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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 2 – Item 2 Sahai

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IUKWC Workshop November 2016: Developing Hydro-climatic Services for Water Security
Session 2.2 AK Sahai

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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 2 – Item 2 Sahai

  1. 1. Extended Range Prediction Activities of IITM Dr. A. K. Sahai Extended Range Prediction Group, Monsoon Mission Indian Institute of Tropical Meteorology, Pune – 411 008, INDIA E-mail: sahai@tropmet.res.in
  2. 2. Why we need ERP Seasonal rainfall anomalies are nearly homogeneous over Indian region during extreme monsoon years (droughts/floods). But, mostly (~70%) monsoon years are normal and during normal years the rainfall anomalies are inhomogeneous over the country, contributing to large degree of spatial variability !!! Adding to this is the variability of rainfall on temporal scales............. Drought (2002) Flood (1961) Normal (1998) Seasonal JJAS rainfall anomaly during drought, flood and normal years Drought (2002) Flood (1961) Normal (1998) Although the prediction of a ‘normal’ all India rainfall may have a comfort factor, it may not be 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. Spatial Variability Temporal Variability
  3. 3. Phase1: Peninsular India Phase2: Central India Phase3: Central India Phase4: North India Phase5: Foothills Phase6: South IO Phase7: Indian Ocean Phase8: Southern tip Composite Rainfall anomalies in different MISO phases Predictability of MISO provides hope for extended range prediction:
  4. 4. Examples of evolution of MISO indices
  5. 5. Statistical Extended Range Prediction: The start of a New ERA • As seasonal prediction has limited applicability for short term planning, the extended range prediction beyond weather scale started growing prominence. • The temporal evolution of atmospheric variables may be considered as a continuous process which can have a mixing of processes arising from multiple scales of motion and some inherent chaotic component. • The goal is to separate the scale motions of our interest (signal) from other scales and the chaotic part (noise). • The larger the signal to noise ratio captured by a particular statistical method for a particular variable, the better the method of prediction.
  6. 6. Different methods of Statistical Extended Range Prediction Prediction of ISO : • Based on Principal Oscillation Pattern (POP): Von Storch and Xu (1990), Clim. Dyn • based on Singular Vector Decomposition: Waliser et al., 1999. J. Clim. • Based on EOF only : Lo and Hendon, 2000, MWR • Based on EOF and Multiple-regression: Jones et al., 2004 • Based on EOF and Analog techniques: Xavier and Goswami., 2007, MWR • Based on Self-Organizing Map and Analogues: Chattopadhyay, Sahai and Goswami., 2008, JAS.
  7. 7. Early Developments FOR Extended Range Prediction over Indian Subcontinents • Initial Gain was realized for flood forecasting . A study by Webster et al., 2004, BAMS (Limitation: End Point Problem) • Goswami et al., 2007 used a EOF based analogue method for prediction of OLR (Limitation: at times OLR and Rainfall mismatch; linear method)
  8. 8. Initial Attempt: A SOM based Non-Linear Analogue Technique Sahai et al., 2006 START OF OUR JOURNEY TOWARDS DEVELOPMENT OF EXTENDED RANGE PREDICTION SYSTEM
  9. 9. JAS 2008
  10. 10. Towards Real-Time Probabilistic ISO prediction Presented in IMSP 2009
  11. 11. Well-predictedNot so well-predicted P4 JGR, 2014
  12. 12. ..Though Statistical Extended Range Forecast gives a reliable forecasts.. Unaccounted error may arise due to variability in predictability skill of predictor themselves. Moving towards Dynamical Prediction….
