“How to use the products of JMA Ensemble Prediction System?”

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“How to use the products of JMA Ensemble Prediction System?”

Takuya KOMORI ( [email protected] )

Numerical Prediction Division Japan Meteorological Agency

2009.05.12-15 1st TRCG Technical Forum2009.05.12-15 1st TRCG Technical Forum

(B2) Applications for TC forecasts

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1. Introduction of the One-Week EPS Products on JMA EPS-WEB

What kind of products can be seen via JMA EPS-WEB?

2. Exercise: a TC Case-study using JMA EPS-WEB How should EPS products be interpreted for practical

weather forecasting?

ContentsContents

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The products in JMA EPS-WEB are recommended by Manual on the GDPFS (WMO No.485).

• In addition to the web-site for public users, JMA provides a web-site for meteorologists and forecasters in foreign countries.

• The special forecast products derived from EPS are disseminated on the website, “JMA EPS-WEB”, supporting the activity of National Meteorological and Hydrological Services (NMHSs) in Asia.

• The data in this website is available for operational weather forecasting in your countries.

JMA EPS-WEBJMA EPS-WEB

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• JMA operates an EPS web-site (EPS-WEB) for supporting the activity of National Meteorological and Hydrological Services (NMHSs).

• The EPS-WEB is intended for NMHSs forecasters, not for public use.

• This web site provides the JMA One-week EPS products.

• Caution! The links to this website are strictly prohibited.

• Address of this web site is ….

JMA EPS-WEB provides visualized EPS products.

Introduction (JMA EPS-WEB)Introduction (JMA EPS-WEB)

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JMA EPS-WEB (Visualized EPS Products)JMA EPS-WEB (Visualized EPS Products)

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Probability of exceeding the 24mm/1day precipitation.

Probability of exceeding the 48mm/1day precipitation.

A A

B B

Area-A: High probability in 24mm/day, while less than 5% in 48mm/day.It will be relative small precipitation, and low probability for heavy precipitation.

Area-B: High probability in 24mm/day and middle percentage in 48mm/day.It will be relative small precipitation. In addition, there is probability for heavy precipitation.

Probability map indicate potential locations of extremely severe weather events exceeding a certain threshold.

Contents: Probability MapContents: Probability Map

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“AllThres.” displays probability maps of 850hPa temperature anomalies, T850anm, exceeding four thresholds at the same valid time. The range of forecast time is from 1-da

y to 9-day with 1 day interval.

T850anm > 2 K T850anm < -2 K

T850anm > 8 K T850anm < -8 K

T850anm > 4 K T850anm < -4 K Severe Weather

Event


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“Sequence” displays selected probability maps from 1-day up to 9-day forecast.

1-day 2-day 3-day

4-day 5-day 6-day

7-day 8-day 9-day

Probability Map - layout and threshold -Probability Map - layout and threshold -

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WesternPacific

Asia

Northern Hemi.

Probability Map - area -Probability Map - area -

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Daily PrecipitationTemperature at 850hPa

Probability Map - elemant -Probability Map - elemant -

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EPSgrams at 70 major cities in RA-II area EPSgrams at 70 major cities in RA-II area

Moscow ABHA COLOMBO CHIANG MAI JINAN MAJURO/MARSHALL IS.

ASTANA BAHRAIN(INT. AIRPORT) MALE BANGKOK QINGDAO KOROR, PALAU WCI.

BISHKEK DOHA INTERNATIONAL AIRPORT ALTAI PHUKET NANJING YAP, CAROLINE IS.

