NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-16-1259-2016Preface: Advances in meteorological hazards and extreme eventsNastosPanagiotis T.nastos@geol.uoa.grhttps://orcid.org/0000-0001-9336-6586DaleziosNicolas R.Laboratory of Climatology and Atmospheric Environment, National and Kappodistrian University of Athens, Athens, GreeceUniversity of Thessaly, Volos, GreecePanagiotis T. Nastos (nastos@geol.uoa.gr)31May201616512591268This 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://nhess.copernicus.org/articles/16/1259/2016/nhess-16-1259-2016.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/16/1259/2016/nhess-16-1259-2016.pdf
This Special Issue of the Natural Hazards and Earth System Sciences (NHESS)
entitled “Advances in Meteorological Hazards and Extreme Events”
hosts fifteen (15) selected papers presented at the
11th International Conference on Meteorology, Climatology and Atmospheric
Physics – COMECAP 2012 – Athens, 29 May–1 June 2012. COMECAP 2012 covered various topics
related to the science of the Atmospheric Environment, which is in fact an
interdisciplinary field, giving the opportunity to understand the physical
systems and environmental processes in an integrated manner. The featured
papers shed light to advances and current trends in the considered
meteorological hazards and extreme events, such as tornadoes, heat waves and
extreme temperature indices, droughts, floods, convective precipitation,
landslides, medicanes (Mediterranean tropical like cyclones) and wildfires,
using recorded datasets, model simulations and innovative methodologies.
Hazard (or cause) may be defined as a potential threat to humans and their
welfare and risk (or consequence) as the probability of a hazard occurring
and creating loss. Unlike hazard and risk, a disaster is an actual
happening, rather than a potential threat, thus, a disaster may be defined
as the realization of hazard. The term environmental hazard has the
advantage of including a wide variety of hazard types ranging from natural
(geophysical) events, through technological (man-made) events to social
(human behaviour) events. Meteorological hazards and extremes constitute
natural environmental hazards caused by atmospheric disturbances. Disaster
risk arises when hazards interact with physical, social, economic and
environmental vulnerabilities. The impact of disaster can be transferred
from one region to another. This, compounded by increasing vulnerability
related to several factors, such as population growth, land pressure,
urbanization, social inequality, climate change, political change, economic
growth, technological innovation, social expectations, global
interdependence, environmental degradation, competition for scarce resources
and the impact of epidemics, points to a future where disasters could
increasingly threaten, among others, the sustainable development of
agricultural regions (Smith, 2013). Sustainable development, socio-economic
improvement, good governance, and disaster risk reduction are mutually
supportive objectives.
Recent research findings suggest that variability of climate, if
encompassing more intense and frequent extremes, such as major large-scale
environmental hazards like droughts, heatwaves or floods, results in the
occurrence of natural disasters that are beyond our socio-economic planning
levels. This is expected to stretch regional response capabilities beyond
their capacity and will require new adaptation and preparedness strategies
(Salinger et al., 2005). Disaster prevention and preparedness should
become a priority and rapid response capacities to climate change need to be
accompanied by a strategy for disaster prevention. Nevertheless, each type
of extreme events has its own particular climate, cultural and environmental
setting, and mitigation activities must use these settings as a foundation
of proactive management. There is an urgent need to assess the forecasting
skills for natural disasters affecting mainly agriculture and other sectors
of the economy in order to determine those where greater research is
necessary. It is well known that lack of good forecast skill is a constraint
to improve adaptation, management and mitigation. Seasonal to interannual
climate forecasting is a new branch of climate science, which promises
reducing vulnerability. Improved seasonal forecasts are now being linked to
decision making for cropping. The application of climate knowledge to the
improvement of risk management is expected to increase the resilience of
farming systems.
