Transcript 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

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
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 2 – November-7-15
-- Part 1 -Recent changes to
operational suites
Page 3 – November-7-15
Summary of recent changes
Major upgrade to
Global Prediction Systems
(Deterministic & Ensemble)…
Major upgrade to Ensemble
Prediction Systems (Global
& Regional)
Adjustments to
High Resolution
Deterministic
Prediction System
Update to Regional Air Quality
Deterministic Prediction System
New Operational Hydrodynamic Simulation System
Additional satellite (CSR,
ATOVS, polar winds)
added to deterministic
systems
Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct
Nov Dec Jan Fev
2013 2014
2012 2013
… accompanied by
upgrades in
Regional
Deterministic
Prediction System
New Regional
Deterministic
Air Quality
Analysis
Mar
METOP-1
added to
GPS-RO
Satwinds
(METEOSAT10) +
ASCAT winds
(METOP-1) added
to DA system
Experimental Regional
Ice Prediction System
(RIPS)
Adjustments to ocean analysis
in seasonal prediction system
(CanSIPS)
Experimental Global
Ice-Ocean Prediction
System (GIOPS)
Page 4 – November-7-15
Upgrades to
Nowcasting
system (INCS)
Experimental
Pan-Canadian High
Resolution Deterministic
Prediction System
Major upgrade of the Global
Deterministic Prediction
System (GDPS):
DA (4Dvar)
forecast model
summary of changes
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
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
physics
outer loop
inner
loop
- from 33km to 25km
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)
- 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
GDPS upgrade parallel suite:
Oct-2012 to Jan-2013,
geopotential height RMSE at day-5 (verification against analyses)
pressure level (hPa)
OLD
NEW
@ day 5
RMSE difference (dam)
N.Hemisp.
OLD – NEW
@ 500 hPa
Day
RMSE (m)
pressure level (hPa)
RMSE difference (dam)
S.Hemisp.
Page 6 – November-7-15
RMSE (m)
Day
Major upgrade of the Global Deterministic
Prediction System (GDPS):
impact of main upgrades since 2001
annual running mean of
day-5 GZ-500hPa RSME
against radiosondes
over N. Hemisphere
latest upgrade
Page 7 – November-7-15
Two upgrades of the Global Ensemble
Prediction System (GEPS)
(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.
Page 8 – November-7-15
[*CRPS = Continuous Rank Probability Score]
OLD
NEW
CRPS(OLD) - CRPS(NEW)
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)
OLD
NEW
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.
Page 9 – November-7-15
Major upgrade Regional of the
Regional 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.
OLD
NEW
-- Part 2 -Ongoing and future projects
Page 11 – November-7-15
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
Page 12 – November-7-15
* NOTE: Most elements also apply to new regional system (RDPS)
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 13 – November-7-15
EnVar: a replacement of 4D-Var
• Overall, EnVar (~10 min) analysis ~6X faster than 4DVar (>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
Page 14 – November-7-15
current 4D-Var
2013-2017: Toward a Reorganization of
the NWP Suites at Environment Canada
Current systems
Global
EnKF
Global
4D-Var
Perturbed
members of
the regional
ensemble
prediction
system (REPS)
Perturbed
members of
the global
ensemble
prediction
system (GEPS)
Global
deterministic
prediction
system (GDPS)
Regional
4D-Var
Regional
deterministic
prediction
system (RDPS)
Page 15 – November-7-15
global system
regional system
2013-2017: Toward a Reorganization of
the NWP Suites at Environment Canada
Increasing role of global ensembles… GDPS4.0
Regional
Ensemble
forecasts
(REPS)
Global
ensemble
forecasts
(GEPS)
Global
EnKF
Background
error
covariances
Global
EnVar
Global
deterministic
forecast
