G. Tetzlaff Universität Leipzig - NUS Tetzlaff.pdf · G. Tetzlaff Universität Leipzig Singapore,...
Transcript of G. Tetzlaff Universität Leipzig - NUS Tetzlaff.pdf · G. Tetzlaff Universität Leipzig Singapore,...
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Extreme Rain and Wind Storms in the
Mid-Latitudes I
G. Tetzlaff
Universität Leipzig
Singapore, 21.04.2009
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Contents Part I1. Introduction2. Mid-latitue weather systems2.1 The structure of mid-lat. Weather systems2.2 Wind : Kyrill 20072.3 Flood : Elbe 2002
Part II3. Damage4. Weather models5. Past events of (extreme) weather and estimates for users6. Wind maximisation (probable maximum gust)7. Rain maximisation PMP (probable maximum precipitation)8. Conclusions
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1. Introduction
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2. Mid-latitude weather systems
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2.1 The structure of mid-latitude
weather Systems
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Differential solar heating
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Difference :
301 – 257 ~ 44 K
! at 1000hPa !
average gradient equ.-pole :
~0.3K/ 100 km
frontal zone gradient :
~5K/ 100 km
~150 m/s in ~300hPa
Two air masses :
Tropical
Polar
Frontal zone
in between,
with
~5-10 times the
average
zonal temperature
gradient
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The Ocean (thin) and Atmospheric (dotted) contributions to the total northwards
heat flux based on the NCEP reanalysis (in PW) by (i) estimating the net
surface heat flux over the ocean (ii) the associated oceanic contribution,
correcting for heat storage associated with global warming and constraining the
ocean heat transport to be -0.1 PW at 68 S (iii) deducing the atmospheric
contribution as a residual. The total meridional heat flux is also plotted (thick)
(Trenberth and Caron 2001).
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Average horizontal transports of a specific property ψ
are fully described
T = ρ u ψ
with
A = A + A‘ for u and ψ,
neglecting ρ‘ and avering with respect
to time and latitudonal circle
four types of transports remain
(two stationary ones with respect to time
and two instationay ones) :
1. stationary flow, constant over the whole latitudonal circle
(stationary cell with horizontal axis)
2. stationary flow, variable over lat. circle (stationary eddies with
vertical axis of rotation)
3. instationary flow, constant over whole lat. Circle (fluctuating
cell with horizontal axis)
4. Instationary flow, variable over lat. Circle (transient eddies,
with vertical rotational axis).
Under the assumptions these four types of transports are
complete.
In the mid-latitudes transports are managed by #4. mainly.
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MMC : mean meridional Circulation (horizontal axis)
Eddies : vortices with vertical axis (Speth et al. 1974)
mid-latitudes
January
Mid-latitudes : typically eddies dominate the scene!
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Transportable specific (per kg) energy
in the atmosphere :
1. potential energie P = g z
2. enthalpie (sensible) energy H = cp T
3. latent energy L‘ = L q
4. kinetic energy K = u2/2
(enthalpie from inner energy U= cvT)
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Petterssen, Weather Analysis and Forecasting 1958
Surface isobars (solid)
and 1000-500 mb
thickness (dashed)
• thickness contours
concentrated between sfc
front and upper level frontal
zone
Basic driver is the
eq.-pole temperature
difference
(necessarily resulting in
differences of
latent, internal,
and potential energies
transient eddies =
mid-lat. cyclones
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mid-lat. :
transient eddies
are the major
constituent of
all weather,
because of the
transport requirements.
typical propagation
velocity of
mid-lat. cyclone
10 m/s.
With the intense
wind and rain „zones“
(fronts) extending over
a few 100 km this results
in a typical
extreme weather duration
of 3 hours to
½ day.
whole cyclone :
a few 1000 km
intense rain :
a few 100 km
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Types of Fronts
• Cold Fronts:
– slope ~1:50 to 1:100
• Warm Fronts:
– slope ~1:100 to 1:200
• Occluded fronts
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Concentration of T-gradient
Wave length
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Low pressure system close to Iceland, NOAA März 2004,
„organised“ clouds
mid-latitudes : eddies with vertical rotation axis
T (frontal zone
at 500 hPa) ~
15K/300km
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mid-latitudes :
wind speed max.!
climate : what can happen where, how strong, how often, for how long?
