Centre report:Recent changes to and plans for the NWP suites of Environment Canada

WGNE-29 – Melbourne, Australia

Ayrton Zadra

RPN – Environment Canada

10-13 March 2014

Page 2 – April 21, 2023

Acknowledgements

Weather Prediction: Martin Charron, Ron Mctaggart-Cowan, Jason Milbrandt, Abdessamad Qaddouri, Claude Girard

Environmental Prediction: Greg Smith, Pierre Pellerin, Vincent Fortin, Stephane Belair

Data Assimilation: Mark Buehner, Jean-Francois Caron, Luc Fillion, Stephane Laroche, Peter Houtekamer

CMC-Development: Normand Gagnon

Page 3 – April 21, 2023

-- Part 1 --Recent changes to operational suites

Page 4 – April 21, 2023

Summary of recent changes

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Fev Mar

2012 2013

Major upgrade to Global Prediction Systems(Deterministic & Ensemble)…

Major upgrade to Ensemble Prediction Systems (Global & Regional)

Update to Regional Air Quality Deterministic Prediction System

METOP-1 added to GPS-RO

New Regional Deterministic Air Quality Analysis

Adjustments to High Resolution Deterministic Prediction System

2013 2014

… accompanied by upgrades in Regional Deterministic Prediction System

Satwinds (METEOSAT10) + ASCAT winds (METOP-1) added to DA system

New Operational Hydro-dynamic Simulation System

Adjustments to ocean analysis in seasonal prediction system (CanSIPS)

Additional satellite (CSR, ATOVS, polar winds) added to deterministic systems

Upgrades to Nowcasting system (INCS)

Experimental Global Ice-Ocean Prediction System (GIOPS)

Experimental Pan-Canadian High Resolution Deterministic Prediction System

Experimental Regional Ice Prediction System (RIPS)

fore

cas

t m

od

el

horizontal resolution - from 33km to 25km - improvements seen in analysis cycle

dynamics- new vertical coordinate

- new vertical grid (from regular to Charney-Phillips)

- reduction of errors in stratosphere

- noise reduction; improved numerical stability and conservation properties

physics

orographic blocking

- amplification of bulk drag coefficient, based on Wells et al. (2008) & Vosper et al. (2009)

- significant reduction of tropospheric errors in winter hemisphere

boundary layer

- turbulent hysteresis effect- reduction of errors associated with frontal inversions; improvement of upper-air scores

outer loop - from 33km to 25km

- more data (AMSU-A and Aircraft) due to increase of # bins

- all changes contributed to forecast improvements, roughly doubling the gain due to model changes

inner loop

TL / AD- from 160km to 100km

- t: from 45min (9 bins) to 18min (21 bins)

background error statistics

- from T108 to T180

minimization:# of iterations

- from 55 (30+25) to 65 (35+30)

DA

(4

Dv

ar)

Major upgrade of the Global Deterministic Prediction System (GDPS):summary of changes

Page 6 – April 21, 2023

GDPS upgrade parallel suite: Oct-2012 to Jan-2013,geopotential height RMSE at day-5 (verification against analyses)

S.Hemisp.

N.Hemisp.OLDNEW@ day 5

pre

ss

ure

le

ve

l (h

Pa

)p

res

su

re l

ev

el

(hP

a)

OLD – NEW @ 500 hPa

Day

Day

RM

SE

dif

fere

nc

e (

da

m)

RM

SE

dif

fere

nc

e (

da

m)

RMSE (m)

RMSE (m)

Page 7 – April 21, 2023

Major upgrade of the Global Deterministic Prediction System (GDPS):impact of main upgrades since 2001

annual running mean ofday-5 GZ-500hPa RSME

against radiosondesover N. Hemisphere

latest upgrade

Page 8 – April 21, 2023

Two upgrades of the Global Ensemble Prediction System (GEPS)

OLDNEW

(1) Feb-2013 upgrade•multi-scale algorithm

•time-step: from 30 to 20min

•horiz. resol.: from 100 to 66km

•vertical levels: from 58 to 74

•topography filter

•reduced thinning of observations (2.7 X radiances)

•improved dynamics and physics Fig.: Global verification (CRPS* error) of temperature at 500hPa against radiosondes, of OLD versus NEW GEPS, showing a gain in predictability of 12h and plus.

