Activities and Plans in Modelling & DA National Centre for Medium Range Weather Forecasting A-50, Sector 62, NOIDA-201309 www.ncmrwf.gov.in
NCMRWF is a Centre of Excellence in Numerical Modelling and Data Assimilation Major Mandates of NCMRWF Ø Development and improvement of weather prediction models for IMD to underpin their forecasting capability Ø Development of Data Assimilation (DA) systems for both Global Forecast System (GFS) & Unified Model (UM) Ø Development of a Seamless prediction system based on UM
History of Global Models Year Model Resolution DA 1989 T L 79L19 (ECMWF) - 1989-1992 R40L18 250 km OI (COLA) 1992 - 2007 T80L18 150 km T170L28 75 km 3D-VAR (SSI) 2007 NGFS (T254L64) 50 km 3D-VAR (SSI) 2009 NGFS (T254L64) 50 km 3D-VAR (GSI) 2010 NGFS (T382L64) 35 km 3D-VAR (GSI) 2011-15 NGFS (T574L64) 23 km Hybrid 3D-VAR 2016- NGFS (T1534L64) 12 km (GSI) 2012 -15 NCUM (N512L70) 25 km 4D-VAR NCUM (N768L70) 2016- 17 km Hybrid 4D-Var
Model Resolution Upgrades have taken Place Whenever there was a major HPC upgrade Year Computing Model Power Resolution 1988-91 234 MF 250 km 1992-99 468 MF 150 km 2000-02 1 GF 150 km 2003-05 28.8 GF 150 km 2005-06 500 GF 75 km 2006-10 1 TF 50 km 2010-15 24 TF 25 km 2015 - 350 TF 17 km
Current Operational Data Assimilation Systems • Hybrid Ensemble Global Data Assimilation System for NCUM • 44 members – ETKF/4D-VAR – 17 km • Hybrid Ensemble Global Data Assimilation System for GFS • 80 members –EnKF/3D-VAR (GSI) – T1534 (SL) ~12km • Global Ocean DA using NEMO-Var – 0.25 Deg
IITM-GEFS IMD-GFS INCOIS- LETKF ODA T1534 (SL) ICs NCMRWF T382 (EL) IMD-extended IITM-seasonal
Increase in Data Reception at NCMRWF FTP (SAT + RADAR) IMD(GTS) 6 70 60 5 50 FTP (GB/day) GTS (GB/day) 4 40 3 30 2 20 1 10 0 0 1997 2001 2003 2006 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year Indian Satellite Data Assimilated q Number of observations (conventional and •OSCAT- Ocean surface winds satellite/radar) has increased by orders of •Megha-Tropiques SAPHIR magnitude over past 10 years. •INSAT-3D AMV q Data assimilated at NCMRWF comparable with •INSAT-3D Sounder Radiance other International Global modelling centres •GPSIPW
BUFR Data not being decoded at NCMRWF
Unified Model at NCMRWF Same Model for Global/Regional/Mesoscale – Seamless model 330 m Delhi Fog Model 1.5 km regional model up to 48 hr forecast 4 km regional model up to 72 hr forecast 17 km global model up to 10 Day forecast Global Ensemble Prediction System – 33 km with 44 members up to 10 Days Coarse resolution coupled model (NEMO+UM)
Operational/Experimental Models • NCMRWF Unified Model (N768L70) – since June 2015 Ø NCUM (17 km) -10 day forecasts at 00 UTC Ø NCUM (17 km) -5 day forecasts at 12 UTC – for RIMES Ø 4-km NCUM-R (with explicit rain processes) running with NCUM-G inputs at 00 UTC for 72 hours – since July 2015 Ø NCMRWF Global EPS – based on NCUM (33 km/44 members) -10 Day Forecasts at 00 UTC (450 nodes -3.5 hrs) Ø 1.5-km NCUM-R (with upgraded physics) - experimental runs for whole Indian domain for 72 hours during Monsoon 2016 (196 nodes; 7 hrs WCT) Ø NCUM-DM (330 m) –Daily 36 hr forecasts based on 00 UTC
Day 2 Forecast: Mean Rainfall (10-30 June 2016) Seamless prediction: Same model for global, regional & convective scale predictions • Obs 1.5 km 4.0 km 17 km Global Positive Impact of higher resolution and better model physics in better prediction • is seen in the case of 1.5 km model
Verification of Day 03 Forecasts against Radiosondes over India Error in 850 hPa winds (m/s) during (2005-2016) 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.
