Introduction
Between the end of May and mid-June 2016, Germany and large parts of
central and southern Europe were affected by an exceptionally large number of
severe convective storms and related extremes such as heavy rainfall, hail,
and tornadoes (Fig. ). Rain totals exceeding 100 mm within a
few hours at several locations in Germany triggered various flash floods and
floods mainly in small catchments. In the town of Braunsbach in the federal
state of Baden-Württemberg, for example, a severe flash flood on 29 May
with a height of up to 3.5 m caused serious damage to more than 80
buildings, of which five were completely lost . Only 3 days later on 1 June, extreme rain in the district of Rottal-Inn in the south
of Bavaria evoked a sudden and dramatic rise in the levels of several creeks
such as the Simbach, where the height increased from 20 cm to more than 5 m
within only 12 h. Subsequently, the village Simbach am Inn experienced the
largest flooding in history. Some of the thunderstorms during the 2 weeks
also produced hail with diameters between 0.5 and 5 cm. A total of 12
tornadoes in 8 days with intensities between F0 and F1 on the Fujita
intensity scale, were recorded and confirmed by the European Severe Weather
Database ESWD;.
Phenomena associated with severe convective storms between 26 May
and 9 June 2016 collected from various sources of information (European
Severe Weather Database, newspaper articles, weather services; heavy
rain •, hail △, and tornadoes ⋆).
The severe thunderstorms caused substantial damage to buildings,
infrastructures, transportation networks, and crops. A large number of roads
and railroads were blocked or severely damaged, and some villages experienced
power outages over a couple of days. Flooded regions such as the district of
Rottal-Inn in Bavaria were completely trapped by the water masses and cut off
from the outside world. According to , the overall losses
associated with the severe convective storms in Europe totaled EUR 5.4 billion,
of which EUR 2.7 billion were insured. In Germany, economic losses accounted for
EUR 2.6 billion with insured losses of EUR 1.2 billion .
The large number of severe thunderstorms developed in an environment with
moist and unstable air masses that persisted over almost 2 weeks. A
large-scale ridge (upper-level high-pressure system)
stretching from Great Britain to Iceland and central Scandinavia caused a
blocking situation and hampered the exchange of air masses during the
episode. The pressure gradient and the resulting wind speed in the lower
troposphere were very weak, particularly in the second half of the storm
episode. Consequently, thunderstorms were almost stationary, resulting in
large precipitation accumulations in local areas.
The objectives of this paper are to highlight the meteorological conditions
that were decisive for the thunderstorm episode, to estimate the severity of
the recorded rain totals, and to put the event into historical context.
Since information about thunderstorm occurrence is not available over a
sufficiently long period, statistical analyses are based on different proxies
that estimate the convective potential of the atmosphere: large-scale weather
patterns derived from reanalysis data and convective parameters obtained from
vertical profiles of radio soundings. Another purpose of our study is to
estimate empirical probability distributions with respect to the variable
cluster length of days with convective weather situations using dichotomous
parameters such as convection-favoring weather types or areal-related
precipitation severity index.
While this paper focuses on the meteorological aspects of the severe
thunderstorm episode, Part 2 will discuss the impact of
selected flash floods in Baden-Württemberg and will present a simplified
method to estimate losses from flash floods. All investigations were conducted
within the frame of the Forensic Disaster Analysis (FDA) approach in
near real time, which is the main research strategy of the Center of Disaster
Management and Risk Reduction Technology
www.cedim.de;.
The paper is structured as follows: Sect. 2 presents the different data sets
that are used and the methods applied. Section 3 discusses the synoptic
background, including precipitation observations and the respective
probabilities in a long-term perspective. In Sect. 4, we assess the
persistence of certain clusters of days with convection-favoring conditions
and estimate their occurrence over the last 5 decades. Lastly, Sect. 5
briefly summarizes the main results and gives some conclusions.
Data and methods
The episode with an exceptional number of severe thunderstorms extended from
26 May to 9 June 2016 (hereafter referred to as STE16). For the long-term
classification of STE16, occurrence probabilities for precipitation,
atmospheric stability, and large-scale weather patterns were assessed with
respect to the 55-year period 1960–2014 (C20/21) for the summer half year
(SHY) from April to September. The study domain for the statistical analyses
comprises the whole area of Germany south of 52∘ N (in case of model
data, also adjacent countries), where most of the convective events occurred
(see Fig. ).
Observational data
Precipitation
Statistical rainfall analyses are based on 24 h REGNIE totals
(REGionalisierte NIEderschläge, regionalized precipitation)
provided by the German Weather Service (DWD). REGNIE is a gridded data set
based on several thousand climate stations (RR collective). Selected station
data are interpolated to a regular grid considering elevation, exposition, and
climatology . The REGNIE area contains 611 grid points in
west–east directions with 5.83∘ E ≤ϕ≤ 16∘ E and
971 grid points in north–south directions with 47∘ N ≤θ≤
55.08∘ N (ϕ is longitude; θ is latitude), but it only
covers Germany. The spatial resolution is approximately 1 km2. It should be
noted that REGNIE data are not homogeneous due to the temporal variability of
the number of rain gauges considered.
In addition to REGNIE, we also used data from selected rain gauges of DWD
stations with hourly resolution. For each day during STE16, we chose the rain
gauge with the highest observed 24 h total. The observation time period for
24 h totals (both REGNIE and rain gauges) is from 06:00 to 06:00 UTC on the
next day, but values are backdated in order to conform with the usual
calendar days.
Lightning
Lightning data for 2001–2014 (SHY) were obtained from the German detection
network BLIDS (Blitz-Informations-Dienst Siemens), which is integrated in the
European EUCLID (European Cooperation for Lightning Detection) network
. We only considered cloud-to-ground flashes (CG) to define
convective days, without further distinguishing among polarity and peak
current. BLIDS data are provided in a spatial resolution of 1 km and a
temporal resolution of 1 ms .