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Summary : dominance of the
mid-lat. cyclones with respect to
atm. motions (boundary layer
extra)
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2.2 Wind : Kyrill 2007
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25. Dezember 1999 Orkan Lothar : Fotoaufnahme: Sturmwurffläche am 26.12.1999 bei Ettlingen, Lkr. Karlsruhe
Maximalböen Karlsruhe 151 km/h, d.h. ~1/1000a
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strong pressure gradient
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Propagation velocity Kyrill
18.01. 01 UTC to 19.01. 01 UTC :
~2400 km/24h = 100 km/h with
~constant core pressure
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>300 km/h
wind in 300 hPa ~ 100 m/s
are more violent processes possible?
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winter storm with gale force winds
and damage; storm „Kyrill“
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Kyrill
Max-gusts
18.01.2007,
Last 24h, in km/h;
wetteronline
Düsseldorf :
~1/100a-
event!
damage :
~5 –10 10^9 EUR
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with 18m/s as the average wind speed there is a band width
of gusts plausible :
standard gust factor (building codes) 1.6 29m/s
thunderstorm (theoret. gust, with u=3.9 T (here 10K) 39m/s
observed gust factor (widely spread) 1.8 32m/s
city gust factor (high roughness) 2.3 41 m/s
airport Düsseldorf observed 2.4 40 m/s
combined influences
of convection +
surface roughness
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Sturmtief Kyrill vom 18.01.2007
0
2
4
6
8
10
12
14
16
18:40 19:00 19:20 19:40 20:00 20:20 20:40 21:00 21:20 21:40 22:00 22:20
Uhr
Tem
pera
tur
/
Nie
ders
ch
lag
960
962
964
966
968
970
972
Lu
ftd
ruck
Temperatur Niederschlag Druck
temperature drop : ~9 C in ~10 minutes;
within that interval : ~10 mm of rain
measurements : Institut für Meteorologie, Universität Leipzig
There is no climate information available for
parameters like : K/s, phase shift between parameters, etc.
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0
5
10
15
20
25
30
18:40 19:00 19:20 19:40 20:00 20:20 20:40 21:00 21:20 21:40 22:00 22:20
0
5
10
15
20
25
30
Temperatur Windspitzen
Kyrill 18.01.2007Measurements Institut für Meteorologie, Universität Leipzig
---Windgeschwindigkeit
gust factor : 25m/s/11m/s = 2.27;
max. gust : 25m/s = 90 km/h
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location Date
376 km/h Pacific, Typhoon Angela, gust 01./02. Nov. 1995
372 km/h* Mt. Washington, 10-Minute-average 12. April 1934
357 km/h Caribik, Hurricane Wilma, gust 18./19. Oct. 2005
335 km/h* Zugspitze, winter storm, gust 12. June 1985
333 km/h Thule (Greenland), winter storm, gust 08. March 1972
324 km/h Dumont d‘Urville (Antarctica), June (1980-1992)
catabatic wind, gust
310 km/h Pacific offshore Japan, Typhoon Tip, gust 12. Oct. 1979
294 km/h* Atlantic (buoy 59N/11W), cyclone Gero, gust 12. Jan. 2005
263 km/h* Brocken (Germany), winter storm, gust November 1984
262 km/h Havanna (Cuba), Hurricane, gust 18. Oct. 1944
216 km/h* Granville (Frankreich), winter storm, gust 16. October 1987
184 km/h* List/Germany, winter storm Anatol, gust 03. Dec. 1999
151 km/h** Karlsruhe, winter storm Lothar, gust 26. Dec. 1999
144 km/h** Düsseldorf, winter storm Kyrill, gust 18. Jan. 2007
*mid-lat.
**mid-lat. Inland, lowlands
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2.3 Flood : Elbe 2002
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Flood in Germany
August 2002
Overall flood damage in Europe amounted to more than $ 20 10^9
Munich Re,
CRED, 2003/07
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Schumann,
2003
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track of the Vb-cyclone bringing moisture northwards,
and forcing winds upslope at central European
mountain ranges
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air flow steered
against orography
(Erzgebirge)
140 mm areal average rains
340 mm peak of the mountain
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The 36h forecast was more accurate than the 12h one!
