JMA Activity in Sub-seasonal Forecasting

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Climate Prediction Division / JMAYuhei Takaya

WWRP/THORPEX/WCRP Sub-seasonal to Seasonal Implementation Planning Meeting2-3 December 2011, Geneva Switzerland

Thanks to M. Harada, N. Adachi, S. Matsueda

• Review of plans• Integrated EPS on the next HPC (2012.6-)

• Ongoing activity• More user-oriented and seamless products:

Extreme Forecast Index (EFI), meteogram• MJO diagnostics with Emphasis on sources of

predictability at monthly time scale

Table of Contents

Climate Prediction Division, JMA2

Toward “Seamless EPS (weekly - monthly)”Toward “Seamless EPS (weekly - monthly)”• Further seamless forecast

• Climate information– Forecast products– Monitoring-forecasting

• Model development• Forecast system

– More efficient– More accurate

Review of plans in sub-seasonal forecasting

Climate Prediction Division, JMA3

Images from http://visibleearth.nasa.gov/

Integrated “seamless” EPS on the next HPC

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Weekly EPS(TL319L60)

2-Week EPS (Early Warning)

Monthly EPS (TL159L60)reforecast

2-Week EPS (TL479L100)reforecast

Monthly EPS (TL319L100)

Week-1 Week-2 Week-3 Week-4

present

FY2014

FY2013

FY201?

2-Week EPS (TL479L100)reforecast

Monthly EPS (TL319L100?)reforecast

2-Week EPS (Early Warning)

1. More user-oriented and seamless products: Extreme Forecast Index (EFI), meteogram　 Masashi Harada and Yuhei Takaya

Ongoing activity

Climate Prediction Division, JMA5

“Seamless” Climate Information

Seamless climate Information on the extreme weather events

Climate Prediction Division, JMA6http://ds.data.jma.go.jp/tcc/tcc/products/climate/

Past Future

Extreme weather forecasting based on EFI

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• EFI (Extreme forecast index): Lalaurette (2003)• Measure of the difference between

a probabilistic forecast and a climate distribution

from Lalaurette(2003)Threshold(eg. 10m wind)

Prob

abili

ty n

ot to

exce

ed th

resh

old

climate

forecast

• Definition of the EFI(ECMWF Newsletter, No.107)

Cumulative distributions offorecast and climate

All forecast members are below the 0 percentile of the climate

All forecast members exceedthe 100 percentile of the climate

How to obtain probabilistic distributions?

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time

Hindcast data(150 members)

Hindcast (reforecast) data(150 members: 5 membersfor each initial day, 30years)

average period(ex. 7days)

JMA operational 1-month forecast(25 members for each initial day)

Forecast CDF

Climate CDF

We produce climate distributions from hindcast runs of two initial days

Examples of EFI-products (1)

• Time series of the EFI(top) and the probability distribution of the forecast and the model climate(7-days average, bottom)

EFI

forecast and model climate

Extreme cold Extreme warm

EFI-Meteogram at London(initial date: 2011/11/10)

EFI-map for 850hPa temperature averaged from 11/11 to 11/16

EFI-Map EFI time series - Meteogram

Examples of EFI-products (2)

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• Warning map for extreme weather events• Possibilities for various extreme weather events

are summarized on one map.

Extreme: EFI ≧ 0.8Above normal: EFI ≧ 0.5

2. Research related to MJO• MJO diagnostics: Emphasis on sources of

predictability at monthly time scale Satoko Matsueda and Yuhei Takaya

• Case study: MJO influence on extratropic circulation

Kengo Miyaoka and Yuhei Takaya Shuhei Maeda

Ongoing activity

Climate Prediction Division, JMA11

MJO index skill

MJO Skill of JMA monthly models•correlation coefficient falls below 0.6 on day 13•predicted phase speed is faster than observed phase speed•predicted amplitude is smaller than observed amplitude

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RMSE

COR

Phase error (PERR)

Relative amplitude difference (AERR)

Lead time (day)

faster

slower

larger

smaller

MJO Life cycle composite OLR/Wind200 Winter

analysis model FT=2week

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Lag-correlation of OLR/U850 in Winter

analysis

Eastward propagation of OLR/U850 anomaly is not well simulated.

Fig. Lag correlation of intraseasonal OLR (shaded) and U850 (contour) averaging 10S-10N at all longitudes against OLR and U850 at an Indian Ocean reference point (OLR:10S-5N,75-100E, U850:1.25-16.25S,68.75-96.25E).

For hindcast, a forecast time of 10 days corresponds lag = 0.

model (FT=10day : lag=0)

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Lag correlationOLR : shadedU850 : contour

Lag correlationOLR : shadedU850 : contour

FT=15dayFT=10day

FT=1day

1800 0 1800 0

0

15

-15

0

15

-15

Case study of MJO influence on the extratropic circulation

Climate Prediction Division, JMA15

Hovmöller diagram of 200-hPa velocity potential anomaly averaged over 10N-10S. (2011/9/1-11/10)

The second strongest MJO during last 30 years

[W/m2]

Wave train dispersed from wave source generated by MJO

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OLR & Velocity Potential 200-hPa anomalies (2011/10/29 - 11/2)

5-day running mean temperature anomalies (Sep.-Nov. 2011)

Sep. Oct. Nov.

NorthernJapan

EasternJapan

WesternJapan

Okinawa & Amami

[W/m2]

Temperatures in Western Japan (+3.4) and Okinawa & Amami area (+2.2) were highest records (since 1961) for the first 10 days of Nov. 2011.

Was the event predicted?JMA monthly forecast for 10/28-11/3 (I.C. 10/20)

850-hPa temperature Western Japan

200-hPa velocity potential and divergent wind anomalies

200-hPa stream function, rain anomalies

The week-2 forecast (I.C.: Oct. 20th) successfully predicted the event.

850-hPa temperature

[K]

[K]

[mm/day]

10-6 [m2/s]

CHI200 analysis T850 analysis T850 JMA model

When active convection is in Indian Ocean, T850 anomaly pattern can be reproduced in Asia.

0 73-3-7

Composite : initial Phase 2 (active convection in Indian Ocean)

Influence of MJO on Asian climate

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FT= 6day

FT= 1day

[K]10-6 [m2/s]

• Review of plans• Integrated “seamless” EPS on the next HPC (2012.6-)

• Ongoing activity• More user-oriented and seamless products:

Extreme Forecast Index (EFI), meteogram• Emphasis on sources of predictability

at monthly time scale (MJO diagnostics)

Summary

Climate Prediction Division, JMA19