Development of High Resolution Models and Its Applications for

Development of High Resolution Models and Its Applications for Weather and Climate Risk Reduction in Indonesia:

BMGBMG

Indonesia Meteorological & Geophysical Agency (BMG)

Mezak A. RatagDirector for Research & Development - BMG

The First International Workshop on Prevention and Mitigation ofMeteorological Disasters in Southeast Asia

Kyoto, Japan, 3-5 March 2008

Recent Development using CCAM

Climate Forecast Applications

BMGBMG Outline• Introduction • The needs of meteorological services at

regency/district scale• Forecasting approach: Introducing CCAM• Some remarks on applications and

dissemination activities

Acknowledgement. The slides on CCAM are mostly based on the material prepared byMarcus Tatcher (CMAR – CSIRO). The results of CCAM presented here are all the outputsof the model run at BMG R&D Centre

Sectorial Applications

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

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%)

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

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

ARARWave-

let


ANFIS

EOF

AOAO--GCMGCM

Multi-regr.

CCA PCANon-Linier

RCMRCM


Statistical Models

EnsembleEnsemble



Downscaling


BMG

SpatialPlanning

Crops

Waterresources

Plantation

Fishery

Energy &Industry

Hidromet.Disaster

ManagementTourism

CCAM

CMAR Introduction

Overview

General introduction to CCAM

It includes:The Conformal Cubic grid

Using the Schmidt transform for regional forecasting

Multiple nesting techniques for downscaling

Topography and land-use datasets

A more detailed discussion of using CCAM for NWP and climate applications will be given in subsequent presentations

CMAR Introduction

Regional climate modelling at BMG (& LAPAN)

Used DARLAM for most of 90s1-way nested limited-area model

For last few years using the conformal-cubic atmospheric model (C-CAM), a variable-resolution global model

avoids boundary reflections

avoids difficulties should forcing model and driven model have different inherent cold or moist biases

can enforce conservation in a proper manner

CMAR Introduction

CCAM technical notes

CCAM employs a Conformal-Cubic grid

Typically each face contains 48x48 grid points (i.e., a C48 grid) and 18 vertical sigma levels (total points = 48x48x6x18)

Devised by Rancic et al., QJRMS 1996

CMAR Introduction

Sigma levels

CMAR Introduction

Gnomonic-cubic grid and panels

Sadourny(MWR, 1972)

Semi-Lagrangian advection study by McGregor (A-O, 1996)

CMAR Introduction

CMAR Introduction

The Conformal-Cubic grid

The Conformal-Cubic (CC) grid provides CCAM with a number of advantages, including:

No singular points (e.g., the north or south pole).No hard boundaries – CCAM is a global model.The grid can be stretched for high resolution forecasts (e.g., 1km).The stretched grid can be repositioned anywhere in the world.

CMAR Introduction

CCAM features

2-time-level semi-implicit hydrostatic (recently, also non-hydrostatic)semi-Lagrangian horizontal advection with bi-cubic spatial interpolationtotal variation diminishing (TVD) vertical advectionunstaggered grid, with winds transformed to/from C-staggered positions before/after gravity wave calculations using reversible interpolationminimal horizontal diffusion needed:

Smagorinsky style; zero is fineCartesian representation of all awkward terms:

calculation of departure points (McGregor, 1996, MWR)advection or diffusion of vector quantities

indirect addressing keeps code simpleweak off-centering (in time) used to avoid semi-Lagrangian "mountain

resonances“careful treatment of surface pressure and pressure-gradient terms near terraina posteriori conservation of mass and moisturegrid is isotropic

CMAR Introduction

CCAM physical parameterizations

cumulus convection:

new CSIRO mass-flux scheme, including downdrafts

evaporation of rainfall

GFDL parameterization for long and short wave radiation

interactive cloud distributions

derived prognostically from liquid water

gravity-wave drag scheme

stability-dependent boundary layer and vertical mixing with non-local option

vegetation/canopy scheme

6 layers for soil temperatures

6 layers for soil moisture (Richard's equation)

option for cumulus mixing of trace gases

CMAR Introduction

CCAM technical description

CMAR Introduction

A uniform C48 grid. Note the (approx) uniform 200km grid spacing.

200km

Schmidt = 1.


CMAR Introduction

60km

750km

The CCAM grid can be stretched (using a Schmidt transformation) to also provide a regional forecast.

Schmidt = 3.33


CMAR Introduction

CMAR Introduction

The Conformal-Cubic grid

The ability to stretch the grid is crucial for generating high resolution forecasts (e.g., 1km).

For example, to model the whole world for 1 day we would need:

At 200km C48 grid 155Mb <1min

At 20km C480 15.5Gb ~9.6hrs

At 2km C4800 1550Gb ~1.1yrs

60km C48 155Mb 2min

(stretched)

Hence, a stretched grid is necessary to make the problem computationally tractable.

CMAR Introduction

Downscaling

The advantage of the stretched grid is the number of grid points is the same (i.e., the same RAM).

