1

High Resolution Climate Modeling in Indonesia

BMGBMG

Indonesia Meteorological & Geophysical Agency (BMG)

Sri Woro B. HarijonoDirector General

Prof. Dr. Mezak A. RatagDirector for Research & Development - BMG

Lessons Learned from Regency / District Scale ApplicationsLessons Learned from Regency / District Scale Applications

WMO Regional Seminar on Enhancing Service Deliveryby National Meteorological and Hydrological Services (NMHSs)

In Regional Association V (South-West Pacific) Kuala Lumpur, Malaysia, 2-6 April 2007

Climate Forecast Applications

BMGBMG Outline• Introduction • The needs of meteorological services at

regency/district scale• Climate forecasting approach• Climate forecast dissemination activities• Lessons learned• Summary

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Sectorial Applications

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BMGBMG

RainyRainySeasonSeason

onsetonset1991-2003Relative to1961-1990

Sumatra

4

BMGBMG

WestJava

RainyRainySeasonSeason

onsetonset1991-2003Relative to1961-1990

BMGBMG

CentralJava

RainyRainySeasonSeason

onsetonset1991-2003Relative to1961-1990

5

BMGBMG

EastJava

RainyRainySeasonSeason

onsetonset1991-2003Relative to1961-1990

Kel-1 : bag sel Haurgelis/ Gabuswetan/

Bangodua

Kel-2 : bag.utara Indramayu

Kel-3 : bag.utara Anjatan/Sukra

Kel-4 : Krangkeng /Karangampel

Juntinyuat/ Sliyeg/Kertasemaya/

Jatibarang/Widasari/Sindang/

Lohbener/ bag.Utara Bangodua

Kel-5 : Kandanghaur/Bongas/bag.utara

Gabuswetan/bag.timur

Anjatan/Lohsarang

Kel-6 : Cikedung /bag.sel.Gabuswetan

/bag.utara Haurgelis/ Lelea

BMG

6

BMGBMG

JEMBER, EAST JAVA

TANAH DATARWEST SUMATRA

BLITAREAST JAVA

MINAHASANORTH SULAWESI

BANDUNGWEST JAVA

MALANGEAST JAVA

REG. CENTER 7

REG. CENTER 8

REG. CENTER 9

REG. CENTER 4

REG. CENTER 10

REG. CENTER 6

REG. CENTER 1

REG. CENTER 3

REG. CENTER 5

NATNAT’’L. L. CENTERCENTER

10 REGIONAL TSUNAMI WARNING CENTERS+ 30 HYDROMET-HAZARDS WARNING SYSTEM

REG. CENTER 2

HorticulturePest Management

Rice ProductionFloodLandslide

Rice Production PlantationFlood

Water ManagementSalt Mining

Pilot sites forClimate Appl.:

45 Regencies/Districts

(~10%)

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National, Regional and Local Weather & Climate ModelsNational, Regional and Local Weather & Climate Models

HorizontalResolution

50 km

30 km

5-10 km

BMGBMG Meso/Local~Kabupaten/

Regency

0

200

400

600

1980 1982 1984 1986 1988 1990 1992

Mon

thly

Rai

nfal

l (m

m/m

onth

)

0

20ObservedStatistical DownscalingDynamical Downscaling

Biak

Dynamical models: experimental, low performance

8

ARARWave-

let

FilterFilterKalmanKalman

ANFIS

EOF

AOAO--GCMGCM

Multi-regr.

CCA PCANon-Linier

RCMRCM

Numerical/Dynamical Models

Statistical Models

EnsembleEnsemble

High Res.High Res.Weather &Weather &

ClimateClimateForecastsForecastsStatistical

Downscaling

DynamicalDownscaling

BMG

SpatialPlanning

Crops

Waterresources

Plantation

Fishery

Energy &Industry

Hidromet.Disaster

ManagementTourism

Climate risk management: Demonstration sites in SE AsiaDiversity of climate hazards + socio-economic systemsMulti-scale partnerships Angat, Angat,

BulacanBulacan

Nusa Tenggara Timur

IndramayuIndramayu

IloIlo

Can Tho

Quang TriQuang Tri

Bali

BMGBMG

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Climate Climate ModelModel

GCMGCM

LAMLAM

StatStat

INTERFACE

Monthly Climate

Data

ENSO & Dipole Mode

Monthly Indexes

CLIM

GEN

Daily Climate

Data

DSSAT

CROP Simulation

MODEL

PRO

DU

CTIVITY PR

EDIC

TION

Integrated ClimateIntegrated Climate--Crop ModelCrop Model

GCM : General Circulation ModelLAM : Limited Area ModelDSSAT : Decision Support System for Agrotechnology Transfer

