Simulations of the Indian Monsoon Variability using the NCMRWF Global Modeling System

1. Simulations of the Indian Monsoon Variability using the NCMRWF Global Modeling System A.K. Bohra & S. C. Kar National Centre for Medium Range Weather Forecasting Ministry of Earth Sciences Government of India ______________________________________________________________________________________________________________________________________________ Celebrating Monsoon- International Conference at IISc, Bangalore

2. NCMRWF is the only Organization in India where Real-Time Global Data Assimilation and Forecast Systems are Run every day NCMRWF is engaged in Research to address issues of Weather & Climate covering all spatial and temporal scales

3. Forecasting System at NCMRWF A new Global Data Assimilation & Forecast System at T254L64 has been implemented recently

4. Issues on Medium Range Weather Forecasting  High Resolution Modeling  Parameterization of Physical Processes  Ensemble Predictions  Data Assimilation Techniques  Assimilation of all the available Data  Diagnostics

5. Global Modeling System Data Assimilation at T170/L28 & T80/L18 Resolutions Global Model at T170/L28 & T80/L18 Resolutions Ensemble Prediction at T80/L18 Resolution 8 Member Ensemble (Breed Vector) Experimental Global Model & Data Assimilation at T254L64 resolutions (2007)

6. The T170L28 Global Model has been developed in house (Kar, 2002)

7. Day-5 RMSE Winds 850hpa against Indian Radiosonde Data Jan-1999 onwards Wind Errors are generally higher in Monsoon season compared to other seasons.

8. The breeding of growing modes method (Toth and Kalnay, 1993). 􀂾 Add and subtract very small arbitrary perturbation to the analysis (initial state) at a given day t0 􀂾 Integrate the model from both the positively and negatively perturbed initial conditions for one day 􀂾 Subtract one forecast from the other 􀂾 Scale down the difference field so that it has the same norm (e.g. rms. of kinetic energy) as the initial perturbation 􀂾 add the difference obtained to the analysis of the next day An Ensemble Prediction System with the T80L18 Global Model, based on the “Breeding of Growing Modes” has been implemented at NCMRWF. Model Resolution: T80L18 Number of Ensemble members: 8

9. Day-4 Forecasts during monsoon season T170L28, T80L18 control Runs

10. Ensemble Spread of Rainfall Ensemble Spread of Rainfall increases as length of forecast increases

11. Difference of Day-3 and Day-1 forecast rainfall & Day-6 and Day1 forecast rainfall Model has a systematic tendency to increase rainfall amount in forecasts over India and Central Bay of Bengal as the forecast length increases The Ensemble spread also become more over the same region as forecast length increases

12. Rainfall forecasts over Gangetic Plains from each member ensembles. Thick Black curve is Ensemble Mean forecast, and Red curve is Observation The Model predicts Monsoon Activity over Gangetic Plain reasonably well 6-days in advance as seen from Ensemble Mean forecasts.

13. Rainfall forecasts over East Coast from each member ensembles. Thick Black curve is Ensemble Mean forecast, and Red curve is Observation The Model predicts Monsoon Activity over East Coast reasonably well in Short-range. As Forecast length increases, Spread becomes larger and confidence on forecasts is reduced.

14. Scatter Diagram- Control Rain Vs Ensemble Spread over the Indian region Not only that Spread increases as the rainfall amount increases, Spread is quite large even when the rainfall amount is low

15. Day-5 Wind RMSE over the Indian Region: July 2005 Wind Error is reduced in Ensemble Mean as Compared to the Control forecast

16. Systematic Errors of U and V component of Wind (Vertical Cross Section) as a function of Forecast Length Pressure Level Forecast Day

18. 2nd Aug-2006 Case

19. Estimated errors in the forecast location of depressions (D), deep depressions (DD) in the forecasts during monsoon 2006

20. New GFS (T254L64) at NCMRWF • Horizontal Resolution: T254 (50 km) • Vertical – 64 levels 15 levels below 800 hPa & 24 levels above 100 hPa • Model Time step: 7.5 min • Data Assimilation improvements: • Enhanced quality control • Improved emissivity computations over snow & ice • Model Improvements: • New sea-ice model (Winton, 2000) • Land surface Model (NOAH LSM with 4 soil levels) • Modified vertical diffusion – Turbulent diffusion length scale reduced from 150 m to 30 m in stable case • LW Radiation - Rapid Radiative Transfer Model (RRTM) – 3 hourly • SW Radiation based on Hou et al. 2002 – hourly • SAS convection

22. Medium Range Weather Forecasts are reasonably good over India. Further Improvements will come by increasing Resolution of Models Improving Physics Packages Improving Data Assimilation Schemes & by Utilizing more data

23. Heavy Rainfall case- Mumbai- Runs using MM5 shows importance of Initial Input to the Model Mesoscale Models at 10km resolution could bring out the heavy rainfall event in Hindcast mode

24. Global Data Assimilation System Data Assimilation at T170/L28 & T80/L18 Resolutions (A 3-dimensional Variational Analysis Scheme) Recently T254L64 Data Assimilation System has been introduced Issues addressed: Development of schemes to assimilate Satellite data (derived products) Assessment- Impact of New data on Analysis & Forecasts Assessment of Model Bias on Analysis Issue to be addressed: Assimilation of Direct Radiance 4-dimensional Variational Analysis