TASHKENT ABU DHABI INTER. AIRPORT ULAANBAATAR KUALA LUMPUR/SUBANG SHANGHAI BRUNEI AIRPORT

DUSHANBE SEEB, INTL AIRPORT KATHMANDU AIRPORT SINGAPORE CHANGI AIRPORT HANGZHOU KOTA KINABALU

ASHGBAT SANA'A KOWLOON HANOI FUZHOU LAOAG

KING KHALED INT. AIR ISLAMABAD TAIPA GRANDE DA NANG GUANGZHOU MANILA

KUWAIT INTERNATIONAL AIRPORT DHAKA SEOUL TAN SON HOA (Ho Chi Minh) HAIKOU PUERTO PRINCESA

BAGHDAD NEW DEHLI/SAFDARJUNG BUSAN VIENTIANE NWSO AGANA, GUAM MACTAN

TEHRAN-MEHRABAD CALCUTTA/DUM DUM JEJU PHNOM-PENH/POCHEN TONG SAIPAN (CG) DAVAO AIRPORT

ESFAHAN BOMBAY/SANTACRUZ TOKYO BEIJING CHUUK, ECI 　

KABUL AIRPORT MADRAS/MINAMBAKKAM YANGON TIANJIN POHNPEI, CAROLINE IS. 　

Contents: EPSgram (Point Forecast)Contents: EPSgram (Point Forecast)

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•Forecast chart and probability map are used to grasp synoptic features.

•In addition to synoptic features, we need the results of ensemble forecasts in a certain grid-point, which is closest to the specified forecast point.

•The image plotted the forecast data as a time series is useful for users near the specified point.

ex. Forecast Point: Tokyo

EPSgram (Point Forecast)EPSgram (Point Forecast)

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Surface variables

1. Surface variable at model surface

Upper air2. Air temperature at 6 levels3. Upper air temperature at 925hPa4. Upper air temperature at 850hPa5. Upper air temperature at 700hPa6. Upper air temperature at 500hPa7. Upper air temperature at 300hPa

EPSgram - Contents -EPSgram - Contents -

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1. Surface variable at model surface

6-hourly surface temperature (Box plot)

6-hourly rainfall (Box plot)

Accumulated Precipitation (from initial time )

6-hourly mean sea level pressure (Box plot)

EPSgram – Surface Products – EPSgram – Surface Products –

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2. Air temperature at 6 levels

6-hourly surface temperature.(Box plot)

925hPa temperature (Box plot)• Left: time series (6-hourly) • Right: Probability (%) not to exceed

threshold

300hPa temperature (Box plot)




EPSgram – Upper Air Products for all Levels –EPSgram – Upper Air Products for all Levels –

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3. Upper air temperature at 925hPa

4. Upper air temperature at 850hPa5. Upper air temperature at 700hPa6. Upper air temperature at 500hPa7. Upper air temperature at 300hPa

6-hourly 925hPa temperature (Box plot).

Probability (%) not to exceed threshold

EPSgram – Upper Air Products for Each Levels –EPSgram – Upper Air Products for Each Levels –

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Initial time

Elem

ent

FT+0 FT+2166-hourly

Surface Temperature (degree C)Surface Temperature (degree C)

Control Member

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Interpretationof box plot

Imageof EPS

distribution

“Box plot” represents the distribution, skewness and outlier of the forecasts in EPS.

Largest value

Upper quartile(third quartile )

Median

Smallest value

Box

Whisker

Prediction value

Each Forecasts

Lower quartile(first quartile )

Description of Box Plot (box-and-whisker diagram)Description of Box Plot (box-and-whisker diagram)

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Long whisker and small box

Most members predict close values.

No extreme “low” values are predicted

Long whisker and box

The long box means wide distribution of EPS forecast.

The uncertainty of forecast is larger.

When the “median” bar is located upper or lower position from the center of the box to some extent, the distribution of EPS prediction is not “Normal” type.

A few member predict extremely “high” value.

Biased median

Box Plot (box-and-whisker diagram)Box Plot (box-and-whisker diagram)

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•Almost all members predict 1-6mm/6hours precipitation.•A few members predict “heavy rain”.

Heavy Rain

Precipitation Rate (mm / 6hours)Precipitation Rate (mm / 6hours)

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High reliability

High uncertainty

5-day forecast

Uncertainty of Forecast by “Box Plot”Uncertainty of Forecast by “Box Plot”

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Perturbed run

Plume Diagram – Accumulated Precipitation (mm) –Plume Diagram – Accumulated Precipitation (mm) –

“Plume Diagram” shows possible accumulated precipitation.

