mesoscale phenomenon. Performing simulations at a high resolution is computationally expensive and time consuming, and therefore it is less feasible to use a high-resolution NWP model at climatological timescales. An appropriate model for climatological studies of snow bands must balance computational expense and accuracy of the simulated physical processes. Case studies of different model configurations are therefore essential to evaluate the model's performance in order to be aware of potential weaknesses and to give a justified interpretation of the climatological results. In a regional climate model the information on the large-scale forcing is given at the lateral boundaries, and regional response to local conditions (like surface information) can be studied. We also use additional models coupled to the regional atmospheric model (RCA4) to study the importance of more accurate sea surface temperature (by using the ocean model NEMO) or impact of surface waves (by using the wave model WAM).
The Rossby Centre of the Swedish Meteorological and Hydrological Institute
(SMHI) has been developing and applying climate models since 1997
(Jones et al., 2011). RCA4 is the latest version of their
regional atmospheric climate model and it is run over many different
Coordinated Regional Climate Downscaling Experiment (CORDEX) domains
(Nikulin, 2013). The domain used in this study (illustrated in red
in Fig. 1) covers Europe. RCA is based on the operational numerical weather
prediction model HIRLAM, although RCA was developed to simulate the
atmosphere based on climatological timescales. The foundation of this
hydrostatic model are primitive equations using terrain-following hybrid
vertical coordinates and a rotated longitude–latitude grid. The original
model employed time steps of 15 min, 40 model layers as vertical
coordinates and a spherical resolution of 0.22
The domain of the NEMO model (in blue) embedded in the RCA European CORDEX domain (in red) (Dieterich et al., 2013).
The atmosphere–ocean interaction is of great importance for the atmosphere's properties and dynamics as well as the entire Earth's climate system. The SST is a crucial factor for the accurate representation of the development of convective snow bands. The Nucleus for European Modelling of the Ocean NEMO (Madec, 2012) is an ice–ocean model based on primitive equations. Its domain covers the North Sea and the Baltic Sea and is shown in blue in Fig. 1. The boundaries at the northern North Sea and the English Channel are kept open and take information of the Atlantic Ocean outside the NEMO-Nordic domain into account (Dieterich et al., 2013). In comparison with the default settings of the RCA4 model, NEMO-Nordic has a very high resolution with a horizontal grid spacing of two nautical miles, corresponding to circa 3.7 km, and 56 geopotential levels at the vertical scale (Dieterich et al., 2013). NEMO can be coupled to RCA in order to exchange information at the interfaces between air and sea or ice. The ice–ocean model provides parameters such as ice fraction and albedo as well as SST to the atmospheric model. In turn RCA4 communicates heat, freshwater, and momentum fluxes to the NEMO model (Wang et al., 2015). The coupling of two independently developed model components such as RCA4 and NEMO can be realized by OASIS3 – the Ocean Atmosphere Sea Ice Soil Simulation Software. This coupler was developed by PRISM, the Project for Integrated Earth System Modelling and is commonly used in the climate modelling community (Valcke, 2013).
Waves have an impact on the roughness length at the water surface, affecting in return the low-level wind field as well as the heat fluxes. RCA4 has been used in connection to a wave model in order to test the sensitivity of this interrelationship. The WAve Model (WAM) is a third-generation full-spectrum prognostic wave model using the basic transport equation (WAMDI Group, 1988), which can be used for an atmosphere–wave coupled system. The WAM model explicitly solves the energy balance equation in order to gain the evolution of the wave spectrum (Janssen, 2004). The European Centre for Medium-Range Weather Forecasts (ECMWF) has been running a coupled system of WAM in communication with an atmospheric component operationally since 1998 (ECMWF, 2016). For the purpose of coupling, WAM and RCA4 have the same resolution and time step frequency in this study. Here the WAM model component is treated as a subroutine which is called by RCA4 with every time step communicating the essential information between the model components. The WAM model provides RCA4 with wave information in exchange for wind field data from the atmospheric model. The important wave data for the RCA model is obtained by a two-dimensional ocean wave spectrum and may involve parameters like wave height and period as well as roughness length. The applied coupled RCA4-WAM setup is similar to that described by Wu et al. (2015) and Rutgersson et al. (2012) only exchanging the roughness length.
