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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5 – Item 5 - AK_Mitra


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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5.5 Ashih K Mitra

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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5 – Item 5 - AK_Mitra

  1. 1. Rainfall Information from NCMRWF Modelling System National Centre for Medium Range Weather Forecasting A-50, Sector 62, NOIDA-201309 Ashis K. Mitra [with inputs from NCMRWF Colleagues ]
  2. 2. Mission of the Centre Major Mandates of NCMRWF To continuously develop advanced numerical weather prediction systems, with increased reliability and accuracy over India and neighbouring regions NCMRWF is a Centre of Excellence in Numerical Modelling and Data Assimilation  Development and improvement of weather prediction models for IMD to underpin their forecasting capability  Development of Data Assimilation (DA) systems for both & Unified Model (UM) & Global Forecast System (GFS)  Development of a Seamless prediction system based on UM
  3. 3. IMD Observations Feedback on observation quality IC & FCST (Local & Global) Central/ State GOVT Sectoral Users (Agriculture , Aviation ….) Public Media Value added products IITM IMD IAF SASE Capacity Building on NWP New Applications Wind energy Water Cycle … BIMSTEC Linkage of NCMRWF with Various Organizations INCOIS satelliteobs. ISRO NCMRWF Numerical Modelling of Weather & Climate IC& FCST IC FirstGuess Ocean and Fishery services
  4. 4. Assimialtion-Forecast System of NCMRWF Data Reception Global Data Assimilation Forecast Models Users SURFACE from land stations Upper Air RSRW/ PIBAL SHIP BUOY Aircraft Satellite High Resolution Satellite Obsn Internet (FTP) RTH, IMD GTS NCMRWF OBSERVATION PROCESSING NKN24x7 NESDIS EUMETSAT NKN proposed dedicated link Observation quality checks & monitoring 4 times a day for 00,06,12,18 UTC Global Analysis: (Hy GSI T574) 4D VAR UM Initial state Global Fcst Models: NCUM Global Model UM-N512L70 10 day FCST UM Based EPS M45 33km ; 10 days once in a day for 00 UTC Visuali- sation IMD INCOIS IITM SASE BARC RIMES Other sectors NKN ISRO MT NKN ~600mb/dy ~ 9 Gb/dy UM Regional 4 km ; 1.5 km 3 days Global Observations
  5. 5. Forecast Products Partners and User Agencies Analysis Fields from Global Model IMD, IITM, INCOIS and SAC Rainfall, Temperature and Winds from Global and Ensemble Models IMD, SWFDP (WMO) Snow and Avalanche Study Establishment/DRDO Bhakra Beas Management Board (BBMB) (Flood Monitoring & Forecasting) Krishna and Bhima-Basin Simulation Division (Maharashtra Govt; flood Forecasting) Wind, Temperature, Rainfall, SW Radiation etc. from Global and Regional Models Wind and Solar Energy Sector  National Institute of Wind Energy (Ministry of New and Renewable Energy) Manikaran Power Limited & Energon Power Resources Pvt Ltd Wind, Temperature, Geo- potential Height and Humidity from Regional Model Nuclear Power Corporation of India Ltd. Weather forecast for Nuclear Power Plants (9) (1) Kaiga (4) Kalpakkam (7) Visakhapatnam (2) Trombay (5) Narora (8) Kudankulam (3) Tarapore (6) Rajasthan (9) Kakrapara Partners and User Agencies of NCMRWF Products
  6. 6. Current Operational Data Assimilation Systems • Hybrid Ensemble Global Data Assimilation System for NCUM • 44 members • Hybrid Ensemble Global Data Assimilation System for GFS • 80 members
