Medium Range Forecasting with NCMRWF Unified Modelling System Presented by Ashis K. Mitra [ with inputs from many ncmrwf colleagues] National Centre for Medium Range Weather Forecasting A-50, Sector 62, NOIDA-201309, India www.ncmrwf.gov.in Outline 1. Operational Set-up 2. Data Usage 3. Monsoon 2015 : Model Bias 4. R&D and new applications 5. Summary Assimialtion-Forecast System of NCMRWF Data Global Observations Reception SURFACE from land stations GTS ~600mb/dy RTH, IMD SHIP BUOY 24x7 Upper Air RSRW/ PIBAL Aircraft Satellite NKN ISRO (MT) NKN NCMRWF OBSERVATION PROCESSING NKN High Resolution Satellite Obsn NKN Internet (FTP) Observation quality checks & monitoring proposed dedicated link Global Model T574L64 UM-N512L70 10 day FCST Users IMD INCOIS IITM SASE Visualisation Global Analysis: (Hy GSI) GEFS M20 4D VAR UM T190L28 Initial state 10 day Fcst RIMES UM Based EPS M45 33km NGFS 4 times a day for 00,06,12,18 UTC EUMETSAT Forecast Models BARC Global Fcst Models:NCUM, ~ 9 Gb/dy NESDIS Global Data Assimilation Other sectors once in a day for 00 UTC IC & FCST IMD Central/ State GOVT Public New Applications Wind energy Water Cycle … ISRO INCOIS IITM value added product Media First Guess feed back on observation qual. satellite obs. (Local & Global) NCMRWF Numerical Modelling of Weather & Climate IC observations IC & FCST ESSO Linkage of NCMRWF with Various Organizations IAF Sectoral Users (Agriculture , Aviation ….) SASE Ocean and Fishery services Capacity Building on NWP *Being Implemented NCAOR Real Time Forecasts: Southern Ocean, Antarctic, Arctic Data assimilation at NCMRWF • NCMRWF has developed a robust data monitoring mechanism for all observations that include conventional, satellite and radar. • It not only helps in maintaining observational network but also helps in producing good forecasts. • Observational System Experiments (OSE) to study impact of data • Assimilation becomes operational if impact of data on forecast system is neutral or +ve. Operational DA UM Short Forecast Output (previous cycle) NCUM 4-D Var Data Assimilation System Background Error Observations Observation Processing Obs Process_Screen Screen Analysis (3DVAR) UM2Jule s UM Short Forecast Output (previous cycle) ASC AT SM Obs ASCAT SM Obs. Processing Configure_LS_N1 44 VAR_N320 (4DVAR) VAR_N144 (4DVAR) SST & SeaIce Analysis (12 UTC) Configure_LS_N32 0 Snow Observatio n (12 UTC) JULES SST & Sea Ice Data (analysis) Preparation Snow Analysis Atmospheric Analysis EKF based Land Assimilation System Soil Moisture Analysis NCUM Short forecast (17 km) Types of observations Assimilated at NCMRWF Observation category Name of Observation. Surface Surface, Mobile, Ship, Buoy (SYNOPs) Upper air TEMP (land and marine), PILOT (land and marine), Dropsonde, Wind profiler Aircraft AIREP, AMDAR, ACARS, Atmospheric Motion Vectors from Geo-Stationary Satellites AMV from Meteosat, GOES-11, GOES-13, MTSAT-1R, MODIS (TERRA and AQUA), POLAR WINDS (NOAA, MODIS, METOP) Scatterometer winds ASCAT winds from METOP satellite NESDIS / POES ATOVS Sounding radiance data 1bamua, 1bamub, 1bmhs,1bhirs3, 1bhirs4 Satellite derived Ozone data NESDIS/POES, METOP-2 and AURA orbital ozone data Precipitation Rates NASA/TRMM (Tropical Rainfall Measuring Mission) and SSM/I precip. rates Bending angles from GPSRO Atmospheric profiles from radio occultation data using GPS satellites MeghaTropique Saphir radiances NASA/AQUA AIRS & METOP/ IASI brightness temperature data IASI,AIRS brightness temperatures INSAT -3D radiance Work initiated Geostationary Satellite Radiances GOES Meghatropiques Oceansat -2 INSAT- 3D NCMRWF Data Handling system RMSVD of INSAT-3D AMVs computed against collocated RS/RW observations (for April 2014) Root Mean Square Vector Difference (RMSVD) Northern