CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-13-455-2017Ensemble cloud-resolving modelling of a historic back-building mesoscale
convective system over Liguria: the San Fruttuoso case of 1915ParodiAntonioantonio.parodi@cimafoundation.orgFerrarisLucaGallusWilliamMaugeriMaurizioMoliniLucaSiccardiFrancoBoniGiorgiohttps://orcid.org/0000-0002-8255-9312CIMA Research Foundation, Savona, ItalyDipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei
Sistemi, University of Genoa, 16145 Genoa, ItalyDepartment of Geological and Atmospheric Sciences, Iowa State University,
Ames, Iowa, USAUniversità degli Studi di Milano, Dipartimento di Fisica, Milan, ItalyAntonio Parodi (antonio.parodi@cimafoundation.org)12May201713545547229September201619October201612March201717March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://cp.copernicus.org/articles/13/455/2017/cp-13-455-2017.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/13/455/2017/cp-13-455-2017.pdf
Highly localized and persistent back-building mesoscale convective systems
represent one of the most dangerous flash-flood-producing storms in the
north-western Mediterranean area. Substantial warming of the Mediterranean
Sea in recent decades raises concerns over possible increases in frequency
or intensity of these types of events as increased atmospheric temperatures
generally support increases in water vapour content. However, analyses of the
historical record do not provide a univocal answer, but these are likely
affected by a lack of detailed observations for older events.
In the present study, 20th Century Reanalysis Project initial and
boundary condition data in ensemble mode are used to address the feasibility
of performing cloud-resolving simulations with 1 km horizontal grid spacing
of a historic extreme event that occurred over Liguria: the San Fruttuoso
case of 1915. The proposed approach focuses on the ensemble Weather Research
and Forecasting (WRF) model runs that show strong convergence over the
Ligurian Sea (17 out of 56 members) as these runs are the ones most likely
to best simulate the event. It is found that these WRF runs generally do
show wind and precipitation fields that are consistent with the occurrence
of highly localized and persistent back-building mesoscale convective
systems, although precipitation peak amounts are underestimated. Systematic
small north-westward position errors with regard to the heaviest rain and
strongest convergence areas imply that the reanalysis members may not be
adequately representing the amount of cool air over the Po Plain outflowing
into the Ligurian Sea through the Apennines gap. Regarding the role of
historical data sources, this study shows that in addition to reanalysis
products, unconventional data, such as historical meteorological bulletins,
newspapers, and even photographs, can be very valuable sources of knowledge in
the reconstruction of past extreme events.
Introduction
Flash floods are phenomena very common to most Mediterranean coastal cities,
accountable for millions of euros of damage and tens to hundreds of victims
every year (Gaume et al., 2009). The north-western Mediterranean area is
affected by such events in a period usually spanning from late summer (the
end of August) to late fall (early December): in this period, the warm
waters of the sea, in combination with large-scale meteorological systems
coming from the Atlantic Ocean, provide a huge amount of energy, namely
latent and sensible heat fluxes, to the atmosphere (Reale et al., 2001; Boni
et al., 2006; Pinto et al., 2013). Heavy precipitation is then triggered by
the typically very steep topography of the coasts: it frequently occurs
that the monthly average rainfall falls intensely in just a few hours and/or a
significant fraction (up to 30–40 %) of the yearly average falls in 1 day
(Parodi et al., 2012; Fiori et al., 2014). Obviously, the losses experienced in
terms of human lives and economic damage in these very densely populated
areas are often dramatic.
Among the flash-flood-producing storms in the Mediterranean area, a
prominent feature is the highly localized and persistent back-building of
mesoscale convective systems (MCSs; Schumacher and Johnson, 2005; Duffourg et
al., 2015; Violante et al., 2016). Such a scenario has often been observed in
the last decade, when Liguria (NW Italy) and southern France have been
repeatedly hit by severe floods: 2010 Varazze and Sestri Ponente, 2011
Cinque Terre and Genoa, 2012 Marseille and Isle du Levant, 2014 Genoa and
Chiavari, 2015 Nice. As shown in several recent works (Parodi et al., 2012;
Rebora et al., 2013; Fiori et al., 2014; Duffourg et al., 2015; Silvestro et
al., 2015; Cassola et al., 2016; Silvestro et al., 2016), convective cells,
embedded in such MCSs, are generated on the sea by the convergence of a warm
and moist south-easterly flow and a northerly much colder and drier one.