  7. 7. T1534 (SL) IC IITM-GEFS INCOIS- LEKF IMD-extended IITM-seasonal IMD-GFS T382 (EL) Utilization of NCMRWF Hybrid-GSI Analysis (GFS based) T1534 EnKF was also developed & Tested without STTP (Stochastic total tendency perturbation)
  8. 8. Global Observations Data Assimilation Initial Condition Forecast Model 10 Days Weather Forecast NCMRWF Global Forecast System
  9. 9. Increase in Data Reception at NCMRWF  Number of observations (conventional and satellite/radar) has increased by orders of magnitude over past 10 years.  Data assimilated at NCMRWF comparable with other International Global modelling centres Indian Satellite Data Assimilated •OSCAT- Ocean surface winds •Megha-Tropiques SAPHIR •INSAT-3D AMV •INSAT-3D Sounder Radiance •GPSIPW 0 10 20 30 40 50 60 70 0 1 2 3 4 5 6 1997 2001 2003 2006 2008 2009 2010 2011 2012 2013 2014 2015 2016 FTP(GB/day) GTS(GB/day) Year FTP (SAT + RADAR) IMD(GTS)
  10. 10. 1.5 km regional model up to 48 hr forecast 17 km global model up to 10 Day forecast 4 km regional model up to 72 hr forecast Global Ensemble Prediction System – 33 km with 44 members up to 10 Days Coarse resolution coupled model (NEMO+UM) UM Partnership Agreement with UKMO, KMA, BoM, NIWA on joint development of “Unified Model” for seamless Prediction Unified Model at NCMRWF Same Model for Global/Regional/Mesoscale – Seamless model
  11. 11. Sl. No Topic Progress (last 6 months) 1. Atmospheric Data Assimilation • Implementation of Hybrid 4D-Var DA system at NCMRWF (using 44 member NCMRWF EPS forecast) in Sept. 2016 • Use of more observation in DA system and Indian satellite radiance observations– MT SAPHIR & INSAT-3D Sounder radiances in NCUM DA system • 3. Regional DA system (3D-Var) with Indian DWR radial wind is under testing 2. Land Data Assimilation system Providing Soil moisture analysis and forecast products to IMD (Agro Met Division); JULES LS Model NCUM Data Assimilation (Global & Regional)
  12. 12. Observation Type Satellite AMSU/MHS radiances NOAA , MetOp Satellites HIRS clear radiances NOAA , MetOp Satellites IASI and AIRS radiances MetOp satellites (IASI), Aqua (AIRS) ATMS & CrIS radiances Suomi NPP MT-SAPHIR radainces Megha-Tropiques Geo Imager – cloud clear IR radiances Meteosat , GOES Satellites Geo Sounder – cloud clear IR Radiances INSAT-3D GPS RO bending angles COSMIC, MetOp/GRAS, GRACE GPS ZTDs Global observations (various locations) Satellite Atmospheric Motion Vectors INSAT-3D, Meteosat-7& 10, GOES-E& W, MTSAT-2/Himavari-8, AQUA, NOAA & MetOp Satellites Scatterometers : Sea-surface winds MetOp Satellites (ASCAT) Surface soil moisture/wetness MetOp Satellites (ASCAT) Aerosol Optical Depth (for dust) AQUA , INSAT-3D (experimental) Satellite Data Used in NCMRWF Unified Model DA System
  13. 13. BHASKARA 350 TF HPC Facility Year Peak Performence 1988-91 234MF 1992-99 468MF 2000-02 1GF 2003-05 28.8GF 2005-06 500GF 2006-10 1000GF 2011-12 24TF 2015 350TF
  14. 14. The decrease in the Error can be attributed to: • increase in the resolution of the model, • increase in the amount of data being assimilated, • improvements in data assimilation techniques, • improvements in model physics/dynamics. Verification of Day 03 Forecasts against Radiosondes over India Error in 850 hPa winds (m/s) during (2005-2016) 0 1 2 3 4 5 6 7 8 9 Jan/05 May/05 Sep/05 Jan/06 May/06 Sep/06 Jan/07 May/07 Sep/07 Jan/08 May/08 Sep/08 Jan/09 May/09 Sep/09 Jan/10 May/10 Sep/10 Jan/11 May/11 Sep/11 Jan/12 May/12 Sep/12 Jan/13 May/13 Sep/13 Jan/14 May/14 Sep/14 Jan/15 May/15 Sep/15 Jan/16 RMSEV Day3 FCST Verification