Control

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A

B C

Period-A: All members predict little precipitation.

Period-B: Many members including control run predict precipitation. Some members predict heavy rain. (sharp gradient in accumulated precipitation)

Period-C: Some member predicts precipitation, which is relatively weak compared to Period-B.

Accumulated Precipitation (mm)Accumulated Precipitation (mm)

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Let’s take a break now, and resume in 10 minutes.

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Exercise Time

“Now, we go on to try some practical exercises using a tropical cyclone events.”

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The forecast products for answering the question are enclosed in CD-ROM.

You can access the forecast products with similar interface to EPS-WEB.

We focus on a forecast of Tropical Cyclone (TC) Initial time: 30 September 2006

The TC located in the western-North Pacific, south of Japan.

Exercise Time: IntroductionExercise Time: Introduction

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•We focus on this TC (Tropical Depression: TD).

Tokyo


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Tropical Cyclone (TC): The generic terms for a non-frontal synoptic scale cyclone originating over tropic or sub-tropic oceans with organized convection and definite cyclonic surface wind circulation. TC causes strong wind and heavy rain near the core with lowest pressure. The TC is categorized by its intensity for Tropical Depression, Tropical Storm, Severe Tropical Storm and Typhoon at western-North Pacific region. The other terms are used at the other regions; ex. Hurricane.

Radar Image

Satellite Image

Surface Wind

Paths of Tropical Cyclone During the 45-year Period 1951-1995

Note: Tropical Cyclone


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Access the following contents:

Exercise\Case_TC\200609301200\index.html

All products are enclosed in the CD-ROM.

Let’s start !!

Exercise Time: Data SetExercise Time: Data Set

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1. At D+0 day (initial time), where is the largest uncertainty in mean sea-level pressure?

Uncertainty = large deviation (spread…)

Look at “stamp map” and check the “spread”.

Hint…

Exercise 1Exercise 1

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1. At D+0 day (initial time), where is the larger uncertainty in mean sea-level pressure (Psea)?

•There is a larger spread in the vicinity of TC.

ANSWER


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1. At D+0 day (initial time), where is the larger uncertainty in mean sea-level pressure (Psea)?

•There is a larger spread in the vicinity of TC.

•The large spread results from intensity of TC (See stamp map of each member).

Control run Ensemble: 19m Ensemble: 20p


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2. At D+3 days, some ensemble members predict another TC (TC_2) east of existing TC.

How many members predict TC_2 with less than 1000hPa of central pressure (Psea) at D+3 days?

Look at “stamp map” at FT=3.0day.

Hint…


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2. At D+3 days, some ensemble members predict another TC (TC_2) east of existing TC. How many members predict TC_2 with less than 1000hPa of central pressure (Psea) at D+3 days?

4-Members

ANSWER

Ensemble: 07p Ensemble: 15p Ensemble: 16m Ensemble: 18m


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2. At D+3 days, some ensemble members predict another TC (TC_2) east of existing TC.

How many members predict TC_2 with less than 1000hPa of central pressure (Psea) at D+3 days?

In addition to 4-members, several members predict a weak tropical low.

Indicating high probability of formation of another TC.

Control-run

The another TC is not clear.

Ensemble: 19m Ensemble: 08m Ensemble: 13p


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T0616

T0617

3 days later

•There are 2-tropical cyclones at 12UTC October 3.

•Although control-run could not predict T0617, several members predict the TC (T0617)-genesis.

Synoptic analysis chart at 12UTC 3 October.

Ensemble: 16mControl-run


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3. Describe the difference between Psea forecast of control run and that of ensemble mean at D+5.5 days (T+132h), and explain the reason for this difference.

•Check the “stamp map”, ensemble mean and control-run forecast.

•The ensemble mean is derived from averaging all ensemble forecasts. See the forecast of each member and compare forecast of control-run with that of ensemble members.

Hint…


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3. Describe the difference between Psea forecast of control run and that of ensemble mean at D+5.5 days, and explain the reason for this difference.