How do we compare with other centres? Error (RMSE) of 850 hPa winds(m/s) against Radiosondes over Tropics (~75 obs) 5 4 RMSEV 3 2 1 0 1 2 3 4 5 Forecast Days ECMWF NCEP UKMO NCMRWF
Real time Verification System for MoES Medium Range Weather Forecast Models Monthly Mean 850 hPa Wind RMSE Global Monthly Mean 500 hPa ACC of over Tropics (5 Jan - 3 Feb 2017) Geopotential Height (5 Jan - 3 Feb 2017)
Linkage of NCMRWF with Various Organizations NCMRWF New Observations Applications (Local & Global) First Guess Numerical Modelling of Wind Weather & Climate Feedback on satellite obs. energy observation quality Water Cycle … IC & FCST IC & FCST IC ISRO IMD INCOIS IITM IMD Value added products IAF Media SASE Sectoral Users Capacity Building (Agriculture , on NWP Central/ Aviation ….) Ocean and State GOVT Fishery services Public
Partners and User Agencies of NCMRWF Products Forecast Products Partners and User Agencies Analysis Fields from Global IMD, IITM, INCOIS and SAC Model Rainfall, Temperature and IMD, SWFDP (WMO) Winds from Global and Snow and Avalanche Study Establishment/DRDO Ensemble Models Bhakra Beas Management Board (Flood Monitoring & Forecasting) Krishna and Bhima-Basin Simulation Division (Maharashtra Govt; flood Forecasting) Wind, Temperature, Wind and Solar Energy Sector à National Institute of Wind Rainfall, SW Radiation etc. Energy (Ministry of New and Renewable Energy) from Global and Regional Manikaran Power Limited & Energon Power Resources Pvt Ltd Models Wind, Temperature, Geo- Nuclear Power Corporation of India Ltd. potential Height and Weather forecast for Nuclear Power Plants (9) Humidity from Regional (1) Kaiga (4) Kalpakkam (7) Visakhapatnam Model (2) Trombay (5) Narora (8) Kudankulam (3) Tarapore (6) Rajasthan (9) Kakrapara
This product is shared with IMD, Agromet & SAC
Recent Developmental Activities
NCUM Activities • Regional DA – Assimilation system (3D-Var) for 4-km regional NCUM has been implemented. Successfully assimilated radial wind observations from Indian DWRs – For calculation of background error, new CVT system has been setup and preparation of background error has started. • FSO system has been setup and experimented for 20 days • IMDAA reanalysis (1979-1988) – 42 + 30 months completed at NCMRWF/Met Office. • NCUM at 330 m set up over Delhi and is being run daily for fog prediction • NGEPS – Impact of ensemble member size (11,22 & 44) » Impact of Horizontal resolution (33 v/s17 km) • JULES 1-D & 2-D simulations for INCOMPASS
Impact of DWR data in Regional NCUM 4 km regional domain
Expt. Name Observational data used GTS Assimilation of observations received from GTS + Satellite (Surface, Sonde, Aircraft, AIRS, ATOVS, IASI, Satwind) GTS+Radar GTS (GTS+ Satellite) and DWR radial wind
Observed Rainfall 24 hr Forecast (GTS) (GTS + Radar) DWR Data used in DA Observed and 24 hr forecast (accumulated) rainfall valid for 03 UTC 27 July 2016
Observed Rainfall 24 hr Forecast (GTS) 24 hr Forecast (GTS +Radar) Observed and 24 hrs accumulated rainfall for Day-1 valid for 03 UTC 10 July 2016
Observed Rainfall 24 hr Forecast (GTS) 24 hr Forecast (GTS +Radar) Observed and 24 hrs accumulated rainfall for Day-1 forecast valid for 03 UTC 6 July 2016
RMSE of 24 hr forecast (valid for 00UTC 27July 2016) (Against Radiosonde Observations) u-wind v-wind
Forecast Sensitivity to Observations (FSO) in NCUM Global system
• Forecast Sensitivity to Observation help us to understand how observations are used by the data assimilation system. FSO v/s OSE • FSO quantifies the impact of all assimilated observations on a selected forecast metric. It shows, if any observation decreases or increases forecast error. • OSE (data denial experiments) gives impact of one selected change to the observation system. • However, FSO (adjoint-based technique) is restricted by the tangent linear assumption, valid up to 24-48 hours forecast only. NWP centers use FSO routinely to monitor their Data Assimilation and Global Observing System. • It helps to assess the impact of specific sensors and provide feedback to data providers. It also helped to improve the Quality Control, Bias Correction, etc.
Forecast Sensitivity to Analysis: • The calculation of forecast sensitivity to analysis is carried out using two 24 hour forecast trajectories by the NCUM global model, which is initialized with the analysis and background, respectively. • The background is a prior model forecast (6-hour forecast), and the analysis is produced by 4D-Var (Hybrid 4D-Var) data assimilation system. • The forecast error will be calculated with respect to the verifying analysis for the forecast of analysis and background. • A forecast error cost function will be defined based on the difference between those two forecast errors. • Along the forecast back trajectory with adjoint of model (PF) is able to calculate the forecast sensitivity to analysis using the forecast error cost function.
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