  15. 15. How do we compare with other centres? Error (RMSE) of 850 hPa winds(m/s) against Radiosondes over Tropics (~75 obs) 0 1 2 3 4 5 1 2 3 4 5 RMSEV Forecast Days ECMWF NCEP UKMO NCMRWF
  16. 16. GPM Core Observatory launched on 27Feb2014 DPR & GMI Heavy rain Moderate rain Light rain Improved: Light Rain Falling snow Rain microphysics Rainfall Analysis (Estimation): Merged Satellite Gauge Data Product (Joint IMD & NCMRWF)
  17. 17. Daily Merged Rainfall Data Moving from TRMM to GPM Earlier 0.5 x 0.5 grid Now 0.25 x 0.25 grid Parallel run Aug 2015 onwards Will be operational from 01OCT2015
  18. 18. GPM
  19. 19. Merged GPM satellite plus Gauge Rainfall Data at 0.25 x 0.25 Now around 2600 gauges
  20. 20. Land Use Land Cover data • NCMRWF Unified Model (NCUM) uses the climatological 18 class IGBP LuLc dataset to derive nine surface types for the JULES land surface scheme. • The IGBP dataset was derived from AVHRR data covering the period between April 1992 and March 1993 and provides data at 30 arc-second (~1km) resolution globally • The climatological LuLc data are replaced with the NRSC/ISRO derived LuLc from IRS-P6 satellite over South Asia and adjoining region. – AWiFS sensor data of IRS-P6 satellite during 2012 to 2013 was used to derive the 18 IGBP surface types with a resolution of 30 sec (~1 km)
  21. 21. LuLc (UKMO & NRSC)
  22. 22. Positive Impact of higher resolution and better model physics Heavy Rainfall Event over Chennai NCUM-G ~17km NCUM-R ~4km GPM ~25km Day 1,2,3 Rainfall (cm/day) Forecast valid for 2 December 2015 Day 1 Day 2 Day 3
  23. 23. 31st August 2016 Delhi Heavy rains was predicted consistently by the 17 km global model - 4 days ahead onwards
  24. 24. The 4-km Model could predict the very high amounts of rainfall -3 days before (31st August Rain Event over Delhi) Observed Rainfall :1st Sep 2016 Day-3 FC Rainfall :1st Sep 2016 4-km Model Observations
  25. 25. Mean Rainfall ~4 km ~17 km
  26. 26. Diurnal rainfall variability for Central India rain episode 1-3 Jul 2016 (averaged for 77-82 ᵒE & 20-25 ᵒN) • Though the peaks are not matching the global forecasts are able to predict the diurnal variability with some delay. • 4km regional model is able to better simulate both the right phase and magnitude compared to the global model. • 4km curve is smoother in UM10.2 compared to UM 8.5 probably due to the moisture conservation invoked in UM10.2.
  27. 27. Soil Moisture Data: Useful for Agriculture and Weather Prediction Land Data Assimilation System , JULES Land-surface Model
  28. 28. (a) Spatial distribution of standard deviation (mm day-1) of daily JJAS rainfall over India from NMSG data set. Locations of seven major river basins are shown. (b) Locations of different regions within the study domain. In Press Met. Applications (RMS), 2016 Rainfall skill at Basin Scale
  29. 29. Time-series of daily domain-mean rainfall for east and west Ganga river basins from NMSG, UKMO and GFS data sets for 2013.
  30. 30. Correlation coefficient and bias of daily basin-mean rainfall from UKMO and GFS data sets with respect to NMSG rainfall for the southwest monsoon period of 2013. EGNG, WGNG, BRHM, GDRI, KRSN and MHND stand for East Ganga, West Ganga, Brahmaputra, Godavari, Krishna and Mahanadi river basins, respectively.
