Post on 16-May-2020
24/02/2014
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Cork eSurge Training20-21 Feb 2014
Modelling Surges
Ad.Stoffelen@knmi.nl
Thanks to:Jan Kroos (SVSD)
Hans de Vries (KNMI)Martin Verlaan (Deltares)
Cork eSurge Training, 20-21 Feb 2014
Cork eSurge Training, 20-21 Feb 2014
1953Total sea level
animation 1953
The animation shows the mean sea level pressure (blue contour lines), the wind at 10 m above the mean sea level (brown flags) and the total sea level (coloured, blue is NAP -5 m, red is NAP +5 m), starting on Jan 31, 1953 10:00 GMT and ending on Feb 2, 1953 0:00 GMT.
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Overview
The need for surge modelling Forecasting in the Netherlands Tides Satellite data Tide and surge propagation on a shelf Surges Forecasting Ensembles
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NL The
Netherlands is very vulnerable with most people living below sea level
The Rotterdam harbour fuels the Dutch economy
Trade and safety interests need trade-off
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Mean Dutch tidal waves
South to North Largest amplitude in South and North
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Mean Dutch tidal levels
Time0:00 12:00
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Normative levels in cmSection Exceedance
Probability
Per year
Station
Surge category / Level
Highest known level
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Results from the past . . .
Commensurate exceedance probability
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Surges and the Maeslantkering
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Accuracy surge forecasts is part of the strategy to close it
The Maeslantkering is closed when:The expected water level in
Rotterdam > 300 cm or Dordrecht > 290 cm above NAP
This occurs once per 7 to 10 years
Surges and the Maeslantkering
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Surges and the Maeslantkering
In 1988 standard deviation surge forecasts σ = 25 cm
MHW Rotterdam 365 cm Safety margin = 95 cm Accuracy forecasts 3*σ = 3*25 = 75 cm Additional margin 20 cm
Water level Rotterdam closure 270 cm Closing frequenty 1 / 2 per 3 years Not tolerable for Rotterdam harbour Surge forecasts need improvement!
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Ship traffic
Rotterdam anchorage Closure of
Maeslantkering causes economic loss
Shipping lanes appear in wind climate too at low winds
As well as platforms
R&D needed
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When building Maeslantkering
Rotterdam harbour threshold requirement:frequenty of closing Maeslantkering max. 1/10 years
MHW Rotterdam 365 Closing water level 300 cm (0.1 y-1) Safety margin (365 - 300) = 65 cm Additional margin 20 cm 45 cm margin for accuracy surge forecast
(3* standard deviation)
Requirement: accuracy surge forecasts Hoek van Holland is < 15 cm
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Progress surge forecasts Developments: High resolution models (weather, surge)
Data assimilation water levels (Kalman filtering)
Improved interaction hydrologists and Meteorologists
Better information exchange hydro-meteo
In 2007 standard deviation (RMSE) surge forecasts reached σ < 15 cm
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Accuracy surge forecasts Hoek van Holland
0
5
10
15
20
25
30
35
1965 1970 1975 1980 1985 1990 1995 2000 2005
RM
SE
in c
m
0
5
10
15
20
25
30
35
# st
orm
vlo
eden
Mens-machine mix SVSD Model DCSM8 # stormvloeden
RMSE computed over period of 10 year
Berekeningsperiode
# st
orm
sur
ges
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Maeslantkering and seiches Mechanical joints can only hold pressure towards land Seiches may lower the seaward water level below the
inland water level A difference of 140 cm is dangerous The closed barrier responds automatically to low sea
level by controlled drifting of the doors This lowers the outward pressure on the doors
Cork eSurge Training, 20-21 Feb 2014
Roles KNMI and RWS
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Cork eSurge Training, 20-21 Feb 2014
History of water level modelsLorentz (1920) Waddenzee, 1 dimensional
Schalkwijk (1947) Hoek van Holland, from wind in 2 sections
Weenink en Groen (1958) 5 sections