These structures are then advected to the land where the combined action of
the aforementioned currents and the topography force them to persist for
several hours over a very localized area (e.g. about 100 km2).
Many flood frequency studies have been carried out, focusing on rainfall
regimes and Mediterranean flood seasonality and type (Barriendos et al.,
2003; Llasat et al., 2005, 2014; Barriendos and Rodrigo, 2006; Boni et al., 2006; Pinto et
al., 2013; Toreti et al., 2015). Due to the exploitation
of both documentary sources and early measurements, these analyses have been
able to go back several centuries; however, their results have been mostly
inconclusive regarding changes in frequency of occurrence. Well-defined
trends have not been found as usually flood frequency oscillates from period
to period with no significant growth, not even in the most recent decades,
regardless of the event's duration (a few hours to days).
The same result applies to precipitation extremes and their possible changes
over the Mediterranean area in recent decades, studied by several authors,
either by empirical or (mainly at-site) extreme value theory approaches (see
e.g. Brunetti et al., 2001, 2004; Alpert et al., 2002; Kostopoulou and Jones,
2005; Moberg et al., 2006; Brunet et al., 2007; Kioutsioukis et al., 2010;
Rodrigo, 2010; Toreti et al., 2010; van den Besselaar et al., 2013). The
temporal tendencies are not fully coherent throughout the region (Ulbrich et
al., 2012) and are conditioned rather by the specific site, the approach used and
the period examined (Brugnara et al., 2012; Brunetti et al., 2012; Maugeri et
al., 2015). Conversely, an increase in precipitation extremes over the
Mediterranean area is generally indicated by climate model scenarios (Alpert
et al., 2002; Giorgi and Lionello, 2008; Trenberth, 2011).
It is therefore still an open debate whether the frequency of these phenomena
is really increasing or if it is merely the perception of both the general
public and scientific community. The latter hypothesis is supported by the
fact that in the last 10–20 years observational capabilities have
substantially increased. For example, in Italy alone, the remotely automated
weather station network has grown to 5000 stations, offering an average
density of about 1/75 station km-2 with a 1 to 10 min sampling rate. At the same time,
the national weather radar network reached a fully operational coverage,
allowing for direct evaluation of the space–time structure of precipitation
(Rebora et al., 2013).
Another factor contributing to enhancement of the perception of an increasing
frequency of extreme precipitation and floods is that it has become much
easier for weather-related disasters to make it to the news (Pasquaré
and Oppizzi, 2012; Grasso and Crisci, 2016) and therefore to the general
public. Moreover, rapidly growing population and soil consumption
increase the exposure of the population to such phenomena (Ward et al.,
2013; European Environmental Agency, 2015).
To better investigate whether extreme precipitation and flood frequency are
really increasing in the Mediterranean, it is important to improve the
exploitation of the information available from past meteorological data. A
contribution to this improvement may come from the development of methods
that identify which ensemble analyses from projects like the 20th Century
Reanalysis Project are able to produce precipitation fields that are
reasonably intense and capable of causing extreme floods.
Study region and Liguria coastal cities affected by the September 1915 event.
This paper focuses on a case study with the aim of investigating the ability
of cloud-resolving grid-spacing atmospheric simulations to capture the main
features of an event causing a very severe flash flood. These simulations are
performed using the Weather Research and Forecasting (WRF; Skamarock et al.,
2008) numerical meteorological model forced by an ensemble of reanalysis
fields from the 20th Century Reanalysis Project (Compo et al., 2006, 2011).
The work is also important to reveal how well fine-scale models
can simulate an event for which observations used to initialize the forcing
model are extremely sparse (see Sect. 4). One prior work, Michaelis and
Lackmann (2013), showed some promising results in the use of WRF for another
historical event, the Great Blizzard of 1888 in New England, but that event was a
mid-latitude cyclone driven by dynamics on a larger scale. More on the
windstorm modelling, Stucki et al. (2015) reconstructed a 1925
high-impact foehn storm in the Swiss Alps.
