Munehiko Yamaguchi, Sharanya S. Majumdar (RSMAS/U. Miami) and multiple collaborators 3 rd THORPEX...

Munehiko Yamaguchi, Sharanya S. Majumdar (RSMAS/U. Miami) and multiple collaborators

3rd THORPEX International Science Symposium

14 Sep. 2009

Coordinated use of targeted observations during TCS-08/T-PARC

Outline of the talk

1. What we did before TCS-08/T-PARC

2. What we did during TCS-08/T-PARC

3. What we learned from TCS-08/T-PARC

I would talk the above 3 from a perspective

of targeted observations

Pretty Simple…

Guideline on targeting products and timelinesAfter the Hawaii meeting in December 2007 (8 months before TCS-08/T-PARC), our targeting group created a guideline on targeting products and timelines because it was found that many institutes could provide the sensitivity analysis guidance and the availability time of each guidance varied from one center to another. (Final version of the guideline was completed in July…)

Singular vectors

•ECMWF

•NRL (based on NOGAPS)

•JMA

•U Yonsei (based on MM5)

Adjoint based SAG

•NTU (ADSSV)

•NRL (based on COAMPS)

EKF

•UKMO (ETKF based on MOGREPS)

•U Miami/NCEP (ETKF based on NCEP + ECMWF ensembles)

•U Washington (EKF based on WRF)

Ensemble variance

•NOAA (based on NCEP ensembles)

10 kinds of SAG in total (many talks and posters about

SAG and targeting during TTISS)

Website providing sensitivity analysis guidance

1. UCAR/EOL T-PARC/TCS-08 website

2. JMA T-PARC website

3. PREVIE system developed by ECMWF in partnership with UK Met Office.

UCAR/EOL JMA ECMWF PREVIEW

We mainly used the PREVIEW system for the comparison of the sensitivity analysis guidance

PREVIEW System

Sensitive Area Predictions (SAPs)

• Automatic submission of 5 fixed areas

• Up to 5 additional areas chosen

interactively

• Flexible choice of targeting time

(t + 18 to 102 h)

and verification time

(t + 36 to 120 h)

TC Date Aircraft

Non-typhoon 2008082600-2008090900 6 Falcon missions

TY Nuri 2008082012 DOTSTAR

TY Sinlaku 2008091000 DOTSTAR

TY Sinlaku 2008091100 - 2008091112

DOTSTAR and Falcon

TY Sinlaku 2008091400 Falcon

TS Sinlaku 2008091600 DOTSTAR and Falcon

TY Hagupit 2008092200 DOTSTAR

STY Jangmi 2008092700 DOTSTAR

TY Jangmi 2008092800 - 2008092812

DOTSTAR and Falcon

TS Jangmi 2008092906-2008100100 3 Falcon missionsJMA activated rapid-scan mode on MTSAT2 satellite between 2008091012-2008091306 and 2008092712-2008092812. Extra rawinsonde observations.

Summary of Missions

Targeting Elluminate Session

The targeting group hold an 1-hour Targeting Elluminate Session before the Daily Planning Meeting when targeting missions might be expected and mainly discussed about

1.Synopsis based on satellite images and analysis fields by global models such as ECMWF, UKMO, GFS and NOGAPS.

2.Available observation resources

3.Forecast uncertainty using ensemble products such as tracks and spread of Z500.

4.Sensitivity analysis guidance

5.Tentative flight plan

Following 3 slides are the slides we really used in the Targeting Elluminate Session starting from 21UTC 9th September 2008 for a flight mission for Typhoon Sinlaku at 00 UTC 11th September 2008.

JMA Typhoon EPS JMA Typhoon EPS

2008.09.09 06UTC ini +132h2008.09.09 06UTC ini +132h

JMA Medium-Range EPS JMA Medium-Range EPS

2008.09.08 012UTC ini +216h2008.09.08 012UTC ini +216h

EPS track forecasts

ECMWF EPS ECMWF EPS

2008.09.08 12UTC ini +120h2008.09.08 12UTC ini +120h

GFS EPS GFS EPS

2008.09.09 12UTC ini +???h2008.09.09 12UTC ini +???h

Sensitivity Guidance: Obs. Time 09/11 00Z

ECMWF NOGAPS JMA

UM ETKF UK ETKF COAMPS

Joint Flight Plan: 09/11 00Z

DOTSTAR (Blue) and Falcon (Red) Flight Plan

Canceled Flight: Mission at 09/12 00Z

DOTSTAR

!!! Canceled !!!