Hemisphere Tropics Southern Hemisphere Low Level(1000hPa – 700hPa) INSAT-3D IR Meteosat-7 IR 3.7 3.8 3.6 3.0 5.3 4.1 Middle Level(700hPa – 400hPa) INSAT-3D IR Meteosat-7 IR 5.2 5.3 3.9 2.1 5.3 5.4 High Level(400hPa – 100hPa) INSAT-3D IR Meteosat-7 IR 7.46 7.48 5.1 3.9 5.3 7.5 OSE with INSAT-3D AMVs In first week of March’14 North west India and central India region received rainfall under the influence of middle level westerly trough Control Run with INSAT AMV shows positive impact on Day 2 rain forecast Improvement in the quality of INSAT AMV Satellite Year Retrieval Method RMSVD Comparison of INSAT-3D and METEOSAT AMV DEC 2015 (against obsn) METEOSAT7 Jan2011 HA - IR window.. (NWP background) 5m/s Kalpana-1 Jan2011 HA- GA(NWP background not used) 11m/s Kalpana-1 Aug2011 HA- GA SGP (navigation correction) 9m/s Kalpana-1 Feb2013 HA - IR window.. (NWP background) 7m/s INSAT-3D Nov2013 (offline) HA - IR window..(NWP background) 8.5m/s INSAT-3D Feb 2014 (offline) HA - IR window.. (NWP background) + Modified QC 8m/s INSAT-3D Apr2014 (offline) HA - IR window.. (NWP background) + Modified QC + Modified processing 5.2m/s INSAT-3D Sep2014 (real ,, ( assimilated since Oct’2014) 4.8 m/s ( Dec’2015 INSAT-3D) Large Error Impact of INSAT-3D AMV on TC Chapala INSAT-3D AMV Observation NCMRWF ANALYSIS with INSAT-3D AMV IR LOW LVL 00Z281015 IR HIGH LVL 00Z281015 Impact on the track of TC Chapala : IC 281015 Verification of Day 03 Fcst against Radiosondes over India (2005-2014) Root Mean Square Error (RMSE) of 850 hPa winds in m/s 9 8 7 6 RMSEV 5 4 3 2 1 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Jul-13 Oct-13 Jan-14 Apr-14 Jul-14 0 The decrease in the RMSE can be attributed to the increase in the resolution of the model, increase in the amount of data being assimilated, improvements in data assimilation techniques and model physics. INSAT-3D Sounder Clear Sky Brightness Temperature (CSBT) Bias Correction Assimilation Bias correction shifted the innovations (O-B) towards zero: Bias correction working fine. Observed BT-Model Background Corrected BT - Model Background Impact on radiances from other instruments Mixed impact of INSAT3D Sounder CSBT on other radiances. NWP Index remains neutral: No Impact due to the assimilation of INSAT-3D CSBT. Next Operational Upgrade Preliminary Results Relative impact of surface observations, Jan 2015 FSO (Forecast Sensitivity to Observation) system is implemented at NCMRWF. This tool enables to study the impact of various observations in the forecast Relative impact of satellite wind obs., Jan2015 Hybrid GSI v/s GSI Soil Moisture Analysis at NCMRWF Data Used in the NCMRWF Soil Moisture Analysis (NCUM) Data Assimilation Method & Analysis Resolution Data Used for Verification of the NCUM analysis 1. Screen level (surface) air temperature & humidity observations 2. ASCAT surface soil wetness observations from MetOP-A satellite (Cband, Level2 product) Nudging technique 1. AMSR2 satellite ~ 25 km resolution observations (global analysis, (X band) 25 x 25 km grid, 4 2. SMOS satellite times daily at 00, observations 06, 12 & 18 UTC at (L-Band) 4 soil levels) 3. UK Met Office (Extented Kalaman soil moisture Filter based Land analysis Data assimilation 4. In-situ soil system is tested moisture successfully. This observations is being of India implemented in Meteorological the operational Department NCUM system) Soil Layers 10 cm 25 cm 65 cm 200 cm Soil Layers (thickness): 0-10 cm, 10-35 cm, 35-100 cm, 100-300 cm Soil Moisture Analysis for last week of Dec 2015 Current Operational Global Models • NCMRWF Global Forecast System (NGFS) T574 with hybrid 3D-Var Data Assimilation (EnKF) 10 day forecast – at 00 UTC 3 day forecast -12 UTC for initializing WRF (RIMES,NPCIL) • NCMRWF Unified Model NCUM (17 km) -10 day forecasts at 00 UTC Current Operational Regional Models 4-km NCUM-R (with explicit rain processes) running with NCUM-G inputs at 00 UTC for 72 hours 1.5-km NCUM-R (with explicit rain processes) trial runs with NCUM-G inputs for 72 hours 3-km WRF runs for 8 NPCIL sites – IC & LBC from 9-km WRF runs from12 UTC GFS inputs 9-km-WRF for RIMES domain – Daily running for 3 days with 12 UTC GFS inputs Current Operational Global EPS • NCMRWF Global Ensemble Forecast System NGEFS (75 km/21 Members) -10 forecasts at 00 UTC • NCMRWF Global EPS – based on NCUM (33 km/44 members) -10 Day Forecasts at 00 UTC (450 nodes -3.5 hrs) The Multiple Time Scales of Climate Weather MJO/monsoon bursts Time scale increasing Annual cycle El Nino/La Nina Decadal Variability Climate Change Some skill exists (potentially) in Intra-seasonal to interannul prediction in some regions. Seasonal Fcst systems are crucial in development of adaptation strategies climate change A host of climate risk management tools are developed for real application Adapt to climate variability today (seasonal)~will help in adapt to climate change tomorrow One Model: hours, days, weeks, Monthly, Seasonal, Inter-annual, Decades, Centuary Consortium: UK, Australia, South Korea, India, , South Africa, New Zealand, (Singapore) Unified Model at NCMRWF (NCUM) Same Model for Global/Regional/Mesoscale/Coupled Seamless Modeling System 1.5-km regional model up to 72 hr forecasts 4-km regional model up to 72 hr forecasts 17-km global model for 10 Day forecasts Global Ensemble Prediction System 33 km, 44 members up to10 Day Forecasts (450 nodes -3.5 hrs) Course Resolution Coupled GS4 2014 :Day-1 to Day-5 Drying Over east coast and Central India and west coast All India Obs/Model rainfall values (cm) JJAS -2015 MODELS Day-1 Day-3 Day-5 NCUM 89.7 91.9 85.8 NGFS 72.1 73.6 76.3 OBS merged gridded rainfall = 80.3 Correlation coefficient: Observed Vs Model predicted daily rainfall MODELS Day-1 Day-3 Day-5 NCUM 0.43 0.38 0.34 NGFS 0.29 0.24 0.23 NCUM: west coast rain predicted Highest rain at each grid 2015:Central India & Orissa, AP reasonable & realistic 4 WIND Rainfall WIND Rainfall Systematic Error Global NCUM 17 km Wet bias Dry bias Rainfall exceeding 1mm/day is rainy day Reduction in the number of rainy days with increasing forecast lead time Crucial over core monsoon region Substantial over the Peninsula Difference (FCST-OBS) in the number of rainy days Also substantial over dry regions of peninsula Difference (FCST-OBS) in the number of rainy days Also substantial over dry regions of peninsula CRA method of rainfall verification NCUM 29 July 2015 Day 1 Day 2 Day 3 GPM ~25km NCUM-R ~ 4km NCUM-G ~17km NCUM-R Day 1,2,3 Rainfall (cm/day) Forecast valid for 16 November 2015 Day 2 Day 3 GPM ~25km NCUM-R ~ 4km NCUM-G ~17km Day 1 Day 1,2,3 Rainfall (cm/day) Forecast valid for 2 December 2015 Diurnal variations of Rainfall (mm/hr) in July 2015 Domain 4.5N to 40.5, 64.5E to 100.5E NCUM-Regional 4km Hours Spin up seen in regional model NCUM-Global 17km Hours The predicted diurnal cycle of precipitation peaks too early in global model Diurnal variations in July 2015 Central India domain (18N to 28N, 73E to 82E) Rainfall (mm/hr) NCUM-Regional 4km Hours Spin up seen in regional model NCUM-Global 17km Hours The predicted diurnal cycle of precipitation peaks too early in global model UM-REG (4km) over Indian domain Convection Experiments Experiment Name Details CNTL Convection Off (Cape_TS=900s) Convection on (Cape_TS=3600s) ~convection off + New tropical setting EXPT1 EXPT7 NCUM-GL ~17KM (PS34) Convection on + CAPE option vertical velocity dependency 3.0 Day-2 Day-1 2.5 Day-3 Bias 2.0 1.5 1.0 0.5 0.0 1 2 3 4 5 Rainfall Threshold (cm/day) 6 7 1 2 6 7 1 2 3 4 5 Rainfall Threshold (cm/day) 3 4 5 Rainfall Threshold (cm/day) 6 7 6 7 1 2 3 4 5 Rainfall Threshold (cm/day) 6 7 1.0 0.8 FAR 0.6 0.4 0.2 0.0 1 2 3 4 5 Rainfall Threshold (cm/day) 1 2 3 4 5 Rainfall Threshold (cm/day) 6 7 UM Nesting suit 1.5Km Day-1 Madhya Pradesh Day-2 (700x450 grid) IC: 00Z 4 Aug 2014 UM Global vn8.5 (25Km) Day-3 Nested UM (1.5Km) Gridded rain analysis SRTM (90m) orography used for 1.5Km domain Obs NS1.5 km Global vn8.5 UM Nesting suit 1.5Km Day-1 Gujarat (600x450 grid) Day-2 IC: 00Z 28 Jul 2014 UM Global vn8.5 (25Km) Day-3 Nested UM (1.5Km) Gridded rain analysis SRTM (90m) orography used for 1.5Km domain Obs NS1.5 km Global vn8.5 Land Cover and land Use Major changes seen on urban, forest and snow tiles NRSC/ISRO data in 1.5 km UM Source CWET(NIWE) NCMRWF Model wind comparison with MERRA – Aug 2015 Model validation Wind Speed Time-series comparison with Met Mast at Jaisalmer (July and Aug 2015) Model validation: Wind direction comparison with Met Mast at Jaisalmer (July and Aug 2015) NCUM EPS System • Model resolution – N400L70 (~33 km at Mid-lat) • No. of members – 44 • IC Perturbation Method – ETKF • Model Perturbation Stochastic physics EPS Products are now available in NCMRWF web site Day-7 NEPS Based On NCUM Day-6 Day-5 Ensemble Value added Products using Model Climatology •MOGREPS Operational Rainfall forecasts (Day-1 to Day-7) •Rainfall Climatology (2007-2015) •Actual (MOGREPS forecast) •Normal (MOGREPSDaily Mean) •Forecast Rainfall Departure Rainfall Forecast Departure: (EnsAv-Clim) Forecasts valid for 17th Nov 2015 Rainfall Forecast Departure: (EnsAv-Clim) Forecasts Valid for 23rd Nov 2015 Rainfall Forecast Departure: (EnsAv-Clim) Forecasts Valid for 2nd Dec2015 Forecast rainfall exceeding the model climatology Rainfall exceeding the 90th percentile of model climatology Rainfall Analysis: Merged Satellite Gauge Data Product (Joint IMD, NCMRWF) GPM Core Observatory launched on 27Feb2014 DPR & GMI Heavy rain Moderate rain Light rain Improved: Light Rain Falling snow Rain microphysics 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 GPM Gridded Radar Rainfall of Chennai Radar rain vs. Model obtained Rain DELHI Satellite Observations Microwave IR ARG AWS Radar Rain Gauge Merged Gridded Rainfall Product Objective Analysis Summary: A high resolution Unified modelling real-time system (Global , Regional) and 4-D Var data assimilation have been implemented on new HPC during 2015. A 45 member global (33 km) UM based real-time EPS has been implemented in Dec 2015 R&D on model particularly for monsoon rainfall has been taken up Model systematic errors at shorter time scale are important – linked to extended and seasonal (and beyond) scales. Model development and DA are crucial for all scales. Various new applications are developed for end users with high resolution model output An ocean-atmosphere coupled model is being implemented for real time use. Major Modelling centers including ECMWF are moving towards Coupled Modelling (Seamless from Days-to-Season) Faster Model Development NCMRWF Coupled Model 1. Stand alone versions of NEMO, CICE were implemented Work on sensitivity tests and diagnosis of NEMO for Indian Ocean region studied 2. A course resolution version of coupled model implemented on IBMP6 (old HPC), 14 Seasons monsoon hind-cast have been completed with GloSea4 setup. Mean Monsoon circulation is reasonable, has intense monsoon compared to Obs. 3. As part of the MoES Extended Range Prediction working group NCMRWF during monsoon 2015 was issuing realtime forecasts to the group based on UKMO outputs. 4. On new Bhaskar HPC higher resolution GloSea5 is being implemented Coupled Modelling for MRF/Cyclone, Extended(up to month) , Seasonal Seamless 5. NEMOVar and Coupled NWP by 2016 December Thank You