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IITM, MoES , Government of India

Seasonal and Extended Range Prediction Activities in India Using CFS. Suryachandra A. Rao (Surya). IITM, MoES , Government of India. Outline of the Presentation. Status of Seasonal Prediction of Indian Summer Monsoon (History and Present)

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IITM, MoES , Government of India

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  1. Seasonal and Extended Range Prediction Activities in India Using CFS Suryachandra A. Rao (Surya) IITM, MoES, Government of India

  2. Outline of the Presentation • Status of Seasonal Prediction of Indian Summer Monsoon (History and Present) • Status of Extended Range Prediction of Indian Summer Monsoon • Work in Progress to Improve Model Biases in CFS V2.0 • Monsoon Mission

  3. Monsoon

  4. HISTORY OF IMD OPERATIONAL FORECASTS ALL STATISTICAL MODELS 1924--1987 Forecast NW INDIA / PENINSULA 1988 16 –PARA MODEL All India Deterministic FORECASTS 1999 Forecasts for 3 REGIONS introduced 2003 8/10 Parameter Model for (Jun-Sept) & July F/C for India 2004 All India Forecast along with 4 Homogeneous Regions of India 2007 New Statistical Models Introduced 16 P Model Continued till 2002 8/10 Para Models Source: Rajeevan

  5. Forecast Performance: 1932-1987 Gadgil, Rajeevan and Nanjundiah (2005, Current Science) Monsoon Prediction: Why yet another failure?

  6. Correlation Coefficients between the observed and 5 AGCM MME hindcasted June-August precipitations (1979-1999) Wang et al. (2005)

  7. Correlation Coefficients between the SST-Rainfall in observations and 5 AGCM MME (1979-1999) Wang et al. (2005) Observations AGCM

  8. Seasonal Prediction of the Indian Monsoon (SPIM) Observed and simulated variation of all-India rainfall: 1985-2004 Note:(1988, 2001, 2002) – consistency among the models; 1994 – Models consistently fail to capture the observed anomaly Potential Prediction Skill is ~0.39

  9. Details of Latest Models

  10. Details of Initialization (in ENSEMBLES) Atmospheric IC:ERA-40 Operational Analysis AMIP type Simulations Ocean IC: Ocean analysis with Wind/SST Perturbations

  11. Details of Initialization (in CFS V2.0) (May Initial Conditions) CFS V2.0 hindcast results are evaluated for Feb., Mar., Apr., May., Initial Conditions.

  12. Rainfall skill Land points (CGCMs) UKMO Depresys UKMO CFS V2.0 ECMWF

  13. Prediction Skill of ISMR in CFS V2.0 CFS v2 Jan IC Correlation=0.37 CFS v2 Feb IC Correlation=0.59 CFS v2 Mar IC correlation=0.33 CFS v2 Apr IC Correlation=0.53 CFS v2 May IC correlation=0.36

  14. Dynamical Seasonal Prediction of Indian Monsoon JJAS Rainfall – 2010 (CFS V1.0) Issues in April IITM CFS T62 IITM CFS T126 T126L64 T62L64 Central Indian drought predicted by CFS model Above normal rainfall over southern peninsular India IMD

  15. Dynamical Seasonal Prediction of Indian Monsoon With Initial Conditions generated within India at (INCOIS & NCMRWF) JJAS Rainfall – 2011 (Issued in March) IITM CFS T62 IITM CFS V2.0 T126 T126L64 T62L64 Central Indian above normal rain predicted by CFS model Below normal rainfall over southern peninsular India IMD Upto 10th September

