Addressing the Livelihood Crisis of Farmers Weather & Climate Services WMO

1. Addressing the Livelihood Crisis of Farmers Weather & Climate Services WMO Short, Medium and Extended Range Forecasting B. Mukhopadhyay

2. Overview………….. • Relevance of Weather prediction to agriculture • Spatial and Temporal Scales of weather phenomena • Predictability • Conventional methods of forecast • Global and Regional NWP Models • Climate models • Integrated Agro advisory services

3. Relevance to the Livelihood Issue Realizationofthepromise Ideal Yield Natural Yieldaidedbyagro-meteorology Yieldwithouttechnology Yieldaidedbytechnology Agromet Technology

4. A View From Space - hierarchical structure of planetary weather

5. Spatial Scales of Motion JUNE MAY Distribution of atmospheric moisture

6. seasonal transition 2 May

7. 16 May

8. 31 May

9. 14 Jun

10. 30 Jun

11. View of an incoming storm low approaching the coast of California taken by SeaWIFS instrument on the OrbView-2 satellite. Cumulonimbus clouds can coalesce to form a huge continuous cloud cell, as seen in this meteosat image of the Balkans Smaller spatial scales

12. Mix of fog and smog in northern India seen by MODIS View of an "anvil cloud". from the International Space Station

15. Very Limited Skill in Extended range time scale (10 days to one month)

16. Predictability & Forecasting Range Time Series ofSolution Time Series ofSolution 30 30 20 20 10 10 Variable X Variable X 0 0 -10 -10 -20 -20 -30 -30 Time t Time t Lorenz system with slight different initials Lorenz system with a forcing Predictability of the Second Kind Predictability of the First Kind LRF targets this signal predictable unpredictable Major forcing is boundary conditions

17. Forecasting Ranges Resolutionvs Lead time

18. ForecastingbySynopticCharts

19. GLOBAL DATA ASSIMILATION (GSI) SOURCE: GTS SOURCE: ftp PREPBUFR non-conventional Fixed Fields (climatology) GSI Analysis 06 hourly FCST FCST FCST 7 days Obs/BG Error Stats Usage Control Coeff tables Land surface 00Z, 12Z

20. Short-range Forecasting Strategy • Triple nested model forecast 27, 9 and 3 km • Cycling • 4 times a day 00Z 06Z Time Obs. Error Model BE Model Obs. Error Model BE Model Observations Observations WRF-Var Global Model WRF-Var Global Model Analysis Model State Forecast Analysis Model State Forecast WRF Model WRF Model

21. 9-km assimilation: GFS model provide background and boundary conditions 0000Z 28 NOV 08 0000Z 27 NOV 08 0030Z 0230Z 0300Z 0130Z 0200Z 0100Z forecast forecast forecast forecast forecast forecast Forecast (21 hrs) ADAS ADAS ADAS ADAS ADAS ADAS ADAS IMDS.20081127.0000 IMDS.20081127.0300 ARPS & ADAS – • Advance Regional Prediction System Model (ARPS) - has been used. • ADAS has been used to assimilate & interpolate, radar data file generated by 88D2ARPS & background and boundary file generated by EXT2ARPS, onto the ARPS grid using GFS Forecast. • Following Diagram is showing ½ hourly assimilation cycle ( first 3 hours) & then 21 hours ARPS Model forecast - For Cyclone Case

22. Generation of Multi-model Forecasts NCEP JMA ECMWF NCMRWF UKMO Forecast (F)= WiFi

23. IMD Multi-model Ensemble (MME) based District level (50 x 50 km) Forecasts • Parameters: • Rainfall • Max and Min temperature • Total cloud cover • Surface Relative humidity • Surface Wind

25. Model Performance Spatial Distribution • Rainfall • Root Mean Square Errors • Mean Errors Categorical Skill scores • POD • TS • BS

