1. 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

2. 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!

3. 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.

4. 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!

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

6. 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

7. Seasonal rainfall anomaly in a flood, normal and a drought years
(Xavier and Goswami, 2007)

8. 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

9. 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

10. 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.

11. Current Status

12. 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 ( ) = 7.5%

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

14. 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

15. 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, oE)
Asian-Pacific MNS (5-30oN, oE)

16. Proposed Activities

17. 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.

18. 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

19. 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

20. Skill of CFS Model in simulating ISMR for different Initial Conditions

21. Skill of CFS Monsoon Prediction
CC=0.43

22. 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

23. 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

24. 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)

25. 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)

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

27. 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

28. 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.

30. 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

32. 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.

33. 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

34. 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)
2.36
Hindcast Runs (for 29 years)
March , April, May IC
18.0
CFS T126L64
4.10
March, April, May IC
30.90
CFS T382L64
10.00
Hindcast Runs (29 years)
March, April, May IC
75.90
AGCM
AMIP Type of Experiments
(31 years)
103788
1.20
OGCM
Simulations
1.00
Research and Sensitivity Experiments
(Data Assimilation Expts.)
For Research and Development of Model
1430.6
Current Critical Requirement is ~50 Tflop machine for 1 year and 707 TB storage. Increment Tflop/year, TB/year

35. 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
18.00
CFS T126L64
30.60
CFS T382L64
75.6
Research and Sensitivity Experiments
To improve the Model ISO Simulations
250.00
Current Critical Requirement is Tflop machine for 1 year and ~370 TB storage. Yearly Increment Tflops/ TB

36. 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 ( 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

37. 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, workstations, 144 ports IB switch!
Internet bandwidth has been upgraded to 100 Mbps!

38. Time lines of the Mission
Setting up nodal point at IITM
Setup CFS V 2.0 model at IITM
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.
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
2016
Will have an Indian Model for Seasonal prediction and Climate
Change studies with improved and useful skill of seasonal prediction

39. 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

40. 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

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

42. 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

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

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

45. CFSV2 Free run
Last 20 years 45

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

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

48. Biases 48 48

49. 49 49

50. Model bias (Rainfall)
CFS v1
CFS v2

51. Model bias (SST)
CFS v1
CFS v2

52. Rainfall (JJAS)
CMAP
CFS2 Noah
CFS2 OSU

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

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

55. Thank You

56. 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

57. Financial Requirements
Upgradation of computer infrastructure for high resolution coupled models: 150 Crores
For National partners (Basic Research/Model Development): Crores
For international (Model development)
collaboration: Crores
For observational programs: 15 Crores
Total budget requirement: 300 Crores

58. 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

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

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

62. 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.

63. 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)