Timmerman (1969) 6 sections
Timmerman (1975) Numerical linear model
Flather, IOS (1976) Numerical non-linear model
GB, D, DK, NL (1981) Model comparison
KNMI/IOS (1986) Numerical non-lineair model
WAQUA/CSM-16 (1991) Numerical non-lineair model (16 km)
GB, B, F, GR, NL (1992) Model comparison
Data Assimilation (1992) Kalman Filter in WAQUA
WAQUA/CSM-8 (1999) 8 km
WAQUA/CSM-8 (2008) Probability forecasts upto 240 hours
WAQUA/DCSMv6 +ZuNo.v4
(2012) 1.6km (leads to DCSMv6)
Cork eSurge Training, 20-21 Feb 2014
Must see
www.dropbox.com/s/odzgofwtp3gkypz/globerefined.avi
Startup of global surge model- Check your own region- Effects of coasts and shelfs - Harmonics
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SectionsSchalkwijk (1947)
Usefulness Large scale storms Investigate meteo error
sensitivity
Not anymore Absolute water levels Small scale systems Quite circumstances
Cork eSurge Training, 20-21 Feb 2014
SectionsSchalkwijk (1947)
Wind effect
Pressure effect
External surge
Back propagation
Total
See next slide
Cork eSurge Training, 20-21 Feb 2014
Surge back propagationExample: Surge along Dutch coast Wind ceases Surge reflects to English coast Surge reflects again and returns 0,5 to 1 day later in
reduced form along the Dutch coast (20 – 30 %) Losses depend on basin shape, bottom and depth
UK NL
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Surge back propagationExample: Surge along Dutch coast Wind ceases Surge reflects to English coast Surge reflects again and returns 0,5 to 1 day later in
reduced form along the Dutch coast (20 – 30 %) Losses depend on basin shape, bottom and depth
UK NLStorm surge
Cork eSurge Training, 20-21 Feb 2014
Surge back propagationExample: Surge along Dutch coast Wind ceases Surge reflects to English coast Surge reflects again and returns 0,5 to 1 day later in
reduced form along the Dutch coast (20 – 30 %) Losses depend on basin shape, bottom and depth
UK NLStorm surge
ReflectionEnglish coast
Cork eSurge Training, 20-21 Feb 2014
Surge back propagationExample: Surge along Dutch coast Wind ceases Surge reflects to Englisch coast Surge reflects again and returns 0,5 to 1 day later in
reduced form along the Dutch coast (20 – 30 %) Losses depend on basin shape, bottom and depth
UK NLStorm surge
ReflectionEnglisch coast
ReflectionDutch coast
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Model forecasts
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Dutch Continental Shelf Model
Implementations of WAQUA Sequence CSM-16 (16 km; 1988), CSM-8 (8 km;
1999), DCSMv5, DCSMv6 (1.6 km; 2012) Also versions for southern North Sea (ZuNo),
coast and main rivers
Cork eSurge Training, 20-21 Feb 2014
Dutch Continental Shelf Model DCSMv6 Improved representation of physical phenomena
such as tide and surge propagation and generation and the role of non-linear interactions therein– Bed friction (tidal wave amplitude; = g |u| u / C(D)2)– Bathymetry (tidal wave phase; u D1/2)– Wind forcing
Increased resolution of 1,6 km (representation) Initially NOOS and ETOPO2 bathymetry, but
optimised for stability and tidal propagation by altimeters
Open boundary where the amplitudes and phases of 22 harmonic constituents are specified
Zijlstra et al., Ocean Dynamics (2013) 63:823–847
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Astronomical CorrectionI. Model tides are not representative of station tidesII. Station tides are estimated by harmonic analysis of past
data (i.e., at average surge level), TSIII. Predicted model tides at stations are obtained without
meteorological forcing, TMIV. Predicted model water levels at stations are obtained with
meteorological forcing, WMV. Estimated water levels are corrected for the tidal
component, W = WM – TM + TS
Astronomical correction improves predicted water levels II and III may have different mean non-linear surge-tide
interaction Applicable only at tide gauge stations
Cork eSurge Training, 20-21 Feb 2014
Bathym.
DCSMv6
Cork eSurge Training, 20-21 Feb 2014
Obs.
Tide gauges (10 min.)
Altimeters 1992-2009 Jason-1/Topex-Poseidon
(~10 days)
Improved amplitude and phase of the actual tidal propagation harmonics
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Altimeter quality
Most uncertainty on the shelf
RSS Altimeter - GOTO0.2 tides RMS Altimeter ascending-desc.