A more integrated approach to environmental hazards has been gradually
attempted using common methodologies, such as risk analysis. Understanding
of extreme events and disasters is a pre-requisite for the development of
adaptation strategies in the context of climate change and risk reduction
within the disaster risk management framework (IPCC, 2012). Extreme events
will have greater impacts on sectors with closer links to climate, such as
agriculture and food security. According to the World Meteorological
Organization (WMO, 2006), some natural hazards are weather events (tropical
and extra-tropical cyclones, tornadoes, thunderstorms, lightning,
hailstorms, high winds, snow storms, freezing rain, dense fog, thermal
extremes and drought). Others are related to weather, climate and water
(floods and flash floods, storm surges, high waves at sea, sand- or dust
storms, forest or bush fires, smoke and haze, landslides and mudslides,
avalanches and desert locust swarms). Each hazard is in some way unique.
Tornadoes and flash floods are short-lived, violent events, affecting a
relatively small area. Others, such as droughts, develop slowly, but can
affect most of a continent and entire populations for months or even years.
An extreme weather event can involve multiple hazards at the same time or in
quick succession. In addition to high winds and heavy rain, a tropical storm
can result in flooding and mudslides. In temperate latitudes, severe summer
weather (thunder and lightning storms or tornadoes) can be accompanied by
heavy hail and flash floods. Winter storms with high winds and heavy snow or
freezing rain can also contribute to avalanches on some mountain slopes and
to high runoff or flooding later on in the melt season.
The special issue “Advances in meteorological hazards and extreme events”
reflects a wide range of papers analyzing meteorological hazards and extreme
events mainly in the Mediterranean area, using recorded data sets, model
simulations and innovative methodologies. This special issue provides
advances and current trends in the considered meteorological hazards and
extreme events, such as tornadoes, heat waves and extreme temperature
indices, droughts, floods, convective precipitation, landslides, medicanes
(Mediterranean tropical like cyclones) and wildfires. We considered useful
to adopt six sub-sections in the structure of the text associated with the
aforementioned extremes discussed in the Special Issue. At the beginning of
each section, the features of the meteorological hazards and extreme events
are briefly described, followed by the presentation of the related research
in the Special Issue.
Tornadoes are extreme phenomena associated with severe convective storms. The Greek
philosopher Aristotle (384–322 BC) in Meteorologica presented perhaps the
most renowned exposition of natural extreme phenomena: “So the whirlwind
originates in the failure of an incipient cyclone to escape from its cloud.
It is due to the resistance the eddy generates and emerges when the spiral
descends to the earth dragging along the cloud that cannot shake off. When
blowing in a straight line it carries along whatever comes by in a circular
motion and overturns and snatches up whatever it meets” (Meteorologica, 371a9-15).
Tornadoes occur in many parts of the world (Fujita, 1973).
Several publications during the last several decades have presented
historical records concerning tornadic activity (e.g. Meaden, 1976; Tooming et al. (1995); Peterson, 1998; Reynolds, 1999; Gayà et al., 2000; Tyrrel,
2003; Macrinoniene, 2003; Dotzek, 2003; Nastos and Matsangouras, 2010;
Brázdil et al., 2012; Rahuala et al., 2012; Matsangouras et al., 2014a).
Two papers in the Special Issue present synoptical analysis and modeling
simulations of tornadoes over Greece.
More specifically, a synoptic analysis of tornadic events (tornadoes,
waterspouts and funnel clouds) over western Greece by means of composite
means and anomalies of synoptic conditions is conducted by Nastos and
Matsangouras (2014). The daily composite means of synoptic conditions are
based on the National Centers for Environmental Prediction–National Center
for Atmospheric Research (NCEP–NCAR) reanalysis data sets, for the period
12 August 1953 to 31 December 2012. The daily composite anomalies are
calculated with respect to 30 years of climatological study (1981–2010) of
the synoptic conditions. The analysis is carried out in terms of seasonal
and monthly variability of composite means and anomalies of synoptic
conditions for specific isobaric levels of 500, 700, 850, 925 hPa and the
sea level pressure (SLP). In addition, an evaluation of the dynamic lifted
index from NCEP–NCAR reanalysis data sets is presented. The daily composite
mean analysis of 500 hPa reveals a trough line across the northern Adriatic
Sea and central Italy, associated with a SW upper-air stream over western
Greece. The maximum composite anomalies are depicted at the isobaric level
of 500 hPa during autumn, spring and summer, against winter when the anomaly
appears at 925 hPa isobaric level. In addition, 48 % of tornado events
during the autumn season occur in pre-frontal weather conditions (cold
fronts) and 27 % developed after the passage of the cold front.