  13. 13. CFS1 CFS2 SOM 2005, P4
  14. 14. Time Line of development of IITM ERPS using CFSv2 2011: Ensemble Prediction System developed, [Abhilash etal., 2014, IJOC] 2012: Bias Correction of CFS forecasted SST implemented [Abhilash etal., 2014, ASL; Sahai etal., 2013, Cur. Sci.] 2013: High Resolution CFST382 implemented [Sahai etal., 2014, CD;Borah etal, 2014, IJOC] 2014: CFS based Grand EPS Implemented [Abhilash etal., 2015, JAMC; Sahai etal., 2015, Cur. Sci] 2015: Forecast for winter and other seasons started 2016: Forecast for Heat Waves started [Applications: Onset Prediction: Joseph etal, 2014, JC; Uttrakhand Heavy Rainfall: Joseph etal, 2014, CD; Skill of CFST126: Abhilash etal., 2014, CD; Comparison 2013 and 2014 June extremes: Joseph etal., QJRMS, 2015; Prediction skill of MJO: Sahai et al., 2016, IITM-RR]
  15. 15. Zonal wind at 1000 hPa from (a) Analysis, (b) Perturbed initial condition and (c) Perturbation. Development of Perturbation Technique Each ensemble member is generated by slightly perturbing the initial atmospheric conditions with a random matrix (random number at each grid point) generated from a random seed. Fraction of the 24 hour tendency of different model variables are added to or subtracted from the unperturbed analysis with random perturbation between -1 and +1 times the 24 hour tendency so that the perturbation follow Gaussian distribution. The perturbed IC, X´x,y,z,t = Xx,y,z,t – n [r ΔXx,y,z,t] where, ΔX = Xx,y,z,t – Xx,y,z,t-1 ; r -> taken from a random matrix and lies between -1 and +1; n -> tuning factor such that 0≤n≤1 We perturb the wind, temperature and moisture fields and the amplitude of perturbation for all variables are scaled according to the magnitude of each variable at a given vertical level. Abhilash et al. (2012, IITM Res Rep); Abhilash et al. (2014, Int . J. Climatol.)
  16. 16. SST Bias from Long Simulation Development of Bias-correction Technique
  17. 17. Spatial correlation between forecasted rainfall and observed rainfall Implementation of High Resolution Version Pentad correlation skill and RMSE w.r.t. observations for CFST126 and CFST382 Sahai et al. 2015, Clim Dyn CFS126-IMD CFS382-IMD OBS IMD Climatological Rainfall bias
  18. 18. OBS IMD CFS126-IMD GFS126-IMD CFS382-IMD MME-IMD Deterministic prediction Skill Probabilistic Prediction Skill Abhilash et al. 2015, JAMC; BAMS Seasonal mean and difference from OBS CFS126 SME CFS382 SME GFSbc SME MME P1 Lead 0.87 0.84 0.87 0.89 P2 Lead 0.85 0.85 0.81 0.88 P3 Lead 0.83 0.82 0.78 0.87 P4 Lead 0.79 0.80 0.76 0.86 Spatial pattern correlation over 40°E-120°E and 40°S-40°N MME has been formulated using 21 ensembles of GFSbc, 11 ensembles of CFS126 and 11 ensembles of CFS382. Hence, total 43 ensemble members were produced independently from 3 variants of CFS model to generate the CGEPS and forecast consensus is done by making simple average among the members. Development of MME
  19. 19. RMSE and spread of MISO indices Considerable improvement in MME is contributed from the increased spread, which overcomes the under-dispersive nature of the individual models in EPS. Abhilash et al. 2015, JAMC; BAMS Bivariate RMSE: RMSE w.r.t. observation Bivariate Spread: Std. Dev of iindividual models w.r.t. Ensemble mean
  20. 20. Relook: Why MME? Comparison of IITM-ERPS with ECMWF
  21. 21. Skill of CFST126/T382 is much less than ECMWF in longer leads Comparison of IITM-ERPS with ECMWF Skill improved due to bias correction How to improve the skill and make it comparable to that of ECMWF?
  22. 22. 0531: Low Pressure System (LPS) over southern tip of peninsula is likely to intensify and move towards Oman coast. This system may dissipate around 11th June and till then the monsoon activity will be weaker than normal over India. 0725: It was forecasted that Monsoon activity will be normal and there is a possibility that it may enter in the break phase around 10th Aug. 0620: There will be a large scale reduction of rainfall during 1st half of July. 0710: Large scale monsoon activity is expected to increase by the end of July resulting in revival of monsoon. 0903: A fresh spell of good rainfall will propagate from Indian ocean to southern peninsula around 20th September and may reach central India around 25th September. 0605: It is likely that by 17th June the offshore trough along the west coast will be established and within one week after that, monsoon may reach central India as a feeble current. Prediction of 2015 SW monsoon season
  23. 23. OBS Verification based on 30th June IC Monitoring and prediction of MISO during 2016
  24. 24. Joseph et al. 2015, J Climate
  25. 25. MOK forecast of 2014 based on IC: 0516 Key points from the forecast:  A low pressure system might form over Bay of Bengal around 25 May 2014 and move northwards.  South-west monsoon of 2014 would make its onset over Kerala on 04 June.  However, the strengthening and progression of the monsoon might be slackened till 15 June due to the presence of a low-level anticyclonic circulation over central India. Afterwards, monsoon might strengthen and progress. Monsoon Onset over Kerala
  26. 26. Progression of ISM 2016 IC: 0605 Peculiar progression of ISM from eastern side (via Bay of Bengal branch) around 17 June was predicted by MME.