  16. Extended Range Prediction of Active and Break Cycles

  17. CFS V2 ISO Analysis Last 20 years of free run

  18. East west space time spectra: OLR anomaly 10S:10N averaged OBS CFSV2

  19. North-south space time spectra: OLR anomaly (20S-30N, 60-100E)

  20. Lead lag correlation plot: PRECIPITATION Ref.series: 75-100E,10S-5N summer winter

  21. Lead lag correlation plot:OLR Ref.series: 75-100E,10S-5N summer winter

  22. Lead lag correlation plot: OLR Ref.series: 70-90E,12N-22N

  23. Lead Lag regressionGPCP and CFS v2

  24. Metric for summer ISO prediction and monitoring EEOF of rainfall averaged between 70oE-95oE

  25. GPCP anomaly GPCP ISO PC1 of EEOF

  26. Phase composite of precipitation anomaly

  27. Interannual variability of Indian monsoon ISOs Behavior of ISOs during Jan-April

  28. Climatology & climatological annual cycle of CFS V1 Averaged over 10N-25N; 70E-85E

  29. 45 day evolution of observation and forecast 2002 May 21 Initial condition 2003 2007 Observation _____ Model ---------- 2006

  30. Forecast skill of CFS Model EOF1 EOF2

  31. Model Bias in CFS V2.0

  32. Rainfall Seasonal cycle Averaged over 10-30N, 70-100E Averaged over Indian land mass

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

  34. Winds at 10m (JJAS)

  35. Difference in Tropospheric Temperature Troposphere Cold bias

  36. Average SWE from 200 Russian Station Delayed Snow Melt in CFS

  37. Percentage Convective precipitation CFS V2 Observations

  38. Percentage stratiform precipitation CFS V2 Observations

  39. High resolution Seasonal Prediction Experiments

  40. High resolution Seasonal Prediction Experiments T382 vs. T126 T382 model bias

  41. ISO variance in T382 CFS V2.

  42. SST Prediction Precip. Prediction

  43. Monsoon Mission Vision All models have serious biases in simulating all aspects of monsoon such as Diurnal Variability Intreaseasonal Variability Seasonal Mean InterannualVariabilty These biases reflect in poor prediction skill of both monsoon weather (short-medium range) and climate (seasonal) Over the past 20 years, although we (India) have made some notable contribution in observational programs, we have made NO tangible contribution in model development/improvement! During the next 10 years we must invest much more resource and manpower in model development/improvement to be countable in the world community!!

  44. The need of Monsoon Mission Weather on Short and Medium Range Climate , Seasonal Mean monsoon Climate Change Decadal prediction To improve forecasts in the country for

  45. Effort started some time back • Made some progress • Still skill far below best in the world Weather on Short and Medium Range • Long history of empirical; No skill improvement • Dynamical Effort just has started now! • We are not counted in dynamical seasonal forecasts Climate , Seasonal Mean monsoon To improve forecasts, need to assess where we stand? Climate Change Decadal predict • We are just thinking of starting! • First effort is to build capacity to make reliable projection monsoon

  46. Weather on Short and Medium Range • To take ourselves to a level comparable to the best in the world and be counted ! Climate ,Seasonal Mean monsoon Short Term Goal….2-3 years Climate Change Decadal predict.

  47. Weather on Short & Medium Range • To become the best in the world! Climate ,Seasonal Mean monsoon Long Term Goal….3-10 years Climate Change Decadal predict.

  48. Encourage basic research in a big way, Better understanding of mechanisms, parameterizations etc • Model Developments for improving forecasts Vigorous focused Basic Research What is required to achieve this Vision? • State-of-art HPC at operational and R&D Centres, petaflop comp. by 2-3yr • Equip Academic organizations with HPC to train, build capacity in modeling • Improvement of observations : Indoos and Modernization of IMD State of art Infrastructure Manpower Development : Training • Advanced, modernized training, job linked • Create jobs, induct laterally

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

  50. Basic Research Mechanism of Interannual Variability • Teleconnection • Local Air-Sea Interaction • Internal Dynamics Parameterization of Physical Processes • Tropical Convection • Proportion of stratiform & Clouds IITM, IISc, IITs, NCMRWF, IMD, Universities

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