Furthermore, the main difference in synoptic patterns between tornado and
waterspout days along western Greece during the autumn season is the maximum
daily composite anomaly over the Gulf of Taranto.
The role of topography in significant tornadogenesis events triggered under
strong synoptic scale forcing over Greece is investigated by Matsangouras et
al. (2014b). Three tornado events that occurred over the last years in Thebes
(Boeotia, 17 November 2007), Vrastema (Chalkidiki, 12 February 2010) and
Vlychos (Lefkada, 20 September 2011) are selected for numerical experiments.
These events are associated with synoptic scale forcing, while their
intensities were T4–T5 (on the TORRO scale), causing significant damage.
The simulations are performed using the non-hydrostatic weather research and
forecasting model (WRF), initialized by European Centre for Medium-Range
Weather Forecasts (ECMWF) gridded analyses, with telescoping nested grids
that allow for the representation of atmospheric circulations ranging from
the synoptic scale down to the mesoscale. In the experiments, the topography
of the inner grid is modified by: (a) 0 % (actual topography) and
(b) -100 % (without topography), making an effort to determine whether the
occurrence of tornadoes – mainly identified by various severe weather
instability indices – could be indicated by modifying topography. The
principal instability variables employed consist of the bulk Richardson
number (BRN) shear, the energy helicity index (EHI), the storm-relative
environmental helicity (SRH), and the maximum convective available potential
energy (MCAPE, for parcels with maximum θe). Additionally, a model
verification is conducted for every sensitivity experiment accompanied by
analysis of the absolute vorticity budget. Numerical simulations reveal that
the complex topography constitutes an important factor during the
17 November 2007 and 12 February 2010 events, based on EHI, SRH, BRN, and MCAPE
analyses. Conversely, topography around the 20 September 2011 event is
characterized as the least significant factor based on EHI, SRH, BRN, and
MCAPE analyses. The evaluation of absolute vorticity budget reveals that in
all cases the simulations with topography are associated with higher values
of maximum absolute vorticity in the area of interest than those without topography.
Heat wave is commonly considered as a period of abnormally and uncomfortably hot
weather with high air humidity. Typically, a heat wave lasts at least two
days (Koppe et al., 2004). Nevertheless, a clear definition of heat waves
has not yet been addressed by the World Meteorological Organization. Despite
the fact that the heat wave concerns a meteorological phenomenon, it could
not be assessed without reference to the related impacts on humans. So, it
would be better to take into account the human sensation of heat against
determining specific thresholds of meteorological parameters. Robinson (2001)
considers a heat wave as an extended period of uncommonly high
atmosphere-related heat stress, which causes temporary modifications in
lifestyle habits and adverse health related problems affecting humans. It is
very likely that heat waves will occur with a higher frequency and duration
by late 21th century, due to global warming (Beniston et al., 2007; IPCC,
2013; Nastos and Kapsomenakis, 2015). A recent research has given evidence
that “Mega-heat waves” such as the 2003 and 2010 events broke the 500 year
long seasonal temperature records over approximately 50 % of Europe
(Barriopedro et al., 2011). Further, the extreme air temperature indices can be divided into three categories:
absolute, percentile and duration indices, defined by the joint
CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices
(Alexander et al., 2006). Five papers in the Special Issue analyze heat
waves, urban heat island and extreme temperature indices, by utilizing both
observations and models' simulations.
The predictability of the Russian heat wave during July and August of 2010
on a seasonal timescale is analyzed by Katsafados et al. (2014). The
dynamical seasonal simulations have been carried out using the
state-of-the-art CAM3 AGCM, designed to produce simulations for several
different dynamical cores and horizontal resolutions. The impact of various
model initializations on the predictability of the event is also
investigated because such comprehensive prognostic systems are sensitive to
the initial conditions due to the chaotic nature of the atmosphere. The
ensemble seasonal simulations are based on a modified version of the
lagged-average forecast method using different lead-time initializations of
the model. The results indicate that only a few individual members reproduce
the main features of the blocking system 3 months ahead. Most members miss
the phase space and the propagation of the system, setting limitations in
the predictability of the event. The results of this study underline the
main difficulties and limitations in the seasonal simulation of such
high-impact weather event.
