The Monsoon Mission

B N GoswamiIndian Institute of Tropical Meteorology

A mission mode project to deliver quantifiable improved forecast of monsoon

weather and climate

The Monsoon Mission

Objectives

To set up a high resolution short and medium range prediction system for monsoon weather and to conduct focused research to improve the present skill.

To set up a dynamical seasonal prediction system and to set up a mechanism to enhance the current skill to a useful level!

BackgroundBackgroundThe academic community in the country has made great advances in understanding variability and predictability of the monsoon. Unfortunately, this knowledge has not been translated to improvement of operational forecasts A state of the art dynamical prediction system for seasonal mean and extended range prediction of active-break spells is not operational in the country!Together with IMD, IITM plans to set up such a system, evaluate its hind-cast skill and make it operational. IITM also plans to put in place a strong R&D plan to continuously improve the skill of these forecasts.

The MissionThe Mission

The Mission’s goal is to build a working partnership between the Academic R & D Organizations and the Operational Agency to improve forecast skill.

Requirement :We all must work on A Modeling Framework! This is the challenge!

IMDOperational Forecasts

IITMSeasonal and

Extended Range

NCMRWFShort and Medium

Range

Why a Mission on Dynamical Prediction of Why a Mission on Dynamical Prediction of Seasonal Mean Monsoon and Extended Seasonal Mean Monsoon and Extended

Range Prediction of active-Break spells?Range Prediction of active-Break spells?• There is great deal of interest on the long

range forecast of the Seasonal mean due to its policy implications and dependence of economy on it.

• A severe drought still influences the GDP by 2-5%

• Hence, it is most important to predict the extremes, the droughts and floods!

• Also, during extremes like severe droughts and floods, the rainfall anomaly over the country is homogeneous and hence even the All India mean Rainfall is useful for agricultural planning

Seasonal rainfall anomaly in a flood, normal and a drought years

(Xavier and Goswami, 2007)

On seasonal mean time scale, only large spatial scales are predictable. Hence, we expect to have skill in predicting only the All India mean rainfall.However, during ‘normal’ monsoon years All India mean is not a meaningful quantity.And 70% of the time, the Indian monsoon is ‘normal’.Hence, a prediction of a ‘normal’ all India rainfall may have a comfort factor, but not useful for agricultural planning.Therefore, in addition to the seasonal mean All India rainfall, we need to predict some aspects of monsoon 3-4 weeks in advance on a relatively smaller spatial scale that will be useful for farmers

Daily rainfall (mm/day) over central India for three years, 1972, 1986 and 1988

The smooth curve shows long term mean.

Red shows above normal or wet spells while blue shows below normal or dry spells

Active-break spells (cycles)

Hence, together with prediction of the seasonal mean, it is proposed that the mission should also include extended range prediction (2-4 weeks in advance) the active and break phases of the monsoon sub-seasonal oscillations.Potential predictability of these phases in this time scale has been established (Goswami and Xavier, 2003, Waliser et al. 2003)Bayesian techniques have already demonstrated useful prediction of the phases at this time scale (Xavier and Goswami, 2007, Chattopahyay et al, 2008) A dynamical framework needs to be put in place and improved.

Performance of Operational Forecast for Performance of Operational Forecast for All India Summer Rainfall (1988-2009)All India Summer Rainfall (1988-2009)

During 7 years (including 2009) error is ≥ 10% with highest during 2002 (20%) and 1994 (18%). Error during 2009 was 15%.

Average Abs Error of Op. forecasts (1988-2009) = 7.5%

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YEAR

RA

INF

AL

L %

OF

LP

A Actual

Forecast

Gadgil et al, 2005, Curr. Sci

•Tends to predict ‘normal’. Can not predict extremes•No improvement of skill in 30 years

JJA

DJF

Correlation bet. Prediction and Correlation bet. Prediction and observation of Precipitationobservation of Precipitation

There is a great need to improve this!!

Current Dynamical

models have little

skill in predicting

Indian monsoon

Wang et al. 2008

Skill of Various Models in Predicting the Asia-Pacific

Monsoon System

El Nino region (10oS-5oN, 80oW-180oW)

WNP (5-30oN, 110-150oE)

Asian-Pacific MNS (5-30oN, 70-150oE)

Models have become good in predicting rainfall over the El Nino region, but they fail over the Asian monsoon region!

