Medium Range Forecasting with NCMRWF Unified Modelling System

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1 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 , India

2 Outline 1. Operational Set-up 2. Data Usage 3. Monsoon 2015 : Model Bias 4. R&D and new applications 5. Summary

3 Global Observations SURFACE from land stations SHIP BUOY Upper Air RSRW/ PIBAL Aircraft Satellite High Resolution Satellite Obsn Internet (FTP) ~ 9 Gb/dy Assimialtion-Forecast System of NCMRWF GTS ~600mb/dy 24x7 NKN Data Reception RTH, IMD NKN NCMRWF OBSERVATION PROCESSING NKN NESDIS ISRO (MT) NKN proposed dedicated link EUMETSAT Global Data Assimilation Observation quality checks & monitoring Global Analysis: (Hy GSI) 4D VAR UM Initial state Global Fcst Models:NCUM, NGFS 4 times a day for 00,06,12,18 UTC Forecast Models Global Model T574L64 UM-N512L70 10 day FCST Visualisation GEFS M20 T190L28 10 day Fcst UM Based EPS M45 33km once in a day for 00 UTC Users IMD INCOIS IITM SASE BARC RIMES Other sectors

4 ESSO Central/ State GOVT observations (Local & Global) IMD Media Public Linkage of NCMRWF with Various Organizations feed back on observation qual. IC & FCST value added product Sectoral Users (Agriculture, Aviation.) NCMRWF Numerical Modelling of Weather & Climate IC & FCST INCOIS IC Ocean and Fishery services IITM IAF SASE satellite obs. First Guess ISRO Capacity Building on NWP New Applications Wind energy Water Cycle *Being Implemented NCAOR Real Time Forecasts: Southern Ocean, Antarctic, Arctic

5 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.

6 UM Short Forecast Output (previous cycle) Observations Obs Process_Screen NCUM 4-D Var Data Assimilation System Background Error Observation Processing Operational DA UM Short Forecast Output (previous cycle) Screen Analysis (3DVAR) UM2Jule s JULES EKF based Land Assimilation System Soil Moisture Analysis ASCAT SM Obs. Processing ASC AT SM Obs VAR_N144 (4DVAR) SST & SeaIce Analysis (12 UTC) SST & Sea Ice Data (analysis) Preparation Configure_LS_N1 44 Snow Observatio n (12 UTC) Snow Analysis NCUM Short forecast (17 km) Configure_LS_N32 0 VAR_N320 (4DVAR) Atmospheric Analysis

7 Surface Upper air Aircraft Observation category Atmospheric Motion Vectors from Geo-Stationary Satellites Scatterometer winds NESDIS / POES ATOVS Sounding radiance data Satellite derived Ozone data Precipitation Rates Name of Observation. Surface, Mobile, Ship, Buoy (SYNOPs) TEMP (land and marine), PILOT (land and marine), Dropsonde, Wind profiler AIREP, AMDAR, ACARS, AMV from Meteosat, GOES-11, GOES-13, MTSAT-1R, MODIS (TERRA and AQUA), POLAR WINDS (NOAA, MODIS, METOP) ASCAT winds from METOP satellite 1bamua, 1bamub, 1bmhs,1bhirs3, 1bhirs4 NESDIS/POES, METOP-2 and AURA orbital ozone data 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 NASA/AQUA AIRS & METOP/ IASI brightness temperature data INSAT -3D radiance Types of observations Assimilated at NCMRWF Geostationary Satellite Radiances Saphir radiances IASI,AIRS brightness temperatures Work initiated GOES

8 Meghatropiques Oceansat -2 INSAT- 3D NCMRWF Data Handling system

9 RMSVD of INSAT-3D AMVs computed against collocated RS/RW observations (for April 2014) Root Mean Square Vector Difference (RMSVD) Northern Hemisphere Low Level(1000hPa 700hPa) Tropics Southern Hemisphere INSAT-3D IR Meteosat-7 IR Middle Level(700hPa 400hPa) INSAT-3D IR Meteosat-7 IR High Level(400hPa 100hPa) INSAT-3D IR Meteosat-7 IR