Giannaros et al. (2014) present the development of a high resolution heat
island modeling system, which could be used in the context of operational
real-time weather forecasting applications. The urban heat island (UHI)
effect is one prominent form of localized anthropogenic climate
modification. The modeling system is built around a state-of-the-art
numerical weather prediction model, properly modified to allow for the
better representation of the urban climate. The model performance in terms
of simulating the near-surface air temperature and thermal comfort
conditions over the complex urban area of Athens, Greece, is evaluated
during a 1.5-month operational implementation in the summer of 2010. Results
from this case study reveal an overall satisfactory performance of the
modeling system. The discussion of the results highlights the important role
that, given the necessary modifications and adaptations, meso-scale
meteorological models constitute a promising tool for the operational
simulation/forecasting of the urban thermal climate and thermal comfort
conditions. Nevertheless, there is still room for improvement of the
presented modeling system
Tolika et al. (2014) make an effort to identify the leading meteorological
conditions over the Mediterranean as well as the corresponding large-scale
atmospheric processes and key synoptic scale features that possibly
contributed to such extreme winter cooling and summer warming in 2012 over
Greece. During the summer and autumn months, numerous regions in the domain
of study experienced record-breaking maximum and minimum temperatures.
Conversely, the winter period was particularly cold and January one of the
coldest months over the last 55 years. The analysis of the cold period
indicates that the synoptic conditions resemble the positive phase of the
Eastern Mediterranean Pattern (EMP). The predominance of these cool
conditions seems to be related primarily to an intense NNW or NNE
atmospheric circulation, as a consequence of the positive EMP phase.
Moreover, the reduction in the floating sea ice emerges as a key driver of
the formation of a low-pressure pattern and the reinforcement of the trough
south of Scandinavia, which in turn strengthened the Siberia High east of
the trough. This reinforcement resulted in a blocking pattern and in
favorable conditions for the EMP formation. The atmospheric circulation
during the prolonged high-temperature period resembles, respectively, the
negative phase of North Sea–Caspian Pattern teleconnection. The observed
positive pole, in conjunction with the strong southwestern circulation,
results in temperature increases and in the development of a smooth pressure
field that contributes to the weakening of the Etesian winds and therefore
to calm conditions over the continental areas.
The extreme temperature indices (ETCCDI; Expert Team on Climate Change
Detection and Indices) in the Mediterranean region of Montenegro for the
period of 1951–2010 are analyzed in terms of trends by Burić et al. (2014),
using four stations in the coastal area of Montenegro – Herceg Novi,
Ulcinj, Budva and Bar – within two periods (before 1980 and after 1980) due
to a well-known climate shift that occurred in the late 1970s. A negative
trend has been calculated for cold nights and cold days at almost all
stations. The most significant trends are obtained for warm conditions over
the investigated area during the period of 1951–2010. The trend found in
this study can be associated with the positive phase of the North Atlantic
Oscillation (NAO) since this pattern is recognized as the main mode of
climate variability in the extratropical Northern Hemisphere. However, a
separately investigated period of 1951–1980 for the region has shown
opposite tendencies and a contrasting trend to the period of 1951–2010 as
well as 1981–2010. This result is possibly due to a well-known climate
shift that occurred in the late 1970s where there is a change in the sign of
trend for warm days and warm nights. These two separately investigated
periods have shown contrasting temperature trends.