In order to give a good representation of the Baltic Sea, regional models with high resolution are essential to reproduce topographical features and substantial processes. In connection with the investigation of the mesoscale processes determining convective snow band events, this study was carried out in two parts: the statistical analysis of snow band events based on an 11-year RCA4 dataset and the evaluation of the use of different regional climate model systems.
Baltic Sea area (in blue,
Criteria for the selection of days with moderate to favourable atmospheric conditions for convective snow bands. The Baltic Sea area and precipitation area are defined in Fig. 2.
First, the atmospheric regional climate model RCA4 was used in a high
resolution to simulate the atmosphere over the 11-year time period from 2000
to 2010 with a spin-up of 2 months. The horizontal RCA4 resolution was set
to 0.16
Overview of the model experiments used for the analysis of an 11-year dataset and the model evaluation based on two case studies.
In the second part of this study, two case studies are presented, comparing
the atmospheric properties simulated by five different model setups in order
to give an assessment of the specific model performance concerning snow
bands. An overview over the five model systems is given in Table 2. The
regional atmospheric climate model RCA4 has been used with resolution of 40 vertical model layers and 0.22
The atmospheric conditions which favour the development and maintenance of convective snow bands show a recurrent pattern in the Baltic Sea region. In order to select a day as convective snow band event the criteria presented in Table 1 must be fulfilled within the respective area (defined as in Fig. 2).
Number of snow band days fulfilling the applied criteria per year
The high-resolution RCA4 simulation from 2000 until 2010 reproduces a total of 121 days with these criteria fulfilled; 49 of these days are within the limits of the stricter thresholds describing favourable conditions for snow band formation. Correspondingly, favourable atmospheric conditions occur for a strong development of convective snow bands over the Baltic Sea on average 4.5 days a year. When including the remaining cases which meet the weaker criteria, a total average of 11 days per year is obtained. Figure 3 displays the distribution of the cases per year and month. It is seen that the total number of days varies between 5 and 22 per year. The months of November and December show the highest frequency of days with atmospheric conditions favourable for snow band development (Fig. 3b).
The maximum 10 m wind speed of all selected snow band days is on average 13.3 m s
The largest impact of convective snow bands on the public life is due to the
heavy snowfall. The intensity of a snow band event may be defined by the
consequent amount of its precipitation at the coast. The scale of the given
snowfall values refers to the volume that the snow would possess in liquid,
rather than solid form. This snowfall parameter, however, does not include
precipitation in liquid form, but only solid snowfall at a scale of melted
ice. As an approximate relationship it is reasonable to assume that one
millimetre of melted snow corresponds roughly to 1 cm of gained
snow depth, although the density of the snow depends strongly on parameters
like temperature and age of the snow (Dubé, 2003). The
selected days indicate a maximum snowfall rate between 0.2 and 3 mm h
Average snowfall rate for all 121 selected days
The regions affected by the precipitation of convective snow bands in the
Baltic Sea area can be seen in Fig. 4. These data represent the hourly
accumulated snowfall as an average value of all selected days (Fig. 4a) as
well as only for the days which meet the criteria for favourable conditions
(Fig. 4b). The precipitation reference sector considered for the selection
criteria (as defined in Fig. 2b) is framed in black. However, the figures
represent a sector that exceeds the precipitation area in order to observe
whether other regions are also affected by enhanced precipitation on the
same days. The average snowfall rate results from the total snowfall of the
selected days (of favourable or favourable and moderate atmospheric
conditions for convective snow bands) normalized by the number of days with
positive snowfall at a specific location. Accordingly, along the Swedish
coast two separate regions are mainly pronounced by a large average daily
precipitation rate. The concavely shaped shore near the Swedish town
Gävle represents the most intense snowfall within the precipitation
sector with up to 3 mm d
A similar picture is obtained regarding the frequency of favourable
conditions for convective snow bands. While the selection criteria for
favourable conditions were fulfilled 4.5 times a year on average,
approximately once per year the Gävle region received snowfall rates
larger than 5 mm d
Frequency of occurrence for favourable atmospheric conditions with
convective snow bands causing a snowfall rate greater than 5 mm d
Two different cases have been selected to study the sensitivity of the model setup. Five different RCA4-based model setups are used (Table 2) considering the atmospheric conditions of convective snow bands. Case 1 concerns a well-studied event in early December 1998 that caused 130 cm solid snowfall within 3 days in the Swedish town Gävle (SMHI, 2015). The less intense case 2 occurred in early February 2001 and had similar synoptic conditions. Cold air was transported from Finland over the Gulf of Bothnia causing extreme snow precipitation at the Swedish coast close to Gävle. While the wind direction in case 1 remained consistent over several days by accumulating snowfall at a restricted area around Gävle, in case 2 the wind direction turned slightly and distributed the precipitation more along the Swedish coast. Both cases lasted 3–4 days, and for a better understanding of the evolution of the atmospheric situation approximately 1-day ahead and 1-day after the snow band have been taken into account for the investigation.