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Tidal input verification
Surge model is calibrated by altimeter analyses Not possible with in situ data in deep waters
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Altimeter data assimilation
Provides correct astronomical input to DCSMv6
Next step: Verify tidal propagation modes within model area
Zijlstra et al., Ocean Dynamics (2013) 63:823–847
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Effect depth and sea bed
DCSMv6 Selected Dutch
stations Sea bed and depth
also affect surge propagation
Better bed and depth representation affects non-linear tide-surge interaction
Default bathymetry Bed roughness 0.026 s/m1/3
Default bathymetry Bed roughness 0.027 s/m1/3
Bathymetry – 2 m Bed roughness 0.027 s/m1/3
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Adaptation sections
More observations than adjusted parameters 100 sections, 200 parameters Minimize SD of differences model-obs www.openda.org
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Non-linear effects
Bottom friction term 2x depth term Depth term 2x advection term
A surge increases water level and makes the tide run faster
Neap tide surge contribution is larger than high tide surge contribution
Synchronized surges and tides interact Large non-linear effects for large surges
(Tide) + (Surge) ≠ (Tide + Surge)
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DCSMv6 2008 validation
Generally satisfying Shallow areas with variable bathymetry and
geometry remain most challenging
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DCSMv6 example
Surge errors dominate over tidal errors
Cork eSurge Training, 20-21 Feb 2014
Kalman filterWeighted mean
Kalman filter K
Used here for water level
221
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22
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x
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)1()(
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2
121
1221
21 K
xxKx
xxww
wxx
x
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Kalman filter flow
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Need for QC
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Kalman filter validation
Clear advantage over first 0.5-1 day Apparent overfitting thereafter for low water levels
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RMS verification high tides
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Bias verification high tides
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Meteorological inputs
Short range, limited area (HiRLAM, ECMWF boundaries) Up to 48 hours forecast 4 times a day: 0, 6, 12, 18 UTC Ready at 3, 9, 15, 21 UTC
Medium range (ECMWF) 48 hours and longer range 2 times a day: 0 and 12 UTC Ready at 7:30 (deterministic) Ready at 9:00 (ensemble)
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Pressure surge
1 hPa PMSL decrease theoretically causes 1 cm surge
Practically ~50% is realized– 1 hPa PMSL decrease theoretically causes 0.5 cm surge
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Ensembles
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Wind surge ensemble
50 ECMWF members, DCSMv6; surge and water level
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From plume to probability
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St. Nicolas surge warning
Integrate the probability in each warning class
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Alarm level probability
Can be done for each vulnerable location
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Case verification
Very serious threat; forecast fine, but lagged
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Case verification
Underforecast in the North however by 0.35 m
Cork eSurge Training, 20-21 Feb 2014
What’s next
Take away remaining uncertainties Mesoscale wind forcing Air-sea interaction (momentum flux) Improving water-level representation inside
estuaries and shallow seas (increased resolution) Data assimilation of heights and winds to
increase predictive quality for the shorter lead times (<12 h)
Steric effects (salinity, SST)
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Cork eSurge Training, 20-21 Feb 2014
Cork eSurge Training, 20-21 Feb 2014
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Cork eSurge Training, 20-21 Feb 2014
Mesoscale meteo oscillations
Large-scale turbulence and convection– Periods from minutes to an hour– Additional surges of a few dm max.– Occurs typically within a day due to unsettled
atmospheric conditions
Cork eSurge Training, 20-21 Feb 2014
OSCAT
50 km
12.5 km
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Maeslantkering closure
Surface winds from HARMONIE model at 1.7 km grid
HARMONIE shows mesoscale structures
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Mesoscale meteo oscillations
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Mesoscale meteo oscillations