The latest email from Wu-san said, “Concerning the weather condition not suitable for a feasible targeting flight, the DOTSTAR mission for 00UTC 12 Sept. has just been called off.”

Due to the weather condition, the planned DOTSTAR mission at 00UTC 12th Sept. was canceled.

Sensitivity region not fully covered: Mission at 09/10 00Z

ECMWF NOGAPS

Obs. Points cover sensitive regions proposed by guidance

Area surrounded by black line is also sensitive

DOTSTAR Flight Plan

JMA Due to the limited aircraft resources, the sensitivity area was not fully covered.

Summary

1. The Guideline on targeting products and timelines were helpful in understanding when and what products were available at each meeting help in Monterey, Tokyo, Taiwan, etc.

2. The Websites were useful to compare the various kinds of sensitivity analysis guidance.

3. The targeting group hold the 1-hour Targeting Elluminate Session when targeting mission was expected.

4. We conducted targeting missions for 4 typhoons.

5. Due to the weather condition and limited aircraft resources, we had to be very flexible in the decision-making process.

Future Challenges

1. Observing System Experiment (OSE): Evaluate the impact of targeted observations based on sensitivity areas.

2. Understand the role of the additional observations in NWP models.It might happen that some observations lead to the deterioration of

model performance.

3. Evaluate fidelity of sensitivity analysis guidance (long targeting and verification time).

Enough room to improve TC track forecasts

0

200

400

600

800

1000

Pos

ition

err

or (

km)

average

Position errors of each TC Track Forecast by JMA/GSM in 2007

Forecast time: 72 hours

Total number of forecast events: 102

Position errors are sorted in ascending order

Various approach to improve forecasts

ObservationData

assimilation

Numerical weather

predictionUser

Current system

•Reducing the errors of deterministic track forecasts is not only the approach.

•Providing confidence information based on ensemble forecasts is also one way to improve TC track forecasts.

•Yamaguchi et. al (2009) developed the Typhoon Ensemble Prediction System (EPS) at the Japan Meteorological Agency and demonstrated that the ensemble spread is an indicator of confidence of TC track forecasts.

0

500

1000

1500

2000

2500

3000

0 4000 8000 12000Ensemble spread of TC positions2 (ensemble spread accumulated from 0 to 120 hour

forecasts every 6 hours)

Pos

itio

n E

rror

s of

Ens

embl

e M

ean

at 5

-day

for

ecas

ts (

km)

Number of sample1 ：　 149

Strong relationship between ensemble spread and position error of ensemble mean track forecasts

1.The TC strength of L is included in this verification

2. Ensemble mean tracks are defined using more than 1 ensemble member

Confidence information provided by ensemble forecasts

How to bring out forecast uncertainties

T=t0 T=t1

Analysis field

Deterministic forecast

Ensemble member

Uncertainty of Analysis field

Forecast Uncertainty

Dramatis PersonaeDramatis Personae

Ensemble spread is a variability (standard deviation) between the members in the ensemble forecast.

Ensemble spread can be used as an indicator of confidence of forecast.

Ensemble spread is a variability (standard deviation) between the members in the ensemble forecast.

Ensemble spread can be used as an indicator of confidence of forecast.

ECMWF NCEP

Sinl

aku

init

iate

d at

12

UT

C 1

0 Se

p. 2

008

Dol

phin

init

iate

d at

00

UT

C 1

3 D

ec. 2

008

Some contradictions as seen among various EPSs

The grey lines are ensemble track predictions.

The black line is the best track.

The black triangles are the forecast positions at 120-h.

Japan

Philippines

Taiwan

The recently established The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database makes it possible to conduct a systematic inter-comparison of global model ensembles and investigate the reason why the ensemble spread changes from one EPS to another.

Specifications of ECMWF, NCEP and JMA EPS

Note thatThe verifications in this study are based on initial time 0000 UTC unless otherwise noted because the most of the airborne observations were conducted centered on 0000 UTC.

Verification results of JMA are based on 1200 UTC because JMA’s EPS is initiated only at 1200 UTC.

