B N Goswami Indian Institute of Tropical Meteorology The Monsoon Mission A mission mode project to deliver quantifiable improved forecast of monsoon weather and climate B N Goswami Indian Institute of Tropical Meteorology

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!

Background The 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 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!

Operational Forecasts IMD Operational Forecasts NCMRWF Short and Medium Range IITM Seasonal and Extended Range

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

Active-break spells (cycles) 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

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.

Current Status

Performance of Operational Forecast for 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%

Tends to predict ‘normal’. Can not predict extremes No improvement of skill in 30 years Gadgil et al, 2005, Curr. Sci

Correlation bet. Prediction and observation of Precipitation Current Dynamical models have little skill in predicting Indian monsoon There is a great need to improve this!! Wang et al. 2008

Skill of Various Models in Predicting the Asia-Pacific Monsoon System Models have become good in predicting rainfall over the El Nino region, but they fail over the Asian monsoon region! El Nino region (10oS-5oN, 80oW-180oW) WNP (5-30oN, 110-150oE) Asian-Pacific MNS (5-30oN, 70-150oE)

Proposed Activities

Proposed Activities Implement 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? 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 Monsoon Pattern 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 Prediction CC=0.43

HPC Facilities at IITM IBM 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

CFS T126L64 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 Runs Carried out on Prithvi(HPC) at IITM 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) Forecast Runs (2010) 27 Ensembles (March IC)

TRMM 0.25 deg. Rainfall dataset Model comparison with TRMM 0.25 deg. Rainfall dataset Realistic simulation of rainfall over Western Ghats. Spreading of rainfall Into eastern Arabian Sea still remains in T126 TRMM rainfall (cm) NCEP-CFS rainfall (cm)

TRMM 0.25 deg. Rainfall dataset Model comparison with TRMM 0.25 deg. Rainfall dataset ISO Variance in the model is reasonably well simulated.

Improving Prediction of that all development work Seasonal Mean Monsoon It is important that all development work should be done on a specified model Coupled Model CFS V 2.0 Basic Research Model Development & Improvement in Physical Parameterization Data Assimilation

The Mission: Modalities Focused mission on ‘Seasonal and Intra-seasonal Monsoon Forecast’ to be launched. Support focused research by national and international research groups with definitive objectives and deliverables to improve CFS model. Support some specific observational programs that will result in improvement of physical processes in climate models.

Model Development National: IISc, IITM, IMD, NCMRWF Super parameterization Improvement of Physical Parameterization Resolution National: IISc, IITM, IMD, NCMRWF International: COLA, NCEP, IPRC, INGV, APCC, GFDL ,JAMSTEC

Proposed 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 Partners National Partners International Partners USA: NCEP, COLA, GFDL,IPRC Brazil: INPE Europe: INGV Asia: JAMSTEC, APCC, CCSR National Partners IISC, IITs, SAC, MOES institutes, Universities

HPC requirements of program on Development of a System for Seasonal Prediction of Monsoon Model 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 3201600 4.10 March, April, May IC 24372180 30.90 CFS T382L64 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 T62L64 Hindcast Runs (for 29 years) May, June, July, Aug, Sep Ics Each month 2 Runs 16991100 18.00 CFS T126L64 40620300 30.60 CFS T382L64 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 ~100 National Training on weather and Climate Science ~10 ~15 ~20 Other Research Programs ~30 For IMD operational ~40 Existing Facility ~70TF ~200TF ~300TF ~400TF Research and Development Activities (IITM/NCMRWF/IMD/INCOIS) Centre of Climate Change Research (50 – 150TF) Monsoon Mission (10-30 TF) Development of Seasonal Prediction System Development of Extended range Prediction System National Training on weather and Climate Science (10TF) National Program for Development of High Resolution Indian National Coupled Model (200-800 TF) South Asian Regional Analysis (100 TF) Operational Activities (IMD/NCMRWF/INCOIS) Seasonal Prediction (5-10 TF) Extended Range Prediction (15-20 TF) Medium Range Prediction (20-50 TF) Short Range Prediction (10-20 TF) Now-Casting (5 TF) Ocean Services (10-50 TF) Total requirement 200TF in Two Years to 1PF in Five years 3PF in Ten years

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!

Time lines of the Mission Setting up nodal point at IITM Setup CFS V 2.0 model at IITM 2010-2011 2011-2012 Identify the strengths and weakness of the model and define the problems for further improvement. Invite the project Proposals on specific model development Carryout research on identified problems together with national/ international partners and demonstrate improvement of skill of seasonal prediction. 2012-2014 An interim review by external experts. Implement the experts suggestions in the proposal and carryout the model development activities and test the model’s skill 2014-2016 2016 Will have an Indian Model for Seasonal prediction and Climate Change studies with improved and useful skill of seasonal prediction

CFS version 2 : Implemented on IITM HPC 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 39

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. 40

ISMR Prediction Skill February IC Corr=0.59 March IC Corr=0.33 April IC Corr=0.53 May IC Corr=0.36

Eastern Pole of IOD JJAS SSTA Skill Feb. IC Corr=0.47 Mar. IC Corr=0.55 Apr. IC Corr=0.73 May IC Corr=0.87

Land point precip over India .vs. SSTA Monsoon-ENSO teleconnection pattern simulated by model well but stronger than observed 43 43 43

Local SST-rain relationship Correlation JJAS SST.vs.JJAS.rainfall 44

CFSV2 Free run Last 20 years 45

Annual Cycle of Extended Region (10-30N and 70-100E) Rainfall Annual Cycle of Indian Land Points Rainfall

Last 20 years climatology (2020-2039) Lon averaged 70-90E Last 20 years climatology (2020-2039) 47 47 47

Biases 48 48

49 49

Model bias (Rainfall) CFS v1 CFS v2

Model bias (SST) CFS v1 CFS v2

Rainfall (JJAS) CMAP CFS2 Noah CFS2 OSU

Rainfall Difference (JJAS) CMAP - Noah CMAP - OSU Noah - OSU

NOTE: here in diff. plot model is regridded into CMAP coarse resolution CFS2 Noah - OSU CFS2 Noah CMAP - Noah CFS2 OSU CMAP - OSU

Thank You

Manpower 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 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 Crores For observational programs: 15 Crores Total budget requirement: 300 Crores

An Example (International Partner, 1 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

Time lines of the national Mission Setting up nodal point at IITM Setup CFS V 2.0 model at IITM 2016-2020 2021 Expected to have a coupled model with reasonable prediction skill For Indian SummerMonsoon rainfall

Observational Programs To improve land surface processes Modelling Ocean Mixing Convection parameterization

Development of Multi Model Ensemble Seasonal 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 IITM As a starting step the following MMEs will be implemented this year Simple Composite MME scheme – SCM Probabilistic 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)