Radio soundings
Atmospheric conditions prevailing during STE16 and C20/21 were estimated from
vertical profiles of temperature, moisture, and wind at four radio sounding
stations in western and southern Germany: Essen (51.41∘ N,
6.97∘ E), Idar-Oberstein (49.70∘ N, 7.33∘ E),
Stuttgart (48.83∘ N, 9.20∘ E) and Munich
(48.24∘ N, 11.55∘ E). The profiles were provided by the Integrated Global
Radiosonde Archive (IGRA) from the National Climatic Data Center
. For the assessment of thermal stability, we used the
surface-based lifted index (SLI) and the convective available potential
energy (CAPE). Both quantities have been identified in various studies to
represent atmospheric stability well
.
Since the movement of thunderstorms is controlled to a large degree by the
wind vector at mid-tropospheric levels (depending on the vertical extent of
the cell), we also considered wind speed and direction at 500 hPa.
Model data
The study domain for the model data is slightly extended to the border
regions and covers the area of 5.5∘ E ≤ϕ≤ 15.0∘ E, 47.5∘ N ≤θ≤ 52.0∘ N.
Only data sets at 12:00 UTC were considered since they best mirror the
prevailing convective conditions. Model data were used to estimate the
large-scale weather situation during both STE16 and C20/21.
CoastDat2
The CoastDat2 reanalysis was employed here for investigating long-term
convection-favoring conditions. The reanalysis was carried out by the
Helmholtz-Zentrum Geesthacht Centre for Materials and Coastal Research in Germany
. CoastDat2 is based on the COSMO (Consortium for
Small-Scale Modelling) model in climate mode, COSMO-CLM Version 4.8
, and it uses the National Centers for Environmental
Prediction–National Center for Atmospheric Research reanalysis
NCEP/NCAR1; as forcing, which in consequence of the
almost constant data assimilation only exhibits a small trend. The model
output is available for the entire European domain in a resolution of
0.22∘, with 40 vertical model layers from 1948 (including 3 years
spin-up time) until today.
CFSv2 operational analysis
Since CoastDat2 data are not available for STE16 yet, we estimated prevailing
weather patterns from Climate Forecast System (CFSv2) Operational Analysis
data, which have been in operation since April 2011 as the successor of the
Climate Forecast System Reanalysis CFSR; .
The CFSv2 data are produced under guidance of the National Centers for
Environmental Prediction (NCEP) and offer hourly data with a global
horizontal resolution of 0.5∘.
Objective weather types of DWD
The objective weather-type classification (OWLK) designed by DWD
differentiates between 40 weather patterns by
quantifying three model parameters in a dichotomous scheme: (i) mean flow
direction AA at 700 hPa with the possibilities of SW, NW, NE, SE, and
an indefinite type XX, (ii) cyclonality CY as the product of geostrophic
vorticity and Coriolis parameter at low (950 hPa) and mid-tropospheric
levels (500 hPa), yielding either cyclonic (C) or anticyclonic (A) flow, and
(iii) humidity index HI represented by the precipitable water with the
climatological daily average removed; positive and negative anomalies are denoted
by M (moist) and D (dry).
The continuous grid point values contained in the reference domain are
converted into one scalar number for each day and each parameter, which is
mapped on a categorical variable in each case using previously defined
thresholds. The four variables are concatenated forming a character code:
AACY950 hPaCY500 hPaHI.
For example, the pattern SWCAM refers to a mainly southwesterly flow,
cyclonic and anticyclonic at 950 hPa and 500 hPa, respectively, and a higher
moisture content compared to climatology.
Grid points near the center of the domain are weighted by a factor of 3,
those located near the margins by a factor of 1, and all points in an
interjacent zone by a factor of 2, so as to restrict the influence of the
outer areas. In this paper, the classification results obtained by DWD are
used, which rely on the reference domain defined by
comprising Germany and parts of the neighboring countries.
Several studies have established a relationship between specific OWLK types
and convective activity in terms of severe hailstorms
or tornadoes . The advantages of
OWLK compared to subjective methods such as the well-known classification of
HB, are the non-ambiguous assignment criteria and the
automated categorization procedure . Even though the Low Central
Europe (Tief Mitteleuropa, TM) circulation pattern prevailed on
6 days according to HB, this pattern is usually not related to severe convection, but to
persistent advective precipitation such as during the 2002 and 2013 German
floods. Hence, we decided not to investigate HB further in this paper.
Convective weather types
It can be shown that OWLK only has limited skill regarding the
identification of ambient conditions favorable for convective activity since
it mainly aims at classifying the synoptic situation in general. Based on the
methodical approach of OWLK, we therefore developed a new objective weather-type classification (conOWLK) with a special focus on convection. This scheme
consists of four parameters: equivalent potential temperature at 850 hPa,
precipitable water, surface-based lifted index (SLI), and vertical velocity
w at 500 hPa. In the former two cases, we removed the average annual cycle
by subtracting the 10-day running mean over the average daily values. The
parameter values for a respective day are obtained analogously to OWLK by
calculating the weighted areal mean over a rectangle now enclosing the study
domain defined in Sect. .
The continuous variables are transformed into discrete ones using
trichotomous parameters instead of dichotomous ones as for OWKL, allowing
those values that can not be allocated clearly to one of the two original
classes to be comprised by a third, neutral class (abbreviated as X). The
thresholds are determined so as to distinguish best between conditions
favoring and inhibiting convection, which is assessed by categorical
verification with respect to lightning data. For this purpose, we calculated
the distribution of the Heidke Skill Score (HSS) on a large range of possible
threshold values for each parameter separately and chose the two values as
thresholds where HSS equals its 90 % quantile.
Convection-favoring weather types among the total of 81 classes were verified
and identified against convective days according to BLIDS data using
categorical verification. In this study, we categorize a specific day as
convective if the flash number inside the inner weighting zone (see
above) exceeds its 75 % quantile. These thunderstorm-related types are
characterized by relatively warm (W) and moist (M) conditions with
instability (I) and either lifting (L) or no vertical motion (X) present,
yielding the two codes WMIL and WMIX. Conversely, the two weather types
inhibiting convection are given by the codes CDSS and CDSX (cold, dry,
stable,
and subsidence or no vertical motion). All other types are referred to as
neutral.
