Model Development Activities at ESSO-NCMRWF

E N Rajagopal

1.5 km gridup to 48 hr forecast

25 km global gridup to 168 hr forecast

Unified Model at NCMRWF (NCUM)Same Model for Global/Regional/Mesoscale! – seamless model

12 km gridup to 48 hr forecast

• Current Status

• New Developments

• Model Diagnostics & Evaluation

• Future Plans

Outline of Talk

Status of Observations at NCMRWF

Observations Assimilated in NCUM

Observation file Observation details

Surface.obstore Land SYNOP, Ship SYNOP, Mobile, AWS, BUOY

Sonde.obstore TEMP (Land & Ship), PILOT, DROPSONDE, Wind Profilers

Aircraft.obstore AIREP, AMDAR

Satwind.obstore GOES, Meteosat, MTSat, INSAT-3D, MODIS, MetOp & NOAA Satellites

Scatwind.obstore ASCAT

GPSRO.obstore Bending angle from GPS satellites (COSMIC, GRAS)

GOESClr.obstore GOES Imager Radiance (Clear)

ATOVS.bufr MetOp & NOAA satellites (including HRPT data)

IASI.bufr MetOp

AIRS.bufr AQUA

Current Status

• NCUM DA -4D-Var operational

• NCUM – 25 km/L70 operational

• 3D-Var Surface Analysis operational from July 2014

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

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

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

New Developments

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

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

Monthly Mean Surface Level (0-10 cm) Soil Moisture (November-2014)

NCUM

UKMO

RMSE (%) of Surface Level Soil Moisture against AMSR2 Obs

November 2014

NCUM

UKMO

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

Verification of UM Surface Soil Moisture Analysis over India(Monsoon -2013)

High Resolution (1.5 km) Regional Modelling

• 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.

NCUM-GLOBAL NCUM-REGIONALGoverning Equations Complete equation (Non-hydrostatic); Deep atmosphere (Model top at ~

80 km)

Horiz. Resolution (N-S x E-W) N512 ~25km (0.234x 0.352) ~1.5km (0.0135*0.0135)

Vertical Layers L70 L70

Forecast Length 10 days (240 hours) 3 days (72 hours)

Model Time Step 600 sec 50 sec

IC/ Data Assimilation 4DVAR Downscaling from global initial condition

Spatial Discretization Finite Difference method

Time Integration /Advection Semi-implicit Semi-Lagrangian scheme

Radiation Process Spectral band radiation (general 2-stream)

Surface Process JULES land-surface scheme

PBL Process JULES Revised PBL

Convection Process Turbulence and mass flux convection Convection in UM becomes less active when the area of grid box is decreased (high resolution). The CAPE timescale is increased reducing the activity of the parameterized convection.

Microphysics Improved mixed-phase scheme based on Wilson and Ballard (1999)

Gravity Wave Drag Gravity Wave Drag due to orography (GWD)

Surface Boundary Condition Climatology or SURF (Surface analysis)

Operation Frequency Once daily (00 UTC)

6hour D.A. cycle Four times daily (00/06/12/18 UTC)

1. Madhya Pradesh (700 x 450) IC: 4th August 2014 Wall Clock Time for 3 day forecast= 5.5 hours (8 nodes IBM-p6)2. Gujarat (600 x 450) IC: 28th July 2014 Wall Clock Time for 3 day forecast= 4 hours (8 nodes IBM-p6)

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

SRTM Orography at 1.5 km used in Regional NCUM

Himalayan Orography (km)

GLOBE Orography at ~25 km used in Global NCUM

SRTM data is at 90 metre resolution and GLOBE data is at 1 km resolution

J&K (3-5 Sep 2014)

J&K

Day 1 Forecast Day 2 Forecast Day 3 Forecast

OB

SG

LO

BA

L

Gujarat

Day-1

Day-2

Day-3

1.5 km GlobalObs

SRTM Regional 1.5kmGLOBE Global 25km

Orography (km) over MP

Obs 1.5 km

Day-1

Day-2

Day-3

Global

MP5-7 Aug 2014

Bhopal DWR reflectivities used to derive rainfall

Outer Grey Circle Represents Radar 250 km Range

Radar

1.5km NCUM

IMD-NCMRWF

Rainfall – 06 Aug 2014

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)

Surface Types (IGBP v/s JULES)

9 surface types for JULES

Broadleaf trees

Needleleaf trees

C3 (temperate) grass

C4 (tropical) grass

Shrubs

Urban

Inland water

Bare soil

Land ice

Merged 18 surface types (NCMRWF)

18 surface types from NRSC over India (30 arc sec data) [AWiFS, IRS P6], 2012-13 period

18 surface types from IGBP (30 arc sec data) [AVHRR], 1992-93 period.