[*CRPS = Continuous Rank Probability Score]

CRPS(OLD) - CRPS(NEW)

Page 9 – April 21, 2023

Two upgrades of the Global Ensemble Prediction System (GEPS)

(2) Oct-2013 upgrade•evolving SST (based on anomaly persistence method)

•extension to monthly forecasts (32 days) once a week

•operational historical forecasts (72 hindcasts per week, over the 1995-2012 period)

OLDNEW

CRPS(OLD) - CRPS(NEW)

Fig.: Verification (CRPS error) of 2-m temperature against SYNOP data over N. America, of OLD versus NEW GEPS, showing improvements due to the use of an evolving SST.

Major upgrade Regional of theRegional Ensemble Prediction System (REPS)

Changes model component only:

- horiz. resolution: from 33 to 15km

- vertical levels from 28 to 40

- improved treatment of stochastic physical tendency perturbations to avoid unrealistic precipitation rates

- improved boundary layer parameterization

Verification: Significant improvements of the scores for all upper-air variables at all levels, as well as screen-level temperature and dew-point depression.

OLDNEW

Page 11 – April 21, 2023

-- Part 2 --Ongoing and future projects

Page 12 – April 21, 2023

Upcoming Global Deterministic Prediction System (GDPS 4.0): assimilation related elements

• EnVar replaces 4D-Var

• Horizontal grids:• Analysis increment: 50km instead of 100km

• Satellite radiance observations:

• Additional AIRS/IASI channels assimilated

• Upgrade RTTOV8 to RTTOV10

• Modified obs error stddev for all radiance observations

• Improved satellite radiance bias correction scheme

• Improved treatment of radiosonde (4D) and aircraft observations

• Assimilation of ground-based GPS data

• Use of new global sea ice concentration analysis (based on 3D-Var)

• 4D Incremental Analysis Update (IAU) replaces digital filter

• Use of sequencer Maestro for R/D/O

* NOTE: Most elements also apply to new regional system (RDPS)

Page 13 – April 21, 2023

Ensemble-Variational assimilation: EnVar

• EnVar uses a variational assimilation approach in combination with the already available 4D ensemble covariances from the EnKF

• By making use of the 4D ensembles, EnVar performs a 4D analysis without the need of the tangent-linear and adjoint of forecast model

• Consequently, it is more computationally efficient and easier to maintain/adapt than 4D-Var

• Hybrid covariances used in EnVar by averaging the ensemble covariances with the static NMC-method covariances

• Future improvements to EnKF should benefit both GEPS and GDPS incentive to increase overall effort on EnKF development

Page 14 – April 21, 2023

EnVar: a replacement of 4D-Var

• Overall, EnVar (~10 min) analysis ~6X faster than 4D-Var (>1 hr) on half as many cpus, even though much higher resolution increments

• Nearly identical configuration of EnVar used for both global and regional systems (unified deterministic analysis)

• Large portions of fortran code already being shared between EnVar and EnKF, unification effort continuing

• Results from both global and regional EnVar are mostly comparable or better than 4D-Var, especially at shorter lead times

• Decision made to replace 4D-Var with more efficient EnVar in GDPS 4.0, if EnVar is at least as good as current 4D-Var

Page 15 – April 21, 2023

Current systems

Global EnKF

Perturbed members of the globalensemble prediction

system (GEPS)

Global deterministic

prediction system (GDPS)

Global4D-Var

2013-2017: Toward a Reorganization of the NWP Suites at Environment Canada

Perturbed members of the regionalensemble prediction

system (REPS)

Regional deterministic

prediction system (RDPS)

Regional 4D-Var

global system regional system

Page 16 – April 21, 2023

Increasing role of global ensembles… GDPS4.0

Global EnKF

Global ensemble forecasts (GEPS)

Global deterministic

forecast (GDPS)

GlobalEnVar

Background error

covariances

2013-2017: Toward a Reorganization of the NWP Suites at Environment Canada

RegionalEnsemble forecasts (REPS)

RegionalDeterministic

forecast (RDPS)

Regional EnVar

global system regional system

Page 17 – April 21, 2023

Global and regional ensembles…

Global EnKF

GlobalEnVar

Background error

covariances

2013-2017: Toward a Reorganization of the NWP Suites at Environment Canada

Regional EnKF

Regional ensemble forecasts (REPS)