(GDPS)
Regional
EnVar
Regional
Deterministic
forecast
(RDPS)
Page 16 – November-7-15
global system
regional system
2013-2017: Toward a Reorganization of
the NWP Suites at Environment Canada
Global and regional
ensembles…
Global
ensemble
forecasts
(GEPS)
Global
EnKF
Regional
EnKF
Background
error
covariances
Regional
EnVar
Background
error
covariances
Global
EnVar
Regional
ensemble
forecasts
(REPS)
Global
deterministic
forecast
(GDPS)
High-res
EnVar
Page 17 – November-7-15
global system
Regional
deterministic
forecast
(RDPS)
High-resolution
deterministic
prediction
system
(HRDPS)
regional system
Dependencies between global systems
•
GDPS:
Current system (1-way dependence):
xb
Bgcheck+BC
xb, obs
4D-Var
xa
GEM (9h fcst)
xb
obs
GEPS:
•
xb
EnKF
xa
GEM (9h fcst)
xb
GEPS relies on GDPS to perform quality control (background check)
for all observations and bias correction for satellite radiance
observations
Page 18 – November-7-15
Dependencies between global systems
•
GDPS:
Current system (1-way dependence):
xb
Bgcheck+BC
xb, obs
4D-Var
xa
GEM (9h fcst)
xb
obs
GEPS:
•
GDPS:
GEPS:
•
xb
EnKF
xa
GEM (9h fcst)
xb
With EnVar (2-way dependence):
xb
xb
Bgcheck+BC
xb
xb, obs
EnVar
obs
EnKF
xa
xa
GEM (9h fcst)
GEM (9h fcst)
xb
xb
2-way dependence (EnVar uses EnKF ensemble of background
states) increases complexity of overall system  2 systems have to
be run simultaneously
Page 19 – November-7-15
Upcoming Regional Deterministic Prediction
System (RDPS)
• RDPS v.3.1.0: Intermittent cycling using 4D/3D-Var
G2
(current operational version)
Global 25km
interpolation
4DVar Xa=100km
D2
D1
Global 33 km
Global 33km
3DVar Xa=100km
R2
R1
LAM-Reg 10 km
LAM-Reg 10km
4DVar Xa=100km
T-6h
T+48h
T
Page 20 – November-7-15
Upcoming Regional Deterministic Prediction
System (RDPS)
• RDPS v.4.0.0: Intermittent cycling using 4D-EnVar based
on global EnKF*
G2
(to be operational late 2014)
Global 25km
interpolation
EnVar Xa=50 km
* EnVar setup in D1 and R1
identical to the GDPS
D2
D1
Global 33 km
Global 33km
D1 and R1 upgrade also
includes (as in the GDPS)
• New Bias Correction
• Radiosondes drift
• Added IR channels
• Ground-based GPS
EnVar Xa=50km
R2
R1
LAM-Reg 10 km
LAM-Reg 10km
EnVar Xa=50km
T-6h
T+48h
T
Page 21 – November-7-15
Continuous Cycling Regional EnKF
Global
...
xa
EnKF
xa
GEM
(66km)
xb
EnKF
xa
Regional
•
•
•
•
•
•
xb
EnKF
xa
...
Driver
Driver
GEM-LAM
(15km)
GEM
(66km)
xb
EnKF
xa
GEM-LAM
(15km)
xb
EnKF
xa ...
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.
Page 22 – November-7-15
High Resolution
Deterministic
Prediction System
(HRDPS)
HRDPS (multi-grid)
HRDPS
(pan-Canadian)
RDPS (10 km)
Page 23 – November-7-15
Main objective for the pan-Canadian HRDPS
 To become the primary source of NWP guidance for day 1 and 2
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
Page 24 – November-7-15
DA for a convective-scale model
• HRDPS: A pan-Canadian 2.5-km forecasting system
• 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
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 10km limited-area EnKF
Page 25 – November-7-15
ICs and BCs:
2.5-km CaLDAS
CaLDAS-screen (2.5 km)
Valid on 25 June 25
2011, 1200 UTC
Page 26 – November-7-15
Near-Surface Soil
Moisture (0-10 cm)
Yin-Yang grid for global forecasting
A two-way coupling method
between two limited-area
models
Yin
Yang
Qaddouri & 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
Page 27 – November-7-15
Options for Semi-Lagrangian Trajectory
Calculations
- Averaging rule : Mid-point / Trapezoidal
- Interpolation :
Linear / Cubic
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:
r i  t
vt   vt  t 

r  r i 1 / 2   tv M
2
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:
v  vD
vt , r   vt  t , r  r i 1 
r  t
 t A
2
2
i
Changing rule is fairly straightforward except for the ‘horizontal’ on
the sphere.