Only the ‘time step’ dt, needs to be reduced.

200km uniform grid (dt = 36 min) 60km stretched grid (dt = 20 min)

CMAR Introduction


As the grid is degraded, its ability to resolve synoptic weather patterns is diminished

However, the cost of stretching is that the grid resolution degrades on the opposite side of the globe.

8km grid over New Zealand

1km grid over New Zealand

CMAR Introduction


Missing data in the highly stretched region is replaced with weather information from the previous, lower resolution forecast

To address this problem, CCAM uses a multiple stretched grid technique to ‘step down’ or downscale the forecast

8km

1km

60km

Sample 60 km C48 grid over IndonesiaBMGBMG

CCAM +3Tropics

Sample 14 km C48 grids over Kalimantan and Papua

BMGBMG

CMAR Introduction

8 km resolution

No hard boundaries at panel edges:Far-field nudging from 60 km model run on this panel

Rest of world8 km resolution throughoutthis panel About 50 km resolution

at this edge

CMAR Introduction


When forecasting the weather, it is important to describe the surrounding topography and vegetation.

The forecast can be significantly affected by:Land or water

Topography

The soil type (e.g., clay, sand, loamy-clay, etc)

The land-use type (e.g., forest, crops, urban, etc)

CMAR Introduction


The CCAM system stores the topography data at three scales:

10km for the whole globe (topo2)

1km for the whole globe (*.DEM)

250m for all of Australia (*.ter).

CCAM will determine what topography data it needs, once the user specifies a latitude and longitude.

NOTE: All CCAM coordinates (i.e., latitude and longitude) are relative to the topography.

CMAR Introduction


Land-use data is based on two datasets:

A 1deg global dataset with 12 land-use categories (SiB).A 6km resolution dataset over Australia with 33 land-use categories (Gratez’s)A 1deg global soil dataset with 10 Zobler categories

Also available is the Ecosystems dataset (Meteo France) :

1km global dataset with 215 land-use categories10km global soil dataset (sand and clay fractions) that is converted to the 10 Zobler categories

CMAR Introduction

CCAM training example (runccam.sh)

Topography andLand-use data

CCAM 200 km(uniform global)

Initial conditions

Processed CCAMOutput

CMAR Introduction

CCAM training example (downscale.sh)

CCAM 200 km simulation(output from runccam.sh)

CCAM 60 kmIndonesia

Initial conditionsinterpolated from

host forecast

Processed CCAMOutput

Topography andLand-use data

(far field nudging)

CMAR Introduction

CMAR Introduction

CSIRO Mk3(changing toHadGEM)

CCAM

NCEP 2.5degreanalyses

CMAR Introduction

Technology uniqueness

Can predict weather for up to 8 daysEasily relocatable anywhere in worldHigh forecast accuracy in targeted areas

operationally run at 1 km resolution over selected areas

Forecast system runs automaticallyNo 'hard' lateral boundaries - less artificial damping

only far-field nudging needed for higher resolution runs

Low computing power required. Approximate run times on standard Linux with a single 3.2 GHz Intel Xeon processor:

1 day at 60 km - 2 mins1 day at 8 km - 14 mins

Nearly linear speedup on parallel machines6 processor version used for operational forecasting

PRAKIRAAN TINGGI GELOMBANG SATU MINGGU KE DEPAN

GELOMBANG DAPAT TERJADI 2,0 M S/D 2,5 M DI : LAUT CINA SELATAN, PERAIRAN ENGGANO DAN LAMPUNG SELATAN, PERAIRAN SELATAN JAWA BARAT, LAUT JAWA BAGIAN BARAT, PERAIRAN KALIMANTAN SELATAN, PERAIRAN SULAWESI UTARA, TELUK TOMINI,PERAIRAN SANGIHE TALAUD, LAUTBANDA BAGIAN BARAT, LAUT SULAWESI BAGIAN TIMUR, LAUT MALUKU BAGIAN SELATAN, LAUT BURU, PERAIRAN BARAT FAK-FAK DAN PERAIRAN UTARA PAPUA .( YANG BERBAHAYA BAGI PERAHU NELAYAN DAN TONGKANG )GELOMBANG DAPAT TERJADI 2,5 M S/D 3,0 M DI : LAUT JAWA BAGIAN TENGAH, PERAIRAN SELATAN KALIMANTAN TENGAH, PERAIRAN SELATAN BANJARMASIN, PERAIRAN MAJENE, PERAIRAN SUMBA, PERAIRAN SULAWESI SELATAN DAN TENGGARA, TELUK BONE, PERAIRAN HALMAHERA, LAUT SERAM, PERAIRAN AMBON, PERAIRAN MALUKU SELATAN DAN PERAIRAN KEP. RAJA AMPAT . ( YANG BERBAHAYA BAGI PERAHU NELAYAN ,TONGKANG DAN FERRY ) DAN PERAIRAN )REKOMENDASI ( PERINGATAN DINI ) GELOMBANG DAPAT TERJADI 3,0 M S/D 6,0 M DI : PERAIRAN SELATAN JAWA TENGAH, SAMUDERA HINDIA SELATAN JAWA TIMUR DAN BALI, PERAIRAN MASALEMBU, SELAT BALI DAN LOMBOK, SELAT ALAS DAN SUMBA, PERAIRAN BALI DAN NUSATENGGARA, LAUT FLORES, LAUT SAWU, SAMUDERA HINDIA SELATAN NUSATENGGARA, LAUT TIMOR, LAUT BANDA BAGIAN SELATAN, LAUT ARU, LAUT ARAFURA, PERAIRAN MERAUKE DAN PERAIRAN SELATAN PAPUA . YANG BERBAHAYA BAGI SEMUA JENIS KAPAL)

