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The Monsoon Mission

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

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The Monsoon Mission

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  1. A mission mode project to deliver quantifiable improved forecast of monsoon weather and climate The Monsoon Mission 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. IMD Operational Forecasts NCMRWF Short and Medium Range IITM Seasonal and Extended Range

  6. 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 Why a Mission on Dynamical Prediction of Seasonal Mean Monsoon and Extended Range Prediction of active-Break spells?

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

  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. El Nino region(10oS-5oN, 80oW-180oW) WNP (5-30oN, 110-150oE) Asian-Pacific MNS (5-30oN, 70-150oE) 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!

  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

  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

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

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

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

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

  30. 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.

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

  32. HPC requirements of program on Development of a System for Seasonal Prediction of Monsoon

  33. Development of a System for Extended Range Prediction

  34. Research and Development Activities at IITM

  35. 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!

  36. Time lines of the Mission Setting up nodal point at IITM Setup CFS V 2.0 model at IITM 2010-2011 Identify the strengths and weakness of the model and define the problems for further improvement. Invite the project Proposals on specific model development 2011-2012 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

  37. CFS version 2 : Implemented on IITM HPC

  38. 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.

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

  40. 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

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

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

  43. CFSV2 Free run Last 20 years

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

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

  46. Biases

  47. Model bias (Rainfall) CFS v1 CFS v2

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