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Towards a National 2.5-km High Resolution Deterministic Prediction System
Jason Milbrandt, Stéphane Bélair, Manon Faucher, Anna Glazer, Ruping Mo
Contributors: Bertrand Denis, Amin Erfani, André Giguère, Jocelyn Mailhot, Ron
McTaggart-Cowan, Richard Moffet
CMOS 2012, Montreal QC May 31, 2012
Science and Technology Directorate
Meteorological Services of Canada
Canadian Meteorological Centre
(CMC)
NWP Research at Environment Canada
(the Canadian weather service)
Data Assimilation and Satellite Meteorology
Research
Numerical Weather Prediction Research
Cloud Physics and Severe Weather Research
Air Quality Research
Meteorological Research
Science and Technology Directorate
Climate Research
NWP Research at Environment Canada
Numerical Weather Prediction Research Section Section de Recherche en Prévision Numérique (RPN)
MANDATE: 1. Develop tools for operational NWP (for CMC*) 2. Conduct scientific studies
*CMC = Canadian Meteorological Center (the Canadian weather service)
NWP Research at Environment Canada
Modeling Systems and Applications at RPN/CMC
OUTLINE OF PRESENTATION 1. Overview of current HRDPS 2. Future HRDPS 3. Related projects
Global Uniform Global Variable
Limited Area (LAM)
Environment Canada's NWP Model GEM (Global Environmental Multiscale)
• non-hydrostatic
• fully compressible
• semi-implicit
• semi-Lagrangian
• one-way self-nesting
• staggered vertical grid (Charney-Phillips)
Côté et al. (1998) Mon. Wea. Rev.
Yin-Yang
Various grid configurations:
LAM = Limited Area Model LAM ≠ High Resolution Model
…
t = 0 t = final
fields from driving model
Initial Conditions / Boundary Conditions Boundary Conditions
Environment Canada’s HRDPS (High Resolution Deterministic Prediction System)
• 1997: Project initiated by CMC/RPN (HiMAP) • Since 1999: Collaboration with PNR • Summer 2001: ELBOW project (MRB and Ontario region) • Since 2002: Collaboration with PYR • Since 2004: Collaboration Quebec region
Other related experimental systems: • 2001: MAP • 2007: MAP-DPHASE • 2008-09: UNSTABLE • 2008-10: Lancaster Sound • 2010: Vancouver 2010 Winter Olympics/Paralympics • 2014: Sochi 2014 Winter Olympics/Paralympics • 2015: Pan-American Games
Environment Canada’s HRDPS (High Resolution Deterministic Prediction System)
• 4 “full-time” grids • 1 “seasonal” grid • Dz = 2.5 km • 58 levels (staggered) • one 24-h daily run (per
domain) • downscaled from RDPS-
15 forecast • Li-Barker radiation • Milbrandt-Yau 2-moment
microphysics
Environment Canada’s HRDPS (High Resolution Deterministic Prediction System)
to be 2 x 36-h integrations (end of summer 2012)
EXP
00 UTC 25 March 2009
REGETA1 à LAM-2.5km
00 UTC 25 March 2009
GSL system à LAM2.5km
• SST (TM) • ice fraction (GL) • sea ice temperature (I7) • sea ice thickness (I8 )
Initialization of the surface fields with output from the GSL coupled system
ICE FRACTION ICE FRACTION
Evolving Orography
For 2.5-km integration, elevation an each grid point starts identical to 10-km grid and evolves gradually to final 2.5-km grid
ELEV
ATIO
N
HORIZONTAL
Peaks created
Valley created
10 km OROGRAPHY 2.5 km OROGRAPHY
Evolving Orography
VERTICAL MOTION VERTICAL MOTION Pa s-1 Pa s-1
Vertical motion along an isolated ridge in an idealized simulation. No evolving-orography (left) is compared with the final step of a 12-h growth period (right).
Nesting from 10 km à 2.5 km involves orographic changes that cause imbalances during nesting: l Gravity waves are generated as the dynamics come into balance
c/o Ron McTaggart-Cowan
Evolving Orography Nesting from 10 km à 2.5 km involves orographic changes that cause imbalances during nesting: l Gravity waves are generated as the dynamics come into balance l Effects of subterranean extrapolation can be long-lived
Δ(Squamish) = -600 m
Extrapolated 6.5°C km-1 lapse rate and constant winds cause an initial error of 7oC at Squamish on the 1-km grid.
Without evolving-orography, this nocturnal inversion cannot be re-established before sunrise in the model.
Observed temperature: 10oC
Model temperature: 17oC
c/o Ron McTaggart-Cowan
INPUT: w, T, p, qv
OUTPUT: • Latent heating • Hydrometeors (cloud, rain, ice,…) ® qc, qr, qi, ...
qc, qr, qi, ...