The paper of Kostopoulou et al. (2014) presents the spatial patterns and
temporal trends in temperature and precipitation and their extremes in the
eastern Mediterranean and Middle East region (EMME), using output from the
Hadley Centre PRECIS climate model. The evaluation results have shown that
the model reproduces the major features of the observed annual cycle and
provided evidence of the accuracy of the model results and associated
uncertainties. The spatial distribution of recent temporal trends in
temperature indicates strong increasing in minimum temperature over the
eastern Balkan Peninsula, Turkey and the Arabian Peninsula. The rate of
warming reaches 0.4–0.5 ∘C decade-1 in a large part of the
domain, while warming is expected to be strongest in summer
(0.6–0.7 ∘C decade-1) in the eastern Balkans and western Turkey. The trends
in annual and summer maximum temperature are estimated at approximately
0.5 and 0.6 ∘C decade-1 respectively. The trends in annual and summer
maximum temperature are estimated at approximately 0.5 and 0.6 degreeC decade-1
respectively. Recent estimates do not indicate statistically significant
trends in precipitation except for individual sub-regions. Results indicate
a future warming trend for the study area over the last 30 years of the
21st century. Trends are estimated to be positive and statistically significant
in nearly the entire region. The annual trend patterns for both minimum and
maximum temperature show warming rates of approximately 0.4–0.6 ∘C decade-1,
with pronounced warming over the Middle Eastern countries.
Summer temperatures reveal a gradual warming (0.5–0.9 ∘C decade-1)
over much of the region. The model projects drying trends by
5–30 % in annual precipitation towards the end of the 21st century, with
the number of wet days decreasing at the rate of 10–30 days yr-1,
while heavy precipitation is likely to decrease in the high-elevation areas
by 15 days yr-1.
Drought is a natural, casual and temporary state of continuous decline in
precipitation and water availability in relation to normal values, spanning
a considerable period and covers a wide area. It is discriminated into
meteorological, hydrological and agricultural drought. It is a local
phenomenon identified by the intensity, duration and extent. Drought impacts
concern a variety of sectors of economy, environment and society of the
affected area (Wang, 2005; Mechler et al., 2010; Dalezios et al., 2012). The
identification of dry areas has been considered two millennia ago. The
classical Greek thought acknowledged that the latitude affects the arid,
temperate and cold zones of the earth. There was a perception that the arid
climates in low latitudes were dry (Nastos et al., 2013). The evaluation of
drought is accomplished by the drought indices, the most important of which
and widely used are the Aridity Index (AI), which is based on the ratio of
annual precipitation and potential evapotranspiration rates (UNESCO, 1979) ,
the Standardized Precipitation Index (SPI), which is based on the
probability of precipitation for any time scale (McKee et al., 1993), Palmer
Drought Severity Index (PDSI), which is a soil moisture algorithm calibrated
for relatively homogeneous regions (Palmer, 1965) and Reclamation Drought
Index (RDI), which is based on a calculation of drought at the river basin
level, incorporating temperature as well as precipitation, snowpack, stream
flow and reservoir levels as input (Weghorst, 1996). Reconnaissance Drought
Index (RDI) proposed by Tsakiris et al. (2007) is one of the most recent
developments in the field of meteorological drought indices. Essentially, it
relates precipitation to the potential evapotranspiration at a location, and
can be considered as an extension of the SPI. The development of Earth
observation satellites from the 1980s onwards promoted the drought
monitoring and detection. The most prominent vegetation index is certainly
the Normalized Difference Vegetation Index (NDVI; Tucker, 1979) that was
first applied to drought monitoring by Tucker and Choudhury (1987). The
index NDVI, by itself, does not depict drought or not drought conditions,
but severity of drought can be defined as the deviation from the mean NDVI
value of a long period (DEVNDVI). One paper in the Special Issue addresses
the risk identification of agricultural drought.
Dalezios et al. (2014) deals with risk identification of agricultural
drought, which involves drought quantification and monitoring, as well as
statistical inference. For the quantitative assessment of agricultural
drought, as well as the computation of spatiotemporal features, one of the
most reliable and widely used indices is applied, namely the vegetation
health index (VHI). The computation of VHI is based on satellite data of
temperature and the normalized difference vegetation index (NDVI). The
spatiotemporal features of drought, which are extracted from VHI, are areal
extent, onset and end time, duration and severity. In this paper, a 20-year (1981–2001)
time series of the National Oceanic and Atmospheric
Administration/advanced very high resolution radiometer (NOAA/AVHRR)
satellite data is used, where monthly images of VHI are extracted.