Regional deterministic

forecast (RDPS)

Regional EnVar

Background error

covariances

High-res EnVar

High-resolution deterministic

prediction system

(HRDPS)

global system regional system

Global ensemble forecasts (GEPS)

Global deterministic

forecast (GDPS)

Page 18 – April 21, 2023

• Current system (1-way dependence):

• GEPS relies on GDPS to perform quality control (background check) for all observations and bias correction for satellite radiance observations

Dependencies between global systems

Bgcheck+BC 4D-Var GEM (9h fcst)

GEM (9h fcst)EnKFxb xb

xbxb xb, obs

obsxa

xaGDPS:

GEPS:

Page 19 – April 21, 2023

• Current system (1-way dependence):

• With EnVar (2-way dependence):

• 2-way dependence (EnVar uses EnKF ensemble of background states) increases complexity of overall system 2 systems have to be run simultaneously

Dependencies between global systems

Bgcheck+BC 4D-Var GEM (9h fcst)

GEM (9h fcst)EnKFxb xb

xbxb xb, obs

obs

Bgcheck+BC EnVar GEM (9h fcst)

GEM (9h fcst)EnKFxb xb

xbxb xb, obs

obsxb

xa

xa

xa

xa

GDPS:

GDPS:

GEPS:

GEPS:

Page 20 – April 21, 2023

Upcoming Regional Deterministic Prediction System (RDPS)

• RDPS v.3.1.0: Intermittent cycling using 4D/3D-Var

G2

D2

R2

D1

R1

TT-6h T+48h

Global 25km

inte

rpo

latio

n

LAM-Reg 10km

4DVar Xa=100km

Global 33 km Global 33km

3DVar Xa=100km

LAM-Reg 10 km4DVar Xa=100km

(current operational version)

Page 21 – April 21, 2023

• RDPS v.4.0.0: Intermittent cycling using 4D-EnVar based on global EnKF*

G2

D2

R2

D1

R1

TT-6h T+48h

Global 25km

inte

rpo

latio

n

LAM-Reg 10km

EnVar Xa=50 km

Global 33 km Global 33km

EnVar Xa=50km

LAM-Reg 10 kmEnVar Xa=50km

(to be operational late 2014)

* EnVar setup in D1 and R1 identical to the GDPS

D1 and R1 upgrade also includes (as in the GDPS)

•New Bias Correction•Radiosondes drift•Added IR channels•Ground-based GPS

Upcoming Regional Deterministic Prediction System (RDPS)

Page 22 – April 21, 2023

Continuous Cycling Regional EnKF

• Regional EnKF starts from the global analysis ensemble.

• 192 ensemble members (same as the global).

• Lateral boundary conditions from the global EnKF.

• Model top around 14 hPa.

• No model parameter perturbations.

• Prepare 21 initial conditions for REPS at 00 and 12 UTC.

EnKF

Global

GEM(66km)

EnKFRegional GEM-LAM(15km)

EnKFGEM

(66km)

GEM-LAM(15km)

xaxa xb

xb

...

xa

xa

EnKFxb

EnKFxb

...

...

Driver Driver

xa

xa

Page 23 – April 21, 2023

RDPS (10 km)

HRDPS(pan-Canadian)

HRDPS (multi-grid)

High Resolution Deterministic Prediction System (HRDPS)

Page 24 – April 21, 2023

Main objective for the pan-Canadian HRDPS

To be accomplished in 2 major steps:

1. Phase 1 (2014)Implementation of an experimental pan-Canadian sub-component• add new domain

• surface ICs supplied by coupled 2.5-km CaLDAS

• hydrometeor fields are “recycled” from the previous 2.5-km run

• modifications to GEM configuration

2. Phase 2 (2015)• upper-air data assimilation cycle

• model/configuration upgrades (physics, vertical resolution, …)

• expansion of coverage

• removal of (remaining) local domains

To become the primary source of NWP guidance for day 1 and 2

Page 25 – April 21, 2023

DA for a convective-scale model

• HRDPS: A pan-Canadian 2.5-km forecasting system

Phase 1 (2014) : No atmospheric DA; Downscaling of the 10-km RDPS analysis; hydrometeors are ‘recycled’ from the previous 2.5-km run (i.e. every 6-h)

Phase 2 (2015) : Continuous cycling using 4D-EnVar (+IAU) based on a 10-km limited-area EnKF