Since the objective of conOWLK is to identify ambient conditions that imply a
very high chance for the development of convection, a significant number of
events are missed. Therefore, it is reasonable to additionally implement a
less strict classification. This is done by developing a multivariate
statistical model based on quadratic discriminant analysis (qdaOWLK). After
the initial learning phase, it assigns a particular day to one of the groups
convective day (yes or no) depending on the values of four continuous
input variables, which are represented by the spatially averaged parameters
used in conOWLK.
First, qdaOWLK is calibrated using CoastDat2 reanalysis and lightning data
(2001–2014). These learning data are divided into two subsets corresponding
to the same groups of convective and
non-convective days, as
were defined in the context of conOWLK, and which can be characterized by two
different multivariate probability density distributions .
Based on this partitioning, an assignment rule in terms of a discriminant
function is developed, which will be used to classify the days before 2001
for which lightning data are not available for.
Since covariance matrices differ significantly between both subsets
(heteroscedasticity), quadratic instead of linear discriminant analysis has
to be performed . Applying a Kolmogorov–Smirnov test to the
time series of the four parameters yields significant deviations from the
normal distribution. Therefore, data are normalized using a Yeo–Johnson power
transformation . In this study, the quadratic discriminant
function δ is derived using a maximum likelihood criterion
. Hence, an arbitrary entity is assigned to the group
exhibiting the higher value of the likelihood function Lm with
m∈{0,1}, corresponding to the populations of non-convective and
convective days.
It can be shown that δ is computed from the input data vector
x for a particular day by
δ=12xT(Σ0-1-Σ1-1)x+xT(Σ1-1μ1-Σ0-1μ0)+12μ0TΣ0-1μ0-12μ1TΣ1-1μ1+12ln|Σ0||Σ1|,
where Σm represents the covariance matrix of population m, the
superscript -1 denotes the inverse matrix, and μm
is the respective sample mean vector. Due to
δ=ln[L1(x)]-ln[L0(x)],
an arbitrary day is classified as convective if δ> 0.
Model performance is assessed by means of leave-one-out cross-validation
. Here, the discriminant function is computed from the sample
of training data excluding the first day, which is then classified by the
model. For a sample of size n this procedure is conducted n times
shifting the day excluded by one step each time. As a result, the n
predictions for convective day (yes or no) are compared with the actual
incidences using categorical verification.
Return periods
To estimate statistical return periods of precipitation totals R, we
applied the classical generalized extreme value (GEV) distribution. Most
appropriate for precipitation statistics is the Fisher–Tippett type I (also
known as Gumbel) distribution , with a cumulative distribution
function (CDF) of
F(R)=exp-expζ-Rβ,
where β and ζ are scale and location parameter, respectively. The
two free parameters of the CDF are estimated by the method of moments
using annual rainfall maxima during C20/21:
β=σ6π,ζ=R¯-γ⋅β,
where R¯ is the sample mean, σ the sample standard derivation,
and γ the Euler–Mascheroni constant (≈0.5772).
The CDF describes the probability of occurrence P of a value R beneath a
threshold Rtrs: F(R)=P(R<Rtrs). Conversely, the return period tRP is related to the probability of
threshold exceedance P(R≥Rtrs)=tRP-1.
Therefore, the CDF can be written as F(R)=1-tRP-1. The
resulting equation for the return period tRP is
tRP(R)=1-exp-expζ-Rβ-1.
Heavy rainfall and precipitation severity index
Heavy rainfall is usually defined either by the exceedance of appropriate
percentiles (e.g., 99 or 99.9 %, see below) or by using a fixed threshold
as a function of duration. In the latter case, we considered the criterion
according to :
Ncr=5⋅D,
with the critical rain rate Ncr representing a threshold for
heavy convective rainfall for conditions prevailing in central Europe
(Germany) with duration D (in minutes) between 30 min and 24 h. The
Wussow criterion is only considered for the assessment of the station
observations (Table ).
Classification results during STE16 based on OWLK and conOWLK using the character coding scheme defined in the text (Sect. and ).
Date
OWLK
conOWLK
26 May
SWCCM
XDXD
27 May
SWCCM
WMID
28 May
SWCCM
WMIL
29 May
SECCM
WMIL
30 May
XXCCM
WMID
31 May
XXCCM
WMXD
1 June
XXCCM
WMXL
2 June
XXCCM
WMID
3 June
NECCM
WMIL
4 June
XXCCM
WMIX
5 June
XXAAM
WMIL
6 June
XXAAM
WXID
7 June
XXAAM
WDID
8 June
NWCAM
WMIL
9 June
NWACD
CDXD
Furthermore, the severity of past rain events is assessed by considering both
intensity and spatial extent in terms of the precipitation severity index PS:
PSξk=1Γ∑i,jRi,jk×FR∣Ri,jk≥Ri,jξ,
where Ri,j represent the rain totals at REGNIE grid points (i,j), k
denotes a certain day, FR is the area of the REGNIE grid points (=1×1km2), Γ is the size of the investigation area, and ξ
are the 99 or 99.9 % percentiles of the distribution function quantified
independently at all grid points during C20/21. In this formulation, all
totals are accumulated that are equal to or exceed the value of the 99 or
99.9 % percentile, respectively, at the respective points. Dividing the
accumulated totals by the size of the investigation area gives the
precipitation severity index PS in m3 water. A similar version of
PS was applied for the
comparison of historic large-scale flood events by .
Whereas the 99 % percentile (1.83th largest total in a year on average) may
contain advective precipitation as well, the 99.9 % percentile (0.18th
largest total) mainly considers heavy convective rainfall. Within the
investigation area, the 99 % (99.9 %) percentiles vary between 15 mm
(30 mm) over the northern lowlands and almost 60 mm (100 mm) over the
peaks of the German Alps and the Black Forest mountains (not shown).
Persistence analysis
Days with widespread thunderstorms tend to form temporal clusters of variable
length, which can be described statistically by the concept of persistence.
In this study, a persistent cluster is defined as a sequence of days (between
1 and 15 days) with the binary parameter taking the value of 1 (event day) or
zero (non-event day). This is a familiar approach known from literature in its
basic form e.g. . In order to facilitate the
appropriate treatment of embedded days, on which the event does not occur
(skip days), we refined the method by specifying that clusters with a length
of up to 7 (15) days may contain at most 1 skip day (2 skip days).