UP DATE DATA TGL. 08 JANUARI 2008

BMG

OceanOceanWaveWaveforecastingforecasting

CCAM CCAM WindwaveWindwave

Fire Danger Rating SystemFire Danger Rating SystemBMG

CCAM CCAM Land/Vegetation Fuel ModelLand/Vegetation Fuel Model

Prediksi dan Observasi Jumlah Kasus DBD Bulanan di Propinsi DKI Jakarta (Tahun 2004-2008)

0500

10001500200025003000350040004500500055006000650070007500

2004 2005 2006 2007 2008Bulan / Tahun

Jum

lah

Kas

usD

BD

Observasi

Prediksi

BMG

Forecasting Dengue OutbreakForecasting Dengue Outbreak

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°

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

-1400000

-1400000

-1200000

-1200000

-1000000

-1000000

-800000

-800000

-600000

-600000

-400000

-400000

-200000

-200000-1

20

00

00

-1

20

00

00

-1

00

00

00

-1

00

00

00

-8

00

00

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°-160000 0

-160000 0

-1400000

-1400000

-120000 0

-120000 0

-100000 0

-100000 0

-800000

-800000

-600000

-600000

-400000

-400000

-200000

-200000-1

20

00

00

-1

20

00

00

-1

00

00

00

-1

00

00

00

-8

00

00

0

-8

00

00

0

-6

00

00

0

-6

00

00

0

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

CCAM CCAM Landslide/Flood Susceptibility MapsLandslide/Flood Susceptibility Maps

ForecastingForecastingFloodsFloodsPotential AreasPotential Areas

Forecasting Tropical CycloneForecasting Tropical Cyclone “Melanie”

CCAM CCAM TC ModuleTC Module

BOROBUDUR

ANCOL

G. BROMO

PRAMBANAN

PLAN FOR 2008

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

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

Day 2 20:00

Wind FieldWind Field

Particle TrajectoryParticle Trajectory

Air Quality & Air PollutionDispersion Model

BMGBMG

‘washing’by rainfall

CCAM CCAM Particle/Pollutant Trajectory ModelParticle/Pollutant Trajectory Model

BMGBMG

TRANSBOUNDARY HAZE TRAJECTORY MODELLINGTRANSBOUNDARY HAZE TRAJECTORY MODELLING

Forest fires & haze in Jambi, SumatraForecasts for 22 -26 January 2008

CCAM +3Tropics

C - BAND Weather Radar- NETWORKBMG

APBN 2006 – 4 lokasi




BMG

AUTOMATIC WEATHER STATION NETWORK

AUTOMATIC WEATHER STATION NETWORKBMG





SATTELITE RECEIVERS BMG

: APBN 2006 – 2007 (3) : APBN 2008 – 2009 (5)

METODE RANET UNTUK DISEMINASI

Earthquake/Tsunami warningWeather forecastClimate forecast

FDRS

Interface Institutions

BMG

NGO-USARanet system.net

Inte

rnet

SERVER

AsiaStar(WorldSpace)

BMG

RANET in Local Gov offices and BMG stations

BMG

ForecastsAdvisoriesWarningsScenarios…

UserUser

ARARWave-

let


ANFIS

EOF

AOAO--GCMGCM

Multi-regr.

CCA PCANon-Linier

RCMRCM


Statistical Models

EnsembleEnsemble



Downscaling


BMG

SpatialPlanning

Crops

Waterresources

Plantation

Fishery

Energy &Industry

Hidromet.Disaster

ManagementTourism

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

Another new challenge: Uncertainty in Decision Making

Contexts…

BMGBMGConcluding RemarksConcluding Remarks

We have described the procedures of high resolution meteorological forecasting applied in Indonesia for various purposes based on dynamical and statistical downscaling in combination with some advanced statistical techniques. The introduction of CCAM is highlighted here

Lessons learned from the implementation of the techniques in producing high resolution meteorological 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 behaviors, institutional framework/ arrangements

BMG

Thank YouThank You

Mt. Lokon, Tomohon, N.S

TerimaTerima KasihKasih

Documents

Development of High Resolution Models and Its Applications for