Frequency of occurrence of convective snow bands fulfilling the
selection criteria for favourable atmospheric conditions (see Table 1)
causing a maximum snowfall rate between 0.5 and 1 mm h
All model systems show similar behaviour for the wind field development. The
maximum 10 m wind speed over the Baltic Sea increases rapidly with the
beginning of the convective snow band event and slowly decreases in the
following days. Due to the wave feedback through the wind–wave interaction
as well as the nonlinear wave interaction in the two-dimensional wave spectrum
calculation of the roughness length in the WAM model, this development showed
in both cases a delay by several hours for the RCA-WAM simulation relatively
to the other models. The comparison with SMHI station measurements indicates
that most models underestimated the 10 m wind speeds at the Swedish coast.
The best representation of the observational wind data has been obtained by
the RCA model with increased horizontal resolution. The mean wind shear
between 975 and 700 hPa was in both cases around 30
The air temperature field indicates for all models clearly that the Baltic
Sea serves as a heat source to the air above. But due to the approaching
cold air mass the temperature over the Gulf of Bothnia decreases rapidly. In
case 1 the maximum 2 m temperature decreases within 2 days by 4
For the high-resolution RCA setups as well as the RCA-WAM the ice cover and
SST is provided to the RCA model by the ECMWF reanalysis data ERA-40. The
spectral resolution of ERA-40 is T159, which corresponds to 1.125
Difference map of the SST by ERA-40 and NEMO for 1 day of case 2.
Maximum sensible heat flux
The SST is furthermore associated with the heat fluxes over the water surface. Accordingly, it is not surprising that the RCA-NEMO model represents the highest heat fluxes among the models for both sensible and latent heat over the Gulf of Bothnia (Fig. 8). However, both case studies show that the RCA-NEMO heat fluxes agree well with all other models before the snow bands arise. The difference develops with the initial occurrence of the snow bands. All models represent an increasing sensible and latent heat flux development throughout the convective snow band event. The maximum heat fluxes are reached at a later stage of the evolution when the cloud bands start to dissolve again. The magnitude of the heat fluxes in the February case has been larger than in the December case. Similar to the temperature change, the evolution for the heat fluxes was also more rapid in case 2.
Boundary layer height horizontally for RCA
The increased wind speeds and heat fluxes during the convective snow band event also cause the boundary layer height to rise. Based on the investigation of the two cases it has been observed that RCA-NEMO represents comparatively high boundary layer heights, while the RCA model with high horizontal resolution tends to give a shallower boundary layer (see Fig. 9). Although case 1 was an intense event the mixed layer height barely exceeds 1 km, which has been defined as a threshold by the criteria in Sect. 2.