Page 28 –14,
November-7-15
Information: Girard et al., MWR 2014, Appendix
Trapezoidal rule for trajectory calculations
Mid-point rule/linear interp
Trapezoial rule/linear interp
Mid-point rule/cubic interp
Trapezoidal rule/cubic interp
Page 29 – November-7-15
Idealized Flow past Topography (Schär’s case): Trajectory calculations using …
Trapezoidal rule
Global Averaged Scores
44 Winter Cases
6-Day Forecasts
Gem Yin-Yang 15km Resolution
Semi-Lagrangian Trajectory Calculations
Blue: Mid-point rule/linear interpolation
Red: Various modifications
Cubic interpolation
Trapezoidal rule/cubic interpolation
Page 30 – November-7-15
-- Appendices --
Page 31 – November-7-15
A) 4D-IAU + selective physics recycling
δ
Incremental Period
Trial Period
Forecast Period (G1)
00Z Run
UTC 21
00
03
06
δ
09
“Analysis”
06Z Run
03





06
09
12
15
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 Page
also
appears to have a strong positive impact
32 – November-7-15
on the semidiurnal tide, apparent in tropical scores
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
wind speed
temperature
operational
proposed for both
radiosonde & aircraft
Impact of proposed changes
12h
•
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
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)
Orography, vegetation,
soils, water fraction, ...
• Atmospheric
forcing
CaLDAS
Screen-level (T, Td)
Stations snow depth
L-band passive (SMOS,SMAP)
MW passive (AMSR-E)
Multispectral (MODIS)
Combined products (GlobSnow)
xb
ASSIMILATION
y
(EnKF approach)
xa = xb+ K { y – H(xb) }
OBS
OUT
• Land surface initial
conditions for NWP
and hydro systems
ISBA
LAND-SURFACE
MODEL
T, q, U, V, Pr, SW, LW
• Observations
STDE
Fig.: Impact of CaLDAS on screen lecel air dew-point temperature forecasts
over Canada, over the summer 2008: operational system versus CaLDAS.
IN
• Ancillary land
surface data
BIAS
with
K = BHT ( HBHT+R)-1
• Land surface
conditions for
atmospheric
assimilation systems
• Current state of
land surface
conditions for other
applications
(agriculture,
drought, ...)
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
Impact of lakes on weather
needs to be captured correctly:
DJF 05-09 daily precip. shown
Tributary flow predicted,
(with data assimilation
of streamflow obs.) @ 500m
Connected to water quality
and ecosystem models:
e.g. predicted wastewater
plume for Montreal
E) EnVar Pre-Final Cycles* vs. 4D-Var
*Using 66-km Ensemble and 25-km 4DVar-based Global Analysis
Radiosonde verification scores – 120 cases, Winter 2011
U
|Vh|
U
|Vh|
Z
T
Z
T
T-Td
T+24h
North AmericaPage 36 – November-7-15
T-Td
T+48h
North America
F) Satellite data assimilation: R&D
2014-2015
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
Page 37 – November-7-15
F) Satellite data assimilation: R&D
2014-2015
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)
Page 38 – November-7-15
G1) Regional Ice Prediction G2) Global Ice-Ocean
Prediction System (GIOPS)
System (RIPS)
• 5km N.American grid
• 3DVar Ice analysis
–
(SAM2-SEEK):
–
–
–
SSMI, AMSR-E, CIS daily charts
• CICE4.1 Ice model
–
• Mercator Ocean Assimilation System
Forced by CMC RDPS
• 48hr forecasts at 0, 6, 18, 24Z
• Experimental implementation:
March 2013
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 39 – November-7-15
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 40 – November-7-15
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 41 – November-7-15
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/t
echnote_geps310_20131204_e.pdf
Page 42 – November-7-15
I) Preliminary results for the new reginal EnKF:
6h forecasts verification against radiosondes (20 days)
REnKF features
UU
VV
• Reduced horizontal
localization distance.
• Variable horizontal
localization distance:
Near surface: 1600km
Near top: 2800km
• Same vertical
localization as the
global.
• Reduced isotropic
model error
perturbation.
GZ
TT
ES
Page 43 – November-7-15
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 44 – November-7-15
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 45 – November-7-15
Elements of Canada’s AQ Forecasting System
Schematic diagram of an AQHI forecast
Numerical
forecast
Forecasted
future
situation
-- Next
hr -Next48
48hr
GEM-MACH
Modelled
forecast values
UMOS-AQ
of O3, PM2.5, NO2
Past and present situation
- Last 48 hr Real-time observations
of O3, PM2.5, NO2
AQHI = 10/10.4*100*[(exp(0.000871*NO2)-1)
+(exp(0.000537*O3) -1)+(exp(0.000487*PM2.5) -1)]
Forecaster
(1 desk/forecast region)
AQHI = 10/10.4*100*[(exp(0.000871*NO2)-1)
+(exp(0.000537*O3) -1)+(exp(0.000487*PM2.5) -1)]
Page 46 – November-7-15
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 47 – November-7-15
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 48 – November-7-15
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.
Page 49 – November-7-15