Convective cells
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Squall lines
Characteristic– Sudden surge wave along a line– Single wave (modest tsunami)
Squall line or organized convection Surge period 30 min to 3 hours Surge up to 70 cm
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Squall line
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Squall line
Squall line
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3 Januari 2012
Squall line in rain radar
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Another meter in IJmuiden, 50 cm Roompot Buiten
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1 nov 2006
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Underpredicted surge Delfzijl
31/10/’6 18Z 1/11/’06 4Z
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Storm and high water Delfzijl (NL)
1/11/’06 6:14
• 0.5 m underpredicted surge by HiRLAM (blue) and ECMWF winds (green)
• OSI SAF QuikScat winds (red) are stronger and/or more directed into the harbour
NRT constellation needed R&D on mesoscales needed Coastal zone winds Extreme winds
1/11/’06 4:03
• Delfzijl
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Sharp trough results in +0.7mAllerheiligenvloed 1 nov 2006
Wind forecast Surge forecast
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Eems Dollard 1 nov 2006
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21 maart 2008 H. van Holland
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Polar Low 21 Mar 2008 12:00
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Polar Low
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Front passage 5/6 Dec 2013
Extra surge at front passage
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Harbour seiches Characteristic
– Sudden surge along the coast reinforces in a harbour
Cause– Atmospheric convective cells above North Sea with
associated wind fluctuations of periods of 1 to 2 hours
– (Long-range) swell
Surge period 1 to 2 hours Surge 0 – 150 cm Duration 1 day or a few days Resonance with harbour basin dimensions
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Harbour seiches
Harbour seiches
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Seiches 5/6 Dec 2013
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Seiche mechanism
Convective-clouds
Sea level30 – 100 km
2 –
4 km
Direction front (N)NW
Seiche at sea 10-20 cm
cold front
Vertical temperature gradiënt typically7-10 gr / km for convective cells
Convective cells
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Convective cells
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Better use of observations More capable computers Improved weather models
NH
ZH
Weather forecasts improve
Cork eSurge Training, 20-21 Feb 2014
Improve meteorology?
Greg.J. Tripoli, Un. Wisconsin
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Bus?
planetary waveslow pressure systemsstorms, frontsorographic circulations
matu
re sy
stem
sd
evelo
pin
g sy
stem
sb
ou
nd
ary
layer
Temperatuur en druk bepalenweerevolutie
Fast
Slow
Temperature and pressure determineweather evolution
Mis
t
Clo
ud la
yer
R
ain
colu
mn
10
V [
m]
100
1
000
10.
000
10 100 1000 10.000 H [km]Shower Front Storm Climate zone World
Wind determinesweather evolution
Cork eSurge Training, 20-21 Feb 2014
Cork eSurge Training, 20-21 Feb 201490ERS-2 scatterometer wave train; missed by HiRLAM
Mesoscale waves
NWP models miss wave
Next day forecast bust in UK and NL
Clouds alone do not depict dynamics well
Rossby wave
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Scatterometers in operation
9:30 LST & 21:30 LST: Advanced Scatterometer ASCAT-A and ASCAT-B carried by the Metop-A and MetOp-B meteorological satellites operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT); operational
12:00 LST & 0:00 LST: OSCAT from the Indian OceanSat-2 scatterometer; operational
6:00 LST & 18:00 LST: HSCAT from the Chinese HY-2A scatterometer; experimental
These all have follow-on instruments
WMO requires 6-hourly OSVW coverage Surges, diurnal cycle, mesoscale convective systems, eddy-scale
ocean applications, air-sea interaction, coastal applications
Cork eSurge Training, 20-21 Feb 2014
ASCAT and QuikScat impactJapan Meteorological Agency
ASCAT has smaller rain effect; splash remains
Cork eSurge Training, 20-21 Feb 2014KNMI Scientific
Review, January 13-
93
Product quality varies in TCs
TC Katrina just before landfall
KNMI SDP25 NOAA DIRTH
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Thinned data at ECMWF Mainly larger scales are assimilated With good impact though
Cork eSurge Training, 20-21 Feb 201495ERS-2 scatterometer wave train; missed by HiRLAM
Mesoscale waves
NWP models miss wave
Next day forecast bust in UK and NL
Clouds alone do not depict dynamics well
Rossby wave
Cork eSurge Training, 20-21 Feb 2014
Extreme winds
capability
NOAA hurricane flights
Ike: highest ASCAT speed ever at the time (75 knots) and we were just there !