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River Elbe at Dresden
(Source: Engel et al., 2002; "Der Elbestrom", 1898)
1342: 824
1432: 830
1501: 857
1655: 8381275: 840
2002: 940
1799: 824
1784: 857
1862: 824
1890: 837
1845: 944 (877)
700
750
800
850
900
950
1000
1200 1300 1400 1500 1600 1700 1800 1900 2000 2100
Year
Leve
l in
cm
700
750
800
850
900
950
1000
Summer
1845, corr.
Winter
runoff coefficient : 0.44
areal rain 139 mm (2 days)
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Extreme Rain and Wind Storms in the
Mid-Latitudes II
G. Tetzlaff
Universität Leipzig
Singapore, 22.04.2009
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3. Damage
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CRED 2007
Changes (linearised) :
dark red ~15%/a
purple ~ 4%/a
red ~-0.1%/a
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CRED 2008
role of climate change?
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Source: VROM (the Netherlands
Ministry of Housing, Spatial Planning
and the Environment)
Tue curve does represent
ONE out of many social
consenus!
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4. Weather models
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The set of equations to describe the relevant atmospheric
processes causing rain and wind storm
consists of 3 conservation equations :
mass, energy, momentum
and the equation of state.
The number of forces acting on an air parcel is 5 :
pressure gradient, Coriolis, inertia, friction, gravity
There are 4 forms of energy to be
considered here : potential, internal, kinetic, latent.
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DKKV Workshop Severe Storms 26-28 March 2007
Limitation: Model grid box large
Grid box 25 km x 25 km
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The AVHRR data resolve 2 km² with four measurements per day the METEOSAT
resolves about 50 km² with 48 measurements per day.
Scaling : theory needs clarification on the temporal and spatial resolution!
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Divergence div u and vertical motion w :
w ~ ∫ div u dz;
with dz ~ 10 km and div u ~ 10-5
w ~ 0.1 m/s in the „intense“ parts.
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Terrain-induced vertical motion
Steeper terrain contributes to greater vertical motion. Terrain-induced vertical motion influences precipitation amount,can lead to gravity waves downstream
hVterrainwt )( hVcos
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after Stull 1988
phase shift of w(z) small
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5. Past events
of (extreme) weather
and estimates for
users
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~2000 mm/day, La
Réunion, cone-shaped
mountain with ~3000m
height
NOAA, 2001
------ 2000mm/d
This is NOT PMP!
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City of Hamburg, 2004
Practical application of extreme weather frequencies
Return period in yearsduration
Design values for precipitation in the building sector
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Probabilities of occurrence of gusts, DWD 2005
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6. Wind maximisation
(probable maximum gust)
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Structure of a mid-lat. cyclone
absolute temperature in 850 hPa
vertical velocity in Pa/s in 500 hPa
Example : Anatol 3.12.1999
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Mechanisms of cylonic formation
PVA
Vorticity in 300 hPa in 10-4 s-1
Geopotential (82000 bis 93000 m2/s2 –
Isolines at 1000 m2/s2) in 300 hPa
WAA)
in 850 hPa in K/h
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Synthetic increase of the
horizontal temperature gradientIncrease T=5 K
„Added“ temperature in K:
Horizontal field in 700 hPa
Cross section at ca. 50 n.Br.
original temperature (Isolines at 2,5 K)
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Reference Temperature difference 2,5 K
Temperature difference 5 K Temperature difference 7,5
K
Undine 6.-7.1.1991
Max winds (gusts) in m/s
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0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
Böen, w
elc
he in 1
/1000 d
es M
odellg
ebie
tes
übers
chritten w
urd
en, in
m/s
Und
ine
(199
1)
Dar
ia (1
990)
Loth
ar (1
999)
1991
1229
12
Wie
bke
(199
0)
Mar
tin (1
999)
1989
0211
06
Silk
e (1
998)
1962
1213
18
Ham
burg
er F
lut (
1962
)
1976
1129
18
1983
0116
12
Cap
ella (1
976)
1984
0113
00
1988
0208
00
Ana
tol (
1999
)
Referenzlauf
stärkster Lauf
16 winter storms: observed vs. „maximised“
gusts in m/s (exceeded in
1/1000 of the model area)
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Frequency of exceedance of gust velocity in
events per year
1E-3
0,01
0,1
1
0 10 20 30 40 50 60 70
vB in m/s
Hä
ufig
ke
it d
er
Üb
ers
ch
reitu
ng
in A
nte
ilen a
m M
ode
llge
bie
t
R
2,5 K
5 K
7,5 K
Radtke 2006
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7. The rain maximation
PMP (probable maximum
precipitation)
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Daily sums of precipitation on the Erzgebirge in August
2002%PMP* %PMP*
11.08. 12.08. 13.08. 3 days 12.08. 3 days
Estimated
Near Res. --- 410 --- --- 103 --- Altenberg
Reservoir 31,5 239,5 24,3 295,3 60 54 Lehnmühle
Reservoir 9,8 280,6 23,2 313,6 70 57 Klingenb.
