Download Urban - Réseau canadien en modélisation et diagnostics du climat

Système de modélisation et d’assimilation de
surface à Environnement Canada
Stéphane Bélair, Bernard Bilodeau, Marco Carrera, Dorothée Charpentier,
Isabelle Doré, Vincent Fortin, Pablo Grunmann, Aude Lemonsu,
Alexandre Leroux, Pierre Pellerin, Wei Yu, and Ayrton Zadra
Recherche en prévision numérique (RPN)
Division de la recherche en météorologie
Environnement Canada
Impact of Surface Processes on NWP:
Short-Range Regional Model (2001)
Near surface soil moisture
m3m-3
Precipitation bias (24-48h)
Old OP model
New surface
scheme with soil
moisture assimilation (OI)
(hPa) Temp.
Better soil moisture resulted in
significant improvements for:
(valid at 1200 UTC 22 October 2004)
(K)
Errors
Low-level air Temp. Errors
• Low-level air temp. and
humidity
RMS
• Diurnal cycle of the PBL
bias
RMS
• Precipitation biases
NOTE: mostly in summer
bias
(K)
48-h integrations
(hour)
(Bélair et al.)
Impact of Surface Processes on NWP:
Medium-Range Global Model (2006)
Near surface soil moisture
m3m-3
120-h, Europe
(valid at 0000 UTC 15 December 2001)
Precipitation Threat Score (Day 4)- SHEF
ISBA + soil moisture
Control
Has been implemented in the global forecasting
system (31 October 2006).
(Bélair et al.)
End-to-End Land Surface System
(under construction)
OBSERVATIONS
LAND SURFACE
DATABASES and
HIGH-RES ANALYSES
TRANSFER MODELS
SURFACE
FIELDS
GENERATOR
OFF-LINE LAND SURFACE
MODELING SYSTEM
(MEC)
GRID PARAMETERS
DOWNSCALING MODELS
LAND SURFACE
INITIAL CONDITIONS
LAND SURFACE
MODELS
(in-line or off-line)
3 compontents
• Fields generator
• Assimilation and analyses
• Models
LAND SURFACE
FORECASTS
or
BOUNDARY CONDITIONS
FOR ATMOSPHERIC AND
HYDROLOGY MODELS
DOWNSCALING MODELS
LAND SURFACE DATA ASSIMILATION SYSTEM
(CaLDAS)
OUTPUT
PROCESSOR
BEST
ESTIMATES
ATMOSPHERIC
FORCING
FORECASTS
MODELS or
CLIENTS
Land Surface Databases
Soil texture
STATSGO in the CONUS (~ 1 km)
AG-Can in Canada (variable resolution)
FAO global (~ 7-8 km)
Updated FAO global (~ 7-8 km)
Vegetation
USGS global (~ 1 km)
GLOBCOVER global (~ 300 m)
SAFORAH Canada (~ 25 m for forests)
Topography
USGS global (~ 1 km)
US Navy global (~ 1 km)
SRTM-DEM Canada (~ 30 m)
SRTM-DEM global (~ 90 m)
CDED Canada (~ 20 m)
USGS-NED US (~ 10 m)
Urban
Satellite ASTER and LANDSAT in-house algorithm
NTDB Canada (Vector data)
Italics = not yet available in Gen-Gen software
Optimal Interpolation Analysis of LAI
Using MODIS (Gu, Bélair, Mahfouf, Deblonde, 2006)
2
2
 LAI a  LAI o   LAI a  LAI b   LAI a  LAI c 
J ( LAI a )  
 
 

o
b
c

 
 