Depending on the strength of the convective snow band, the amount of the total precipitation can vary vastly. A slight turning of the wind may furthermore lead to a distribution of the lake effect snow along the coast rather than the accumulation in one restricted area. Case 1 indicated a significantly greater accumulation of precipitation than case 2, partly due to the consistent wind direction. The hourly precipitation of case 1 has however been more intense for a longer period of time. Regardless, for both cases the high-resolution RCA models as well as the RCA-NEMO system indicate considerably higher precipitation rates than the original RCA or RCA-WAM. The 48 h accumulated total precipitation of case 2 reached up to 20 mm and can be compared for the different models in Fig. 10. In case 1 the precipitation of 2 days went even up to 45 mm. Very remarkable were the results of the RCA simulation with increased horizontal resolution. The local maxima reach significantly higher values and the confined precipitation area is represented in more detail. When comparing the model performance for the location of a weather station in Gävle with the measured precipitation, as in Fig. 11, it is clear that all models have difficulties in representing the exact time and location of the snowfall. For case 1, almost all models underestimate the actual precipitation. Only the RCA model with high horizontal resolution exceeds the measurements for 1 day. In case 2, on the other hand, all models represent maximum precipitation rates at a time period that did not show any precipitation in the measurement data, while the measured precipitation showers in Gävle were not recognized by the models.
The 2-day accumulated total precipitation of all models for case 1 in comparison and a cropped satellite image of 1 February 2001 (MODIS, 2016).
The simulations by RCA and RCA-WAM resulted in a rather weak development of the convective snow bands and the comparison with observational data showed that the atmospheric conditions were often underestimated by these two models. The coupling of the atmospheric RCA model with the wave model component WAM employed a different roughness length computation at the sea. Hence, the largest impact was observed on the wind field. The magnitude of the maximum 10 m wind speed reaches similar values; however, the RCA-WAM model possesses a time shift due to the wave feedback on the roughness length and wind speed. With regard to all other investigated parameters, RCA and RCA-WAM show very similar results and the different roughness length calculation due to the coupling of RCA and WAM has a negligible impact on the atmospheric conditions describing convective snow band events.
Time series of the precipitation measurements of the SMHI station in Gävle in comparison with the model results for this location. The solid line shows the model simulation at the closest grid point to the measurement site and the shaded area indicates the variation of this result according to the directly neighbouring grid points.
The coupled atmosphere–ocean system RCA-NEMO benefited clearly from the high
resolution SST of the NEMO-Nordic component. The reanalysis ERA-40 data
which otherwise provided the uncoupled RCA model with the SST had a coarser
resolution than the original RCA itself. For water bodies of the size of the
Baltic Sea, this resolution is insufficient and the quality of the
simulation is impaired as local extremes cannot be represented. The
interpolated ERA-40 SST shows a negative bias towards the NEMO SST as well
as independent datasets such as OISST. The difference is significant with up
to 5
A high resolution is of great importance when it comes to the regional modelling of mesoscale high-impact events. Increasing the resolution of the atmospheric RCA model resulted in a great improvement for the model performance. Both high-resolution simulations indicate larger values for the local maximum of the 10 m wind speeds over the Gulf of Bothnia. The best agreement with observational data, however, was obtained by the RCA model of increased horizontal resolution. Although the temperature and heat fluxes did not show any impact from an increased resolution in any direction, the high horizontal resolution RCA was able to represent the mesoscale atmospheric circulation process associated with convective snow bands in more detail and resolved the local precipitation rate and area more precisely when compared to the other models.
Finding a method to select convective snow band events is not straightforward. Even though the atmospheric conditions are typical and can be described by certain criteria, the thresholds applied imposed a larger impact on the number of days which are selected. Although the criteria themselves were chosen based on references describing snow bands developing in other regions of the world, they are generally valid and the specific values for the selection were adjusted for the Baltic Sea region. The determination of two different categories distinguishing between weak and moderate conditions hints at a need to be more careful with the pool of days fulfilling only weak conditions. Since convective snow bands arise from cloud bands that may initially not give any precipitation, it is not always clear how to define the transition and where to set the threshold for heavy precipitation due to snow bands. The average precipitation did not appear very intense, as some days have been selected with rather low snowfall rates of snow bands which could not have developed as strongly. According to the results of the case studies, the RCA model often underestimates the exact amount of precipitation. The increased resolution improves the results on the representation of the snow bands; however, for a long dataset of 11 years it is very computationally intensive to run the simulations in such a high horizontal resolution as was done in the case studies. Also, the use of the ERA-40 data as sea surface input has shown its drawbacks, and caused the RCA model to simulate snow bands in a less intense evolution. The exact amount of the snowfall and its return period should therefore be interpreted with caution. However, the result for the distribution and the relative amount of the snowfall can be understood in a qualitative rather than a quantitative sense. The Gävle region possessed the largest average snowfall rates and the shortest return periods for comparatively high hourly snowfall rates as a result of snow bands developing over the Gulf of Bothnia. It seems natural that convective snow bands which require a cold air outbreak over the warmer sea will develop more frequently in the most northern part of the Baltic Sea. The shape of the Gulf appears furthermore conducive to the generation of convergence zones for the initial formation and the bay-shaped coast in the Gävle region enhances the precipitation due to orographic forcing once again. The snow precipitation occasionally reaches 100 km inland; however, the most intense lake effect snow falls approximately within a radius of 50 km.