Proposed ActivitiesProposed ActivitiesImplement NCEP CFS v2.0 model and identify

strengths and weaknesses of the model. Work towards rectifying the weaknesses in the CFS

model and incorporate new physic/ parameterization schemes to improve the simulations/prediction skill of the CFS model .

Based on changes made to the CFS model, develop an Indian Model which will have the capability to better simulate and predict monsoon rainfall.

Why CFS Model System?Why CFS Model System?Through the NOAA-MoES MoU Institutional

support from NCEP will be available.For predicting monsoon rainfall, skill of no

coupled model is good. However, amongst the existing model systems, skill of CFS seems to be on the better side. It also has a reasonable monsoon climatology

Appears to be a system upon which future developments could be built

Skill of Various Models in Simulating the Climatological

Seasonal Mean MonsoonPattern of Climatological Mean JJAS Precip

Annual Cycle of Climatological Precip

CFS Simulates Seasonal Cycle Better than Other Models

Skill of CFS Model in simulating ISMR for different Initial

Conditions

Skill of CFS Monsoon PredictionSkill of CFS Monsoon Prediction

CC=0.43

HPC Facilities at IITMIBM Power 6

Nimbus Sun Cluster

7.2 TF Peak Performance

2.3 TF

P6-575 Processors AMD Opteron

192/384 No. of CPU/cores

64/256

1.5 TB Total Memory 512 GB

20 TB Online Storage 4 TB

80 TB Near Online Storage

48 TB

• The NCEP CFS Components– T126/64-layer version of the CFS

• Atmospheric GFS (Global Forecast System) model– – Model top 0.2 mb

– – Simplified Arakawa-Schubert convection (Pan)

– – Non-local PBL (Pan & Hong)

– – SW radiation (Chou, modifications by Y. Hou)

– – Prognostic cloud water (Moorthi, Hou & Zhao)

– – LW radiation (GFDL, AER in operational model)

• GFDL MOM-3 (Modular Ocean Model, version 3)– – 40 levels

– – 1 degree resolution, 1/3 degree on equator

CFS T126L64

CFS Runs Carried out on Prithvi(HPC) at IITM

Run T62 T126

Free Run 100 Years 50 Year

Hindcast (May IC) 28 Years (15 ensembles)

28 Year (15 Ensembles)

Hindcast (March IC) ------ 10 Years (6 Ensemble)

Sensitivity Experiments

28 Years (10 Ensembles)

28 Years (10 Ensembles)

Forecast Runs (2010) 27 Ensembles (March IC)

27 Ensembles (March IC)

TR

MM

rainfall (cm)

NC

EP

-CF

S rainfall

(cm)

Realistic simulation of rainfall over Western

Ghats. Spreading of rainfallInto eastern Arabian Sea still remains in

T126

Model comparison

withTRMM 0.25

deg. Rainfall dataset

ISO Variance in themodel is reasonably

well simulated.

Model comparison

withTRMM 0.25

deg. Rainfall dataset

Improving Prediction of Seasonal Mean Monsoon

Coupled ModelCFS V 2.0

Basic Research

Model Development & Improvement in

Physical Parameterization

Data Assimilation

It is important that all development work

should be done on a specified model

The Mission: ModalitiesThe Mission: Modalities

o Focused mission on ‘Seasonal and Intra-seasonal Monsoon Forecast’ to be launched.

o Support focused research by national and international research groups with definitive objectives and deliverables to improve CFS model.

o Support some specific observational programs that will result in improvement of physical processes in climate models.

Model Development

Resolution Super

parameterization

National: IISc, IITM, IMD, NCMRWF International: COLA, NCEP, IPRC,

INGV, APCC, GFDL ,JAMSTEC

Improvement of Physical

Parameterization

Proposed modalities to achieve mission objectivesProposed modalities to achieve mission objectives

• IITM to coordinate the effort.

• Proposals to be invited from National as well as international Institutes on very specific projects and deliverables through which improvement of the CFS model are expected.

• Provisions for funding the National partners as well as the international partners will be year marked.

• The Proposal partners will be allowed to use the HPC facility at IITM which will be suitably enhanced for this purpose.

• Funding for students, post docs and some scientists time (consultancy) and some minor equipments may be provided.