In this study, the case under investigation was a very intense flash-flood-producing event that occurred in 1915 in eastern Liguria (20–25 km east of
Genoa, Liguria region capital city), affecting San Fruttuoso, a small hamlet
near Portofino, and the coastal cities of Santa Margherita Ligure, Rapallo
and Chiavari (Fig. 1). Based on the newspapers of the time and documentary
sources, after relatively light rain during the night between 24 and 25 September,
in the early morning of 25 September, the area was hit for a few
hours (07:00–11:00 UTC) by violent rain that triggered widespread flash
flooding and a devastating debris flow. This landslide half demolished the
San Fruttuoso 1000-year-old abbey and laid down a thick layer of sand and
rocks to form a still-existing 20 m wide 2 m deep beach (Faccini et al.,
2009), today a very popular seaside resort. Based both on the observations
of the time (wind speed and direction, rainfall, observed lightnings) available
for north-western Italy, and on the model simulations, the occurrence of a
back-building MCS is suggested.
The paper is organized as follows. In Sect. 2 the 1915 convective event is
presented. Section 3 describes the WRF model setting performed. Results are
discussed in Sect. 4. Conclusions are drawn in Sect. 5.
Meteorological scenario
The synoptic and mesoscale information for this event are available both from
the 20th Century Reanalysis Project (Compo et al., 2006, 2011) and from the
weather bulletins issued on a daily basis by the Italian Royal Central Office
for Meteorology (Regio Ufficio Centrale di Meteorologia e Geodinamica).
The 20th Century Reanalysis Project is an effort led by the Earth System
Research Laboratory (ESRL) Physical Sciences Division (PSD) of the National
Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute
for Research in Environmental Sciences (CIRES) at the University of Colorado
to produce a reanalysis dataset covering the entire 20th century,
assimilating only surface observations of synoptic pressure, monthly sea
surface temperature and sea ice distribution. The observations have been
assembled through international cooperation under the auspices of the
Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative and
working groups of the Global Climate Observing System (GCOS) and World Climate
Research Program (WCRP). The project uses an ensemble filter data
assimilation method, which directly yields each six-hourly analysis as the
most likely state of the global atmosphere, and it also gives estimates of the
uncertainty in that analysis. This dataset provides the first estimates of
global tropospheric variability spanning from 1851 to 2012, with a six-hourly
temporal resolution and a 2.0∘ grid spacing. This study adopts 20th
Century Reanalysis Project version 2C, which uses the same model as version 2
with new sea ice boundary conditions from the COBE-SST2 (Hirahara et al.,
2014), new pentad Simple Ocean Data Assimilation with sparse input (SODAsi.2)
sea surface temperature fields (Giese et al., 2016) and additional
observations from ISPD version 3.2.9 (Whitaker et al., 2004; Compo et al.,
2013; Krueger et al., 2013; Hirahara et al., 2014; Cram et al., 2015).
Image panels show the following: (a) 500 hPa geopotential, (b) 850 hPa temperature and (c) sea level
pressure on 25 September 1915 at 06:00 UTC (20th Century Reanalysis
Project mean fields over the 56 ensemble members).
The weather bulletins issued by the Italian Royal Central Office for
Meteorology include weather maps at 07:00 and 20:00 UTC and data (sea level
pressure, wind direction and speed, temperature, cloud cover, cloud
direction, state of the sea, weather of the past 24 h and notes) from about
125 Italian stations.
Panel (a) shows the same field as in Fig. 2c, but over the same area of the map in
(b).
Panel (b) shows sea level pressure isobars on 25 September 1915 at 07:00 UTC, as provided by the Italian Royal Meteorological Service.
Panel (a) shows 2 m temperature and panel (b) shows 2 m specific humidity on 25 September 1915 (06:00 UTC) over the study region (20th Century Reanalysis mean fields over the 56 ensemble members).
Panel (c) shows surface temperature isotherms on 25 September 1915 (07:00 UTC), as provided by the Italian Royal Meteorological Service.