Application is implemented in Thessaly, which is the major agricultural
drought-prone region of Greece, characterized by vulnerable agriculture. The
results show that agricultural drought appears every year during the warm
season in the region. The severity of drought is increasing from mild to
extreme throughout the warm season, with peaks appearing in the summer.
Similarly, the areal extent of drought is also increasing during the warm
season, whereas the number of extreme drought pixels is much less than those
of mild to moderate drought throughout the warm season. Finally, the areas
with diachronic drought persistence can be located. Drought early warning is
developed using empirical functional relationships of severity and areal
extent. The adopted remote-sensing data and methods have proven very
effective in delineating spatial variability and features in drought
quantification and monitoring.
Heavy convective precipitation typically occurs with moist deep convection. The excess water vapor in
rising air parcels condenses to form a cloud. The heat released through this
condensation can help to sustain the convection by warming the air further
and making it rise still higher, which causes more water vapor to condense,
so the process feeds on itself. Doswell et al. (1996) have said that in
order to produce moist deep convection three ingredients are needed: (1) the
environmental lapse rate must be conditionally unstable, (2) there must be
enough lifting so that a parcel will reach its level of free convection,
(3) there must be enough moisture present that a rising parcel's associated
moist adiabat has a level of free convection. In mid-latitudes, convective
precipitation is associated with cold fronts (often behind the front),
squall lines, and warm fronts in very moist air. Graupel and hail indicate
convection. Besides, warm rain, precipitation produced solely through
condensation and accretion of liquid, is known to be important in the
tropics (Rogers, 1967; Houze, 1977). However, the warm rain process may also
play a critical role in heavy convective precipitation events in
midlatitudes as well, resulting in many flash floods and landslides. Four
papers in the Special Issue investigate the flood risk, severe
thunderstorms, rainfall intensity and landslides by implementing specific
modelling and techniques.
The hydrological effects of multi-temporal land use changes in flood risk
within the catchment area of Yialias River have been studied by Alexakis et
al. (2014). They apply a hydrological model to simulate the main components
of the hydrologic cycle, in order to study the diachronic effects of land
use changes. For the implementation of the model, land use, soil and
hydrometeorological data were incorporated. The climatic and stream flow
data were derived from rain and flow gauge stations located in the wider
area of the watershed basin. In addition, the land use and soil data were
extracted after the application of object-oriented nearest neighbor
algorithms of ASTER satellite images. Subsequently, the cellular automata
(CA)–Markov chain analysis was implemented to predict the 2020 land
use/land cover (LULC) map and incorporate it to the hydrological impact
assessment. The preliminary results denoted the crucial role of urban sprawl
phenomenon as well as the significant change of land cover regime in the
increase of runoff rate within the spatial limits of a catchment area and
highlighted the importance of searching land use regime with the use of
satellite remote sensing imageries. In addition, the implementation of
CA–Markov provided indication of the potential impact of land use change on
flood vulnerability in the near future.
An alternative methodological tool for the prediction of severe
thunderstorms occurring over a specific area has been developed by Korologou
et al. (2014). Northwestern Peloponnese is chosen to illustrate the proposed
tool, because many thunderstorms with heavy rainfall have occurred there
with disastrous impacts, for the period January 2006–June 2011. The
synoptic scale circulation is examined throughout the troposphere along with
satellite images, lightning data and synoptic observations of weather
stations. Well-known instability indices are calculated and tested against
synoptic observations. Taking into account the severity of the incidents,
the performance of the indices was not as good as expected. Then, the Local
Instability Index (LII) was inferentially drawn by using them. The LII is a
threshold function that consists of the low-level moisture, a practical
approximation of the CAPE, the terrain heating effect and a formalized
operational experience. The results reveal that the LII has satisfactory
total performance (75 %) over the northwestern Peloponnese.