Input to JULES land surface scheme in UM

LuLc (UKMO & NRSC)

Surface Type Fraction

NRSC data shows recent changes in urban, forest and bare soil tiles.

Bare soil fraction

Urban tile fraction

NRSCIGBP

IGBP

NRSC

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.

Average rainfall over (74.5-78 E & 33-36.5 N)

Observation(NCMRWF- IMD)

NCUM(ISRO NRSC)

NCUM(IGBP)

19.26 mm 11.15 mm 8.90 mm

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%)

3hrly averaged OLR count of Kalpana, Control, Entrainment

Arabian sea (65-74oE,15-23oN)

Central India (71-89oE, 17-27oN)

Bay of Bengal (85-100oE, 10-20oN)

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%)

Impact of better physics in coupled model (GA2.0 v/s GA3.0)

GA3.0 has reduced rainfall biases

NEMO Ocean Model simulated SST & MLD (Apr-Sept)

without chlorophyll with chlorophyll

The reduction of 0.5 C in SST bias and 10m in MLD bias is observed in the experimentUse of real time chlorophyll observations from OCM for ocean initialization would provide improvements

Clim with chloro without chloro

SST Bias Annual cycle of MLD (m)

Model Verification against Analysis

Inter-comparison of models at NCMRWF

Global ACC: 500 hPa Z (Jan 2015)

Wind RMSE: 850 hPa (Tropics)

Model performance during the monsoon season-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).

Rainfall VerificationAug-Sept 2014

NGFS shows higher MEat higher lead times

Mean Error (8-38 °N, 68-98 °E)

RMSE magnifies thelarge errors in the isolatedcases (rare events).

RMSE (8-38 °N, 68-98 °E)

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

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

POD: Fraction of observed ‘yes’ events predicted correctly.

Higher POD in NCUM,ACCESS-G and UKMOACCESS-G has highest POD

FAR: What fraction of predicted ‘yes’ events did not realize??

Higher FAR in ACCESS-G

•UKMO has higher ETS for lower thresholds

•ACCESS-G has higher ETS for higher thresholds

ETS: How well did the forecast "yes" events correspond to the observed "yes" events (accounting for hits due to chance)?

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

Synoptic System

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

Peak CC RMSEObs : 269mm UKMO : 207mm .37 19.2mmNCUM : 169mm .20 18.7mmACCESS-G : 119mm .20 18.8mm

Srinagar Rainfall (4th Sept 2014)

All models fail to capture the peak rainfall amounts along the west coast

Rainfall peaks over central India captured by UKMO

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)

Extreme Dependency family of scores

Extreme Dependency Score

Extreme Dependence Index

Symmetric Extremal Dependence Index

This family of scores tell what is the association between observed and forecast rare events.

Contingency TableObserved Total

Yes No

Forecast Yes hits False alarms Forecast YesNo misses Correct negatives Forecast no

Total Observed Yes Observed No Total

•Standard scores fail to show the differences in the scores near the tails

•Extreme Dependency score is able to bring out the difference in model performance for higher rainfall thresholds

EDS, SEDI and EDI all range from -1 to 1; 0 indicating no skill and 1 indicating perfect skill.

Summary

• JJA Mean Rainfall– NCUM :Day-1 to Day-5 Drying

– Forecast skill (ETS) reasonable for lower rainfall thresholds

– Frequency bias : over forecasting at lower thresholds and under forecasting at higher thresholds.

• JJA Maximum Rainfall– Rainfall over central India (UKMO realistic), ACCESS-G and NCUM

underestimate

– NCUM :Day-1 to Day-5 Drying

– Rainfall along the west coast reducing in NCUM

• EDS, EDI and SEDI– Extreme dependency family of scores highlight relative skill at higher thresholds.– UKMO forecasts have relatively better skill in predicting the extremes.

Assessment of GC2 (ENDGame+GA6.0)

•NWP rainfall outputs over the Indian monsoon region against NCMRWF Merged Satellite Gauge (NMSG) daily observed rainfall dataset.

•Test runs of (i) Old N512 (ii) GC2 N512 and (iii) GC2 N768

Study period: Day-1 to Day-6 forecasts, 6-July to 15 September 2012 (72 days).