Probable PartnersProbable PartnersInternational Partners

USA: NCEP, COLA, GFDL,IPRCBrazil: INPEEurope: INGVAsia: JAMSTEC, APCC, CCSR

National PartnersIISC, IITs, SAC, MOES institutes, Universities

HPC requirements of program on Development of a System for Seasonal

Prediction of MonsoonModel Simulation Tflops Data (TB)

CFS T62L64

Free runs (100 Years) 1339200 2.36

Hindcast Runs (for 29 years) March , April, May IC 10194660

18.0

CFS T126L64

Free runs (100 Years) 3201600 4.10

Hindcast Runs (for 29 years) March, April, May IC 24372180 30.90

CFS T382L64

Free runs (100 Years) 12672000 10.00

Hindcast Runs (29 years) March, April, May IC 96465600

75.90

AGCM AMIP Type of Experiments (31 years) 103788 1.20

OGCM Simulations 2278125 1.00

Research and Sensitivity

Experiments(Data Assimilation

Expts.)

For Research and Development of Model 1506271530

1430.6

Current Critical Requirement is ~50 Tflop machine for 1 year and 707 TB storage. Increment 20-50 Tflop/year, 100-200 TB/year

Development of a System for Extended Range Prediction

Model Simulation Tflops Data (TB)

CFS T62L64Hindcast Runs (for 29 years)

May, June, July, Aug, Sep IcsEach month 2 Runs

1699110018.00

CFS T126L64Hindcast Runs (for 29 years)May, June, July, Aug, Sep Ics

Each month 2 Runs 40620300 30.60

CFS T382L64

Hindcast Runs (for 29 years)May, June, July, Aug, Sep Ics

Each month 2 Runs 160776000 75.6

Research and Sensitivity

Experiments

To improve the Model ISO Simulations 655162200 250.00

Current Critical Requirement is 27.0 Tflop machine for 1 year and ~370 TB storage. Yearly Increment 20-50 Tflops/ 50-100 TB

Research and Development Activities at IITM

Program Current Requirement (Peak in TF)

By 2013 By 2016

Centre for Climate Change Research ~75 ~125 ~150

Monsoon Mission•Development of Seasonal Prediction System•Development of a system for extended range prediction of Active / break spell

~75 ~100 ~125

National Training on weather and Climate Science

~10 ~15 ~20

Other Research Programs ~10 ~20 ~30

For IMD operational ~30 ~40 ~75

Existing Facility ~70TF ~200TF ~300TF ~400TF

Up gradation Plan for Computing Facility

Existing HPC System at IITM is being upgraded with additional 101 IBM P575 nodes with 60.2 TF peak power to achieve more than 70TF peak performance. Additionally 4 high end servers, 10 workstations, 144 ports IB switch!

Internet bandwidth has been upgraded to 100 Mbps!

2010-2011 Setting up nodal point at IITMSetup CFS V 2.0 model at IITM

2011-2012Identify the strengths and weakness of the model and

define the problems for further improvement. Invite the project Proposals on specific model development

2012-2014Carryout research on identified problems together with national/international partners and demonstrate improvement of skill of

seasonal prediction.

2014-2016An interim review by external experts. Implement the experts

suggestions in the proposal and carryout the model development activities and test the model’s skill

2016 Will have an Indian Model for Seasonal prediction and Climate

Change studies with improved and useful skill of seasonal prediction

Time lines of the Mission

CFS V1 CFS V2

AGCM resolution

T62 T126

Ocean Model MOM3 MOM4

Land Surface Model

OSU 2-layer model

NOAH 4-layer model

Sea Ice Climatology 2-layer Sea-ice model

CFS version 2 : Implemented on IITM HPC

CFS v2 Coupled Runs

Examined skill of seasonal prediction of Indian monsoon by CFS v2 generated by NCEP

CFSv2 T126 free coupled runs performed at IITM HPC. Model has been integrated in coupled mode for 30 years with initial condition of 12 December 2009.