26. Inter-comparison : Day-5 forecast of Cum. Rainfall (cm) OBS MME ENSM

27. Inter-comparison of day-5 forecast of Cum. Rainfall (cm) UKMO ECMF NCEP OBS NCMR JMA

28. RMSE 2009 :Day-1 NCEP NCMR ECMF UKMO JMA MME ENSM

29. RMSE 2009 :Day-5 ECMF UKMO NCEP NCMR JMA MME ENSM

30. Inter-comparison of model performance: RMSE2009

31. Mean Errors (F-O) :Day-5 ECMF UKMO NCEP NCMR JMA MME ENSM

34. Intra-seasonal Variability of Monsoon type systems-prediction of 15 days to 1 month • The active/break cycles -manifestations of sub-seasonal fluctuations of the northward propagating ITCZ. • Since the time scale is sufficiently long so that much of the memory of the atmospheric initial conditions is lost, and it is probably too short so that the variability of the ocean is not large enough, which makes it difficult to beat persistence. • However, an important source of predictability at this time range is the Madden–Julian oscillation (MJO).

35. Madden-Julian Oscillation (MJO) Typical Variables Used for MJO Analysis Cloudy Low OLR Clear High OLR U200 Rainfall U850 • Intraseasonal Time Scale: ~40-60 days • Planetary-Scale: Zonal Wavenumbers 1-3 • Baroclinic Wind Structure • Eastward Propagation • E. Hem: ~5 m/s, Surf.+Conv.+Circ. Interactions • W. Hem: ~ > 10 m/s, ~Free Tropospheric Wave • Tendency to be Equatorially Trapped • Strong Seasonal Dependence: • NH Winter: Eastward Propagation • NH Summer: ~Northeast Propagation • Significant Interannual Variability • Potential Role of Ocean/SST Feedback • Convection Has Multi-Scale Structure • Significant Remote and Extra-Tropical Impacts

36. Experimental Forecast for two weeks (Empirical models) 1) Real-time Multivariate MJO Index– for 15 days Based on the first two Empirical Orthogonal Functions (EOFs) of the combined fields of near-equatorially-averaged 850 hPa zonal wind, 200 hPa zonal wind, and satellite-observed outgoing longwave radiation (OLR) data. Wheeler and Hendon (2004)). 2) US CLIVAR MJO ForecastProject - Available through Climate Prediction Centre, CPC). 3) MJO forecast in the form of OLR anomalies (a) Xavier and Goswami (1997) – Uses the analog method for the forecasting of OLR pentad anomalies at lead time period of 1 pentad to 4 pentads (b) Jones et al., (2004) - The model uses principal components (PC’s) of empirical orthogonal function analysis of 20-90 days anomalies of OLR. (Jones et al. 2004 J. Climate). 4) Self Organising Map - Developed by IITM is based on a non-linear pattern recognition technique known as Self Organising Map (SOM), which predicts rainfall using dynamical indices.  Currently upgraded and used real time rainfall as input in the forecasting of probability of rainfall anomaly over central India. (IMD and IITM presently working together)

37. Experimental Forecast for two weeks-1 month (Dynamical Coupled models) 1) ECMWF Monthly forecast 51 ensembles - (days 5 to days 32) – Forecast in four weekly averages. (Runs once a week) 2)JMA/Tokyo Climate Center51 ensembles – (day 2 to day 29) -Forecast in two weekly averages and one fortnightly averaged. (Runs once a week). 3) NCEP CFS 4 ensembles - Forecast as daily averages (Runs daily). Model runs every day with 4 ensemble members with 2 days lag of Ocean initial condition 4)Experimental Multi-model forecast system for India : For two weeks after bias correction, based on ECMWF and NCEP coupled model

38. ECMWF forecast for Dry spell of September, 2009 Valid for 14-20 Sep Valid for 21-27 Sep Lag Week 1 Lag Week 2 Lag Week 3

39. Experimental Monthly Probability ForecastBased on Real time NCEP coupled model outputs Total 60 forecast ensemble members 4 Ensembles/day with 15 days of the Month (0) • Probability forecast (in %) for month 1 • Above Normal • Below Normal Model Hindcast Climatology from 25 years (1981-2005)

40. Forecast (Tmax & Tmin) For March 2010 Based on February ICs Observed Tmax anomaly, Mar 2010

41. DWR at VSK Doppler Weather Radars …Means of Nowcasting and very short range forecasting • Wind information • Water content in clouds in different phases • Digital output Increases forecast accuracy dramatically over the next few hours and appreciably over 24 HRS

42. Nowcasting