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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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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
Contents: Probability MapContents: Probability Map
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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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JMA EPS-WEB (Visualized EPS Products)JMA EPS-WEB (Visualized EPS Products)
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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)
700hPa temperature (Box plot)
850hPa temperature (Box plot)
500hPa 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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JMA EPS-WEB (Visualized EPS Products)JMA EPS-WEB (Visualized EPS Products)
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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
Exercise Time: IntroductionExercise Time: Introduction
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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
Exercise Time: IntroductionExercise Time: Introduction
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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
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.
•The large spread results from intensity of TC (See stamp map of each member).
Control run Ensemble: 19m Ensemble: 20p
Exercise 1Exercise 1
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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…
Exercise 2Exercise 2
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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
Exercise 2Exercise 2
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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
Exercise 2Exercise 2
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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
Exercise 2Exercise 2
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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…
Exercise 3Exercise 3
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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.
Exercise 3Exercise 3
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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.
Exercise 3Exercise 3
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5.5 days later
Exercise 3Exercise 3
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…
Exercise 4Exercise 4
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4. When is the TC closest to Tokyo? Answer using the time-series of control-run forecasts in Tokyo.
•The lowest pressure in control-run is predicted on 06UTC October 6.
ANSWER
Exercise 4Exercise 4
T+0h
Control-run (green pointsgreen points)
T+216h
Element
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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 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
Exercise 4Exercise 4
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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…
Exercise 5Exercise 5
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5. When does the accumulated precipitation exceed 200mm in ensemble members (earliest time) in Tokyo?
12UTC October 4.
ANSWER
Exercise 5Exercise 5
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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
Exercise 5Exercise 5
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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
Exercise 6Exercise 6
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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
Exercise 6Exercise 6
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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
Exercise 6Exercise 6
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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?”