25. Reception of conventional observations at NCMRWF & UKMO (daily averaged for 1-14th Aug 2006) Observational Data Reception at the Centre Conventional Data Through GTS- Good. We need to have Bilateral Mechanism to get NON-GTS Data

27. Reception of Satellite observations at NCMRWF & UKMO This has been a Weakness- We are working on to utilize More satellite Data at the Centre With usage of Radiance Data Our data utilization shall go up. We also need bilateral Arrangement

28. atmospheric motion vectors (upper –level-winds) from Geo-Satellite sea-surface wind speed and direction (scatterometer QSCAT ~100km res.) sea-surface wind and precipitable water (SSM/I ~ 75 km res.) temperature & moisture from NOAA sounder channels (ATOVS ~120km) Present Status QSCAT INSAT,METEOSAT,GOES, GMS NOAA Temp. & moisture SSM/I

29. 2007 2006 Satellite Winds (High Density)for GOES,METOSAT,GMSMETEOSAT-5 Satellite Winds INSAT,METEOSAT,GOES, GMS AIREP/AMDAR Observations ACARS Observations

30. EXP OPER Verification of accumulated rainfall predictions (Day 1 & 3) for METEOSAT-wind experiment (Sept. 2005) TRMM OBS

31. Assimilation of Doppler Radar Wind profiles in Mesoscale model Das et al. (2006)

32. Climate Prediction Program of NCMRWF First-time in India: Monsoon Simulation: MONEG Seasonal Simulation using Global Model Real-time Monthly Prediction for Monsoon Long-term Simulations AMIP-type Sensitivity Studies (Seasonal) Aerosol & Climate (Simulation Studies) Real-time Monitoring & Prediction of MJO/ISO Impact of Solar Variability on Climate Simulations Regional Climate Modeling (Dynamic Downscaling) Atmospheric Chemistry and Climate Climate and Crop Yield

33. Climate Prediction Program of NCMRWF  A new perspective of Continuum of Prediction: blurring distinction between Shorter-term and Longer-term climate predictions. An Initial-value problem. Knowledge of the current state of Atmosphere, Oceans, Cryosphere, and Land Surface Climate Models: with the highest possible Resolutions Ability to relate the Structure, Parametrizations and Performance of models Practical approach  Unified: Models aimed at different time-scales and phenomena may have large commonality but place emphasis on different aspects of the system. Models to include Atmospheric Chemistry, Carbon Cycle, evolving Vegetation, etc. Theoretical basis of Predictability: what Predictions and what Techniques to attempt

34. Seasonal Prediction & Application to Society(SeaPrAS) To improve the capacity in India’s Resource Management to cope with the impacts of Climate Variability A Platform for Policymakers & Resources Managers to have access to, and make use of, information generated by Climate Prediction Models. To provide the Planners with more Reliable Seasonal Climate Prediction Information and Guidance on who could be the Potential Beneficiaries of the Predictions. Idea is to develop a Multi-Model Ensemble Seasonal Prediction System. Associated Application Systems will also be developed for Energy Demand Water Resource Management Agriculture- Drought Prediction, Crop Yield. Work is in Progress towards this end.

35. SeaPrAS Seasonal Prediction Issues IN-GLM1 Model has been integrated for 1982-2004 using Initial conditions (NCEP Reanalysis) from April 15-20, May 01, May15 of each year with Observed SST (Reynolds). Model has been integrated for 1982-2004 using Initial conditions from April 15 each year With Climatological SST. Integrations of other versions of the model are in progress

36. SeaPrAS

37. SeaPrAS

38. SeaPrAS

39. SeaPrAS ROC Contingency Table for Deterministic Forecasts Hanssen and Kuipers score (HKS)

40. SeaPrAS 1994 Model 1988-1987 Model 1988-1987 Obs 1994 Obs 2002 Model 1997 Model 2002 Obs 1997 Obs

41. SeaPrAS Probabilistic Seasonal Prediction Ensemble Mean is the Signal Ensemble Spread is the Noise having Normal Distribution Three-Category Probabilistic Prediction Scheme has been developed following Kharin & Zwiers (2004) Calibrated Seasonal Prediction will be produced. Reliability Diagram JJAS Rain Above Normal Tropics Red Indian Region Purple

42. SeaPrAS Does the Global Model respond to SST? What about Internal Variability? Can we achieve some prediction skill from Initial Condition alone? Model was integrated for 24 monsoon seasons with Clim. SST. & Compared with those runs with Obs. SST Obs SST Run Clim SST Run Model Climate is similar for both the runs

43. SeaPrAS Corr. Obs & Model rain Run with Obs. SST Corr. Obs & Model rain Run with Clim. SST Though the model climates are similar, the correlation of simulated Rainfall with observation is higher in runs with observed SST.

44. Corr. Coeff. Precip Regions where not a single model nor any MME Corr. Coeff. is more than 0.3 Not a Single Model nor any MME Scheme provide Corr. Coeff. More than 0.3 in the Shaded Region JJA-Precip Corr=0.34

45. Summary NCMRWF leads the Country in Global Modeling & Data Assimilation. Current Forecasts are reasonably Good Further Improvements in the Forecasting System are being carried out to improve the quality of Medium Range Predictions. NCMRW has already initiated research to develop a Reliable Seasonal Prediction System for the benefits of various Socio-Economic sectors

46. Thank You!