BMG

Basic MapLandslide & FloodSusceptibility Maps

BakosurtanalDitjen. Geologi & SDM

Dep. Kimpraswil

Sumber: Peta Rawan Longsor DGTL, Prediksi Curah Hujan BMG dan Prediksi Probabilitas Hujan LAPAN

PETA ANTISIPASI BENCANA LONGSOR PADA MUSIM HUJAN 2002-2003 DI PULAU JAWA

BATAS KABUPATEN

Non DPMDaerah Perhatian1Daerah Perhatian 2Daerah Perhatian 3aDaerah Perhatian 3b

LEGENDA

11°

11°

10°

10°

9°

9°

8°

8°

7°

7°

6°

6°

5°

5°

4°

4°

104°

104°

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106°

106°

107°

107°

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108°

109°

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110°

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112°

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113°

114°

114°

115°

115°

116°

116°-1600000

-1600000

-1400000

-1400000

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-1000000

-1000000

-800000

-800000

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-600000

-400000

-400000

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-200000-1

20

00

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-1

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-1

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00

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-8

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0

-8

00

00

0

-6

00

00

0

-6

00

00

0

Sumber: Peta Rawan Banjir Dept.Kimpraswil, Curah Hujan BMG dan Prediksi Probabilitas Hujan LAPAN

PETA ANTISIPASI BENCANA BANJIRPADA MUSIM HUJAN 2002-2003 DI PULAU JAWA

BATAS KABUPATEN

Non DPMDaerah Perhatian1Daerah Perhatian 2Daerah Perhatian 3aDaerah Perhatian 3b

LEGENDA

Dpmbmg

11°

11°

10°

10°

9°

9°

8°

8°

7°

7°

6°

6°

5°

5°

4°

4°

104°

104°

105°

105°

106°

106°

107°

107°

108°

108°

109°

109°

110°

110°

111°

111°

112°

112°

113°

113°

114°

114°

115°

115°

116°

116°-1600000

-1600000

-140000 0

-140000 0

-1200000

-1200000

-100000 0

-100000 0

-800000

-800000

-600000

-600000

-400000

-400000

-200000

-200000-1

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00

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-1

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-8

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RainfallForecast

GIS Gridding&

SusceptibilityClassification

Climate & WeatherObservation Data

BMG

Hazard Atlas & Prediction MapHazard Atlas & Prediction MapFor Landslides & Floods For Landslides & Floods

BMG

BMG

Universities,Research Institutes

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Fire Danger Rating SystemFire Danger Rating SystemBMG

Day 2 20:00

Wind FieldWind Field

Particle TrajectoryParticle Trajectory

Air Quality & Air PollutionDispersion Model

BMGBMG

‘washing’by rainfall

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BMGBMG

Air Pollution Dispersion ModelCase Study: West Sumatra

BMGBMG

Tree Diagram for 30 VariablesSingle Linkage

Euclidean distances

Link

age

Dis

tanc

e

40

60

80

100

120

140

160

180

200

B

138

B

137

B

133

B

131A

B

180

B

140

B

185B

B

126A

B

181A

B

185

B

136

B

127A

B

68X

B

181B

B

151A

B

CLL

B

173A

B

196A

B

UJB

B

127

B

163C

B

163A

B

166

B

174

B

147

B

160

B

163G

B

163

B

170

B

162

Linkage Distance

Plot of Linkage Distances across StepsEuclidean distances

Step

Link

age

Dis

tanc

e

40

60

80

100

120

140

160

180

200

220

0 3 6 9 12 15 18 21 24 27 30

Positioning & Positioning & DataData

CollectingCollectingClustering Zoning Forecasting

Forecasts; Model:(1,0,1)(1,0,1) Seasonal lag: 36Input: T2BANDUN

Start of origin: 1 End of origin: 504

Observed Forecast ± 90.0000%

-150

-100

-50

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50

100

150

200

250

300

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MultiMulti--ModelModelDevelopDevelop--

mentment

ValidationValidation

in collaboration with Dept of Agriculture, IPB, IRI, ADPC

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ForecastsAdvisoriesWarningsScenarios…

UserUser

ARARWave-

let

FilterFilterKalmanKalman

ANFIS

EOF

AOAO--GCMGCM

Multi-regr.