Lack of buoy data > 20 m/s
ASCAT lacks H pol and sensitivity
Tested VH for Post-EPS
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ASCAT Ultra High ResolutionArea of 2 by 2
Centered around
19N 129E
(NE of Philippines)
26-10-2010 00:36
12.5 km
Coastal ASCAT wind product available at KNMI
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ASCAT Ultra High Resolution
6.25 km
Sharper shear lines, divergence patterns
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• Noisy
• Needs improved QC on footprint level
• MSS ?
• Rough eye as also witnessed by SFMR
• Do you want such products ?
3.125 km
ASCAT Ultra High Resolution
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Spatial representation
We evaluate area-mean (WVC) winds in the empirical GMFs 25-km areal winds are less extreme than 10-minute sustained in situ
winds (e.g., from buoys) So, extreme buoy winds should be higher than extreme scatterometer
winds Extreme NWP winds are again lower due to lacking resolution (over
sea)
Wind scales
0
10
20
30
40
0 25 50 75 100 125 150 175 200
Distance (km)
Win
d s
peed
(m
/s)
BuoyASCATECMWF
Cork eSurge Training, 20-21 Feb 2014101
Standards in TC classification
Hurricane standards are based on 1 ormin mean winds, not on 25-km mean winds !
Need for unification in names ?! DTU Summerschool, 2011
Cork eSurge Training, 20-21 Feb 2014102
WVC size WVC size is
50/25/.. km Extreme winds are
smeared out How to translate
scatterometer winds to hurricane categories ?
Same guidance in tropics as extratropics ?
Typical factor of 1.5-2.0 between 10-min winds and scatterometer winds
WVC size
DTU Summerschool, 2011
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Data Assimilation Systems (DAS) are cycled at the cut-off time Observation timeliness is subtracted from the cut-off point (t) Only in initial part of the analysis window observations are used This corresponds to a “duty cycle” percentage
What is the duty cycle for current operational HiRLAM implementations ?
Data Assimilation System Cycling
Analysis Window
Cut-offL2B tUsed
indow
Cut-offt
Analysis W
L2B Used
Cork eSurge Training, 20-21 Feb 2014
QRT timeliness ≡ 30 minutes, NRT ≡ 120 minutes Earth rotates at 825 km/h east (true at 60 N), such that the number of
data assimilation cycles can be approximated from SizeX (Lon) NRT 50% or less, while QRT about 70-100% observation data use
Rapid cycles with short cut-off are most sensitive to timelinesshttps://hirlam.org/trac/wiki/HirlamInventory/Operational
Model DMIT15
DMIM09
FMIRCR
FMIMB71
AEMETONR
KNMID11
Met.ieI10
Met.no12
SMHI C22
EMHIETA
LHMS HL8
Dxy km 16.5 9.9 16.5 7.48 17.6 11 11 11.88 22 11 8.8
SizeX km 10065 7227 9603 3606 10243 8976 7194 10264 6732 4026 1637
SizeY km 9372 7385 7392 2693 7462 7150 4664 8292 6732 3080 1637
Cycle hr 6 6 6 6 6 3 6 6 6 6 6
Cut-off min 100 100 120 120 120 120 110 125 115 120 120
QRT/cycle min 250 250 270 270 270 180 260 275 265 270 270
NRT/cycle min 160 160 180 180 180 90 170 185 175 180 180
QRT/cycle % 69 69 75 75 75 100 72 76 74 75 75
NRT/cycle % 44 44 50 50 50 50 47 51 49 50 50
Need for Quasi Real Time
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Prospect based on HiRLAM operations
~70-100% of scatterometer QRT L2B winds can be used in current HiRLAM implementations
< 50% of scatterometer NRT L2B winds can be used in HiRLAM These numbers vary for other NRT and QRT specs.