JMA’s EPS is the Medium-range EPS, not the Typhoon EPS.

What I did

Calculation procedures:1.Download u and v of initial ensemble fields at 1000, 925, 850, 700, 500, 300, 250 and 200 hPa through ECMWF’s TIGGE site.2.Convert the latitude-longitude coordinate into the x-y coordinate centered on the central position of Sinlaku.3.Calculate ensemble perturbations (u’ and v’) of all ensemble members at each vertical level.4.Calculate kinetic energy defined as u’^2+v’^2 from the results of 3.5.Calculate grid- and ensemble-averaged kinetic energy at each vertical level over the 2000km x 2000km domain and draw the vertical profile.6.Calculate vertically- and ensemble-averaged kinetic energy over the 2000km x 2000km domain and draw the horizontal distribution.

I compared the kinetic energy of ensemble initial perturbations for Typhoon Sinlaku, using TIGGE data from ECMWF, NCEP and JMA* (Medium range EPS, not Typhoon EPS).

I calculated the following two elements through 10th to 19th of September 2008.

1. Vertical profile of the kinetic energy

2. Horizontal distribution of the kinetic energy

Best track and intensity of Typhoon Sinlaku

Synopsis in a before-recurvature stage

500 hPa 250 hPa

Geopotential height (solid line) and stream function (dash line)

Sinlaku was located west of the Pacific High. The High is not strong enough to interact with the typhoon, so the steering caused by the Pacific High is weak. Sinlaku moved very slowly at that time; less than 10 km h-1.

Synopsis in a during-recurvature stage

500 hPa 250 hPa


Sinlaku was located in a confluent area induced by the westerly jet and the southerly flow at the west edge of the Pacific

Synopsis in a after-recurvature stage

500 hPa 250 hPa


Sinlaku was sandwiched by both features; it was located north of the Pacific High and south of the westerly jet, being advected by the confluent westerlies.

Vertical profile of KE in a before-recurvature stage

Note that the scale of NCEP is 10 times as large as that for ECMWF and JMA

Vertically averaged horizontal distribution of KE in a before-recurvature stage

ECMWF NCEP JMA

Storm-relative coordinate with the domain of 2000 km x 2000kmStorm-relative coordinate with the domain of 2000 km x 2000km

2000km

2000km

ECMWF NCEP JMA

Vertically averaged kinetic energy of ensemble initial perturbationsVertical profile of

kinetic energy of ensemble initial

perturbations

2000km

JMA

ECMWF

NCEP

Comparison of kinetic energy of ensemble initial perturbations for Typhoon Sinlaku (2008)

Storm-relative coordinateStorm-relative coordinate

2000km 1

0 S

ep. 0

0Z

(bef

ore-

recu

rv.)

15 S

ep. 0

0Z

(du

rin

g-re

curv

.)18

Sep

. 00Z

(a

fter

-rec

urv

.)

Temperature and specific humidity perturbation

I did the same verifications for temperature and specific humidity perturbations.

Total energy = ½ {(u’2 + v’2)

+ cpT’T’/Tr

+ Lc Lc q’ q’/cp/Tr}

cp is the specific heat of dry air at constant pressure, T’ is a temperature perturbation about the control analysis, and Tr = 300 K is a reference temperature. Similarly, Lc is the latent heat of condensation and q’ is a specific humidity perturbation.

! Kinetic energy

! Available Potential energy

! Specific humidity energy

ECMWF NCEP JMA

Vertically averaged APE of ensemble initial perturbationsVertical profile of

APE of ensemble initial perturbations

2000km

JMA

ECMWF

NCEP

Comparison of APE of ensemble initial perturbations for Typhoon Sinlaku (2008)

Storm-relative coordinateStorm-relative coordinate

2000km 1

0 S

ep. 0

0Z

(bef

ore-

recu

rv.)

15 S

ep. 0

0Z

(du

rin

g-re

curv

.)18

Sep

. 00Z

(a

fter

-rec

urv

.)

NCEP JMA

Vertically averaged SHE of ensemble initial perturbationsVertical profile of

SHE of ensemble initial perturbations

2000km

JMA

NCEP

Comparison of SHE of ensemble initial perturbations for Typhoon Sinlaku (2008)

Storm-relative coordinateStorm-relative coordinate2000km

10

Sep

. 00Z

(b

efor

e-re

curv

.)15

Sep

. 00Z

(d

uri

ng-

recu

rv.)