CCA PCANon-Linier

RCMRCM

Numerical/Dynamical Models

Statistical Models

EnsembleEnsemble

High Res.High Res.Weather &Weather &

ClimateClimateForecastsForecastsStatistical

Downscaling

DynamicalDownscaling

BMG

SpatialPlanning

Crops

Waterresources

Plantation

Fishery

Energy &Industry

Hidromet.Disaster

ManagementTourism

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Science Forum Science-Policy Forum

CREDIBLECREDIBLE LEGITIMATELEGITIMATE

SALIENTSALIENT

BMGClimate Forecast Dissemination Activities

Science-Policy-User Forum

Science Forum Science-Policy Forum

CREDIBLECREDIBLE

SALIENTSALIENT

BMGClimate Forecast Dissemination Activities

LEGITIMATELEGITIMATE

Crucial roles of intermediaries

14

Science Forum Science-Policy Forum

CREDIBLECREDIBLE

SALIENTSALIENT

BMG

Climate Forecast Dissemination Activities

LEGITIMATELEGITIMATE

Asia-Pacific Network on Global Change Research (APN) Project 2006-2008

““ Increasing Adaptive Capacity of Farmers to Extreme Climate EvenIncreasing Adaptive Capacity of Farmers to Extreme Climate Events and ts and Climate Change through ScienceClimate Change through Science--PolicyPolicy--Community NetworkingCommunity Networking””

Science-Policy-Community Forum

Excerpts from the Excerpts from the WMO EXPERT MEETING ONWMO EXPERT MEETING ONREVIEW OF CURRICULUM INREVIEW OF CURRICULUM INAGRICULTURAL METEOROLOGY AGRICULTURAL METEOROLOGY

(14(14--16 March 2007, New Delhi, India)16 March 2007, New Delhi, India)

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First reason of scant use of agrometeorological services

Lack of cooperation between

the institutions providing information and relevant advisories andthose responsible for their transfer to the farming community

Second reason of scant use of agrometeorological services

Insufficient education and training of the user community, including the farm advisory services, that provide specific agricultural advice from general weather information

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Data support systems have grown enormously in Data support systems have grown enormously in importance, particularly in the applications in the importance, particularly in the applications in the

industrialized world. industrialized world. The same applies to research.The same applies to research.

Training, education and extension have followed Training, education and extension have followed these developments but appreciably less these developments but appreciably less

((StigterStigter, 2007), 2007)

Society and economics are no meteorologySociety and economics are no meteorologybut consequences and use but consequences and use

((that is managementthat is management!)!)of water, radiation/heat and air of water, radiation/heat and air

in society and economics, in society and economics, which strongly influence various aspects and which strongly influence various aspects and

behaviourbehaviour of the society and economyof the society and economyand vice versaand vice versa

slowly became an undercurrent slowly became an undercurrent in applied meteorology.in applied meteorology.

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Translated to poorer countries, Translated to poorer countries, sociosocio--economic aspectseconomic aspects of for example irrigation, of for example irrigation, storage, agroforestry, floods, drought, erosion storage, agroforestry, floods, drought, erosion

and desertification, frostand desertification, frost,, wind protection, simple wind protection, simple artificial growth conditions, sustainable farming artificial growth conditions, sustainable farming

and related users/farmersand related users/farmers’’ income income became after all became after all

additional prioritiesadditional priorities in the undercurrent in the undercurrent of applied meteorology.of applied meteorology.

A <-------|-------> B <------ |-------> C| || |E2E2 E1E1

E1 = Meteorological Action Support Systems on Mitigating Impacts of Disasters

E2 = Meteorological Services Supporting Actions of Producers

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AA = Sustainable livelihood systems

BB = Local adaptive strategiesLocal adaptive strategies (knowledge pools based on traditional knowledge and indigenous technologies)

+ Contemporary knowledge poolsContemporary knowledge pools (based on science and technology)

+ Appropriate policy environmentsAppropriate policy environments (based on social concerns and environmental considerations, scientifically supported and operating through the market where appropriate)

CC = Support systems to meteorological services: data + research + education/training/ extension + policies

A <-------|-------> B <------ |-------> C| || |E2E2 E1E1

E1 = Meteorological Action Support Systems on Mitigating Impacts of Disasters

E2 = Meteorological Services Supporting Actions of Producers

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Of course in each of the three domains one makes use of data, research, education/training/ extension and policies, but only in the C domain are they to be considered of a purely supportive nature. In the B and A domains and in E2 and E1 they are (or should be) used/carried out in action.