MOIST PROCESSES
Single cloudy grid element – interaction with NWP model:
For NWP models at the “convective scale” (Dx < 4 km), no longer need a CPS – clouds are considered to be resolved
à cloud / precipitation processes are treated by a grid-scale condensation scheme
Cloud Microphysical Processes
Dxx
xx eDNDN la -= 0)(For each category x = c, r, i, s, g, h:
Six hydrometeor categories: 2 liquid: cloud, rain 4 frozen: ice, snow, graupel, hail
Prognostic variables
qx, Nx (12)
RAIN
GRAUPEL HAIL
SEDIMENTATION SEDIMENTATION
VAPOR
ICE CLOUD
VDvr VDvs NUvi, VDvi
CLci, MLic, FZci CLcs
CNig CNis, CLis
CLri CLih
CLsh
CLir-g CLsr-h
CLir-g CLsr-g
CLch CNsg
CNgh
MLgr
CLcg
VDvg
CLir
VDvh self-
collection self-
collection
CLrh, MLhr,SHhr
NUvc, VDvc
CNcr, CLcr
CLsr CLrs
MLsr, CLsr SNOW
Milbrandt-Yau* 2-Moment Microphysics Scheme
* Milbrandt and Yau (2005a,b)
RAIN
ICE (pristine crystals)
SNOW (large crystals / aggregates)
GRAUPEL HAIL (ice pellets)
CLOUD (CLW)
Hydrometeor Mixing Ratios, qx
10 – 30 microns (maritime CCN)
0.1 – 1 mm
10 – 50 microns 0.1 – 4 mm
0.5 – 2 mm < 0.5 mm
RAIN
ICE (pristine crystal)
SNOW (large crystals / aggregates)
GRAUPEL HAIL (ice pellets)
CLOUD (CLW)
DRIZZLE STRATIFORM RAIN
RIME-SPLINTERING
Mean-Mass Diameters, Dmx
RN1 – Liquid Drizzle RN2 – Liquid Rain FR1 – Freezing Drizzle FR2 – Freezing Rain SN1 – Ice Crystals SN2 – Snow SN3 – Graupel (snow pellets) PE1 – Ice Pellets (re-frozen rain) PE2 – Hail (total) PE2L – Large Hail
Precipitation types from microphysics :
VIS1 (liquid fog)
VIS2 (rain)
VIS3 (snow)
3D fields for VISIBILITY due to fog, rain, and snow (parameterizations based on observations taken during FRAM)
km
km
km
VIS1 = f (qc,Nc)
VIS2 = f (RRN2)
VIS3 = f (RSN2)
*Gultepe and Milbrandt (2007)
VIS1 (liquid fog)
VIS2 (rain)
VIS3 (snow)
km
km
km
VISIBILITY due to the combined effects of
liquid FOG, RAIN, and SNOW:
1)l n ( --= e xV I S be
1
31
21
11 -
÷øö
çèæ ++=
V IV I SV I SV I S
3D fields for VISIBILITY due to fog, rain, and snow (parameterizations based on observations taken during FRAM)
VIS1 (liquid fog)
VIS2 (rain)
VIS3 (snow)
VIS (fog + rain + snow) km
km
km
km
km
3D fields for VISIBILITY due to fog, rain, and snow (parameterizations based on observations taken during FRAM)
0.995
Visi
bilit
y (m
)
Real-Time Verification Examples (from SNOW-V10 site)
How much snow will fall?
mm
Accumulated Precipitation (liquid-equivalent)
Accumulated Precipitation (unmelted)
mm
x 10 ?
i.e. snow depth QPF
Proposed Method – MICROPHYSICAL PROCESSES Source: Ware et al. (2006), Wea and Forecasting
Observed SOLID-LIQUID ratios: • can range from 3:1 to 100:1 • average value approximately 10:1 • varies geographically
mm
Solid-to-Liquid Ratio* Accumulated Precipitation (liquid-equivalent)
* Milbrandt et al. (2012) MWR
mm
Accumulated Precipitation (liquid-equivalent)
Accumulated Precipitation (unmelted)
mm
i.e. snow depth
Num
ber o
f Grid
Poi
nts
10:1
Solid-to-Liquid Ratio
Source: Roebber et al. (2003), Weather and Forecasting
10:1
Solid-to-Liquid Ratio
grid area
06 00 18 12 06 00 18 12 00 18
Current Experimental WEST Run • 1 LAM-2.5km runs per day, 24-h • Nested from 6-h forecasts of 00z-REG-15
RDPS-15
HRDPS LAM-15
LAM-2.5
06 00 18 12 06 00 18 12 00 18
Future Operational WEST Runs • 2 LAM-2.5km runs per day, 36-h • Nested from 6-h forecasts of 00z- and 12z-REG-10 runs
HRDPS RUN 1
RDPS-10
LAM-10
LAM-2.5
HRDPS RUN 2
RDPS-10
LAM-10
LAM-2.5
Current HRDPS
Future HRDPS
Current: (near future) • multi-grid (2.5 km) - 2 x 36-h (west domain) - 1 x 24-h (other domains) • downscaled from RDPS • 58 levels • IC surface fields from ISBA
HRDPS Configuration
Future: • single grid (2.5 km) - 4 x 36-h • 70 - 80 levels • IC surface fields from CaLDAS • upgraded microphysics • upper-air assimilation cycle
• LAM 250-m grids (e.g. over cities) Next generation HRDPS
HRDPS Future Plans 1. Operational WEST-2.5 domain
- operational status of WEST; 2 x 36-h - upgrade of GEM version à Implementation in progress
2. National-2.5 – STAGE 1
- single, national grid - 2 x 36-h - increased vertical resolution - high-resolution surface fields (CaLDAS) - upgrade to microphysics - reduced spin-up (recycling PHY bus) à 2013
3. National-2.5 – STAGE 2
- 4 x 36-h - upper-air data assimilation cycle (EnVar*) à 2015
* Buehner et al. (2010a,b)
W
M
A L
LAM2.5 windows: West (W), East (E), Maritimes (M), Lancaster (L), Arctic (A)
N1 (ni x nj = 2904 x 1674)
N2 (ni x nj = 2524 x 1334)
Prototype National-2.5: RUN 1
The CANADIAN LAND DATA ASSIMILATION SYSTEM (CaLDAS*)
ISBA LAND-SURFACE
MODEL
OBS
ASSIMILATION
xb
y (with ensemble Kalman filter
approach)
xa = xb+ K { y – H(xb) }
K = BHT ( HBHT+R)-1
with
IN OUT 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) Surface stations snow depth L-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, ...