  27. 27. The revival of monsoon due to the formation of a LPS around 13-14 September and subsequent westward movement was forecasted well from 08 September IC. Withdrawal of ISM 2016 (IC: 0908)
  28. 28. Year June Rainfall Departure from Mean 2001 219.0 35.6 2002 180.1 9.4 2003 179.9 9.8 2004 158.7 -0.8 2005 143.2 -9.5 2006 141.8 -12.7 2007 192.5 18.5 2008 202.0 24.3 2009 85.7 -47.2 2010 138.1 -15.6 2011 183.5 12.2 2012 117.8 -28.0 2013 219.8 34.4 2014 92.4 -43.5 Observed June Rainfall during 2001-14 Joseph et al. 2016, QJRMS
  29. 29. The model has remarkable skill in predicting the June extremes !!! ERP of June extremes by CGEPS MME Joseph et al. 2016, QJRMS
  30. 30. IC: 05 JunePrediction of Heavy Rainfall Events Uttarakhand event in June 2013 Evolution of Potential Vorticity (PV; x10-7 s-1) anomalies at 700 hPa and mean sea level pressure Forecast OBS Joseph et al 2016, Clim. Dyn.
  31. 31. Low Pressure System (LPS) over southern tip of peninsula is likely to intensify and move towards Oman coast. This system may dissipate around 11th June and till then the monsoon activity will be weaker than normal over India. Cyclone “Ashobaa” during Onset phase of 2015 monsoon IC: 0531 Prediction of Cyclogenesis MME OBS
  32. 32. MME OBS IC: 11 May Cyclone Roanu in May 2016 Prediction of Cyclogenesis
  33. 33. OBS model MJO Forecast during March-April 2009 period
  34. 34. Prediction of Heat Waves
  35. 35. Heat Wave in May 2016 Observed Heat Index Predicted Heat Index IC: 0516 Prediction of Heat Waves
  36. 36. Prediction of North-East Monsoon (NEM)
  37. 37. Hindcast Skill for Post Monsoon/NEM NEM SLK NEM and SLK region Climatological rainfall NEM (SLK) region exhibits useful skill up to 3(2) Pentad Hindcast Skill
  38. 38. Area averaged rainfall over NEM region during 2015 predicted by MME The CGMME system predicted the above normal rainfall activity over Chennai and NEM region well in advance. The CGMME system able to capture this high impact continuous rainfall activity during the last week of November and first week of December around Chennai region from 4th pentad lead
  39. 39. Application to Model Output • Bias Correction and Downscaling 45
  40. 40. SOM based Bias correction and Downscaling: Application to ERP of Mahanadi flood in September 2011 Sahai et al 2016, Clim. Dyn. 0.30 0.47 0.17 0.31 0.22 0.60 0.49 0.47
  41. 41. Critical Success Index of Predicting Anomalies • CSI of predicting dry anomalies in precipitation forecast from CFSv2 and IITM_ensemble is higher in northwestern region whereas lower in Himalayan range and southern peninsula • Runoff and Soil moisture shows higher CSI than precipitation and temperature due to persistence in Initial conditions. Precipitation Temperature Runoff Root Zone Soil moisture
  42. 42. Prediction of Drought : 15th July 2009 Better predictability of precipitation and temperature from bias-corrected IITM ensemble along with persistence in Initial hydrologic condition lead to better forecast of anomalies in runoff and root-zone soil moisture Precipitation Temperature Runoff Root zone Soil Moisture
  43. 43. ERP has: • Application in agricultural • Application on health sector • Application on energy sector • Application in urban/rural planning • Application in extremes prediction • Application in dam/watershed management • Application in insurance/reinsurance The current version of ERP System has been transferred to IMD.
  44. 44. picture from Nasa Thank You http://www.tropmet.res.in/erpas/index.php

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