and other model cycles
There is a general tendency to 3-h cycling and fast cut-off in the coming years to better exploit fast observations (like EARS), increasing the need for QRT delivery in regional NWP
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ASCAT scatterometer
Europe’s contribution
Cork eSurge Training20-21 Feb 2014
ASCAT-A and ASCAT-B come together
Convectivedownbursts
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Triple collocation result ASCAT winds are veryaccurate
ASCAT error SD is smallerthan representativenessvector error SD
Buoy errors appear large(current, wind variability)
ECMWF winds appear smooth and biased lowon average
In extreme weather muchlarger deviations will occur
See also Vogelzang et al., JGR, 2011
ECMWF ScaleError SD U m/s V m/s
Buoy 1.44±0.02 1.59±0.02
ASCAT 1.05±0.02 1.29±0.02
ECMWF 1.32±0.02 1.18±0.02
Scatterometer ScaleError SD U m/s V m/s
Buoy 1.21±0.02 1.23±0.02
ASCAT 0.69±0.02 0.82±0.02
ECMWF 1.54±0.02 1.55±0.02
Representativeness (r2) 0.78±0.02 1.00±0.02
Trend U m/s V m/s
ASCAT 0.99 0.99
ECMWF 0.97 0.96
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OceanSat-2 scatterometer
OSCAT
International collaboration and cal/val team
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Independent verification
KNMI processing delivers best verifying OSCAT winds
More extended verification needed
Naoto Ebuchi, Tokai Un., Japancoaps.fsu.edu/scatterometry/
meeting/past.php#2013
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Independent verification
Naoto Ebuchi, Tokai Un., Japan, coaps.fsu.edu/scatterometry/meeting/past.php#2013
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OSCAT Monitoring• KNMI OWDP speed bias against
the global UK MetOffice NWP model (background) in March 2012
• Uncorrected OWDP• NSCAT2 GMF
• OWDP version with orbit-height based backscatter bias correction in dB
• NSCAT3 GMF
www.nwpsaf.org
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OSCAT impact in TC forecasts
• Mean position errors (of MSLP minimum) of the 2011/2012 Tropical Cyclones in the south-west Indian Ocean as forecast with the regional Aladin Réunion NWP model (Dominique Mékiès, 2013).
• ASCAT is used in both.
Cork eSurge Training, 20-21 Feb 2014
Impact of assimilated observations on Forecast Error Reduction
[C. Cardinali, ECMWF]
The forecast sensitivity to observations measures the impact of the observations on the short‐range forecast (24 hours). The forecast sensitivity tool developed at ECMWF computes the Forecast Error Contribution (FEC) that is a measure (%) of the variation of the forecast error (as defined through the dry energy norm) due to the assimilated observations.
May 2013 versus May 201212% Smaller Global FcError2% FcError Reduction due to GOS
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HY2A Scatterometer
EvaluationAd.Stoffelen@knmi.nl
Anton Verhoef
Same approach as for OSCAT Data exchange Offer expert and GS support
Cork eSurge Training, 20-21 Feb 2014
Collocation result - u (559551 wind vectors)
-20 -10 0 10 20Model u component (m/s)
-20
-10
0
10
20
Scattero
mete
r u c
om
ponent (m
/s)
-20 -10 0 10 20
-20
-10
0
10
20
Collocation result - v (559557 wind vectors)
-20 -10 0 10 20Model v component (m/s)
-20
-10
0
10
20
Scattero
mete
r v c
om
ponent (m
/s)
-20 -10 0 10 20
-20
-10
0
10
20
Collocation result - speed (559540 wind vectors)
0 5 10 15 20 25Model wind speed (m/s)
0
5
10
15
20
25
Scattero
mete
r w
ind s
peed (
m/s
)
0 5 10 15 20 250
5
10
15
20
25Collocation result - direction (518777 wind vectors)
0 90 180 270 360Model wind direction (deg)
0
90
180
270
360
Scattero
mete
r w
ind d
irection (
deg)
0 90 180 270 3600
90
180
270
360 KNMI L2B vs ECMWF
1.48 m/s
1.44 m/s 1.44 m/s
10.58 deg
OWDP as used for QSCAT and OSCAT
-1.7 dB 0 correction -0.0001 linear outer
beam correction No outer swath WVCs No low wind adaptation
Speed bias removed Low winds introduced Rain issue reduced Scores similar to
QuikScat and OSCAT
Cork eSurge Training, 20-21 Feb 2014
Case study: closure Maeslantkering
Part of the Dutch Delta Works plan (initiated after the 1953 flooding disaster) to protect the South-Western part of the Netherlands for high sea levels
Closed for the first time:– 9 November 2007
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Cork eSurge Training, 20-21 Feb 2014
surface wind speed
AN: 2007110900FC+6
VT: 2007110906
Note the maximumwind speed in
South-West Netherlands for
conv+scat experiment
121110987
654321
no assim
conv + ascat + qscat
ECMWF
verifying qscat
Closure Maeslantkering …zooming in to The Netherlands
Cork eSurge Training, 20-21 Feb 2014
Maeslantkering closure
Surface winds
Harmonie shows structures not observed by QuikSCAT
Note: QuikSCAT footprint is about 50 km2
Cork eSurge Training, 20-21 Feb 2014
OSCAT
50 km
12.5 km
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Cork eSurge Training, 20-21 Feb 2014
Hurricane winds Discussion on highest ever peak winds
from Haiyan in the media, but impossible to measure!