18 S

ep. 0

0Z

(aft

er-r

ecu

rv.)

ECMWF’s perturbations

1. ECMWF perturbs wind and temperature and does not perturb specific humidity.

2. In the before-recurvature stage, the ECMWF wind perturbation has a peak at 700-hPa on average and is largest in the near environment of the typhoon. Looking at each ensemble member, the maximum amplitude is found to be 4.4 m s−1, appearing about 700 km away from the typhoon center while the amplitude within 100 km from the typhoon center is only 1.6 m s−1 at most.

3. As the typhoon moves northward, the amplitude above 500-hPa becomes larger, corresponding to the change in the area of highest amplitude from the typhoon surroundings to the synoptic features north of the typhoon.

4. As with the wind perturbation, the temperature perturbation also has a peak in the mid-troposphere (e.g., the maximum amplitude in the before-recurvature stage is 2.6 K at 500-hPa and about 500 km away from the typhoon center, implying that the perturbation has little influence on the warm core structure in the inner region).

5. The vertical profiles of the wind and temperature perturbations are quite similar to those of perturbations seen in TEPS at JMA, that also uses singular vectors targeted for TCs (Yamaguchi et al 2009).

NCEP’s perturbations

1. NCEP perturbs all components; wind, temperature and specific humidity.

2. The amplitude of the wind perturbation is larger than ECMWF, especially in the upper troposphere. For example, it is 9.2 times as large as ECMWF at 200-hPa in the before-recurvature stage; the amplitude averaged over the 2000 km × 2000 km domain about the typhoon center is 3.4 m s−1. This trend is common in the other stages.

3. In the before-recurvature stage, there are large amplitudes in the temperature and specific humidity perturbations within about 300 km from the typhoon center. Looking at each ensemble member, the maximum amplitude of temperature (specific humidity) perturbation is found to be 2.1 K (1.8 g kg−1), which appear at 250-hPa (700-hPa). Considering that the temperature anomaly due to the warm core structure in the non-perturbed field (not shown) is about 4.0 K at 250 hPa, the temperature perturbation strengthens the warm core structure by about 50 %. The specific humidity perturbation increases the moisture by 16 % at 700-hPa with respect to the non-perturbed field.

JMA’s perturbations

1. JMA also perturbs all components; wind, temperature and specific humidity.

2. JMA’s perturbations are characterized by the large amplitude of the specific humidity perturbation. For example, it is 3.7 times as large as NCEP at 925-hPa in the before-recurvature stage; the amplitude averaged over the 2000 km × 2000 km domain about the typhoon center is 1.25 g kg−1.

3. The perturbation area is not in the typhoon surroundings but mainly south of the typhoon. This is because JMA uses moist singular vectors for creating the perturbations and they are not targeted for each TC, but for the entire tropics. That is why the amplitude south of the typhoon becomes smaller as the typhoon moves north.

4. On the other hand, the amplitude of the wind perturbation is small. For example, it is a quarter of ECMWF at 700-hPa in the before-recurvature stage; the amplitude averaged over 2000 km × 2000 km domain about the typhoon center is 0.24 m s−1. This trend is common in the other stages.

How do the perturbations modify the symmetric and asymmetric wind field of Typhoon?

Symmetric wind field

ECMWF NCEP

Tangential wind at 850-hPa (before recurvature stage)Tangential wind at 850-hPa (before recurvature stage)

Black: CTL

Grey: Ensemble member

1. The size of the typhoon (radial profile of the symmetric component of tangential wind) is similar among the ensemble members in each EPS;

2. The range of maximum tangential wind is less than 1 m s−1

3. The radius of the maximum tangential wind does not change significantly;

4. The differences between ECMWF and NCEP are much larger than the differences caused by the initial perturbations in each ensemble.

5. These trends are common in other stages.

Asymmetric wind field

Steering flow at 500-hPa (before recurvature stage)Steering flow at 500-hPa (before recurvature stage)

Black: CTL

Grey: Ensemble member

ECMWF NCEP

The steering flow is defined here as the asymmetric flow at 500-hPa averaged over 300 km from the typhoon center.