• Grid points: 2584 x 1334

• Forecasts up to +48-h

• Should eventually replace the RDPS as the main guidance for short-term forecasts in Canada

Page 26 – April 21, 2023

CaLDAS-screen (2.5 km)

Valid on 25 June 25 2011, 1200 UTC

Near-Surface Soil Moisture (0-10 cm)

ICs and BCs: 2.5-km CaLDAS

Page 27 – April 21, 2023

Yin-Yang grid for global forecasting

A two-way coupling method between two limited-area modelsQaddouri & Lee, 2011: The Canadian Global Environmental Multiscale model on the Yin-Yang grid system, QJRMS 137, 1913-1926)

• No poles + global quasi-uniform grid => simplification of numerical schemes:

– semi-Lagragian scheme without considering fluid parcel trajectory as great circle

– explicit numerical diffusion solver

• More balanced computational load for scalability purposes when compared to lat-lon grids

Yin Yang

Page 28 – April 21, 2023

- Averaging rule : Mid-point / Trapezoidal- Interpolation : Linear / Cubic

Mii t

tttt vrr

vvr

2/

21

22

,, 1DA

ii t

tttt

vvrrvrvr

Here we compare mid-point rule and trapezoidal rule for the calculation of displacements Dr in the semi-Lagrangian scheme.

The mid-point rule (a time mean followed by a space interpolation) can be described as follows:

where i is for iterations being made due to the non-linear nature of the process, while the trapezoidal rule (a space interpolation followed by a space-time mean) can be written:

Changing rule is fairly straightforward except for the ‘horizontal’ on the sphere.

Information: Girard et al., MWR 2014, Appendix 14, Trapezoidal rule for trajectory calculations

Options for Semi-Lagrangian Trajectory Calculations

Page 29 – April 21, 2023

Mid-point rule/linear interp Trapezoial rule/linear interp

Mid-point rule/cubic interp Trapezoidal rule/cubic interp

Idealized Flow past Topography (Schär’s case): Trajectory calculations using …

Page 30 – April 21, 2023

Global Averaged Scores

44 Winter Cases6-Day Forecasts

Gem Yin-Yang 15km Resolution

Semi-Lagrangian Trajectory Calculations

Blue: Mid-point rule/linear interpolationRed: Various modifications

Cubic interpolation

Trapezoidal rule

Trapezoidal rule/cubic interpolation

Page 31 – April 21, 2023

-- Appendices --

Page 32 – April 21, 2023

21UTC 00 03 06 09

Incremental PeriodTrial PeriodForecast Period (G1)

03 06 09 12 15

δ

δ

“Analysis”

Analysis increment (δ) is applied as δ/N , where N is # of timesteps in 6h assimilation window (T-3h to T+3h).

Increments are allowed to evolve following the 4D B matrix available in EnVar. Some physical quantities (cloud condensate and PBL quantities) from the

previous integration (background) are recycled into the next integration. When IAU and physics recycling are combined, model spin-up is virtually

eliminated. The replacement of DF by IAU also appears to have a strong positive impact on

the semidiurnal tide, apparent in tropical scores

00Z Run

06Z Run

A) 4D-IAU + selective physics recycling

B) Upgrades and Improvements to the MSC Data Processing for Radiosonde and Aircraft Data

• Increased volume of data: selection of observations according to model levels

• Revised observation error statistics

• Revised rejection criteria for radiosonde data based on those used at ECMWF

• Horizontal drift of radiosonde balloon taken into account in both data assimilation and verification systems

• Bias correction scheme for aircraft temperature reports

operationalproposed for both

radiosonde & aircraft

Impact of proposed changes

• General short-range forecast improvements above 500 hPa in both wind and temperature fields

• The temperature forecast biases are significantly improved due to the bias correction scheme for aircraft below 200 hPa and to the new rejection criteria for radiosonde humidity data above

• See Laroche & Sarrazin 2013, Weather and Forecasting, 28, pp 772-782

wind speed temperature

12h

48h

Fig.: Verification scores against radiosondes over the N. Hemisphere, Jan-Feb 2009 (dash = bias; solid = stde)

C) The new Canadian Land Data Assimilation System (CaLDAS)(in 2013)

ISBALAND-SURFACE

MODEL

OBS

ASSIMILATIONxb

y(EnKF approach)xa = xb+ K { y – H(xb) }

K = BHT ( HBHT+R)-1

with

ININ OUTOUT

• Ancillary land surface data

• Atmospheric forcing

• Observations

• Land surface initial conditions for NWP and hydro systems

• Land surface conditions for atmospheric

assimilation systems

• Current state of land surface

conditions for other applications

(agriculture, drought, ...)