Maximum 1-minute sustained winds are also difficult to know
Scatterometer winds are mentioned in 20% of ALL NHC TC discussions; these are calibrated against NOAA hurricane-hunter winds (SFMR & dropsondes)
Scatterometers measure 25-km scale winds and are much less extreme than peak winds
Current scatterometers either capture rain and/or saturate at 40 m/s; ASCAT-SG will measure extremes (2022)
The impact of a hurricane surge is catastrophic and depends on wind speed, wind direction, wind duration and wind fetch as e.g. depicted by NWP wind forecasts
Forecast errors need to be monitored against observations for surge quality assurance TC Rita
Cork eSurge Training, 20-21 Feb 2014
Global constellation users
OSCAT Beta UsersCanada
China
Europe
Hong Kong
India
Japan
Russia
SH
USA
OSI SAF Message List South America
Oceania
Europe
Other
Canada
China
Hong Kong
India
Japan
Korea
Russia
Taiwan
USA
KNMI OSCAT experimental winds were already distributed all over the globe to beta users (left)
All EUMETSAT SAF wind product service messages are popular world wide (now include OSCAT; right)
Service messages also through EUMETCAST and JPL PODAAC China is using constellation data right now
Cork eSurge Training, 20-21 Feb 2014
NWP SAF software users
AfricaChinaEuropaIndiaOther AsiaRussiaSouth AmericaUSA
Concerns all versions of AWDP, SDP and OWDP (247 users) In addition to the wind products, also EUMETSAT SAF wind processing codes
are popular world wide Both Ku-band processing codes (31) and AWDP (26) are popular in China
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Cork eSurge Training, 20-21 Feb 2014
Training EUMETRAIN Marine Forecasters Course:
webcast on Measuring Winds from Space http://www.eumetcal.org/courses/course/view.php?id=112§ion=1
Training Course Applications of Satellite Wind and Wave Products for Marine Forecasting http://vimeo.com/album/1783188 (video)
Forecasters forum http://training.eumetsat.int/mod/forum/view.php?f=264
Xynthia storm case http://www.eumetrain.org/data/2/xynthia/index.htm
EUMETrain ocean and sea week http://eumetrain.org/events/oceansea_week_2011.html (video)
NWP SAF scatterometer training workshop http://research.metoffice.gov.uk/research/interproj/nwpsaf/scatterometer/data_assimilation_workshop/
Use of Satellite Wind & Wave Products for Marine Forecasting http://classroom.oceanteacher.org/course/view.php?id=103
Satellite and ECMWF data vizualisation http://eumetrain.org/eport/smhi_12.php?
Cork eSurge Training, 20-21 Feb 2014
Summary
WMO expresses a global need for scatterometer winds every 6 hours
The CEOS OSVW virtual constellation contributes to resolve the earth’s surges
The constellation partners make an effort to exchange their public resources for their own and the global public good
Exchange with ISRO operational NSOAS exchange is starting up The constellation winds do indeed lead to global societal
and economic benefits in diverse application areas, including surges
L-band (extreme) winds are emerging as well as SAR winds which are clearly complementary
Needs are NRT, coastal, extremes, and mesoscale (seiches); any priority or other needs?
Cork eSurge Training, 20-21 Feb 2014
ASCAT-A ASCAT-B
t = 50 minutes
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Cork eSurge Training, 20-21 Feb 2014
OSCAT
That’s why we needed wellies in Venice !
Cork eSurge Training, 20-21 Feb 2014
Cork eSurge Training, 20-21 Feb 2014
All maps are at tf=60 [h]; which model surge plot 1-4 corresponds to which map A-D?
ModelObs. 1
3 4
2A
DC
B