10 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

11 Improvement in the quality of INSAT AMV Satellite Year Retrieval Method RMSVD (against obsn) METEOSAT- 7 Jan HA - IR window.. (NWP background) 5m/s Comparison of INSAT-3D and METEOSAT AMV DEC 2015 Kalpana-1 Kalpana-1 Kalpana-1 INSAT-3D Jan Aug Feb Nov (offline) INSAT-3D Feb (offline) INSAT-3D INSAT-3D Apr (offline) Sep (real HA- GA(NWP background not used) HA- GA SGP (navigation correction) HA - IR window.. (NWP background) HA - IR window..(nwp background) HA - IR window.. (NWP background) + Modified QC HA - IR window.. (NWP background) + Modified QC + Modified processing,, ( assimilated since Oct 2014) 11m/s 9m/s 7m/s 8.5m/s 8m/s 5.2m/s Large Error 4.8 m/s ( Dec 2015 INSAT-3D)

12 Impact of INSAT-3D AMV on TC Chapala INSAT-3D AMV Observation NCMRWF ANALYSIS with INSAT-3D AMV IR LOW LVL 00Z IR HIGH LVL 00Z Impact on the track of TC Chapala : IC

13 Verification of Day 03 Fcst against Radiosondes over India ( ) Root Mean Square Error (RMSE) of 850 hpa winds in m/s RMSEV 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 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.

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28 INSAT-3D Sounder Clear Sky Brightness Temperature (CSBT) Assimilation Bias correction shifted the innovations (O-B) towards zero: Bias correction working fine. Bias Correction Observed BT-Model Background Corrected BT - Model Background Impact on radiances from other instruments Mixed impact of INSAT- 3D Sounder CSBT on other radiances. NWP Index remains neutral: No Impact due to the assimilation of INSAT-3D CSBT. Next Operational Upgrade

29 Relative impact of surface observations, Jan 2015 Preliminary Results 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

30 Hybrid GSI v/s GSI

31 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 ~ 25 km resolution (global analysis, 25 x 25 km grid, 4 times daily at 00, 06, 12 & 18 UTC at 4 soil levels) (Extented Kalaman Filter based Land Data assimilation system is tested successfully. This is being implemented in the operational NCUM system) 1. AMSR2 satellite observations (X band) 2. SMOS satellite observations (L-Band) 3. UK Met Office soil moisture analysis 4. In-situ soil moisture observations of India Meteorological Department Soil Layers 10 cm 25 cm 65 cm 200 cm Soil Layers (thickness): 0-10 cm, cm, cm, cm

32 Soil Moisture Analysis for last week of Dec 2015

33 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

34 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

35 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)

36 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

37 One Model: hours, days, weeks, Monthly, Seasonal, Inter-annual, Decades, Centuary Consortium: UK, Australia, South Korea, India,, South Africa, New Zealand, (Singapore)

38 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

39 2014 :Day-1 to Day-5 Drying Over east coast and Central India and west coast

40 All India Obs/Model rainfall values (cm) JJAS MODELS Day-1 Day-3 Day-5 NCUM NGFS OBS merged gridded rainfall = 80.3 Correlation coefficient: Observed Vs Model predicted daily rainfall MODELS Day-1 Day-3 Day-5 NCUM NGFS

41 NCUM: west coast rain predicted Highest rain at each grid 2015:Central India & Orissa, AP reasonable & realistic 4

42 WIND Rainfall

43 WIND Rainfall

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45 Systematic Error Global NCUM 17 km

46 Wet bias Dry bias

47 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

48 Difference (FCST-OBS) in the number of rainy days Also substantial over dry regions of peninsula

49 Difference (FCST-OBS) in the number of rainy days Also substantial over dry regions of peninsula

50 CRA method of rainfall verification NCUM 29 July 2015

51 NCUM-R Day 1 Day 2 Day 3 NCUM-R ~ 4km GPM ~25km NCUM-G ~17km Day 1,2,3 Rainfall (cm/day) Forecast valid for 16 November 2015