1. The ensemble members are dispersed around the non-perturbed member in both EPSs.

2. The change in the steering flow of NCEP is larger than ECMWF; In the before-recurvature stage, it is 0.67 m s −1 for NCEP and 0.49 m s−1 for ECMWF on average.

3. These trend are common in other stages, probably due to the relatively large amplitude of initial perturbations

Perturbation evolution for Sinlaku

Growth of kinetic energy of asymmetric wind component of ensemble perturbations

A 2-day time series of the kinetic energy of the storm-relative asymmetric component of each wind perturbation at 500-hPa. The kinetic energy is calculated as the difference between asymmetric wind components of the non-perturbed member (control analysis and forecast) and each ensemble member to investigate how the steering flow of the ensemble members is different from that of the non-perturbed member.

ECMWF NCEP

Before recurvature stage (00Z 10th Sep. 2008)Before recurvature stage (00Z 10th Sep. 2008)

Definition of

kinetic energy of asymmetric wind component

(uasym_i − uasym_c)2 + (vasym_i − vasym_c)2,

where (uasym_i, vasym_i) and (uasym_c, vasym_c) are the asymmetric horizontal wind fields of the i’th ensemble member and the control, respectively.

Ensemble member with the largest growth (ECMWF Ensemble member 44)

Before recurvature stage (00Z 10th Sep. 2008)Before recurvature stage (00Z 10th Sep. 2008)

3000km

3000km

Track of EPS member with the largest growth (ECMWF)

Ensemble member 43Ensemble member 44

Westernmost (left) and easternmost (right) course among all EPS members

Definition of

kinetic energy of ensemble initial perturbations

(ui − uc)2 + (vi − vc)2,

where (ui, vi) and (uc, vc) are the horizontal wind fields of the i’th ensemble member and the control analysis about which the ensemble is constructed, respectively.

ECMWF NCEP

Sinl

aku

00U

TC

13

Sep.

Sinl

aku

00U

TC

10

Sep.

Growth of kinetic energy of asymmetric wind component of ensemble perturbations

A 2-day time series of the kinetic energy of the storm-relative asymmetric component of each wind perturbation at 500-hPa. The kinetic energy is calculated as the difference between asymmetric wind components of the non-perturbed member (control analysis and forecast) and each ensemble member to investigate how the steering flow of the ensemble members is different from that of the non-perturbed member.

Definition of

kinetic energy of asymmetric wind component

(uasym_i − uasym_c)2 + (vasym_i − vasym_c)2,

where (uasym_i, vasym_i) and (uasym_c, vasym_c) are the asymmetric horizontal wind fields of the i’th ensemble member and the control, respectively.

2007 2008

1 da

y fo

reca

sts

3 da

y fo

reca

sts

Relationship of spread of ensemble track forecasts between ECMWF and NCEP

r = 0.27

r = 0.21r = 0.56

r = 0.26

Summary

1. Ensemble perturbations and their growth around a tropical cyclone are investigated using the THORPEX Interactive Grand Global Ensemble (TIGGE).

2. Vertical and horizontal distributions of initial perturbations produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP) and the Japan Meteorological Agency (JMA) are compared for Typhoon Sinlaku

3. The amplitudes and distributions of the perturbations are found to be different among the 3 centers: before, during and after recurvature.

4. The growth rate of the asymmetric component of wind perturbations (that control the steering flow) is much higher in the ECMWF ensemble than that of NCEP, usually leading to a relatively large ensemble spread of tracks in ECMWF for forecasts beyond 3 days. Due to the relatively large amplitudes of their initial perturbations, NCEP generally possesses a larger ensemble spread at forecast times of order 1 day.

Interactive forecast system

ObservationData

assimilation

Numerical weather

predictionUser

Current system


adaptive observations

sensitivity analysis

ObservationData

assimilationUser

A sensitive analysis technique is needed to maximize the effect on a numerical prediction and to minimize the cost of the observations.

sensitive area

Adaptive observations©Vaisala

©JAXA

© ＮＡＳＡ

Numerical weather

prediction

2nd year in RSMAS

On singular vector based sensitivity analysis for tropical cyclones in a non-divergent barotropic framework

Title:

Motivation:How sensitive are the fast-growing perturbations to the intensity, size and asymmetry of the initial TC-like vortex?