This step is necessary because the criteria for an event day might not be
fulfilled on sporadic days, although convective predisposition clearly
persists. Each time the algorithm identifies the end of a cluster according
to these rules, the actual length, exclusive of skip days, is stored. The
approach described is a top-down one: it considers only the maximum cluster
length and prevents longer clusters from being split into two or more
sub-clusters. Dividing the absolute number of clusters with length n by the
number of all clusters yields the relative frequency of a cluster with length
n. Due to the large data volume, this can be perceived as an approximate
measure for probability. Persistence analysis as described above is applied
to precipitation severity index PSξ (Eq. ), large-scale
weather types, and compound events with low stability and weak flow.
Weather situation
Warm and moist air masses in combination with large-scale lifting by shallow
surface lows persisted during STE16 over wide parts of Germany. Steep
environmental lapse rates due to surface heating by solar radiation, cooling
at mid and upper troposphere levels by cold air advection, and upper-level
troughs created an environment favoring the development of various
thunderstorms. Due to the very weak horizontal flow at mid-tropospheric
levels – particularly in the second half of the period – the various
thunderstorms were almost stationary, resulting in large precipitation
accumulations over limited areas.
Synoptic overview and atmospheric characteristics
At the end of May and beginning of June, large parts of Europe were
influenced by atmospheric blocking. Such a blocking event is characterized by
large amplitude and long-lasting negative potential vorticity anomalies
located beneath the dynamical tropopause . They establish most
frequently over the North Atlantic and the northeastern Pacific during all
four seasons but most frequently in autumn and winter. The usual westerly
flow over Europe is blocked due to the presence of a high-pressure system
over the North Atlantic or northern Europe. Such a block, termed as
Omega-block in Europe, may persist over several days up to several
weeks with enormous consequences for the regional weather and climate
.
During STE16, the structure of the 500 hPa mean geopotential height over
Europe and the North Atlantic was characterized by a massive high-pressure
system, which stretched from Great Britain to Iceland and central
Scandinavia. The high-pressure block was flanked by two upper-level troughs
(Fig. a). To the west a trough made its way southwards
towards the Azores, whereas over eastern Europe another trough extended
southwards to the Black Sea and Turkey.
500 hPa geopotential height over Europe: (a) averaged
between 27 May and 10 June 2016 and (b) departure from normal
1979–2005 (Image source: NOAA/ESRL Physical Sciences Division, Boulder
Colorado, http://www.esrl.noaa.gov/psd).
The 500 hPa geopotential anomalies with respect to the long-term mean
(1979–2005) show some positive anomalies of around 20 hPa occurring near
Iceland, whereas negative anomalies of the same magnitude were present in the
mid-Atlantic Ocean around the Azores (Fig. b). Unlike
usual blocking situations, an area of low pressure was cut off
beneath the northward bulging high-pressure ridge. Low 500 hPa geopotential
values could be identified over Poland, Germany, and France, accompanied by weak
pressure gradients, resulting in low wind speeds at mid-tropospheric levels.
The weak upper-level trough over central Europe corresponded with shallow
surface lows, one of which extended between Poland and France and persisted
until 9 June. Consequently, western and southern Germany remained under the
influence of low pressure with moist and warm air, while drier air gradually
prevailed in the northeast. At the beginning of the blocking event, moist and
warm air was advected ahead of a deep trough northeastwards towards central
Europe. Later in June, with the stationary low pressure being present
across central Europe, moisture was maintained mainly by evapotranspiration
from local sources and advection from nearby countries.
The most intense and fatal rain events occurred on 29 May over southwestern
Germany. In the town of Braunsbach, for example, flash floods and landslides
had devastating consequences (see Fig. a). During that day,
the upper-level trough approached southern Germany from France and
Switzerland. It was associated with massive positive vorticity advection and
the advection of warm and moist air at lower and mid-tropospheric levels,
which both reached their maximum values during the evening hours. Together,
they provided a large uplift across western Bavaria and
Baden-Württemberg. The preexisting air mass was unstably stratified,
leading to negative SLI values of -5 and -2 K in Munich and
Stuttgart, respectively (Fig. ).
Time series of (a) surface lifted index (SLI) and
(b) horizontal wind speed in 500 hPa (vH,500 hPa) at
12:00 UTC at four German sounding stations during STE16, including the
thresholds (TH) defined in Sect. .
Gridded rain totals (REGNIE) over (a) 24 h for 29 May 2016
and (b) 15 days (26 May to 9 June 2016, STE16).
The first thunderstorms on that day occurred over Bavaria already before noon.
While the rain area extended towards western Baden-Württemberg, new and
intense thunderstorms formed north of the Alps, followed by isolated heavy
thunderstorms aligned from Munich to Salzburg and Nuremberg to Stuttgart. In
the evening, a large mesoscale convective system (MCS) Type II, which usually
develops from preexisting single cells, covered all of Baden-Württemberg,
western Bavaria, eastern Rhineland-Palatinate, and southern Hesse with a size
of roughly 60 000 km2. At 19:00 UTC, a line of violent thunderstorms
stretched over several hundred kilometers from Passau in eastern Bavaria to
Mannheim in the northwestern tip of Baden-Württemberg. Various
convective cells embedded in the eastern and northern edge of the MCS
affected mainly the same region in the north of Baden-Württemberg, leading
locally to rainfall totals in excess of 100 mm within a few hours (see
Fig. a).
During the entire STE16 period, atmospheric stability across Germany (and
central Europe) was low. The SLI computed at four sounding stations shows
values below zero on almost every day (Fig. a, recall
that negative SLI values express instability). SLI was particularly low at
the beginning and in the second half of STE16. The values for
CAPE100 hPa, which is calculated based on start values of the
lifted curve being mixed over the lowest 100 hPa, were above
400 J kg-1 on most days, with maximum values between 800 and
1100 J kg-1.
Apart from the first 2 days, wind speed at 500 hPa was exceptionally low,
with values between only 2 and 15 m s-1
(Fig. b). Especially at the two stations located in
southern Germany, Stuttgart and Munich, where most of the flash floods
occurred, the values dropped below 10 m s-1 on 31 May and remained on
a very low level until the end of STE16. Only the two stations situated in
the center of Germany showed wind speeds of around 10 m s-1 or
slightly above. The general wind direction at 500 hPa, until 28 May, was predominantly from the west at all four
stations considered. Afterwards, the flow turned to southerly (29–30 May)
and mainly easterly directions that prevailed until 6 June. The last 3 days
were again dominated by westerly flow. Note, however, that wind directions
during calm winds have a very limited applicability.