Northeast winds also often lead convective snow bands to the Västervik region; however, the average snowfall here is not as intense nor as frequent. Convective snow bands occur often in multiple band structures and it is common that various regions along the coast are affected by the lake effect snow when the atmospheric conditions are favourable in a large area. Hence, the Gdansk region was also perceived to experience convective snow bands on the same days as the Swedish coast, when all criteria were fulfilled as for convective snow bands along the Swedish coast (see Table 1 applied to the regions represented in Fig. 2).
Since convective snow bands occur in different strengths around the year, a statistical analysis as performed in the first part of this study must define the range of snow band criteria depending on their intensity. The present study includes snow bands that caused moderate snowfall. However, in order to investigate stronger snow band events with hazardous consequences a longer period of time should be studied with stricter criteria for the precipitation since their occurrence it not as frequent.
The investigation of an 11-year RCA4 climate model dataset indicated the heaviest and most frequent lake effect snow due to convective snow bands in the Baltic Sea area affecting the Gävle region. The Västervik region at the Swedish coast and even the Gdansk area at the Polish coast also experienced enhanced snowfall on days of favourable atmospheric conditions for snow bands. When including days of moderate conditions for convective snow band development, a total of 11 days per year on average results. Most convective snow band events occur in the months of November and December, when the sea surface is still warm from the summer and the cold air approaches frequently from the cold Finnish land.
The application of RCA4 in different model setups has indicated for the two case studies that any of the investigated RCA4 model configurations simulate the atmospheric conditions for convective snow bands and fulfil the criteria established in previous research. Nevertheless, significant differences have been observed between the different model systems.
The RCA and RCA-NEMO model varied largely due to the different SST input provided by coarse reanalysis data or the high-resolution ocean model respectively. The coupled RCA-NEMO model provided a superior representation of the sea surface with significantly higher SST values when comparing with the ERA-40 data. The direct impact of the higher NEMO SST on the heat fluxes and convective development manifests itself through a more intense convective snow band development and higher local precipitation rates. Even if on a larger scale all models agree well on the overall precipitation area, the exact location on a smaller scale as well as the amount and time of the snowfall remain a challenge. The models differed to a great extent in the amount of accumulated precipitation. The largest precipitation rates were given by the two high-resolution models as well as the atmosphere–ocean model.
Since the atmosphere–ocean interaction is of great importance for the regional climate modelling of events like convective snow bands, the coupling with the high-resolution ocean model NEMO is advantageous compared with the use of the coarse reanalysis data used in the original RCA model. Moreover, the increased resolution of the atmospheric RCA model had a positive impact on the model results. The high horizontal resolution led to an especially significant improvement in the representation of the cloud bands, the precipitation area, as well as the wind speed. Based on the investigation of the two cases the use of a coupled atmosphere–ocean system in connection with a high horizontal resolution of the atmospheric component is suggested for a more accurate representation of convective snow bands in regional climate models.
The data used in this study can be acquired by contacting the corresponding author.
The authors declare that they have no conflict of interest.
This work was done within the Centre of Natural Disaster Science (CNDS), Uppsala University. Lichuan Wu is supported by the Swedish Research Council (project 2012-3902). Edited by: M. Reckermann Reviewed by: two anonymous referees