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Model Development Activities at ESSO-NCMRWF

Explore the current status, new developments, and future plans of the NCUM model at NCMRWF, focusing on observational assimilation, data generation, model implementations, and forecasting capabilities. Learn about soil moisture assimilation, regional modeling, and land use impact evaluations.

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Model Development Activities at ESSO-NCMRWF

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  1. Model Development Activities at ESSO-NCMRWF E N Rajagopal

  2. Unified Model at NCMRWF (NCUM) Same Model for Global/Regional/Mesoscale! – seamless model 1.5 km grid up to 48 hr forecast 12 km grid up to 48 hr forecast 25 km global grid up to 168 hr forecast

  3. Outline of Talk • Current Status • New Developments • Model Diagnostics & Evaluation • Future Plans

  4. Status of Observations at NCMRWF

  5. Observations Assimilated in NCUM

  6. Current Status • NCUM DA -4D-Var operational • NCUM – 25 km/L70 operational • 3D-Var Surface Analysis operational from July 2014

  7. New Developments • Generation of SST and Sea-ice for NCUM from high resolution (5 km) OSTIA SST • Generation of snow analysis for NCUM from imssnow dataset (4 km) • Implementation of 3D-Var surface analysis in NCUM using SYNOP data (Temp & Humidity) • Implementation of Nudging Scheme for Surface Soil Moisture in NCUM • Assimilation of surface soil moisture derived from ASCAT in NCUM

  8. New Developments • Attained capability to create ancillary files for various resolutions of NCUM using CAP utility. • Use of NRSC/ISRO derived LuLc from IRS-P6 satellite over South Asia and adjoining region in NCUM. • Assimilation of INSAT-3D AMVs in NCUM from 1st January 2015 • Efforts are going on to ingest INSAT-3D CSBT and Megha-Tropiques SAPHIR radiances (under IMDAA project’s young scientist training) in NCUM

  9. New Developments • Implementation and testing of 1.5 km Nested model • 17 km global model implemented and tested • Migration to the next generation UM environment based on Rose/cylc has been accomplished • Mirroring of UM shared repository available through cloud • Sensitivity study with convection in UM • Diagnostic assessment of monsoon behavior in Coupled UM on sub-seasonal scale (Training under UoR NMM project) • New Products • Dust forecasts from NCUM • Visibility forecasts from NCUM

  10. New Developments

  11. Soil moisture assimilation scheme based “nudging” technique is operational from July 2014

  12. ASCAT Surface Soil Wetness in the assimilation system ASCAT surface soil wetness observations in the assimilation System (1-15 Sept 2014) ASCAT observations used in assimilation system at NCMRWF (a typical day) 00 UTC 06 UTC 12 UTC 18 UTC

  13. Monthly Mean Surface Level (0-10 cm) Soil Moisture (November-2014) NCUM UKMO

  14. RMSE (%) of Surface Level Soil Moisture against AMSR2 Obs November 2014 NCUM UKMO

  15. Verification of UM Surface Soil Moisture Analysis over India (Monsoon -2013) Soil moisture analysis is able to capture large variabilities seen in the in-situ observations IMD soil moisture observations are not used in the analysis

  16. High Resolution (1.5 km) Regional Modelling

  17. The high resolution regional model at 1.5 km resolution is embedded within a coarser resolution global model (25 km). • Both global and regional models are setup using latest version of UM8.5-GA6.1. • NASA’s 90 metre SRTM topographic data is used to generate the regional model’s orography.

  18. Nested regional model at 1.5 km resolution has been successfully implemented and run for 3 days for Gujarat, Madhya Pradesh, Odisha, J&K and Delhi domains • Madhya Pradesh (700 x 450) IC: 4th August 2014 Wall Clock Time for 3 day forecast= 5.5 hours (8 nodes IBM-p6) • Gujarat (600 x 450) IC: 28th July 2014 Wall Clock Time for 3 day forecast= 4 hours (8 nodes IBM-p6)

  19. Himalayan Orography (km) SRTM Orography at 1.5 km used in Regional NCUM GLOBE Orography at ~25 km used in Global NCUM SRTM data is at 90 metre resolution and GLOBE data is at 1 km resolution

  20. J&K (3-5 Sep 2014) Day 1 Forecast Day 3 Forecast Day 2 Forecast OBS J&K GLOBAL

  21. Obs 1.5 km Global Day-1 Gujarat Day-2 Day-3

  22. Orography (km) over MP GLOBE Global 25km SRTM Regional 1.5km

  23. Obs 1.5 km Global Day-1 MP 5-7 Aug 2014 Day-2 Day-3

  24. Rainfall – 06 Aug 2014 1.5km NCUM IMD-NCMRWF Radar Bhopal DWR reflectivities used to derive rainfall Outer Grey Circle Represents Radar 250 km Range

  25. 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)

  26. Surface Types (IGBP v/s JULES) Input to JULES land surface scheme in UM

  27. LuLc (UKMO & NRSC)

  28. Surface Type Fraction Bare soil fraction IGBP • NRSC data shows recent changes in urban, forest and bare soil tiles. Urban tile fraction NRSC IGBP NRSC

  29. Impact of land use/land cover - JK Rainfall Results shows an improvement of regional rainfall pattern with the use of new realistic land use land cover data from ISRO NRSC.