This is an essential difference that so far is most often not recognized, because, following developments in developed countries, we are everywhere too obsessed with methodologies and science as such.

The closest action is most often in E1. These actions are the bulk of our good intentions to mitigate impacts of disasters and to use the four ingredients of the support systems in applications in the real world.

Unfortunately they have often little to do with the needs of the A-domain, in which meteorological services should deliver support for users/farmers’ decisions/actions.

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E2 supporting actions are actual meteorological services, that relief

constraints under the livelihood conditions of the local users/farmers.

This means that needs assessments with respect to meteorological services for the user/farming systems concerned

should come first.

• However, the pushing is from right to left.• Exemplifying from the relation between

industry and physics in the fifties, a pushing from left to right is possible.

• Changes in what pushes breeding research show that the same is possible in meteorological services: A-domain guides C-domain.

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curricula at post graduate and lower levels curricula at post graduate and lower levels should pay attention to the enormous should pay attention to the enormous

need for training need for training of meteorological extension personnel at the of meteorological extension personnel at the

intermediate level.intermediate level.

In non-industrialized countries, training of intermediaries would go a long way in solving these problems for various groups of all but the richest and best educated farmers. Training programs at all levels must therefore be adapted to national and regional needs.

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In recent operational developments this includes developing extension around the establishment of meteorological services.Particularly in all poorer countries.Intermediaries should there be

the ones in direct contact with the user communities.

The first kind of meteorological intermediarieswould be close to the centers where the meteorological information useful for decision-makers is generated.

The second kind of (meteorological) extension intermediaries should be closest to the users/farmers and operate exclusively in the A-domain, using E2 guidance. They should learn to articulate the needs of the user/farmer communities better and detect meteorological components that need attention.

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The education and in service training of these two kinds/classes of meteorological extension intermediaries is an essential part of the new approach,that appears necessary in education, training and extension in agricultural meteorology as well as in other applications of meteorology.

A 2007 World Bank Report A 2007 World Bank Report on poverty in Indonesia, on poverty in Indonesia,

that was highly reviewed, says:that was highly reviewed, says:

““Change happens when intermediaries Change happens when intermediaries get involved to help the poorget involved to help the poor””

This definitely also applies toThis definitely also applies toour kinds of intermediaries.our kinds of intermediaries.

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Activity

Prayer ceremony post-harvest

Water Users Asso. exec. Meeting

Water Users Asso. members meeting

Canal and road maintenance

Securing land from open grazing

Land preparation

Nursery

Seedling transplant

Fertilizing

Weeding

Pest and disease management

Draining field water & canal rehab

Harvesting

Collecting fee from farmer members

Evaluation & planning for next season

Dec Feb Mar Apr Oct NovJan May Jun Jul Aug Sep

Activity Schedule for Planting Season. Irrigated Paddy, Nusa TenggaraTimur (Kupang Dist.)

BMGBMG

W W

W/C W/C

W/C W/C

W/C W/C

C

BMG

IPB

Universities

Agriculture Office at District Level

Marine and Fishery Forestry

Health Tourism

Aviation - Maritime

Directorate of Plant ProtectionDept. of Agriculture

Related institutions

BMG

Translation of Climate OutlookScientific Language Operational Language(“Below Normal”) = (Lack of water)

Provision of Climate OutlookIn “meteorological language”

Conversion of Operational Language into e.g. Crop Management Strategies

Dissemination of Information to Farmers and evaluation of Farmers

Response

BMG

Change Crop Pattern !Change Planting Time !Change Crop Variety !

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ARARWave-

let

FilterFilterKalmanKalman

ANFIS

EOF

AOAO--GCMGCM

Multi-regr.

CCA PCANon-Linier

RCMRCM

Numerical/Dynamical Models

Statistical Models

EnsembleEnsemble

High Res.High Res.Weather &Weather &

ClimateClimateForecastsForecastsStatistical

Downscaling

DynamicalDownscaling

BMG

SpatialPlanning

Crops

Waterresources

Plantation

Fishery

Energy &Industry

Hidromet.Disaster

ManagementTourism

BMG

IPB / BMG/ Ditlin / ADPC

Field Guide I (PL I ) at District or Sub-district Level

Field Guide II (PL II ) at Sub-district or Village Level

or Farmer Group

Farmer Group and Farmers Family

e. Type of Weather Instrumentf.Exerciseg.Exercise (Advance)h.Climate Application (Assessment)

a. Weather and Climate Scienceb. Atmosphere Dynamic Sciencec. Climate Analysisd. Weather/ Climate Information