52 Day 1 Day 2 Day 3 GPM ~25km NCUM-R ~ 4km NCUM-G ~17km Day 1,2,3 Rainfall (cm/day) Forecast valid for 2 December 2015

53 Diurnal variations of Rainfall (mm/hr) in July 2015 Domain 4.5N to 40.5, 64.5E to 100.5E NCUM-Regional 4km NCUM-Global 17km Hours Spin up seen in regional model Hours The predicted diurnal cycle of precipitation peaks too early in global model

54 Diurnal variations in July 2015 Central India domain (18N to 28N, 73E to 82E) Rainfall (mm/hr) NCUM-Regional 4km NCUM-Global 17km Hours Spin up seen in regional model Hours The predicted diurnal cycle of precipitation peaks too early in global model

55 UM-REG (4km) over Indian domain Convection Experiments Experiment Name CNTL EXPT1 EXPT7 Details Convection Off (Cape_TS=900s) Convection on (Cape_TS=3600s) ~convection off + New tropical setting NCUM-GL ~17KM (PS34) Convection on + CAPE option vertical velocity dependency

56 Day-1 Day-2 Day Bias Rainfall Threshold (cm/day) Rainfall Threshold (cm/day) Rainfall Threshold (cm/day) FAR Rainfall Threshold (cm/day) Rainfall Threshold (cm/day) Rainfall Threshold (cm/day)

57 UM Nesting suit 1.5Km Day-1 Obs NS1.5 km Global vn8.5 Madhya Pradesh (700x450 grid) Day-2 IC: 00Z 4 Aug 2014 UM Global vn8.5 (25Km) Nested UM (1.5Km) Day-3 Gridded rain analysis SRTM (90m) orography used for 1.5Km domain

58 UM Nesting suit 1.5Km Day-1 Obs NS1.5 km Global vn8.5 Gujarat (600x450 grid) Day-2 IC: 00Z 28 Jul 2014 UM Global vn8.5 (25Km) Nested UM (1.5Km) Day-3 Gridded rain analysis SRTM (90m) orography used for 1.5Km domain

59 Land Cover and land Use Major changes seen on urban, forest and snow tiles NRSC/ISRO data in 1.5 km UM

60 Source CWET(NIWE)

61 NCMRWF Model wind comparison with MERRA Aug 2015

62 Model validation Wind Speed Time-series comparison with Met Mast at Jaisalmer (July and Aug 2015)

63 Model validation: Wind direction comparison with Met Mast at Jaisalmer (July and Aug 2015)

64 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 NCUM EPS System

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66 Day-7 NEPS Based On NCUM Day-6 Day-5

67 Ensemble Value added Products using Model Climatology MOGREPS Operational Rainfall forecasts (Day-1 to Day-7) Rainfall Climatology ( ) Actual (MOGREPS forecast) Normal (MOGREPSDaily Mean) Forecast Rainfall Departure

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69 Rainfall Forecast Departure: (EnsAv-Clim) Forecasts valid for 17 th Nov 2015

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71 Rainfall Forecast Departure: (EnsAv-Clim) Forecasts Valid for 23 rd Nov 2015

72 Rainfall Forecast Departure: (EnsAv-Clim) Forecasts Valid for 2 nd Dec2015

73 Rainfall exceeding the 90 th percentile of model climatology Forecast rainfall exceeding the model climatology

74 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

75 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

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78 Gridded Radar Rainfall of Chennai

79 Radar rain vs. Model obtained Rain DELHI

80 Microwave Satellite Observations IR AWS ARG Radar Merged Gridded Rainfall Product Objective Analysis Rain Gauge

81 Summary: A high resolution Unified modelling real-time system (Global, Regional) and 4-D Var data assimilation have been implemented on new HPC during 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.

82 Major Modelling centers including ECMWF are moving towards Coupled Modelling (Seamless from Days-to-Season) Faster Model Development

83 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

84 Thank You