  30. Sensitivity Studies with NCUM Convection Active monsoon spell in 2013 - 72-hr fcst from NCUM (75 km) Entrainment rate increased by 25% OBS Control Entrainment (+25%) Arabian sea (65-74oE,15-23oN) Central India (71-89oE, 17-27oN) • Results: • Total rainfall (t+72) from Entrainment (+25%) shows better correlation with observed rainfall. • Control shows more frequency of deeper clouds in Arabian sea compared to Entrainment(+25%) Bay of Bengal (85-100oE, 10-20oN) 3hrly averaged OLR count of Kalpana,Control, Entrainment

  31. Impact of better physics in coupled model (GA2.0 v/s GA3.0) GA3.0 has reduced rainfall biases

  32. NEMO Ocean Model simulated SST & MLD (Apr-Sept) SST Bias Annual cycle of MLD (m) with chlorophyll without chlorophyll Clim with chloro without chloro • The reduction of 0.5 C in SST bias and 10m in MLD bias is observed in the experiment • Use of real time chlorophyll observations from OCM for ocean initialization would provide improvements

  33. Model Verification against Analysis

  34. Global ACC: 500 hPa Z (Jan 2015) Inter-comparison of models at NCMRWF

  35. Wind RMSE: 850 hPa (Tropics)

  36. Model performance during the monsoon season-2014

  37. Rainfall Verification Aug-Sept 2014 • Model Forecast Daily Rainfall (cm/day) • NCUM & NGFS • Observed Daily Rainfall (cm/day) • IMD-NCMRWF [Merged Sat + Gauge] • 0.5° x 0.5° grid resolution • Continuous type gridded Verification statistics using Model Evaluation Tools • 0.5° x 0.5° grids; over Indian region (8-38 °N, 68-98 °E).

  38. Mean Error (8-38 °N, 68-98 °E) NGFS shows higher ME at higher lead times

  39. RMSE (8-38 °N, 68-98 °E) RMSE magnifies the large errors in the isolated cases (rare events).

  40. Rainfall Verification NCUM, UKMO and ACCESS-G • JJAS Verification of rainfall forecasts • Mean monsoon rainfall • Mean and extreme rain cases • Verification scores for extremes (tails) • Flooding in Srinagar

  41. Forecasts overestimate the Rainfall along the gangetic plains Average rainfall along the west coast and NE India seem realistic. Rainfall along west coast is drying up in NCUM

  42. POD: Fraction of observed ‘yes’ events predicted correctly. Higher POD in NCUM,ACCESS-G and UKMO ACCESS-G has highest POD

  43. FAR: What fraction of predicted ‘yes’ events did not realize?? Higher FAR in ACCESS-G

  44. ETS: How well did the forecast "yes" events correspond to the observed "yes" events (accounting for hits due to chance)? • UKMO has higher ETS for lower thresholds • ACCESS-G has higher ETS for higher thresholds

  45. Synoptic System NGFS: Pattern is missed; Few peaks are captured UKMO: Pattern is captured; peaks are better captured NCUM: Pattern is captured (Day-1); pattern & peaks missing in Day-3 & Day-5

  46. Synoptic System NGFS: Pattern is missed; Few peaks are captured UKMO: Pattern is captured; peaks are better captured NCUM: Pattern is captured (Day-1); pattern & peaks missing in Day-3 & Day-5

  47. Srinagar Rainfall (4th Sept 2014) Peak CC RMSE Obs : 269mm UKMO : 207mm .37 19.2mm NCUM : 169mm .20 18.7mm ACCESS-G : 119mm .20 18.8mm

  48. All models fail to capture the peak rainfall amounts along the west coast Rainfall peaks over central India captured by UKMO

  49. ETS tells how the forecast ‘yes’ events correspond to observed ‘yes’ events (accounting for random hits) POD tells what fraction of the observed "yes" events were correctly forecast BIAS (frequency bias) tells how the forecast frequency of ‘yes’ events compare with observed frequency of ‘yes’ events • FAR Fraction of predicted events that did not occur ETS & POD scores are very low for high rainfall thresholds. Lower rain thresholds over forecast (BIAS>1) Higher rain thresholds under forecast (BIAS<1)

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