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Modeling the Contribution of Tropical Cyclones in Monthly Precipitation Forecast
with NCEP Ensemble Prediction Systems
CHRISTIAN DOMÍNGUEZ-SARMIENTO
MALAQUIAS PEÑA-MENDEZ 1
Presentation
• Concepts and background
• Diagnostic Efforts
• Proof of Concept
• Experiments using the 20-member GEFS
2
3
Type of TC tracks (clusters) and their contribution to seasonal precipitation
Seasonal rainfall (from May to November) 1979-2009
NARR DATA
%
Main Developing Regions according to the cluster
4 ºK
Location of tropical cyclogenesis according to the cluster
HIGH-
RESOLUTION
(0.25º) BLENDED
ANALYSIS OF
DAILY SST AND
ICE, OISSTV2
Climatic Modeling TCs: Status • Circulation patterns are well-simulated in GCMs but TC structures
are not well-represented (Barnston et al. 2010).
• High resolution (20-50 km) GCMs are able to simulate TC-like vortices with TC strength (Zhao et. al 2010).
• CGCMs are used to carry out dynamical seasonal forecasting of tropical cyclone activity (Klotzbach et al 2011). However, tracks and forecasting of rainfall produced by TCs are still developing concepts.
• The index GPI (Genesis Potential Index) identifies which region has capacity for developing vortices according to global circulation patterns and it was tested for El Niño Events (Camargo et al. 2007).
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CFSv2 (T126) PCP ANOMALIES IN JULY, 2005 TC Emily: 11 jul – 21 jul
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Mm/month
CFSv2 (T126) PCP ANOMALIES IN JULY, 2005 Lead time: 0.5 month
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• The CFSv2 was initialized in July 1st and was integrated during a month (until August 1st), 10 days in advance for Emily’s cyclogenesis.
• The plot A represents the monthly accumulated precipitation from CFSR and plot B
represents the forecast monthly accumulated precipitation from CFSRR. The CFSv2 Reforecast of Hurricane Emily (2005) did not capture the precipitation extreme that happened in Northern Mexico.
CFSR - Reanalysis Data CFSRR – Reforecast Data
A B
Mm/month Mm/month
CFSv2 (T126) PCP ANOMALIES IN JUNE, 2010 TC Alex: 25 jun – 2 jul
8
Mm/month
Main assumptions
1. The models are good at representing and predicting the large-scale patterns of circulation and other environmental conditions such as SST, involved in the tropical cyclogenesis.
2. There is a diagnostic tool (index), which connects the large-scale conditions with the likelihood of a region can trigger TC development (downscaling).
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Presentation
• Concepts and background
• Diagnostic Efforts
• Proof of Concept
• Experiments using the 20-member GEFS
10
GENESIS POTENTIAL INDEX (GPIs) • GPI proposed by Camargo et al. 2007, is based on absolute
vorticity at 850 mb, relative humidity at 600 mb, Potential Intensity (proposed by Emanuel and Nolan 2004 and base on SST, prmsl, mixing ratio and air temperature) and wind shear.
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2
332/3
5 1.017050
10
shear
potV
VGPI
• GPI modified by Murakami et al 2010, considers an additional element to develop deep convection: omega at 500 mb.
1.0
1.01.01
705010
2
332/3
5 shear
potV
VGPI
Climatology of GPI
NARR (1982-2012) SST > 26º C
Location of tropical
cyclogenesis from HURDAT
Mask Filtering
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There is a clear signal in the absolute vorticity, rh, PI and wind shear, before and after the cyclogenesis day.
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Seasonal GPI
Median
25%-75%
Min-Max Jun Jul Aug Sep Oct Nov Dec
Months
0
10
20
30
40
50
60
70
80
90
100
110
GP
I
Seasonal GPI from NARR (Maximum and Minimum)
1982-2009
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Approach: Creating likelihoods of cyclogenesis will require a threshold
The tropical cyclogenesis of a TC that makes landfall can happen any time, so an unique threshold is necessary for our approach
Presentation
• Concepts and background
• Diagnostic Efforts
• Proof of Concept
• Experiments using the 20-member GEFS
15
Subseasonal scale and GEFS model
• The sub-seasonal forecast is a link between the weather and seasonal prediction. The time range of this timescale is from 2 weeks to 2 months.
