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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

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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

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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





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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





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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

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%

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





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Likelihood of cyclogenesis ocurrence (%) for Alex 20100625

I.C. 2010062100

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The GPI shows a signal from the IC 4 days ahead

%

Vortex locations at 5 experiments

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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

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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

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Questions?

Hurricane Rita (September 17-26 de 2005)

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