How does the perturbations (sensitivity region) affect track forecasts?

Methodology:Using the SPECTRAL ELEMENT OCEAN MODEL (M. Iskandarani et al. 1995), singular vectors are computed for various initial conditions.

Sharanya S. Majumdar1

Melinda S. Peng2

Carolyn A. Reynolds2

David S. Nolan1

1. Rosenstiel School of Marine and Atmospheric Science, University of Miami

2. Marine Meteorology Division, Naval Research Laboratory

Acknowledgments

Thank you for listening

Present status of tropical cyclone track forecasts

The Japan Meteorological Agency (JMA) provides tropical cyclone track forecasts in the form of a probability circle, which is a circular range into which a tropical cyclone is expected to move with a 70% probability at each valid time. The radius is determined statistically from the recent verification results of track forecasts.

Forecast time 　(hours)

Direction of movement (deg.) Speed of movement (V)

V = < 15 kt 15kt < V = < 30 kt

V > 30 kt

12 60 NM 60 NM 100 NM

24

Before recurvature (180-310) 80 NM

100 NM 150 NM During recurvature (320-000) 90 NM

After recurvature (010-170) 100 NM

48




72




Probability circle

Radius of Probability Circle

Best track

Best track

Typhoon CHABA

Initial: 2004.08.28 12 UTC

Typhoon CHABA

Initial: 2004.08.28 12 UTCTyphoon MARIA

Initial: 2006.08.06 12 UTC

Typhoon MARIA

Initial: 2006.08.06 12 UTC

Deterministic forecasting is as good a guess as anyAnother day,

the deterministic forecast by JMA/GSM is wrong…

One day, the deterministic forecast by

JMA/GSM is perfect!

Red line: JMA/GSM Black line: Best track

Why is a probabilistic approach needed ?

0

200

400

600

800

1000

Pos

ition

err

or (

km)

average

Position errors of each TC Track Forecast by JMA/GSM so far in 2007




JMA begins operation of the Typhoon EPS

The 20 km GSM, which will become operational from 21st Nov, will support both TC track and intensity forecasting.

The Japan Meteorological Agency (JMA) has developed a new ensemble prediction system (EPS) known as the Typhoon EPS, aiming to further improve both deterministic and probabilistic forecasting of TC movements. We will start operation of the Typhoon EPS no later than the beginning of the typhoon season in 2008 following preliminary operation since May 2007.

Pos

ition

err

or (

km)

Num

ber

of S

ampl

es

Forecast time (hours)

Compared with control forecasts, ensemble mean forecasts statistically have smaller errors, especially after four-day forecasts.

Black: Control Run

Red: Ensemble Mean

Black: Control Run

Red: Ensemble Mean

The Typhoon EPS provides better deterministic forecasts

Forecast uncertainty changes day by day (1)

Best track

Example of probabilistic forecast.

There is only one conceivable scenario!



Each ensemble member having a common track scenario means that the scenario is highly likely. People can act accordingly, e.g. those in areas where predictions show no possibility of the typhoon striking can avoid taking unnecessary action against it.

Deterministic forecast (red line)

Initial: 2004.08.28 12 UTC


Initial: 2004.08.28 12 UTC

Typhoon EPS(11 members: red to orange)

(blue: control forecast)



Each ensemble member having a common track scenario means that the scenario is highly likely. People can act accordingly with full confidence, e.g. they can prepare for possible damage well in advance.


Initial: 2006.12.02 12 UTC


Initial: 2006.12.02 12 UTC

Medium-range EPS(51 members: red to orange)

(green: ensemble mean forecast)






Best track



Initial: 2006.08.07 00 UTC


Initial: 2006.08.07 00 UTCExample of probabilistic forecast

Forecast uncertainty is quite large.

Example of probabilistic forecast

Forecast uncertainty is quite large.

Even if the most likely solution (or deterministic forecast) is wrong, with several other scenarios presented, people can act accordingly, e.g. they can prepare for possible damage well in advance.

Best track

Deterministic forecast Typhoon EPS(11 members: red to orange)

(blue: control forecast)

1. Reliability index

0

200

400

600

800

1000po

sitio

n er

ror

(km

)

average

?