Benhamrouche et al. (2015) assess the spatial and temporal distribution of
the daily precipitation concentration index (CI) in Algeria (south
Mediterranean Sea). CI is an index related to the rainfall intensity and
erosive capacity; therefore, this index is of great interest for studies on
torrential rainfall and floods. Forty-two daily rainfall series based on
high-quality and fairly regular rainfall records for the period from 1970
to 2008 have been used. It is concluded that in Algeria, the essential features
of climate in different regions are characterized by narrow climatic zones
close to the coast, under the combined influence of the sea. The relief of
the soil, the latitude and the diversity of climates in Algeria lead to a
very different rainfall distribution. The daily precipitation CI results
allowed the identification of three climate zones: the northern country,
characterized by coastal regions with CI values between 0.59 and 0.63; the
highlands, with values between 0.57 and 0.62, except for the region of
Biskra (CI = 0.70); and the southern region of the country, with high
rainfall concentrations with values between 0.62 and 0.69.
An original approach to set up a mosaic of 18 local rainfall thresholds, in
place of a single regional threshold, to be used in civil protection warning
systems for the occurrence of landslides at regional scale is proposed by
Segoni et al. (2014a). The proposed approach is based on the use of a
software program named MaCumBA (explained and discussed in detail in Segoni
et al., 2014b), which allows for statistical intensity–duration rainfall
thresholds to be identified by means of an automated and standardized
analysis of rainfall data. The automation and standardization of the
analysis brings several advantages that in turn have a positive impact on
the applicability of the thresholds to operational warning systems.
Moreover, the possibility of defining a threshold in very short times
compared to traditional analyses allows subdividing the study area into
several alert zones to be analysed independently, with the aim of setting up
a specific threshold for each of them. The authors focus on how the physical
features of the test area influence the parameters and the equations of the
local thresholds, and found that some threshold parameters can be put in
relation with the prevailing lithology. In addition, the possible relations
between effectiveness of the threshold and number of landslides used for the
calibration are investigated. The findings of the study demonstrate that the
effectiveness of a warning system can be significantly enhanced if a mosaic
of site-specific thresholds is used instead of a single regional threshold.
Mediterranean Tropical Like Cyclones (TLC) known as Medicanes
are meso-scale extreme low pressure systems, resembling the structure of
tropical cyclones, as they captured by satellites. Their intensity appears
much weaker than tropical hurricanes; however, some of them have reached
tropical hurricane strengths. Emanuel (2005) indicated that their genesis is
triggered when an upper-level cut-off low is advected over an area,
resulting in air mass lifting and cooling causing convective instability. It
is of high concern their structure and evolution (Pytharoulis et al., 2000;
Homar et al., 2003; Moscatello et al., 2008), the model physics in
simulating the structure and intensity (Miglietta et al., 2015). These
meso-scale systems with diameter usually less than 300 km have a rounded
structure and a warm core, as well as intense low sea level pressure
(Businger and Reed, 1989). Strong winds, heavy precipitation and
thunderstorms are associated with the incidence of medicanes, causing
occasional severe damages in private property, agriculture and communication
networks, or resulting in flooding of populated areas, posing a risk to
human life (Nastos et al., 2015). Two papers in the Special Issue evaluate
the simulations of medicanes and typhoons.
Akhtar et al. (2014) examine the ability of the coupled atmosphere–ocean
model COSMO-CLM/1-D NEMO-MED12 with atmospheric grid spacings of 0.44, 0.22,
and 0.08∘ (about 50, 25, and 9 km, respectively) and an ocean grid
spacing of 1/12∘ to simulate 11 historical medicanes (Mediterranean
hurricanes), which exhibit some similarities to tropical cyclones. The
strong cyclonic winds associated with medicanes threaten the highly
populated coastal areas around the Mediterranean basin. To reduce the risk
of casualties and overall negative impacts, it is important to improve the
understanding of medicanes with the use of numerical models. The results
show that at high resolution, the coupled model is able to not only simulate
most of medicane events but also improve the track length, core temperature,
and wind speed of simulated medicanes compared to the atmosphere-only
simulations. Besides, the coupled model is an effective tool for simulating
extreme events such as medicanes. The presented coupled model can be a
useful tool for studying tropical-like storms, particularly the ocean
feedback effects. The impact of coupling on the vertical structures of
medicanes and other important parameters such as precipitation and air–sea
fluxes should be analyzed in detail. A full three-dimensional ocean model
can be used for long-term climate simulations and future projections of
these extreme events.