Spatial distribution of maximum return periods of (a) 24 h
and (c) 7-day totals and (b, d) the day of occurrence
during STE16 derived from REGNIE data; return periods are estimated with
respect to the control period C20/21 (SHY).
Time series of the annually accumulated precipitation severity index
PSξ for all REGNIE grid points of the investigation area including
5-year running mean during C20/21.
According to DWD analysis , the OWLK pattern that prevailed on
the first 3 days (26 to 28 May) was SWCCM, indicating moist southwesterly
flow with cyclonic rotation at both levels (Table ). Several
studies identified this pattern to be most related to severe thunderstorm
occurrence in Germany . After the first 3 days, flow
direction became mainly indefinite (XX) due to the very weak winds connected
to the low pressure gradients. On
all days, atmospheric moisture was increased, yielding the weather types
XXAAM and XXCCM. These two types have been found to promote thunderstorms as
well, for example with a probability of 10 % for damaging hail
.
Maximum observed 24 h totals R24 h (in mm) at any rain gauge of DWD (name and coordinates) for each day in
STE16 and
beginning of the record and corresponding return period tRP (in years). Different durations D are shown (if available)
together with the related heavy rainfall criterion Ncr (in mm).
Date
Station [coordinates]
Data since
R24 h
tRP
Duration D
Ncr
26 May
Bitburg [49.98∘ N, 6.53∘ E]
1951
37.8
< 5
in 10 h (28.3 mm in 6 h)
54.8 (42.4)
27 May
Eppendorf [50.80∘ N, 13.24∘ E]
1951
47.4
< 5
28 May
Siegen (Kläranlage) [50.85∘ N, 8.00∘ E]
1931
59.0
10–15
in 4 h
34.6
29 May
Gundelsheim [49.28∘ N, 9.16∘ E]
1888
122.1
> 200
30 May
Kall-Sistig [50.50∘ N, 6.52∘ E]
1947
63.5
20–25
in 13 h (53.2 mm in 5 h)
62.4 (38.7)
31 May
Simbach/Inn [48.27∘ N, 13.02∘ E]
1951
74.6
5–10
42.8 mm in 6 h
42.4
1 June*
Hamminkeln-Mühlenrott
1931
120.3
> 200
[51.72∘ N, 6.58∘ E]
2 June
Wernigerode [51.84∘ N, 10.77∘ E]
1951
61.0
10–15
60.9 mm in 5 h
38.7
3 June
Hohenpeißenberg [47.80∘ N, 11.01∘ E]
1801
61.3
5–10
in 6 h
42.4
4 June
Lenggries (Sylvenstein)
1931
87.5
5–10
[47.69∘ N, 11.57∘ E]
5 June
Karsdorf [50.94∘ N, 13.70∘ E]
1978
65.7
5–10
6 June
Frankenblick-Mengersgereuth-Hämmern
1969
44.5
< 5
44.1 mm in 1 h
17.3
[50.39∘ N, 11.13∘ E]
7 June
Durbach-Ebersweier [48.50∘ N, 7.99∘ E]
1951
62.3
10–15
in 4 h (52.0 mm in 1 h)
34.6 (17.3)
8 June
Fellbach [48.81∘ N, 9.27∘ E]
1941
67.8
25–30
9 June
Neureichenau-Duschlberg
1931
38.7
< 5
[48.79∘ N, 13.73∘ E]
* Malfunction at Simbach am Inn on 1 June 2016 08:00–11:00 UTC. Complementation with data of the corresponding grid point of radar data yields approximately R24 h≈ 120 mm
(≈ 90 mm in 6 h) and a return period tRP of 135–140 years; heavy rain criterion Ncr for 24 h (6 h) totals: 84.5 mm (42.4 mm), fulfilled in both cases.
As discussed in Sect. , the original OWLK was not
designed especially for the analysis of convective conditions. Therefore, we
developed a new classification (conOWLK) optimized in this regard. According
to conOWLK and using CFSv2 reanalysis data (Sect. ), 6 days can
be classified as convection favoring (WMIL or WMIX). These two weather types
are characterized by near-zero false alarm rates but with a considerable
number of missed events with respect to the occurrence of convective days
(not shown). This feature is equivalent to the statement that a
categorization as WMIL or WMIX is by approximation sufficient, but not necessary for the actual
incidence of lightning. Due to the strict design of conOWLK, WMIL and WMIX
coincide with a very high probability for the development of severe
thunderstorms. Thus, the number of 6 out of 15 days exhibiting one of these
weather types has to be considered as fairly high.
Precipitation
The majority of thunderstorms that developed during STE16 showed a typical
diurnal cycle peaking in the afternoon and early evening. On most of the
days, intense rainfall affected only small areas, with extensions of only a few
square kilometers. According to radar data, the diameters of the cloud bursts
were in the order of several hundreds of meters to 1 or 2 km.
Rainfall totals
Thunderstorms with at least 50 mm rainfall totals within 24 h occurred on
11 days during STE16 (Table ). Most of the rain fell within
only a few hours. As shown in Table for selected rain
gauges, the Wussow criterion (Eq. )
was met at seven of the eight chosen stations, for which hourly data are
available. The largest rain amount during this period was observed at
Gundelsheim (∼ 50 km west of Braunsbach) with 122.1 mm on
29 May 2016. On this day, heavy rainfall associated with the large MCS caused
widespread totals in excess of 50 mm (see also Fig. a).
Around Simbach in Bavaria, a maximum of 74.6 mm was recorded on 31 May,
triggering another devastating flood. Unfortunately, the rain gauge had a
malfunction on 1 June for 4 h. Filling up the gap with DWD radar data gives
a daily total of about 120 mm on this day, which may be even higher than the
given value in Table . Hence, Simbach would be the only
station with a Germany-wide maximum on 2 days during STE16.