According to the reanalysis fields, the baroclinic circulation over Europe at
06:00 UTC on 25 September (i.e. a few hours before the most intense phase
of the event) is quite typical for heavy precipitation events over the study
area, with an upper-level trough over Great Britain leading to a diffluent
flow over the Ligurian Sea area, in combination with a widespread high-pressure block over eastern Europe and southern Russia (Fig. 2a). The diffluent
flow over the Ligurian Sea area is associated with warm air advection at
850 hPa from the southern Mediterranean towards northern–western
Mediterranean coastlines (Fig. 2b). Further information is provided by the
mean sea level pressure (MSLP) field at the European scale: both the Italian
weather map (07:00 UTC, Fig. 3b) and the reanalysis field (06:00 UTC,
Figs. 2c and 3a) show an elongated trough over the western Mediterranean and
a prominent ridge over south-eastern Europe, representing a blocking
condition on the large scale. The pressure gradient between the Gulf of Lyon
and the northern Adriatic Sea is about 12 hPa, according to both Fig. 3a and
b. The Italian weather map also gives evidence of a high-pressure ridge
extending into the Po Valley, which causes a significant surface pressure
gradient between the western part of the Po Valley and the Ligurian Sea (about
3 hpa), as well as between the eastern and the western parts of the Po
Valley (about 4 hPa). This high-pressure ridge is present in the reanalysis
MSLP field too (06:00 UTC, Fig. 3a), even though it is much less evident
than in the Italian weather map.
Quantitative precipitation estimates (QPEs) for
07:00 UTC 24 September–07:00 UTC 26 September 1915.
On the mesoscale, at 06:00 UTC, a significant 2 m temperature difference,
around 3–4 ∘C, is apparent from 20th Century Reanalysis Project
fields between the Po Valley and the Ligurian Sea (Fig. 4a), as well as a
significant 2 m specific humidity gradient (Fig. 4b). The temperature
difference is also confirmed by the available observations at 07:00 UTC
provided by the Italian Royal Central Office for Meteorology (Fig. 4c).
These mesoscale features represent the necessary ingredients for the
generation of a back-building MCS offshore of the Liguria coastline, as
observed in the 2010, 2011 and 2014 high-impact weather events in this region
(Parodi et al., 2012; Rebora et al., 2013; Fiori et al., 2014).
The back-building MCS hypothesis is supported by the 48 h quantitative
precipitation estimates (QPEs) for the period from 07:00 UTC 24 September to 07:00 UTC 26 September (Fig. 5). The rain gauges (64)
contributing to this map have been provided by different datasets such as the
European Climate Assessment & Dataset project (Klein Tank et al., 2002;
Klok and Klein Tank, 2009), the KNMI Climate Explorer dataset (Trouet and Van
Oldenborgh, 2013), the Italian Meteorological Society (SMI; Auer et al.,
2005), the Piedmont Region climatological dataset (Cortemiglia, 1999) and
the Chiavari Meteorological Observatory (Ansaloni, 2006).
The QPE map clearly shows a v-shaped elongated pattern, very similar to the
ones observed for the aforementioned events in Liguria. Based on historical
information on sub-daily rain rates, it can be estimated that during the most
intense phase of the event, the rainfall depths reached up to 400 mm in
approximately 4 h (07:00–11:00 UTC on 25 September) in some rain gauges
(Faccini et al., 2009). As a consequence of this intense and highly localized
rainfall, the coastal cities of Rapallo, Santa Margherita Ligure, Chiavari and
San Fruttuoso suffered very serious damages (Fig. 6), with a death toll
of around 25–30 people. Interestingly, as in the case of the Genoa 2014 event,
very intense lightning activity was documented by the Italian Royal Central
Office for Meteorology (Fig. 7).
Rapallo flash-flood impacts on 25 September 1915 (courtesy
of the real estate agency Bozzo in Camogli).
Thunderstorm and lightning activity reports (red circle) on
25 September 1915, as provided by the Italian Royal Meteorological Service.
ARW-WRF model simulations
The model simulations have been performed using the Advanced Research Weather
Research and Forecasting Model (hereafter as ARW-WRF, version 3.4.1). Initial
and boundary conditions were provided by the 20th Century Reanalysis Project
Version version 2c (Compo et al., 2006, 2011). The ARW-WRF model was applied
for each of the 56 members of the ensemble provided by the 20th Century
Reanalysis Project database.