February ICCorr=0.59

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Norm_imd_jjas-anom Norm_Feb_IC

-3

-2

-1

0

1

2

3

Norm_imd_jjas-anom Norm_mar_IC

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Norm_imd_jjas-anom Norm_Apr_IC

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Norm_imd_jjas-anom Norm_May_IC

March ICCorr=0.33

April ICCorr=0.53

May ICCorr=0.36

ISMR Prediction Skill

Eastern Pole of IOD JJAS SSTA Skill

-3

-2

-1

0

1

2

3

Nor_ERSST Nor_Feb_IC

Years

-3

-2

-1

0

1

2

3

Nor_ERSST Nor_mar_IC

Years

-3

-2

-1

0

1

2

3

Nor_ERSST Nor_Apr_IC

-3

-2

-1

0

1

2

3

Nor_ERSST Nor_May_IC

Years

Feb. ICCorr=0.47

Mar. ICCorr=0.55

Apr. ICCorr=0.73

May ICCorr=0.87

Land point precip over India .vs. SSTA

Monsoon-ENSO teleconnection pattern

simulated by model well but stronger than

observed

Correlation JJAS

SST.vs.JJAS.rainfall

Local SST-rain relationship

CFSV2 Free runLast 20 years

Annual Cycle of Extended Region

(10-30N and 70-100E) Rainfall

Annual Cycle of Indian Land Points

Rainfall

Lon averaged 70-90E

Last 20 years climatology (2020-

2039)

Biases

Model bias (Rainfall)

CFS v1

CFS v2

Model bias (SST)

CFS v1

CFS v2

CMAP

CFS2 Noah

CFS2 OSU

Rainfall (JJAS)Rainfall (JJAS)

CMAP - Noah

CMAP - OSU

Noah - OSU

Rainfall Difference (JJAS)Rainfall Difference (JJAS)

CMAP

CFS2 Noah

CFS2 OSU

CFS2 Noah - OSU

CMAP - Noah

CMAP - OSU

NOTE: here in diff. plot model is regridded into CMAP coarse resolution

Thank You

Manpower Requirement at IITMManpower Requirement at IITM

Two/Three Programmers with M.Tech Qualifications

Post-doctoral fellows/Project Scientists: 10 (Ph.D Qualification)

One administrator/2 Contract UDCs for coordination of the project proposals

Financial RequirementsFinancial Requirements

Upgradation of computer infrastructure for high resolution coupled models: 150 Crores

For National partners (Basic Research/Model Development): 75 Crores

For international (Model development)

collaboration: 60 CroresFor observational programs: 15 Crores

Total budget requirement: 300 Crores

An Example (International Partner, 1 An Example (International Partner, 1 Year Budget)Year Budget)

Post Doctoral Fellow/Scientist for 1 year (88,000 USD/Year)= 44 lakhs

Principal Investigator’s 3 months part salary (20,000 USD/Year)= 10 lakhs

Computational Time on HPC (3 months) = unknown

Miscellaneous Expenses (travel, paper page charges etc.) = 8 lakhs

Total Budget/Year ≈ 1.5 Crores

2016-2020 Setting up nodal point at IITMSetup CFS V 2.0 model at IITM

2021 Expected to have a coupled model with reasonable prediction skillFor Indian SummerMonsoon rainfall

Time lines of the national Mission

ObservationalPrograms

To improve land surface processes

Modelling

OceanMixing

Convectionparameterizatio

n

Development of Multi Model Ensemble Seasonal Development of Multi Model Ensemble Seasonal Prediction System for Indian Monsoon Prediction System for Indian Monsoon

Model Outputs from the following models will be obtained from respective agencies for MME

The core model system on which developments will be carried out will be the CFS system of NCEP. Initially we start with the CFS1.1 and replace it by CFS2.0 if available within next six months.

Multi Model Ensemble approach to be used in IITMMulti Model Ensemble approach to be used in IITM

As a starting step the following MMEs will be implemented this year

1.1.Simple Composite MME scheme – SCMSimple Composite MME scheme – SCM

2.2.Probabilistic MME scheme – GAUSProbabilistic MME scheme – GAUS

Model weights inversely proportional to the errors in forecast probability

associated with the model sampling errors

A parametric Gaussian fitting method for the estimate of tercile-based

categorical probabilities i.e Above Normal(AN), Near-Normal(NN) and

Below-Normal(BN).

Coupled models that will be used in MME: POAMA (BMRC),

NCEP-CFS T61 (NCEP),

NCEP-CFS T126 (IITM)

CCSM3 (APCC)