• We ran the 20-member GEFS model for 15 days:
– The first 8 days at T574L64 resolution (25 km)
– The last 8 days at T126L64 (100 km)
The sea surface temperature anomaly is damped with an e-folding time of 90 days during the course of the forecast.
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Concept
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Model integration
Classical Climate Approach (CPC) for seasonal scale:
Model I.C. Number of
TC
Model integration
New Approach for subseasonal timescale:
Model I.C. Number of
TC Index
Large Scale
The new approach tries to go beyond the Accumulated Cyclone Energy (ACE) index
Range: no more than 2 weeks. Predictability of the first kind
Monitoring GPI
If GPI exceeds a threshold, a vortex perturbation will
be made
Computing likelihood of tropical cyclogenesis in the ensemble
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IC 1
IC 2
IC 3
IC 4
IC 5
etc etc
Index value
An intersection of the members will identify in which region the vortex perturbation has to be
initialized
Example of likelihood:
13 (members)/ 20 (total) = 65%
Intersection
Likelihood of Cyclogenesis (%) 4 days in advance
19
%
20
13
2
01
1 2
01
0
NO SIGNAL IN THESE TWO CASES
Percentage of TC cyclogenesis captured by GPI (Applied to GEFS)
20
0
20
40
60
80
100
2010 2011 2012 2013
PER
CEN
TAG
E O
F TC
CYC
LOG
ENES
IS
CA
PTU
RED
BY
GP
I (%
)
YEAR
• In the years of 2010 & 2011, GPI showed a signal to predict TC cyclogenesis 4 days in advance in the Gulf of Mexico and the Caribbean region. •However, since GEFS (resolution, layers, assimilation method and so on) was modified in 2012, GPI signal improved. •Tropical cyclogenesis of TCs that affected Mexico in 2013 was well captured by GPI.
REG
ULA
R
REG
ULA
R
GO
OD
BET
TER
Detect the region of possible cyclogenesis
(according to GPI)
Insert an artificial vortex (AV) in the forecasted region
Get ensembles of spatial and time
distribution (how many days do we
expect to get in order to make an ensemble
of 100 members)
Assess results comparing the
ensemble (track and rainfall) with HURDAT
and a precipitation database
Run GEFS without the AV at the forecasted
region and time
Get the difference between a AV and no-
AV outputs.
Proof of concept: Experimental Design h
First, we will focus on Alex (201006)
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Presentation
• Concepts and background
• Diagnostic Efforts
• Proof of Concept
• Experiments using the 20-member GEFS
23
Likelihood of cyclogenesis ocurrence (%) for Alex 20100625
I.C. 2010062100
24
The GPI shows a signal from the IC 4 days ahead
%
Vortex locations at 5 experiments
25
The same location was used for exp 2 and 3 but the ICs were changed. The exp2 was initialized in June 22th and exp3 was initialized in June 23th
INITIAL CONDITIONS
26
REA
L
INIT
IAL
C
ON
DIT
ION
S
MO
DIF
IED
IN
ITIA
L
CO
ND
ITIO
NS
Relative vorticity at 850 mb is shaded and wind at the same level is in arrows
The artificial vortex in the GEFS’ 20 members
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Black line – BEST TRACK green lines – 20 member forecast 28
Distribution of Vortexes in space and time
Black line – BEST TRACK Red lines – control of 4 experiments 29
OTHER EXPERIMENTS FORECAST 5-DAY ACCUMULATED PCP
I.C. JUNE 23th 2010
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Forecast precipitation initialized on 23 June 2010 showed that when a TC-like vortex was inserted, precipitation patterns changed depending on TC location and intensity.
Remaining Work • Create likelihoods of TC precipitation based on
100-member experiment
• Incorporate the likelihoods into the accumulated monthly precipitation forecast by GEFS (eventually, sub-seasonal scale)
• Carry out more TC cases
31
Questions?
Hurricane Rita (September 17-26 de 2005)
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