Panel (a) shows domains for the numerical simulations of the Genoa 1915
event, d01 (Δ=25 km), d02 (Δ=5 km) and d03 (Δ=1 km). Panels (b–e) compare the topography over the d03 area for d01,
d02, d03 and native 1 km grid spacing.
The ARW-WRF model is configured for this case study based on the results
achieved in the ARF-WRF modelling of the Genoa 2011 and Genoa 2014 v-shape
convective structures (Fiori et al., 2014, 2017). Three nested domains
(Fig. 8a), centred on the Liguria region, were used, with the outer nest d01
using 25 km horizontal grid spacing (61 × 55 grid points), the
middle nest d02 using 5 km grid spacing (181 × 201 grid points) and
the innermost nest d03 using 1 km grid spacing (526 × 526 grid
points). Figure 8b–e provide the comparison between the topography over the
d03 area for d01, d02, d03 and the native 1 km grid spacing (for numerical
stability reasons, given the very large number of ensemble members, initial
conditions for 1 km d03 are interpolated from 5 km d02, as in Fiori
et al., 2014).
The benefits of a high number of vertical levels have been demonstrated in
Fiori et al. (2014), and thus the same higher number of vertical levels (84)
is adopted in this study. Since the grid spacing ranges from the regional
modelling limit (25 km) down to the cloud resolving one (1 km), two
different strategies have been adopted with regard to convection
parameterization. For domain d01 we adopted the new simplified
Arakawa–Schubert scheme (Han and Pan, 2011) as it is also used by the 20th
Century Reanalysis Project with 2.0∘ grid spacing. Conversely, a
completely explicit treatment of convective processes has been carried out on
the 5 km d02 and 1 km d03 domains (Fiori et al., 2014).
The double-moment Thompson et al. (2008) scheme for microphysical processes
has been adopted: this scheme takes into account ice species processes, whose
relevance in this case study is confirmed by the intense lightning activity
observed during the event, by explicitly modelling the spatio-temporal
evolution of the intercept parameter Ni for cloud ice. Furthermore, the
Thompson scheme was shown to perform the best for the Genoa 2011 and
Genoa 2014 studies (Fiori et al., 2014, 2017). With regard to the results in
Fiori et al. (2014) about the role of the prescribed number of initial cloud
droplets – Ntc – created upon autoconversion of water vapour to
cloud water and directly connected to peak rainfall amounts, a maritime value
corresponding to a Ntc of 25 × 106 m-3 has
been adopted.
It is important to highlight that the availability of the 56 members ensemble
is a key strength in the present study, which enables estimates of
uncertainties associated with dynamical downscaling down to the ARF-WRF
1 km d03 domain.
Minimum divergence time series (1/s) for members 1, 13, 22 and 37.
Panels (a–d) and (g–l) show the hourly QPF and 10 m wind fields
corresponding to the period with the minimum divergence values in Fig. 9
for members 1 and 13 (the convergence line trace in the most active phase
is red dashed). Panels (e–f) and (m–n) show the Lightning Potential Index
accumulated over the same 4 h period and the 36 h QPF respectively
for members 1 and 13.
Results and discussion
A fundamental ingredient for the occurrence of back-building MCSs is the
presence of a persistent and robust convergence line: the availability of a
large 1 km ARF-WRF dynamically downscaled ensemble (56 members) allows the
exploration of how many members produce such a convergence line over the
northern part of the Ligurian Sea region where most of such MCSs form (Rebora
et al., 2013). A convergence line is classified here as persistent and robust
if the minimum value of the divergence within the study area is less than
-7 × 10-3 s-1 for at least 4 h in a row. The
divergence threshold equal to -7 × 10-3 s-1 corresponds
to the 99.95 % percentile of the divergence values computed in every grid
point within the region
7.50–10.25∘ E, 43.75–44.50∘N, in Fig. 8 for each ensemble member in the period
12:00 UTC 24 September – 00:00 UTC 26 September (with a 30 min time
resolution).
Panels (a–d) and (g–l) show the hourly QPF and 10 m wind fields
corresponding to the period with the minimum divergence values in Fig. 9
for members 22 and 37 (the convergence line trace in the most active phase
is red dashed). Panels (e–f) and (m–n) show the Lightning Potential Index
accumulated over the same 4 h period and the 36 h QPF respectively
for members 22 and 37.