Technology

Modules 1-2

Modules 3-4

• To translate operational language into farmers language • To disseminate information and technology to farmers• To train farmers based on learning by doing approach• To facilitate and motivate farmers to adopt the technologies

• To translate scientific language to operational language• To train PL 2 in technology and Methodology (see & listen)• To provide inputs to PL 2 in designing detail program for

operational project

Function

Function

BMG

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Priority Areas of Climate Field School( Java )

BMG

Priority Areas of Climate Field School( Sumatra )

BMG

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Priority Areas of Climate Field School( Kalimantan )

BMG

Priority Areas of Climate Field School( Sulawesi )

BMG

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Priority Areas of Climate Field School( Bali )BMG

Priority Areas of Climate Field School( East Nusa Tenggara)BMG

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Another new challenge: Uncertainty in Decision Making

Contexts…

Department of Engineering and Public Policy

Carnegie Mellon University

Mapping words to

probabilities

Probability that subjects associated with the qualitative description

0.00.20.40.60.81.0

Almost certain

Probable

Likely

Good chance

Possible

Tossup

Unlikely

Improbable

Doubtful

Almost impossible

range of individual upper bound estimates

range of individual lower bound estimates

range from upper to lower median estimate

Qua

litat

ive

desc

riptio

n of

unc

erta

inty

use

d

Figure adapted from Wallsten et al., 1986.

This figure shows the range of probabilities that people are asked to assign probabilities to words, absent any specific context.

IPCC TGCIA MeetingMaynooth, 2004

30

Department of Engineering and Public Policy

Carnegie Mellon University

The bottom lineWithout at least some quantification, qualitative descriptions of uncertainty convey little, if any, useful information.The climate assessment community is gradually learning this lesson. As you know Steve Schneider and Richard Moss have worked hard to promote a better treatment of uncertainty in the work ofthe IPCC. At my insistence, in its 2000 report the U.S. national assessment synthesis team gave quantitative definitions to five probability words:

BMGBMG

Tree Diagram for 30 VariablesSingle Linkage

Euclidean distances

Link

age

Dis

tanc

e

40

60

80

100

120

140

160

180

200

B

138

B

137

B

133

B

131A

B

180

B

140

B

185B

B

126A

B

181A

B

185

B

136

B

127A

B

68X

B

181B

B

151A

B

CLL

B

173A

B

196A

B

UJB

B

127

B

163C

B

163A

B

166

B

174

B

147

B

160

B

163G

B

163

B

170

B

162

Linkage Distance

Plot of Linkage Distances across StepsEuclidean distances

Step

Link

age

Dis

tanc

e

40

60

80

100

120

140

160

180

200

220

0 3 6 9 12 15 18 21 24 27 30

Positioning & Positioning & DataData

CollectingCollectingClustering Zoning Forecasting

Forecasts; Model:(1,0,1)(1,0,1) Seasonal lag: 36Input: T2BANDUN

Start of origin: 1 End of origin: 504

Observed Forecast ± 90.0000%

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200

250

300

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MultiMulti--ModelModelDevelopDevelop--

mentment

ValidationValidation

More attention to the quality of data :More attention to the quality of data :the instruments (maintenance, calibration, environment)the instruments (maintenance, calibration, environment)

the observationthe observationand, last but not least,and, last but not least,

the observersthe observers

Preliminary result :60% not functioning

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Designing & Production of Automatic Weather Station (AWS)

BMGBMG

The need:Weather & Climate forecast information that:

is localized

timely

in easily understandable language

Capacity to generate the localized information

Experience in communicating probabilistic scientific information for practical use by end users

BMGChallenges for Provider to Meet

Agricultural End Users Needs

Challenges for Provider:

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BMGBMGConcluding RemarksConcluding Remarks

We have described the procedures of high resolution climate forecasting applied in Indonesia for various agricultural purposes based on dynamical and statistical downscaling in combination with some advanced statistical techniques

Lessons learned from the implementation of the techniques in producing high resolution climate forecasts at regency/district scale in Indonesia indicate some advantages of this multi-model approach:• computationally inexpensive • provide local information in the form of

probability density function risk management

Crucial impedances in the dissemination of climate information: diversity in “language”, socio-economic behaviours, institutional framework/arrangements

BMG

Thank YouThank You

Mt. Lokon, Tomohon, N.Sulawesi

TerimaTerima KasihKasih