*Carrera et al. (2012) (to be submitted to J. Hydromet)
For details, see Stéphane Bélair
Current Levels (58): Alternative Configuration (48):
Examining alternative configurations of vertical levels
Testing increased vertical resolution in PBL: à Expected improvements to winds and temperature
0.950 0.950
0.995 0.995
Wind Speed (knots) Temperature (°C)
nk = 58 nk = 72
c/o Natacha Bernier, RPN (EC)
Current Levels (58): Alternative Configuration (48):
Examining alternative configurations of vertical levels
Removable with
lid-nesting*
* McTaggart-Cowan et al. (2011)
- Nucleation of Cloud Droplets Currently, CCN-type is specified - Prognostic graupel density*
MARITIME
CONTINENTAL 103
100
10-1
0.01 0.1 1.00 10.0
SUPERSATURATION (%)
101 NCCN
(cm-3)
102
Improvement to Microphysics Scheme
* Milbrandt and Morrison (2012) (to be submitted to JAS)
00:00
01:00
10 km
2.5 km
1 km
FROST-2014 (Forecasting for the Russian Olympics Sochi Testbed)
GEM-LAM Set-up
250-m domain
1 km
(full domain)
Current GEM Nesting Strategy for FROST-2014
TE Related work on LAM-250 m configuations: (Leroyer et al., in preparation)
2.5km
1 km
250 m
LAM_15 48hrs
REG_15 48 hrs
LAM_2.5 44 hrs
06 18 00 06 12 18
02Z
00 00 12 UTC
00Z
04Z LAM_1.0 42 hrs
LAM_250m 41 hrs
01Z
14 Aug 14 Aug 2008 16 Aug 15 Aug
Tethered balloon launch
MSC Analysis product
TE
Point Atkinson
Pitt-Meadows airport
Vancouver airport
White Rock
x ♠ ♦
♣
x
x
Sunset
Westham Island (‘rural’)
Oakridge Tethered balloon
Richmond radiometer
Ceilometer
♠ Lidar UBC
Topography < 50 m < 100 m < 150 m > 500 m
EPiCC network in Vancouver Available data for 14-15 August 2008
Environment Canada permanent weather stations EPiCC sites
Data from the Vancouver EPiCC network http://www.epicc.uwo.ca A. Christen, B. Crawford, I. McKendry, D. Von Der Kamp
TEB inputs
Related work on LAM-250 m configuations:
Diurnal cycle of the sea and land breezes Vertical Motion () and Wind Vectors (knots) at ~ 160 m AGL
(2008 14 Aug. 0500 LST – 15 Aug. 0500 LST)
Pa s-1
Knots
Vancouver
l 300 x 300 points l ∆x: 250 m l ∆t: 10 s l 57 vertical levels - 1st u-level :10 m - 15 levels < 500 m - 26 levels < 1500 m
c/o Sylvie Leroyer (EC)
POSSIBILITY for future fog-forecasting model: Local 3D fog model
• driven from HRDPS (2.5 km) • very high-resolution (vertical) • lid in mid-troposphere • full physics
… t = 0 t = final
Initial Conditions / Boundary Conditions Boundary Conditions à Driven by LAM-2.5 km model
Advantages of a cloud-scale deterministic NWP system:
1. Topographic forcing is better resolved - orography, vegetation, land-water boundaries
2. Better physics - high-res surface data assimilation - no need for a CPS - can use a detailed microphysics scheme
à Improved ability to forecast high-impact weather
THANK YOU
CTR
EXP
Use gauge-based scores to compare?