Accumulated 15-day rain totals in Germany widely exceeded a height of 100 mm
(Fig. b). In several parts of Baden-Württemberg and
Bavaria even more than 250 mm was observed, which is far more than the
climatological mean for the entire month of June. Dry conditions prevailed
only towards the North Sea and Baltic Sea region as well as in most parts of Brandenburg, where values rarely
exceeded 40 mm.
Return periods
Return periods estimated with respect to C20/21 allow for assessing the
observed totals in the historic context. Based on
Eq. (), we determined return periods for each day and
each grid point during STE16 based on REGNIE 24 h totals. Additionally, we quantified 7-day return periods shifted by 1 day during STE16, yielding nine 7-day subintervals.. Afterwards, we identified
the highest return period at each grid point, both for 24 h and 7-day
totals. These results together with the days and time intervals of maximum
return periods are shown in Fig. .
Daily totals reveal widespread return periods larger than 40 years but with
several hot spots of more than 200 years, especially around Braunsbach and
the far west of Germany (Fig. a). The temporal distribution
(Fig. b) shows that 29 May, and to a lesser degree 1 June,
represent the dominant days for these totals. Minor return periods between 40
and 60 years occurred in the Simbach area, which can be explained by the
malfunction of the most important rain gauge in that area on 1 June.
As shown in Table , most of the observed daily maxima
recorded at selected rain gauges had return periods of at least 5 years, with
the two outstanding events of 29 May (Gundelsheim) and 1 June
(Hamminkeln-Mühlenrott) exhibiting values of more than 200 years. The
reconstructed data at Simbach on 1 June would yield high return periods of
about 140 years as well.
Considering the 7-day totals, a westward shift of the affected area is
obvious. Emphasis now is on Rhineland-Palatinate west of Frankfurt
(Fig. c), again with enclosed areas of very high return
periods including the upper catchment of the river Ahr (along the border between
Rhineland-Palatinate and North Rhine-Westphalia). The majority of the maximum
return periods occurred during the first 7-day period from 26 May to 1 June
(Fig. d, pink), leading to the most serious flood along the river
Ahr ever reported. Note that 24 h totals in some regions were so
exceptionally high that they also massively affected the 7-day totals and
corresponding return periods.
Comparing the spatial distributions of 24 h and 7-day return periods (and
additionally of 3-day and 14-day periods, which are not shown), it can be
concluded that in most regions heavy precipitation occurred just on 1 or
2 days during STE16 (e.g., Braunsbach or Simbach). Only a few regions such as
Rhineland-Palatinate, especially the upper Ahr catchment, experienced
rainfall on more than 2 days. This fact is a typical feature of heavy
convective rainfall, which was more or less randomly spread over large parts
of Germany during STE16.
Persistence analysis
Aside from exceptional rainfall totals with return periods in excess of
200 years, another peculiarity of STE16 was the almost daily occurrence of
severe thunderstorms somewhere in the investigation area. For this reason, we
investigate how often persistent clusters of days with convective weather
conditions occur in the long-term mean (C20/21) in terms of heavy rain events
based on the precipitation severity index PSξ
(Sect. ), large-scale weather patterns
(Sect. ), and compound events with low stability and
weak flow (Sect. ).
Heavy rainfall
The time series of the annually accumulated precipitation severity index
PSξk (Eq. ) for the two percentiles 99 and 99.9 % based
on REGNIE totals are qualitatively similar (Fig. ). In
both cases, the temporal variability is high, with values in a range between
14 865 and 116 983 for PS99 and between 432 and 36 125 for
PS99.9. An interesting feature is the long-term oscillation inherent in
both time series, but it is more pronounced in the case of the
convection-dominated PS99.9 index. The FFT (fast Fourier transform) power spectrum (removal of
linear trend) reveals the highest peaks for a periodicity between 2 and
3 years as well as a large peak for 13 years (not shown). Possible reasons
for these oscillations remain unclear since a direct link to weather patterns
or stability cannot be established. The two time series in
Fig. also show positive (linear) trends, which, however,
are statistically insignificant (α>5 %) due to the large
volatility.
During STE16, PS99.9>0 at any location (REGNIE grid point) in the
investigation area occurred on 10 consecutive days, whereas PS99>0 was
reached on 14 days. To assess the exceptional nature of this
persistence, we estimated the occurrence probability of clusters with a
length between 2 and 15 extreme rainfall days during C20/21 (see Sect. ). Extreme rainfall days are defined when
PSξ>0 at any location across the investigation area, which best
represents the spatial characteristics of STE16 with spatially varying
hot spots of heavy rainfall.
Relative frequency of clusters of consecutive days exceeding the threshold of PSξ for the 99 and 99.9 % percentiles during C20/21.
According to Fig. , the occurrence probability of
a 14-day cluster with PS99>0 is 0.56 %. Aside from the year of 2016,
this cluster occurred only four times during C20/21 (1970, 1997, 2000, and
2006). For PS99.9>0, the maximum cluster had a length of 10 days, and
occurred three times in C20/21 (1963, 1972, and 2002; see
Fig. ). Thus, the probability of such a cluster is
even lower with a value of 0.30 %.
Time series of different cluster lengths with respect to consecutive
days exceeding the threshold of PS99.9 during C20/21.
Figure also shows that on most of the days,
PSξ is not exceeded in the entire investigation area (60.0 % of all
days for the 99, and 83.9 % for the 99.9 % percentile). As expected, the
relative frequency of occurrence substantially decreases with increasing
cluster length, with some exceptions. For example for PS99.9, a
cluster length with 10 days has a higher probability than a cluster of only
9 days (0.3 % vs. 0.18 %, or two vs. three events, respectively). This
apparently counterintuitive behavior can also be observed in the
probabilities of the clusters for the weather types and the compound low
stability and weak flow events (see Figs. and
). In both cases, however, the changes affect
different cluster lengths. The reason for this behavior remains unclear, but
might be the consequence of the large natural variability of convective
weather as already indicated by the large volatility of PSξ.
Same as Fig. , but for days classified as
convective according to
quadratic discriminant analysis; red: cluster length observed during STE16.
Same as Fig. , but with respect to
consecutive days classified as convective according to quadratic
discriminant analysis during C20/21.