Screen-level (T, Td)Stations snow depthL-band passive (SMOS,SMAP)MW passive (AMSR-E)Multispectral (MODIS)Combined products (GlobSnow)

T, q, U, V, Pr, SW, LW

Orography, vegetation, soils, water fraction, ...

CaLDAS

BIAS STDE

Fig.: Impact of CaLDAS on screen lecel air dew-point temperature forecasts over Canada, over the summer 2008: operational system versus CaLDAS.

D) Water cycle prediction system based on coupled numerical models

• Focus on Great Lakes and St. Lawrence watershed:– Great Lakes: 2-way coupled atmos.-ocean model (GEM+NEMO)

– Watershed: 1D model of land-surface + routing (MESH)

– St. Lawrence: 2D hydrodynamic model (H2D2)

▪ Includes pollutant transport model and habitat models

Tributary flow predicted,(with data assimilation

of streamflow obs.) @ 500m

Connected to water qualityand ecosystem models:

e.g. predicted wastewaterplume for Montreal

Impact of lakes on weatherneeds to be captured correctly:DJF 05-09 daily precip. shown

Page 36 – April 21, 2023

E) EnVar Pre-Final Cycles* vs. 4D-Var

T+24hNorth America

U |Vh|

Z T

T-Td

U

Z T

T-TdT+48hNorth America

|Vh|

*Using 66-km Ensemble and 25-km 4DVar-based Global Analysis Radiosonde verification scores – 120 cases, Winter 2011

Page 37 – April 21, 2023

Satellite data assimilation at EC

To be assimilated within EnVar late 2014 or 2015•Upgrade of AIRS & IASI, add Cris (~140 channels each)•Add ATMS (~16 channels)•Inter-channel observation error (IR & MW sounders)•Higher density of radiances (from 150 km to much lower)•GPS-RO extended to surface

Currently the object of research•Assimilation of surface-sensitive channels over land•Higher temporal assimilation based on simulations (OSSE) in view of upcoming hyperspectral IR sounders on GEO •Ozone assimilation from various sensors•Remote sensing of CO2

F) Satellite data assimilation: R&D 2014-2015

Page 38 – April 21, 2023

Canadian satellite missions with link to operational meteorology

• Radarsat constellation (3 satellites, funded, 2018 launch)

- Main applications: sea ice mapping and ocean surface wind

• Polar Communications and Weather (PCW, 2 satellites in HEO, under review, Planned for 2021)

- Same applications as MTG-FCI, GOES-R-ABI, but filling high latitude gap

(15 min imagery, multispectral, 100% coverage 60-90oN)

F) Satellite data assimilation: R&D 2014-2015

Page 39 – April 21, 2023

G1) Regional Ice Prediction System (RIPS)

• 5km N.American grid

• 3DVar Ice analysis – SSMI, AMSR-E, CIS daily charts

• CICE4.1 Ice model– Forced by CMC RDPS

• 48hr forecasts at 0, 6, 18, 24Z

• Experimental implementation: March 2013

G2) Global Ice-Ocean Prediction System (GIOPS)

• Mercator Ocean Assimilation System (SAM2-SEEK):

– Sea surface temperature – Temperature and salinity profiles– Sea level anomaly from satellite altimeters

• 3DVar Ice analysis• Daily blended ice-ocean analysis and

10day forecast• Model configuration:

– ORCA025 (~1/4°), <15km in Arctic– NEMOv3.1, LIM2-EVP

• Experimental implementation: Jul 2013

Page 40 – April 21, 2023

H) Future plans for Canadian EPS

• In 2014:– Ensemble layer in NinJo– Horizontal resolution of 50 km for the GEPS– Provide trial fields error statistics for EnVAR

– NAEFS-LAM (exchange of REPS and SREF data)

• In 2015-2016: – Better soil properties via assimilation with CALDAS and stochastic perturbations– New Yin-Yang model grid

– Model top at 0.1 hPa (80 km)– Regional EnKF– Increase horizontal resolution for both systems in function of the

available computer power.– Stochastic convection

Page 41 – April 21, 2023

Within the next five years...