Surface pressure stations assimilated every 6 h in the
period 12:00 UTC 24 September 1915–00:00 UTC 26 September 1915.
Using the threshold above, 17 of the 56 ARW-WRF runs (30 % of the total)
exhibit a persistent and robust convergence line in the considered period,
while the remaining 39 do not produce it or it is not persistent. In
particular, the time series of divergence for four members (1, 13, 22 and 37
respectively) show that the minimum is reached (Fig. 9) at approximately the
same time when hourly QPF (quantitative precipitation forecast) exceeds
50 mm h-1 (Fig. 10a–d
and g–l, members 1 and 13; Fig. 11a–d and g–l, members 22 and 37); the
other 13 members are not shown as they behave very similarly. The four
representative members also exhibit large QPFs over the whole 36 h of the
simulations (Fig. 10f and n, members 1 and 13; Fig. 11f and n, members 22 and
37), even though significant differences both in the total amount and in the
spatial distribution are found. Significant values of the Lightning Potential
Index (LPI; Yair et al., 2010), in good agreement with the observations of
the Italian Royal Central Office for Meteorology, are shown in Fig. 10
(panels e and m, members 1 and 13) and Fig. 11 (panels e and m, members 22
and 37).
However, most of the back-building MCS-producing members are affected by a
non-negligible location error (see panels f and n of Figs. 10 and 11 for the
four selected members) with respect to the observed daily rainfall map
(Fig. 5). This feature is largely due to a predominance of the south-easterly
wind component over the north-westerly one (coming from Po Valley), thus
pushing the convergence line too far northwestwards (red dashed line), close to
the western Liguria coastline. This discrepancy is explained by the highly
localized spatio-temporal nature of this event, by the comparatively low
spatial density of the surface pressure stations assimilated by the 20th
Century Reanalysis Project over the western Mediterranean region (Fig. 12)
and by the relatively coarse characteristics (2.0∘ grid spacing, and
6-hourly temporal resolution) of the 20th Century Reanalysis Project forcing
initial and boundary condition data. For instance, the primary wind
convergence area over the sea and the inland area affected by the rainfall
(6.5–10.5∘ E, 43.5–45.5∘ N) is represented by only a few
(two to three) 20th Century Reanalysis Project grid points.
Rainfall depth bias and MAE for each 1 km d03 WRF member. Red
markers represent the 17 members, producing robust and persisting convergence
lines over the Ligurian Sea.
To quantitatively examine precipitation errors for each ARW-WRF ensemble
member, a bias and mean absolute error (MAE) analysis of the 36 h
(12:00 UTC 24 September–00:00 UTC 26 September) QPF versus the 48 h QPE
(07:00 UTC 24 September–07:00 UTC 26 September) is undertaken by comparing
the available 64 rain gauges with the nearest grid points of the 1 km d03.
The use of different time periods for QPE and QPF is not an issue as most of
the observed precipitation reported for Liguria fell in a time span
encompassed in the run time of the simulations. The results (Fig. 13) show
that most of the 56 ARF-WRF members have a negative bias of roughly
10–40 mm, largely explained by the widespread ensemble underestimation of
the extreme rainfall depths over the coastal cities of Santa Margherita
Ligure, Rapallo and Chiavari. The 17 selected members (red markers) show an
average bias of -22 mm and a MAE of 40 mm, while the remaining 39 members
have an average bias of -31 mm and a MAE of 42 mm. Also for the
17 selected members, the bias is largely explained by the stations mostly
affected by the MCS, and it reduces to -8 mm when Chiavari, Cervara Abbazia and S.
Margherita Ligure are excluded from the comparison.
Clusters pair statistics for the 12 members out of 17, showing
significant values (above 0.8) of the total interest function.