The time series of the different clusters do not reveal any systematics;
rather, their occurrence has a large stochastic component
(Fig. ). However, whereas the number of shorter
clusters (2–5 days) increased during C20/21, the number of larger clusters
(> 5 days) decreased slightly. The lower number of larger clusters cannot
compensate for the increase of shorter clusters, leading to an overall positive
trend. However, due to the large annual variability and the small number of
clusters, a robust statement about an increase or decrease cannot be derived
from this analysis.
Large-scale weather types
Considering the original OWLK provided by DWD, a cluster length of 8 days (1
skip day) regarding the weather types XX..M (where . denotes either cyclonic
(C) or anticyclonic (A) vorticity in each case) was observed during STE16
(see Table ). Adding the first 3 days exhibiting the weather type SWCCM,
which has been shown to favor thunderstorm formation as well
(Sect. ), even yields an 11-day cluster (2 skip
days). Both lengths have never occurred before since the beginning of the
OWLK record in 1979.
The total of 6 convective days according to conOWLK in the investigation area
subdivide into three clusters of lengths: 3, 2, and 1 days on 28–29 May and
3–5 and 8 June, respectively (Table ). It is interesting to
note that these clusters are separated by several days assigned to the group
of neutral weather types. This finding can be attributed to the concept of
combining single trichotomous parameters. Recall that conOWLK classifies a
day as neutral or convection-inhibiting if just one of the parameters is
slightly below the considered threshold, whereas the other three parameters
may be well above.
As already discussed in Sect. , only days with a very high
probability for strong convective development will be classified as
convection favoring concerning all four parameters. Except for the first and
last day, when the convective situation was not fully developed, all days
were categorized as warm, and all but 2 days were assigned to the moist and
unstable class. In contrast, 6 days with large-scale subsidence (w<0)
were detected. Consequently, neglecting the lifting parameter w would yield
two clusters of 4 days and one cluster of only 1 day. Since STE16 was
characterized by spatially varying hot spots of severe convective activity, it
can be assumed that convection was mainly triggered by mesoscale flow
convergence in the boundary layer instead of large-scale lifting, except
for the large MCS on 29 May. Thus, strong local vertical velocity maxima may
be overcompensated for by subsidence in other regions.
These findings suggest that conOWLK is too strict when persistence analysis
with respect to the presence of a high convective predisposition is attempted instead of focusing on
days characterized by very high probabilities of strong thunderstorm events
only. Therefore, another type of classification abbreviated as qdaOWLK is
used, which does not rely on a simple combination of parameters exceeding the
respective thresholds. Quadratic discriminant analysis, as introduced in
Sect. , provides a suitable statistical model that is
based on the parameter values calculated by the conOWLK algorithm as well,
but it leaves out mapping these parameters on categorical variables.
Cross-validation with respect to the occurrence of convective days yields a
HSS of 0.53. According to this model and using CFSv2 data, a cluster length
of 11 convective days was detected during STE16 (2 skip days).
As for the precipitation severity index PSξ, we also estimate the
empirical relative frequency distribution of different cluster lengths with
respect to qdaOWLK. For this purpose, a time series of the binary variable
convective day was computed for C20/21 using the discriminant model.
As shown in Fig. , two thirds of all clusters exhibit a
length of 1 or 2 days. Cluster durations of more than 15 days have not
yet occurred since the beginning of the reference period. Less than 1 % of all cluster lengths exceed 10 days. Consequently, the 11-day
cluster of convective days has to be considered as highly exceptional. Due to
the sample size of 811 clusters, the relative frequencies obtained can be
interpreted by approximation as probabilities.
All cluster lengths exhibit a strong interannual variability
(Fig. ), which is characterized by a large
stochastic component, as was the case regarding days with heavy rainfall
(Fig. ). In some years, we observe remarkable extrema,
for example in 1989, when 14 clusters with a length of 2–3 days and only one cluster of
4–5 days occurred. Conversely, 1991 was generally characterized by weak
convective activity (2 clusters with 2–3 days and 1 cluster with 4–5 days).
A 10–11-day cluster occurred only five times (1964, 1972, 1977, 1994, 1997).
In contrast to the time series of heavy rainfall clusters, no long-term
increases or decreases are visible.
Time series of compound low stability and weak flow events at
12:00 UTC during STE16 at four German sounding stations for days, on which
(a) the basic (BC) and (b) the strict criteria (SC) are
fulfilled (represented by the dots).
Compound events with low stability and weak flow
In the last step, we investigate compound events with low stability and weak
flow prevailing during STE16 with the same methods as applied above. The
following two threshold combinations based on surface lifted index (SLI) and
horizontal wind speed in 500 hPa (vH,500 hPa) are considered in
the analysis:
Basic criterion (BC): SLI < 0 K and vH,500 hPa< 10 m s-1,
Strict criterion (SC):SLI <-1.3 K and vH,500 hPa< 8 m s-1.
The thresholds representing the BC are defined by the maximum of the daily
minima at all four sounding stations during STE16 (see
Fig. ), representing the prevailing atmospheric
conditions during STE16 in the investigation area. In that period, the BC
criterion was fulfilled on 13 days (sounding stations Stuttgart and/or
Munich, see Fig. a). The SC is defined in the same way as
BC, but neglecting the day with the highest values; it was fulfilled at the
Munich station with a cluster length of 10 days (2 skip days, see
Fig. b)
Based on sounding measurements and reanalysis data (CoastDat2), we
investigated the frequency of varying cluster lengths for both criteria
(BC–SC) during C20/21. According to Fig. , the
relative frequency of cluster lengths greater than or equal to 13 days (BC) or
10 days (SC) is very low (< 1 %). This applies to both
the analysis based on the Stuttgart sounding as well as to that considering
(3 × 3) grid point averages from CoastDat2 near Stuttgart and
Munich. Regarding wind speed vH,500 hPa solely, an almost
identical behavior is found. It has to be noted that longer cluster lengths
for BC–SC combinations based on sounding data occur less frequently compared
to those based on model data, which means that those events are probably
overestimated in CoastDat2.
