Towards a Pan-Canadian 2.5-km

High Resolution Deterministic Prediction System

Jason Milbrandt, Stéphane Bélair, Manon Faucher, Anna Glazer

Environment Canada(RPN and CMC)

SAAWSO Project WorkshopApril 22-24, 2012

Modeling Systems and Applications at CMC/RPN

Global Uniform Global Variable

Limited Area (LAM)

Environment Canada's NWP ModelGEM (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

• 4 “full-time” grids

• 1 “seasonal” gridz = 2.5 km

• 58 levels (staggered)

• one 24-h daily run (per domain)

• downscaled from RDPS-10 forecast

• Li-Barker radiation

• Milbrandt-Yau 2-moment microphysics

Environment Canada’s HRDPS(High Resolution Deterministic Prediction System)

2 x 42-h integrations

• 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)

0600 1812 0600 1812 0018

Experimental EAST, MARITIME, ARCTIC Runs• 1 LAM-2.5km runs per day, 24-h• Nested from 6-h forecasts of 00z-REG-10

RDPS-10

HRDPS

LAM-10

LAM-2.5

0600 1812 0600 1812 0018

HRDPSRUN 1

RDPS-10

LAM-10

LAM-2.5

HRDPSRUN 2

RDPS-10

LAM-10

LAM-2.5

Operational WEST Runs• 2 LAM-2.5km runs per day, 42-h• Nested from 6-h forecasts of 00z- and 12z-REG-10 runs

Current HRDPS

Future HRDPS

W

E

M

AL

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)

Current: (near future)• multi-grid (2.5 km)

- 2 x 42-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 48-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 42-h- upgrade of GEM version

2. National-2.5 – STAGE 1- single, national grid- 2 x 48-h- increased vertical resolution- high-resolution surface fields (CaLDAS)- upgrade to microphysics- reduced spin-up (recycling PHY bus) 2014

3. National-2.5 – STAGE 2- 4 x 48-h- upper-air data assimilation cycle (En-Var*) 2016

* Buehner et al. (2010a,b)

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

Improved ability to forecasthigh-impact weather

The CANADIAN LAND DATA ASSIMILATION SYSTEM The CANADIAN LAND DATA ASSIMILATION SYSTEM (CaLDAS*)(CaLDAS*)

ISBALAND-SURFACE

MODEL

OBS

ASSIMILATION

xb

y (with ensemble Kalman filter

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)Surface 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, ...

*Carrera et al. (2012)(to be submitted to J. Hydromet)

For details, see Stéphane Bélair

INPUT:w, T, p, qv

OUTPUT:• Latent heating• Hydrometeors (cloud, rain, ice,…) qc, qr, qi, ...

qc, qr, qi, ...

MOISTPROCESSES

Single cloudy grid element – interaction with NWP model:

For NWP models at the “convective scale” (x < 4 km), no longer need a CPS – clouds are considered to be resolved

cloud / precipitation processes are treated by a grid-scale condensation scheme

Cloud Microphysical Processes

Dxx

xxeDNDN 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

SEDIMENTATIONSEDIMENTATION

VAPOR

ICECLOUDV

Dvr

VD

vs

NU

vi,

VD

vi

CLci, MLic, FZciCLcs

CNig

CN

is,

CL i

s

CLri

CL i

h

CL s

h

CLir-g

CLsr-h

CLir-g

CLsr-g

CLch

CNsg

CNgh

ML g

r

CL c

g

VD

vg

CLir

VD

vhself-collection

self-collection

CLrh,MLhr,SHhr

NU vc, VD vc

CN

cr,

CL c

r

CLsr CLrs

MLsr, CLsr SNOW

2-Moment Microphysics Scheme*

* Milbrandt and Yau (2005a,b)

10 – 30 microns(maritime CCN)

0.1 – 1 mm

10 – 50 microns0.1 – 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 DrizzleRN2 – Liquid RainFR1 – Freezing DrizzleFR2 – Freezing RainSN1 – Ice CrystalsSN2 – SnowSN3 – 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)ln( extVIS

1

3

1

2

1

1

1

VISVISVISVIS

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

Vis

ibili

ty (

m)

Real-Time Verification Examples(from SNOW-V10 site)

Prototype National-2.5: RUN 1

00:00

01:00

0600 1812 0600 1812 0018

Experimental EAST, MARITIME, ARCTIC Runs• 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

0600 1812 0600 1812 0018

Recycling of Hydrometeor Fields

RDPS-10

Consecutive 6-h runs (LAM-2.5)

FLOW fields

CLOUD fields

ICs from RDPS analysis

ICs from 6-h forecast of previous 2.5-km run (cycle)

0600 1812 0600 1812 0018

Recycling of Hydrometeor Fields

RDPS-10

HRDPS

Consecutive 6-h runs (LAM-2.5)

FLOW fields

CLOUD fields

ICs from RDPS analysis

ICs from 6-h forecast of previous 2.5-km run (cycle)

00:00

Recycling of Hydrometeor Fields

CURRENT SET-UP of Deterministic NWP System

10 km

2.5 km

1 km

250 m

m

Configuration for FROST-2014

DEMOS – 24 Jan 2013 (heavy rain/snow)

Near-surface winds:2.5 km

DEMOS – 24 Jan 2013 (heavy rain/snow)

Near-surface winds:1 km

DEMOS – 24 Jan 2013 (heavy rain/snow)

Near-surface winds:250 m

DEMOS – 24 Jan 2013 (heavy rain/snow)

250 m

24-h Accumulated SNOW

2.5 km1 km250 m

mm

DEMOS – 24 Jan 2013 (heavy rain/snow)

24-h SNOW 24-h RAIN

2.5 km1 km250 m

DEMOS – 24 Jan 2013 (heavy rain/snow)

Differences are not due to precipitation phase; 2.5-km run appears to underestimate the orographic enhancement

THANK YOUTHANK YOU