  31. 31. MAE and RMSE of daily basin-mean rainfall from UKMO and GFS data sets w.r.t. NMSG rainfall for the southwest monsoon period of 2013. EGNG, WGNG, BRHM, GDRI, KRSN and MHND stand for East Ganga, West Ganga, Brahmaputra, Godavari, Krishna and Mahanadi river basins, respectively.
  32. 32. Summary: Both the models have useful skill for different regions and basins. For the Indus and Krishna basins, even the Day 5 forecasts are seen to be skilful. UKMO Day 5 forecasts are generally more skilful at all seven river basins
  33. 33. Global Ensemble Forecast System • NCMRWF –GEPS (33 km with 44 members) runs daily with 00 UTC inputs – based on UM • IITM-GEFS (35 km with 20 members) runs daily with 00 UTC inputs – based on GFS (Initial Conditions from NCMRWF) Current Operational Global Ensemble Forecast Systems  Ensemble prediction systems are useful for generating probabilistic forecasts – which will highlight the uncertainties associated with a forecast and will be useful in forecaster’s in their decision making
  34. 34. Heat Wave :21 May 2016: Locations: NW India, Central and North Peninsular and Rajasthan (T>500C). Probabilistic forecasts of Maximum Temperature from Global Ensemble Prediction System Day-5 Forecast shows high probability of Tmax > 420C
  35. 35. Probabilistic Forecasts of Rainfall using Global Ensemble Prediction System Day-6 Rainfall Probability (2-6 cm) Forecast over MP for 11 July 2016
  36. 36. Ensemble Model Forecasts For Tropical Cyclone ’Roanu’ 17-23 May 2016 Ensemble Tracks Strike Probability
  37. 37. Verification and Inter-comparison of NCUM-G and NEPS Monsoon 2016 (JJAS) • IMD-NCMRWF (Sat+Gauge) Rainfall Analysis (03 UTC) • NCUM-G Rainfall forecasts (03 UTC) • NEPS (44 member) Ensemble Mean Rainfall Forecasts (00 UTC) • 0.25 x 0.25 grid spacing • Verification over India (8-38E/68-98N) (Land only) – Mean, Maximum – Bias Score and ETS
  38. 38. Drying from Day-1 to Day-5 Peninsula, WG, Arakan Coat Rainfall peaks – • Overpredicted over central India • Underpredicted over BoB
  39. 39. • Over-predicted rain • Over core monsoon region in Day1 • Gangetic plains and NE India on all days • Under-predicted rain •Over parts of peninsula & NW India
  40. 40. ETS (JJAS 2016)
  41. 41. NCUM Regional Data Assimilation System: 4km 1. DA System for 4 km resolution NCUM Regional Model (3D-Var). 2. New Background error – being prepared. 3. Assimilation of DWR Radial winds – Experimental Valid for 03 UTC 17 May 2016 (03 hr forecast) Control (Global NCUM) Experiment (Regional DA with DWR RW obs)
  42. 42. Regional Data Assimilation System: 4 km Analysis increments (T): 00 UTC 10/06/2016 • 3D-Var DA System for 4 km Regional Model. • New Background error – being prepared. • Assimilation of DWR Radial winds – efforts are going on.
  43. 43. High Resolution (1.5 km) Regional Modelling with NCUM • The high resolution regional model at 1.5 km resolution is embedded within a coarser resolution global model (25 km). • NASA’s 90 metre SRTM topographic data is used to generate the regional model’s orography
  44. 44. 1.5 km Resolution Regional Model Global (17km) UM_REG (4 km) UM_REG (1.5 km) • STRM filtered orography is used for UM_REG (1.5km) . The issue of model blow up due to steep gradient in Himalayan orography was resolved by smoothening with a 1-2-1 filter. • NRSC LU/LC used for veg.frac ancillary in both 4-km & 1.5km models • UM_REG (1.5 km) uses latest Tropical settings + moisture conservation + stochastic physics
  45. 45. 15-30 June 2016
  46. 46. Role of Jawadi hills in modulating rainfall over Chennai domain and oceanic region. 24 hour accumulated rain valid for December 2, 2015 from (a) CNTL (b) JHR runs and (d) (b) minus (a). (C) Forecasted rain amount from Global model (17km) which is the parent model to the 1.5km nested model for the same date.