There is a weak low-pressure area south of JAPAN

ANSWER

There is a strong-low (TC) near Tokyo, Japan.


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ANSWER

•The Forecast of TC position is different between ensemble members. Some member predict TC north of JAPAN, the other south of JAPAN. The spread around JAPAN is very large.

•In ensemble mean forecast, the low pressure of TC is cancelled by averaging the forecasts of ensemble member.

Control-run Spread Member – 01m Member – 23m

Ensemble - mean

3. Contd.


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5.5 days later


Synoptic analysis chart at 00UTC October 6.

•Synoptic analysis chart indicates low pressure systems, south of Japan

•In this case, control-run could show good performance to predict a strong low.

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4. When is the TC closest to Tokyo? Answer using the time-series of control-run forecasts in Tokyo.

•The “EPSgram” is useful for point forecasts.

Hint…


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•The lowest pressure in control-run is predicted on 06UTC October 6.

ANSWER


T+0h

Control-run (green pointsgreen points)

T+216h

Element

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•The “EPSgram” is very useful for a forecast focusing on a certain point.

•The control-run forecast is plotted by green marks.

•The lowest pressure in control-run is predicted on 06UTC October 6.

Control- runControl- run(lowest pressure) (lowest pressure)

EPSgram at Tokyo for Psea

Analysis


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•The “EPSgram” is useful for point forecasts.

5. When does the accumulated precipitation exceed 200mm in ensemble members (earliest time) in Tokyo?

Hint…


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5. When does the accumulated precipitation exceed 200mm in ensemble members (earliest time) in Tokyo?

12UTC October 4.

ANSWER


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5. When does the accumulated precipitation exceed 200 mm in ensemble member (earliest time) at Tokyo.

• The EPSgram for accumulated precipitation indicate that the amount of accumulated rainfall at Tokyo point will exceed on 12UTC 4 October.

EPSgram for accumulated rainfall at Tokyo

Probability of heavy rainfall


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6. At which point does the probability of the precipitation of 48 mm/day exceeds 20 % at D+5 days?

Select among following points; Fukuoka, Tokyo and Wakkanai.

• Probability of Heavy Rain => “probability map” …

Hint…

Fukuoka

Tokyo

Wakkanai


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6. At which point does the probability of the precipitation of 48 mm/day exceeds 20 % at D+5days? Select among following points; Fukuoka, Tokyo and Wakkanai.

Tokyo.

Probability map of precipitation over 48 mm/day at D+5days.

Fukuoka Tokyo

Wakkanai

ANSWER


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6. At which point does the probability of the precipitation of 48 mm/day exceeds 20 % at D+5days? Select among following points; Fukuoka, Tokyo and Wakkanai.

• Tokyo.

•Probability map indicates the distribution of severe weather events with percentage.

Probability map of precipitation over 48 mm/day at FT=5days.

Fukuoka Tokyo

Wakkanai


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1. In this case, the uncertainty of TC track is very large. => See the spread in “Stamp Map”.

2. The control-run did not predict new TC genesis, while the ensemble prediction indicates the probability of new TC genesis. => See the forecast field in “Stamp Map”

3. The ensemble mean forecast is not always the most likely forecast. In this case, the TC in ensemble mean is predicted with weak and wide low pressure area, because the disagreement of TC positions between the ensemble members cancelled out the TC in each member by averaging. => See the forecast field in “Stamp Map”

4. It is important to pick up the scenarios of TC track and intensity from ensemble forecasts. => See the forecast field in “Stamp Map”

Summary of ExerciseSummary of Exercise

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5. The “EPSgram” indicates the period of heavy rain and the maximum accumulated rain at a certain point. => See the “EPSgram”.

6. The “Probability map” gives the distribution of the probability of extreme event, ex. high temperature and heavy rain. => See the “Probability Map”

Contd.

Summary of exerciseSummary of exercise

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Thank you for your kind attention.“Questions or Comments?”

“How to use the products of JMA Ensemble Prediction System?”

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