Because traditional verification measures (e.g. point-to-point verification
measures) applied to QPF are greatly influenced by location errors (Mass et
al., 2002), a deeper understanding of QPF performance in the WRF ensemble is
gained by performing object-based verification using the method for
object-based diagnostic evaluation (MODE; Davis et al., 2006a, b), intended
to reproduce a human analyst's evaluation of the forecast performance. The
MODE analysis is performed using a multi-step automated process. A
convolution filter is applied to the raw field to identify the objects. When
the objects are identified, some attributes regarding geometrical features of
the objects (such as location, size, aspect ratio and complexity) and
precipitation intensity (percentiles etc.) are computed. These attributes
are used to merge objects within the same forecast and/or observation field to
match forecast and observed objects and to summarize the performance of the
forecast by attribute comparison. Finally, the interest value combines the attributes (the centroid distance, the boundary
distance, the convex hull distance, the orientation angle difference, the
object area ratio, the intersection divided by the union area ratio, the
complexity ratio and the intensity ratio) computed in the object analysis in a
total interest function,
providing an indicator of the overall performance of matching and merging
between observed and simulated objects. In the present study, the relative
weight of each attribute used the default setting in MODE (National Center
for Atmospheric Research; NCAR, 2013). The displacement errors including
centroid distance and boundary distance were weighted the greatest in the
calculation of total interest.
QPE regridded at 10 km grid spacing (a) and QPF from
members 1 (b), 13 (c), 22 (d) and 37 (e), regridded
at 10 km grid spacing (lower panels). Dots identify the areas of paired
clusters.
In our experiment we have empirically chosen the convolution disk radius and
convolution threshold, so that this choice would recognize precipitation
areas (at least roughly 50 × 50 km or so) similar to what a human
would identify. For each ARF-WRF ensemble member, the 36 h (12:00 UTC
24 September–00:00 UTC 26 September) QPF is compared with the 48 h QPE
(07:00 UTC 24 September–07:00 UTC 26 September), both bilinearly
interpolated to the same 10 km grid. This grid spacing represents a good
compromise between the native 1 km ARF-WRF grid spacing and the 40 km
average distance between the available 64 rain gauges. After a set of
experiments, we fixed the value of the convolution radius to one grid point
and the threshold of the convoluted field to 75 mm. Selected using the minimum divergence criterion, 12 out of
the 17 members show
significant values (above 0.8) of the total interest function (Table 1). This
value is slightly higher than the default one (0.7) used by MODE to match
paired objects in order to restrict our analysis to the best-simulated
events. Despite the limited observations available in 1915, our ensemble
performs relatively well when considering object-based parameters.
Specifically, when examining paired observed and modelled clusters, these
12 members demonstrate useful skill for centroid distance, providing a
quantitative sense of spatial displacement of forecast; forecast
area and/or observed area, providing an objective measure of over- or
under-prediction of areal extent of the forecasts; forecast intensity
50–observed intensity 50 and forecast intensity 90–observed intensity 90,
providing objective measures of median (50th percentile) and near-peak (90th
percentile) intensities found in the objects; and the already-mentioned total
interest, a summary statistic derived from the fuzzy logic engine with
user-defined interest maps for all these attributes plus others
(Table 1).
From member 1 at 06:00 UTC 25 September 2015, panel
(a) shows 2 m potential temperature together with the 10 m
horizontal wind vectors, while panels (b–g) show vertical cross
sections of potential temperature, vertical velocity, water vapor, rain
water, snow and graupel mixing ratios along the cross section (green dotted)
shown in (a).
Indeed it is impressive that small displacement errors averaging only
114 km,
with a standard displacement of only 62 km, are obtained, despite the very
crude initialization of a 1915 reanalysis case. In a much more recent set of
cases, Duda and Gallus (2013) found an average displacement distance
(absolute error) of 105 km for initiation of systems. Squitieri and
Gallus (2016) show that centroids of forecasted MCSs in their sample of 31
relatively recent events in the US Great Plains are usually over
100 km or more removed from the centroids of the observed MCSs. Similarly
good performance of the ensemble exists for areal coverage, rainfall
intensity (although there is a 30–40 % underestimate) and overall
characteristics of the forecasted objects as implied by the interest value.
Selected members 1, 13, 22 and 37 (Fig. 14) have total interest values above
0.93 (close to 1 is good) and their paired clusters distance, namely the
distance between centroids of observed and simulated rain regions, is around
100 km.