  47. 47. NCUM-G (17km) NCUM-CSM (1.5km) NCUM-DM (330m) 330 mt Model: Domain, Orography (m) Delhi airport 28.544° N,77.113 °E Delhi City 28.7041° N, 77.1025° E
  48. 48. • A dense fog set in at 6:30 AM and started clearing by 8:30 AM 12th Dec 2015 • 24 hour model simulations made starting from 06 UTC 11th Dec 2015 • t+(19, 20, 21 hr) forecasts valid for 6.30, 7.30, 8.30 IST of 12th Dec 2015 examined A Case Study: 330 mt model
  49. 49. DM model (calc_prob_of_vis=0.5) Ndrop_surf= 7.5e7 and z_surf=0.0 Moisture conservation=off 7.30 AM 8.30 AM 6.30 AM
  50. 50. • Western disturbances (WDs) affect J&K and neighbourhood during winter • WDs are the embedded low pressure systems in the mid-latitude westerlies • During DJF (2014-15) at least 9 WDs led to heavy rainfall and snowfall – Heavy rains over the hilly regions causing to flash floods and landslides are reported • NCMRWF Significant Weather Forecast Summary (SWFS) issued for each of the WDs included circulation (500hPa flow), Rainfall and Snow over N India based on – Deterministic forecast model (NGFS) – Probabilistic forecast model (NGEFS) Winter Precipitation/Snowfall
  51. 51. Day 7 forecast valid for 15-12-2014 Snowdepth forecast
  52. 52. • Ocean-Atmosphere-Land Coupled Model is already tested • Real time runs 2017 Jan/Feb • 25km/L85 atmosphere & 25 km/L75 Ocean • Interactive Sea-Ice Model • Seamless from Days-to-Season Coupled Modelling and Ocean Initialisation For extending temporal range of Fcst (Days to Season)
  53. 53. Stand-alone Ocean Model NEMO L75 10 X 10 0.250 x 0.250 grid L75 : In Coupled Setup
  54. 54. Sea Ice Simulation in Coupled Model Hindcast
  55. 55. Global Ocean Data Assimilation (for initialising coupled model) NEMOVar Implemented and Tested for June 2016 data Made real-time from Nov 2016 Produces Global Ocean Analysis [and 10-days Fcst]
  56. 56. Indo-UK Field Campaign: Monsoon NCMRWF is actively involved in INCOMPASS & BoBBLE (NERC-MoES) Projects – for better understanding of small scale processes for model development (Land-surface and Air-sea Intercation in monsoon) Currently Several India and UK Institutes are working together in this Joint MoES-NERC Monsoon Project. IMPROVED MONSOON PREDICTION
  57. 57. Air-Sea Interaction in Bay Monsoon Land Surface-PBL-Convection Gliders and ORV MoES-NERC Joint Work Understand and Predict Monsoon 1. INCOMPASS 2. BOBBLE 3. SWAAMI IMPROVED MONSOON PREDICTION
  58. 58. Future Plans  To increase the global model resolution to 12 km.  To implement very high resolution regional model of 1.5-km resolution over Indian region for prediction of high impact weather.  To implement a high resolution (4 km) regional data assimilation system which will have the capability to assimilate Radar and other high resolution datasets  To Implement a 12 km global ensemble prediction system (NITI Ayog Project) (around 40 member)  To implement a high resolution (25 km) atmosphere-ocean coupled modeling system- “ Coupled NWP Model” for week-2 forecasts  Develop new application areas (Wind/Solar energy, Water resource management, Flood, strategic users)  Develop and improve new products (Visibility, Fog, Dust etc.) HPC required for these activities: 4 PF along with 5 PB storage
  59. 59. Thank You