Current Application

A

Based on ensemble spread or a cluster analysis technique, we can optimize the size of the probability circle.

In addition, a reliability index such as A, B and C, where A is the highest reliability, might be easy to understand even for the general public.

Future Application

7 9 09月日時10 09日時

11 09日時

12 09日時

13 09日時

14 09日時

15 09日時

16 09日時

17 09日時

12 21日時13 21日時

14 21日時

台風経路図2007 7 17 9年月日時

Current Application

In the stage where a typhoon is going along with a subtropical jet, forecast uncertainty is relatively large in the direction of movement compared with that in the crosswise direction.

How good an indicator are circles?

Uncertainty in a forecast track is represented with a round shape, whose radius is decided based on a statistical method.

Orange: JMA Typhoon EPS, Green: JMA Medium-range EPS

Pink: ECMWF Medium-range EPS, Blue: JMA TYM

8 30 09月日時

31 09日時

01 09日時

02 09日時

03 09日時

04 09日時

05 09日時

06 09日時

07 09日時

08 09日時08 15日時

05 09日時

05 21日時

06 21日時

07 21日時

台風経路図2005 9 8 15年月日時

Current Application Future Application

The area representing forecast uncertainty could be optimized by using ensemble spread, which changes day by day and typhoon by typhoon.

2. Practical representation of TC-threatened areas

Uncertainty in a forecast track is represented with a round shape, whose radius is decided based on a statistical method.

3. Typhoon Strike Probability Map

%

The figure represents the probability that typhoon DURIAN will pass within a 120-km radius during a given 24-hour period.

Black line: Best track during the given 24-hour period

Grey line: Best track from the initial time to the starting time of the given 24-hour period.

Sun Mon Tue Wed Thu Fri Sat Sun

26 27 28 29 30 1 2 3

Typhoon DURIAN

Initial: 2006.11.28 12 UTC

Typhoon DURIAN

Initial: 2006.11.28 12 UTC

JMA plans to start five-day forecasts

TC track forecasts covering five days will be introduced thanks to both the development of NWP systems and implementation of the Typhoon EPS.

Future Issues

2. Further understanding as to what causes forecast uncertainties in TC track forecasts

T-PARC

1. Further discussion on how to use uncertainty information associated with TC track forecasts

Future issues to be addressed include the following two points:

At T-PARC, we aim to reduce forecast uncertainties in TC track forecasts by performing airborne adaptive observations.

T-PARC

Summary

JMA will start the Typhoon EPS no later than the beginning of the typhoon season in 2008.

TC track forecasts covering five days will be introduced thanks to both the development of NWP systems and implementation of the Typhoon EPS.

Uncertainty information associated with TC track forecasts will be provided using the Typhoon EPS.

T-PARC will help us to further address TC predictability and improve NWP systems.

We would like to enhance our relationships more in order to consider more beneficial use of probabilistic TC forecasts.

Reference

申し訳ありません。

適当な参考文献がありません。

謝謝

Typhoon Strike Probability Map for Typhoon MARIA

The figure represents the probability that typhoon MARIA will pass within a 120-km radius during the period from the initial time, 00 UTC on 7 Aug 2006, to three days ahead, 00 UTC on 10 Aug 2006.

Black line: Best track

Typhoon MARIA

Initial: 2006.08.07 00 UTC

Typhoon MARIA

Initial: 2006.08.07 00 UTC

Why is a probabilistic approach needed ?

0

200

400

600

800

1000

Pos

ition

err

or (

km)

average

Position errors of each TC Track Forecast by JMA/GSM so far in 2007




Various approach to improve TC track forecasts

ObservationData

assimilation

Numerical weather

predictionUser

Current system


adaptive observations

sensitivity analysis

observationData

assimilationUser

A sensitive analysis technique is needed to maximize the effect on a numerical prediction and to minimize the cost of the observations.

sensitive area

Adaptive observations©Vaisala

©JAXA

© ＮＡＳＡ

Numerical weather

prediction

Munehiko Yamaguchi, Sharanya S. Majumdar (RSMAS/U. Miami) and multiple collaborators 3 rd THORPEX...

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Transcript of Munehiko Yamaguchi, Sharanya S. Majumdar (RSMAS/U. Miami) and multiple collaborators 3 rd THORPEX...