Haghroosta et al. (2014) have conducted numerical experiments using the
Weather Research and Forecasting (WRF) model to determine the best
combination of physics parameterization schemes for the simulation of sea
surface temperatures, latent heat flux, sensible heat flux, precipitation
rate, and wind speed that characterize typhoons. Through these experiments,
several physics parameterization options are exhaustively tested for typhoon
Noul, which originated in the South China Sea in November 2008. The model
domain consists of one coarse domain and one nested domain. The resolution
of the coarse domain is 30 km, and that of the nested domain is 10 km. The
model simulation results are compared with the Climate Forecast System
Reanalysis (CFSR) data set through the use of standard statistical
measurements. The results facilitate the determination of the best
combination of options suitable for predicting each physics parameter. Then,
the suggested best combinations are examined for seven other typhoons and
the solutions are confirmed. Finally, the best combination is compared with
other introduced combinations for wind-speed prediction for typhoon Washi in 2011.
The contribution of this study is to have attention to the heat fluxes
besides the other parameters. Overall, the performance of the WRF model is
acceptable and satisfactory for prediction of important parameters related
to typhoon intensity over the South China Sea region.
The frequency of large wildfires and the
total area burned have been steadily increasing, with global
warming being a major contributing factor. Drier conditions will increase
the probability of fire occurrence. Longer fire seasons will result as
spring runoff occurs earlier, summer heat builds up more quickly, and warm
conditions extend further into fall (Running, 2006). More fuel for forest
fires will become available because warmer and drier conditions are
conducive to widespread beetle and other insect infestations, resulting in
broad ranges of dead and highly combustible trees (Joyce et al., 2008).
Increased frequency of lightning is expected as thunderstorms become more
severe (Price, 2009). Heat waves, droughts, and cyclical climate changes
such as El Niño can also have a dramatic effect on the risk of
wildfires. Although, more than four out of every five wildfires are caused
by people. There is a variety of fire danger rating systems used worldwide,
including the Canadian Forest Fire Weather Index System (CFFWIS) used in
Canada (van Wagner, 1987), the National Fire Danger Rating System (NFDRS)
used in the USA (Deeming et al., 1977) and the McArthur Forest Fire Danger
Index (FFDI) used in Australian forests (McArthur, 1967). In Europe, some
well-known indices include the Finnish Fire Index (FFI), developed by the
Finnish Meteorological Institute (Venäläinen and Heikinheimo, 2003);
the Portuguese index (ICONA, 1988); and the Italian index (IREPI) proposed
by Bovio et al. (1984). One paper in the Special Issue evaluates Canadian
Fire Weather Index (FWI) over Greece.
Karali et al. (2014) evaluates the Canadian Fire Weather Index (FWI) over
Greece. FWI is a daily meteorologically-based index designed in Canada and
used worldwide (including the Mediterranean basin) to estimate fire danger
in a generalized fuel type, based solely on weather observations. The
evaluation of the FWI is performed using available current fire observations
for Greece for a 15 year period and the index is confirmed to be capable of
predicting fire occurrence. The critical threshold values of FWI for fire
occurrence (one fire per day per area unit) vary spatially, increasing as we
move from the northwest to the southeast of Greece. Three critical fire risk
threshold values could be established: FWI = 15 for western Greece,
FWI = 30 for northern Greece and FWI = 45 for eastern and southern Greece
(including eastern continental areas, the large Aegean Sea islands and
Crete). These thresholds are not applicable for the small Aegean islands due
to the complex local terrain and the small number of fire events. Future
fire risk projections suggest a general increase in fire risk over the
domain of interest, with a very strong impact in the eastern Peloponnese,
Attica, central Macedonia, Thessaly and Crete. In the near future, 15 to
20 additional critical fire risk days are expected in western and northern
Greece. For eastern and southern Greece the increase reaches up to 10 days
per year. For the distant future, the same pattern applies, with an increase
of 30 to 40 days for western and northern Greece and 20 to 30 for eastern
and southern Greece.
Acknowledgements
The authors of this review article would like to thank the authors and
co-authors of the papers included in this special issue, as well as the
anonymous reviewers for their valuable comments and recommendations.
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