The availability of high-resolution simulations allows one to gain a deeper
understanding of the dynamics of the San Fruttuoso 1915 storm evolution. The
physical mechanism responsible for the generation of the back-building
mesoscale convective systems in this area has been recently explained by
Fiori et al. (2017). Taking advantage of the availability of both
observational data and modelling results at the micro-α
meteorological scale, Fiori et al. (2017) provide insights about the
triggering mechanism and the subsequent spatio-temporal evolution of the
Genoa 2014 back-building MCS. The major finding is the important effect of a
virtual mountain created on the Ligurian sea by the convergence of a cold and
dry jet outflowing from the Po Valley and a warm and moist low-level
south-easterly jet within the planetary boundary layer.
The same mechanism is also active for this case. Let us consider as an
example the convective flow field at 06:00 UTC on 25 September 1915 (see
Fig. 15), as predicted by member 1 of the ensemble. Panel (a) shows the 2 m
potential temperature field together with the 10 m horizontal wind vector
field: the colder and drier jet outflowing from the Po Valley and the warmer
and moister air from the southern Mediterranean Sea are evident. Panel (a)
also shows, by means of the green dotted cross section (45∘), the
thin potential temperature layer (virtual mountain) in front of the actual
Liguria topography (panel b). This acts, as described in Fiori et al. (2017),
to produce strong convective cells in panel (c) (updraft velocity above
10 m s-1), with the apparent back-building on the western side (less
mature and intense cells around 8.4∘ latitude). The main updraft
produces vertical advection of water vapour (panel d), thus resulting in
significant production of rainwater (panel e), snow (panel f, significantly
advected inland by the upper-level south-westerly winds) and graupel
(panel g).
Conclusions
Highly localized and persistent back-building MCSs represent one of the most
dangerous flash-flood-producing storms in the north-western Mediterranean
area. A historic extreme precipitation event occurring over Liguria on
September 1915, which seems to be due to one of these systems, was
investigated in this paper by both means of a large collection of
observational data and means of atmospheric simulations performed using
the ARF-WRF model forced by an ensemble of reanalysis fields from the 20th
Century Reanalysis Project.
The results show that the simulated circulation features are consistent with
the hypothesis of a highly localized back-building MCS over the Ligurian Sea and
that the ARF-WRF runs, driven by a significant fraction of the members of the
20th Century Reanalysis Project ensemble, produce fields that are in
reasonable agreement with the observed data.
The proposed approach was to focus only on the ARF-WRF runs showing strong
convergence so as to get the best depiction of the event. Thus, we suggest
that, when using datasets such as the 20th Century Reanalysis Project, it is
important to consider that the physics–dynamics are likely to play a role in
the events of interest and to follow a similar technique to selectively use
the reanalysis ensemble members best displaying the key physics–dynamics of
the event. Future work should further test an approach like this one to get a
better understanding of how well the same convergence detection approach in
regional climate model simulations of past and future climate (e.g. Pieri et
al., 2015, at cloud-permitting grid spacing) can quantify possible changes in
back-building MCS precipitation processes.
Concerning data collection, this study showed that in addition to the use
of reanalysis products, other sources of data, such as newspapers,
photographs and historical meteorological bulletins, can be essential
sources of knowledge. Focusing on historical meteorological bulletins,
future work on this particular case and similar ones occurring along the
north-western Mediterranean coastline will explore the use of bogus
observations or other preprocessing techniques to alter lower-tropospheric
conditions at model initialization time to better match actual observations,
which may result in a better location of the convergence line and
consequently simulation of the precipitation event.
Data are available at TechnicalInfo (2017).
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the Italian Civil Protection Department and by the
Regione Liguria. The ground-based observations were provided by the Italian Civil
Protection Department and the Ligurian Environmental Agency. The rain gauge
data were courtesy of the European Climate Assessment & Dataset project,
the KNMI Climate Explorer dataset, the Italian Meteorological Society,
the Piedmont Region climatological dataset and the Chiavari Meteorological
Observatory. Antonio Parodi would like also to acknowledge the support of the
FP7 DRIHM (Distributed Research Infrastructure for Hydro-Meteorology,
2011–2015) project (contract number 283568). Thanks are due to CINECA,
where the numerical simulations were performed on the Galileo System,
project-ID: SCENE. W. Gallus appreciates the opportunity for a research visit
at the University of Milan. Edited by: S.
Bronnimann Reviewed by: two anonymous referees
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