Same as Fig. , but for days fulfilling
the (a) BC and (b) SC criteria for compound events with low
stability and weak flow at the sounding station of Stuttgart (blue dotted
line), 3 × 3 grid point averages near Stuttgart (blue solid line)
and Munich (red solid line), and for days with a certain spatial extent
(Ac, green solid line). Clusters for wind
speed near Stuttgart according to the BC–SC criteria are also indicated (light dotted line).
Cluster lengths during STE16 are marked as black vertical lines. See text for
further details.
The climatological distributions of the BC–SC combinations show a distinct
north-to-south gradient (not shown). Therefore, regions in the south of
Germany show a higher frequency of both the mean distribution of days with
compound low stability and weak flow and events with longer cluster lengths
(cf. Stuttgart vs. Munich in Fig. ). This fact can be
explained by the lower stability in southern Germany in the mean
.
Finally, we examined the spatial extent of
the SLI–wind combinations in the investigation area. In the first step, we
identified the area that fulfilled the BC–SC combinations on each day during
STE16 based on CFSv2 analysis. Accordingly, both criteria were reached in an
area of at least Ac=14×104 km2. This value is
considered as a lower threshold for the convective area. In the second step,
now based on CoastDat2, we checked for each day during C20/21 whether the
BC–SC combination was fulfilled over an area of at least Ac or
not. The persistence analysis of that time series is also shown in
Fig. . For both BC–SC combinations, the relative
frequency is higher compared to the persistence without considering the
spatial extent (Stuttgart and Munich). For example, the probability of a
cluster length greater than or equal to 13 or 10 days is between 3.0 and
3.5 % for BC and SC. Finally, we identified the total area with
2.7×106 km2, where these compound events (according to the BC
criterion) prevailed during the 13 days in STE16. This affected area was
unique until now and was never reached in C20/21, where the area was normally
between 0.8 and 1.9×106 km2 for the same (or even higher)
cluster length.
Conclusions
The severe thunderstorm episode in May–June 2016 in Germany (STE16) was
investigated with respect to rain intensity and the presence of
convection-favoring conditions in comparison to a 55-year control period
C20/21 (1960–2014). For the latter, we considered different proxies such as
convective parameters obtained from
soundings and large-scale weather patterns computed from reanalysis data. We
estimated empirical probability distributions with respect to variable
cluster lengths of consecutive days with convective weather situations based
on the different proxies.
The results illustrate that the interaction of convection-favoring weather
patterns, low thermal stability, and weak wind speed provided important
boundary conditions for the extraordinary thunderstorm anomaly observed. Due
to atmospheric blocking, these conditions persisted over almost 2 weeks.
The low wind speed at mid-tropospheric levels ensured that convective cells
were almost stationary, leading to locally extreme rain accumulations of more
than 100 mm, yielding return periods in excess of 200 years for both 24 h
and 7-day totals.
From the persistence analysis it can be concluded that the number of days
with prevailing extreme precipitation or convection-favoring conditions
during STE16 was extraordinary, but not unique. This conclusion, however,
depends on the proxy considered. For the precipitation severity index PS
based on the 99.9 % percentiles, for example, it was found that a 10-day
cluster as observed during STE16 has a probability of only 0.3 %. Compound
events with low stability and weak mid-troposphere flow estimated from
soundings are rare, but they occurred several times during C20/21. A cluster with
a length of 13 days for these conditions, for example, has a probability
between 0.1 and 0.2 % in southern Germany. The total area affected during
13 days, where these compound events prevailed, however, was unique in 2016
and has never occurred to that extent during the last half century.
Large-scale weather patterns dominating STE16 can be best described by both
the objective weather-type classification OWLK and two specific
convection-based classification schemes. A cluster length of 8 days
exhibiting the OWLK type XX..M (where .. denote either a cyclonic or
anticyclonic circulation pattern in 950 or 500 hPa, respectively) was
absolutely unique and has never occurred since 1979 (start of the OWLK
calculation by DWD). The code XX here mirrors the weak wind speed in the
lower troposphere as a peculiarity of STE16, while M reflects the relatively
high humidity. However, OWLK was not designed especially for the
detection of conditions conducive to thunderstorms. Therefore, a new
classification scheme (conOWLK) optimized with respect to convection was
developed, yielding specific convection-favoring weather patterns, which
coincide with a very high probability of severe thunderstorm events.
Additionally, a discriminant model (qdaOWLK) was applied, which provides a
measure for a generally high convective predisposition in a less strict
sense. According to qdaOWLK, we estimated that less than 1 % of all
clusters termed as convective exceeded a length of 10 days.
Thus, the 11-day cluster of STE16 has to be considered as highly exceptional.
A potential weakness of our research is that the examinations mainly rely on
gridded REGNIE 24 h totals and different proxies for convection, which both
have low spatial and temporal resolution. However, high-resolution data sets
such as radar, satellite, or lightning data are not available over a
sufficiently long period of at least 30 years. In case of REGNIE data, it has
to be considered that the regionalization approach and the limited number of
stations lead to a spatial smoothing of the rain fields. Consequently, local
rain maxima are underestimated by REGNIE. However, this underestimation is of
systematic nature and affects all years, making long-term statistics also
reasonable for convective rainfall. In terms of convective parameters and weather patterns, we are aware that
those proxies do not allow establishment of a direct link to
individual convective systems. For statistical analyses, however, these data
sets have a suitable prediction skill, as has been shown by various studies
e.g.,.
For the question of whether climate change was a driver of STE16, as was
frequently asked in the aftermath by the media, it is important to note that
the severe thunderstorms were triggered in an environment with moderate
temperatures around 20–25∘ C. Thus, the potential relation between
temperature increase, moisture increase, and a shift in the distributions of
CAPE or SLI as shown, for example, by , does not apply to the
STE16 event. In our opinion, the large annual and
interannual variability of convective activity across Germany and Europe
visible in the time series of all proxies investigated matter much more. The drivers of this
variability, however, are not yet well understood.
In the next step we intend to scrutinize the reasons that are most decisive
for the temporal variability of severe convective storms and related
atmospheric conditions and to separate dynamical and thermodynamical
processes. The first results showing a clear relation between thunderstorm
days in several European regions and the North Atlantic Oscillation (NAO)
index are promising.