•All model data interpolated to 0.5x0.5 grid

Mean daily rainfall (mm) from 6 Jul-15 Sep 2012

Top: Old N512

Bottom: GC2 N768

Middle: GC2 N512

Mean daily rainfall (mm) from 6 Jul-15 Sep 2012

Top: Old N512

Bottom: GC2 N768

Middle: GC2 N512

•GC2 NWP N512 & N768 perform marginally better over the Indian monsoon region.

•GC2 captures synoptic scale rainfall variability better

•GC2 shows better demarcation (lower rainfall region) between high rainfall Monsoon Trough and foothills of Himalayas

Summary of GC2 Evaluation

TC Prediction from Regional NCUM

TC Name(Intensity)

Simulation period in 24-h intervals

Obs. Landfall (LF) No. of Forecast

Hudhud (VSCS)

00UTC of 08 - 13 October 2014 06 UTC 12 Oct. 2014 (Visakhapatnam)

04

Lehar(VSCS)

00 UTC of 24 - 29 November 2013

08 UTC 28 Nov. 2013(Machilipatnam)

04

Phailin(VSCS)

00UTC of 09 - 13 October 2013 17 UTC 12 Oct. 2013 (Gopalpur)

03

Direct Position Error (DPE)

Direct Position Error (DPE)

TCs Name Different ICs (00 UTC)

Obs. LF time LF Errors (km) % of ImprovementNCUM Reg_UM

Hudhud (October 2014

IC08

06 UTC 12 Oct. 2014 (Visakhapatnam)

307.64 129.42 57.93

IC09 224.7 185.48 17.45

IC10 168.78 103.5 38.67

IC11 67.55 62.61 7.31

Lehar

(November 2013

IC2408 UTC 28 Nov. 2013(Machilipatnam)

578.8 NO --

IC25 458.13 434.44 5.17

IC26 329.38 266.65 19.04

IC27 111.13 72.33 34.91

Phailin (October 2013)

IC0917 UTC 12 Oct. 2013 (Gopalpur)

83.97 34.97 58.35

IC10 43.36 34.97 19.34

IC11 38.51 15.28 60.32

Landfall (LF) errors in NCUM and Regional UM

Future Plans

Future Plans –NCUM DA

• Maximize the use of observations in the assimilation system, especially the Indian observations– Efforts are in the final stages to include the INSAT-3D sounder

and imager as well as Megha-Tropiques SAPHIR radiances– MTSAT imager radiance data in NCUM

• Improvement of the DA system – Move towards hybrid 4D-Var DA based on 44 member

ensemble (ETKF) system – A high resolution regional 4D-Var assimilation system will be

implemented. •  Observation Sensitivity Studies

– The “tools” to study the “Forecast Sensitivity to Observation” (FSO) has been implemented. This will help to identify the impact of different observations being used in the NCUM system.

– OSE & OSSE studies – with INDCOMPASS

Future Plans -NCUM

• Resolution of global deterministic model to be increased to 17 km this year on the new HPC

• Evaluation of Regional NCUM at 4 km/1.5 km

• Move towards high resolution ensemble forecasting with more ensemble members (~33km (global)/44 members)

• Incorporate better land surface data (land-use/land-cover, vegetation, soil moisture, soil temperature etc.) over Indian region with support from NRSC/ISRO

• Land surface DA based on Extended Kalman Filter

Coupled Modelling-Plans

• Implementation of a higher resolution coupled model (Atmos:75kmL85 & Ocean: 25kmL75)

• Implementation of NEMO-Var Ocean Data Assimilation (25kmL75)

Involvement in NMM Projects

• CAWCR – Rainfall verification (CRA) – 1 Trained

• Met Office – IMDAA – 1 scientist visiting MO (Sept-Mar)

• UoR – 1 Scientist visited during Sept-Dec 2014

• Imperial College – Wind Energy

• TERI - Diurnal Variation of model rainfall (NCUM)

• FSU/IISc. - GFS Error

New HPC will be commissioned Soon

350 TF1038 compute nodes

Thank You

Mission Targets

• To implement the Unified Model (NCUM) at 25 km at NCMRWF. The resolution to be subsequently increased to 17 km/12 km.

• To implement regional version of NCUM at 12 km/4-km/1.5-km resolution over Indian monsoon region for high impact weather.

• To implement 4-D VAR system and develop capability for assimilating data/radiances from upcoming Indian Satellites and DWRs

• To implement a high resolution Ensemble Prediction System (EPS) based on NCUM. - NGEPS

• To implement a NCUM based atmosphere ocean coupled modeling system- “ Coupled NWP Model” for week-2 forecasts