• The ensemble approach will become mainstream– Next-Gen SCRIBE will incorporate the ensemble

paradigm– Model resolution will become very attractive to

forecasters▪ Regional EPS at 10 km grid spacing with dedicated data

assimilation▪ Global EPS at ~20-25 km grid spacing▪ Research on ensemble forecasting will be performed at 1-3

km grid spacing, but no operational kilometer-scale EPS within 3-5 years

Page 42 – April 21, 2023

Improvements to the GEPS in 2013

• February implementation :– Better analyses (higher resolution, more observations)– Only one surface scheme (ISBA, Noilhan and Planton, )– Limitation of the stochastic Physics Tendency Perturbations when

convection occurs– See the technical note of Gagnon et al. 2013 :

▪ http://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/op_systems/doc_opchanges/technote_geps300_20130213_e.pdf

• December implementation:– Evolutive SST (monthly forecasting on thursdays)– Operational reforecasting over the last 18 years (with 4 members)– See the technical note of Gagnon et al. 2013:

▪ http://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/technote_geps310_20131204_e.pdf

Page 43 – April 21, 2023

I) Preliminary results for the new reginal EnKF: 6h forecasts verification against radiosondes (20 days)

• Reduced horizontal localization distance.

• Variable horizontal localization distance: Near surface: 1600kmNear top: 2800km

• Same vertical localization as the global.

• Reduced isotropic model error perturbation.

UU VV

GZTT

ES

REnKF features

Page 44 – April 21, 2023

J) Canadian AQ Forecasting System

• Primary messaging tool is the Air Quality Health Index (AQHI)

• Main target is urban areas > 100,000 population

• On-line forecast model GEM-MACH provides guidance on AQHI component values (NO2, O3, PM2.5) and meteorological fields out to 48 hours

Page 45 – April 21, 2023

Canada’s National Air Quality Health Index (AQHI)

• Follows example of Canadian national UV index

• Year-round, health-based, additive, no-threshold, hourly AQ index

• Developed from daily time-series analysis of air pollutant concentrations and mortality data (Stieb et al., 2008)

• Weighted sum of NO2, O3, & PM2.5 concentrations

• 0 to 10+ range

Page 46 – April 21, 2023

Forecasted future situation- Next 48hr -

Modelled forecast values of O3, PM2.5, NO2

Forecaster (1 desk/forecast region)

Schematic diagram of an AQHI forecast

AQHI = 10/10.4*100*[(exp(0.000871*NO2)-1)+(exp(0.000537*O3) -1)+(exp(0.000487*PM2.5) -1)]

AQHI = 10/10.4*100*[(exp(0.000871*NO2)-1)+(exp(0.000537*O3) -1)+(exp(0.000487*PM2.5) -1)]

Numerical forecast- Next 48 hr - GEM-MACHUMOS-AQ

Past and present situation - Last 48 hr -

Real-time observations of O3, PM2.5, NO2

Elements of Canada’s AQ Forecasting System

Page 47 – April 21, 2023

GEM-MACH

• GEM-MACH is a multi-scale chemical weather forecast model composed of dynamics and physics (GEM) and on-line chemistry modules

• Operational configuration of GEM-MACH includes– limited-area-model (LAM) grid configuration for North America

– 10-km horizontal grid spacing, 80 vertical levels to 0.1 hPa

– 2-bin sectional representation of PM size distribution (i.e., 0-2.5 and 2.5-10 μm) with 9 chemical components

– forecast species include O3, NO2, and PM2.5 needed for AQHI

Page 48 – April 21, 2023

RDPS and Operational GEM-MACH Grids

• EC’s limited-area regional deterministic prediction system (RDPS) provides required initial and boundary conditions for GEM-MACH

• GEM-MACH’s grid points are co-located with RDPS grid points

RDPS grid (blue); GEM-MACH grid (red)

Page 49 – April 21, 2023

Ongoing developments for GEM-MACH

• Operational configuration: Lengthen forecast from 48 to 72 hours Include wildfire emissions

• Global configuration for assimilation/piloting purposes 12-bin version for AOD assimilation Simplified stratospheric chemistry for the assimilation

of ozone and GHGs.