Reservoir 20,8 218,0 11,1 249,9 55 45 Malter
Reservoir 26,0 217,2 12,8 256,0 54 47 Gottleuba
•MGN - Maximierte Gebietsniederschlagshöhen or PMP (data DVWK, 1997) „traditional
estimate of PMP following the structure of WMO guide-lines.
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River Main at Würzburg
(Source: SpektrumWasser 1, München 1998, p. 55, 77)
Italic: approximate values1342: 3350
1845: 2170
1909: 1800
1682: 2250
1595: 2000
1451: 2250
1442: 24501546: 2350
1633: 2000
1784: 2650
1882: 1670
1000
1500
2000
2500
3000
3500
4000
1200 1300 1400 1500 1600 1700 1800 1900 2000 2100
Year
Runoff
in m
³/s
1000
1500
2000
2500
3000
3500
4000
Summer
Winter
flood of July 1342
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Increase of precipitation depth
with decrease of preciptation area,
Elbe flood August 2002
Station Zinnwald
Model, area
Mulde catchment area
Elbe catchment area,
DresdenElbe catchment
area, total
Model, point
0
100
200
300
400
500
0,010,11101001000100001000001000000
Precipitation area in km²
Pre
cip
itation d
epth
in m
m/d
ay
With 1342 river Main values : ~800 mm/day
We estimated the areal rains for the 1342-flood event.The basic
orographic setting is similar to the one of the Erzgebirge
only for a different range of mountains. The 1342 flood was heaviest
where the contributaries originating in the mountain range united.
Applying the „1342-rains“ to the Erzgebirge : almost doubling the rains would occur.
Even allowing for errors in the estimates it calls for closer inspection of PMP-estimates.
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Quantification of topographic effects on predicted precipitation for typical Elbe-catchments
in the Erzgebirge
Validation of model results, confirmation of of orographic effects.
Radar-derived precipitation [mm] accumulated for 6h on May, 15th 2004 with a strong
upper-level flow from the north-northwest.
U
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EinzugsgebietTalsperre Malter
Dresden
Orographic profile
distance in m
Catchment Malter
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CAPE very small!
Height dependent temperature and
dew point temperature
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Quantification of topographic effects on predicted precipitation for typical Elbe-catchments
in the Erzgebirge
Influence of orography shape on structure of induced lifting for
standard rain conditions. The effects of stability (Froude Number)
are distinct.
Cross-section of vertical velocity w [m/s]
as a result of LM simulations
U
U
bell-shaped
ridge
vs.
Erzgebirge
orography
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Vertical profiles for w :
1. Standard case
2. Maximum found from sensitivity analysis using LM
-1,0 -0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0
0
2000
4000
6000
8000
10000
12000
exponential decay
adapted from LM
z [m
]
w*
dRR/km aus MAXRR: 5.5K/km, 15m/s, rH~98%
0 0,5 1 1,5 2 2,5 3
1
2
3
4
5
6
7
8
9
10
11
12
z [km
]
dRR/km [mm/(h*km)]
no scaling of w
w * exponential, Hw=5km
w* adapted from LM
resulting liquid water
release per km of height
Model runs are used to estimate the „maximum“ profile of w(z)
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Orograpgically induced precipitation
height
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The relation between
precipitation rate and vertical velocity w is linear.
The precipitation rate almost doubles per 10 K
change of the dew point temperature (at saturation level).
In real cases the maximum precipitation
is not reached (in observations the
highest values amounted to about 70%).
Precipitation rates show high sensitivity.
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Rain in orography are influenced by :
vertical wind velocity (wind speed and slope)
vertical specific humidity profile
vertical profile of Froude-Number/Brunt-Väisälä-Frequency
vertical wind shear
wind drift of precipitation
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8. Conclusions
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Simple conceptual models can give
orientation estimating PMG and PMP.
Most critical seems to be the lack of relevant information
(how well is time and site dependant air flow
known, …?).
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Thank you!