LAI a   o LAI o   c LAI c
 c2
αo =
 o2   c2 , and αc =
 o2
 o2   c2
2
Land cover databases do not provide
information on LAI (usually specified using a
look-up table). LAI is important for
evapotranspiration. Using the LAI analysis from
MODIS (or other instruments) could reduce an
important source of errors.
Land Surface Databases
Soil texture
STATSGO in the CONUS (~ 1 km)
AG-Can in Canada (variable resolution)
FAO global (~ 7-8 km)
Updated FAO global (~ 7-8 km)
Vegetation
USGS global (~ 1 km)
GLOBCOVER global (~ 300 m)
SAFORAH Canada (~ 25 m for forests)
Topography
USGS global (~ 1 km)
US Navy global (~ 1 km)
SRTM-DEM Canada (~ 30 m)
SRTM-DEM global (~ 90 m)
CDED Canada (~ 20 m)
USGS-NED US (~ 10 m)
Urban
Satellite ASTER and LANDSAT in-house algorithm
NTDB Canada (Vector data)
Italics = not yet available in Gen-Gen software
Orography and Urban Areas for the VO-2010
External Surface Modeling System
Topography and the Vancouver urban area
Topography from SRTM (30 m)
Urban LULC from Lemonsu et al. (2007)
Computational grid: 1400 x 1800 (100 m)
Example of Urban Covers: Montreal
(Vector database, 44 classes)
RPN’s Surface Modeling System
Water
Sea ice
Urban
Soil and
vegetation
Glaciers
Soils and vegetation
ISBA, CLASS, Force-Restore
Water
Simple scheme with constant surface
temperature (Lake model eventually)
Urban covers
TEB
Glaciers
Force-restore scheme (with snow), module
from CLASS
Sea ice
3-layer model with snow on top
Snow
Very simple scheme over glaciers and sea ice;
better in ISBA, CLASS and TEB, SNTHERM (IPY
project)
External Land Surface System:
Refining CMC’s Forecasts at the Surface
Global
(33 km)
MODEL OUTPUT
Regional
(15 km)
Local
(2.5 km)
Urban
(200 m)
1 day
MODEL OUTPUT
2 days
MODEL OUTPUT
Grid size
10 days
ATMOSPHERIC FORCING
External
Surface
Model
With horizontal
resolution as high as
that of surface
databases (e.g., 200 m)
10 days
Cost of the external surface modeling system is much less
than an integration of the full atmospheric model
External Surface Modeling System
ATMOSPHERIC FORCING from GEM (forecasts)
INITIAL SURFACE
CONDITIONS
Temperatures
Soil water content
Soil ice content
Snow characteristics
Urban surfaces wetness
Near-surface air characteristics (temperature, humidity, winds)
Surface pressure
Incident radiation (solar and infrared)
Precipitation (rain and snow)
Low res forcing
DOWNSCALING MODELS
High res forcing
EXTERNAL SURFACE MODEL
LAND SURFACE
CHARACTERISTICS
Topography
Roughness
Land/water fractions
Soil texture
Natural cover types
Urban cover types
Glaciers
OUTPUTS
All surface prognostic variables
Low-level air temperature and humidity
Low-level winds (from adaptation+roughness)
Surface fluxes (coupling with atmos models)
Surface runoff and drainage (coupling with hydro)
Snow in Mountains:
The Effect of Downscaling Air Temperature
Snow Water Equivalent
(kg/m2)
Orography
(GEM)
15 km
ALB
ALB
ALB
ALB
1 km
(MEC)
(Valid 0000 UTC 1 December 2006)
Urban Environment (UHI):
The Effect of High-Res Databases
OKC urban area
Near-surface air temperature
(K)
27-hr 250-m forecasts
of near-surface air
temperature over
Oklahoma City using
the Town Energy
Balance scheme,
MEC offline, valid at
0000 local time, 18
July 2003
(Lemonsu et al. 2007, in preparation)
Canadian Land Surface Data Assimilation
System (CaLDAS) – under construction
ATMOSPHERIC
FORCING
ANCILLARY DATA
Ancillary data
Vegetation types (USGS,
GLOBCOVER)
Soil texture (STATSGO,
FAO)
Water bodies
Cities (NLCD, CMC)
Topography (USGS,
SRTM, CDED, USGSNED)
–
–
–
–
–
Meteorological Analyses
- low-level winds, temps,
and humidity
Precipitation analyses
- meteorological forecasts
- Canadian Precipitation
Analysis (CaPA)
Radiation analyses
Best estimates from models and
assimilation cycles
Downscaling
Land schemes ISBA, CLASS
Regional - North America (1-2 km)
OFF-LINE SURFACE MODELING SYSTEM
Global (5-10 km)
Integrated on CMC's supercomputer
Transfer models (emission, backscatter, surface layer…)
Surface temperature
From remote sensing:
GOES, … (IR)
OBSERVATIONS
Vegetation
From remote sensing:
MODIS, AVHRR (NDVI)
Snow on ground
From surface obs and
remote-sensing:
SSM/I, MODIS (visible, MW)
Freeze/thaw
From remote-sensing:
QuikScat (Ku-band)
Hydros (L-band)
SSM/I (Microwave)
Soil Moisture
low-level air characteristics
IR heating rates
C-band radiances
L-band Tb and o (Hydros)
-
Variational Assimilation of Soil Moisture and
Surface Temperatures (Simplified 2D-Var)
15 July 2005
Cost function
J ( x) 




T
1
1
T
x  x b B 1 x  x b  y  H (x)  R 1 y  H (x) 
2
2
Linear hypothesis
H x   x  H x  H x
In this technique, the linear observation operator H
is evaluated using a finite difference approach,
from two perturbed model integrations.
Also, the minimum of J(x) is directly obtained from
 J ( x)  0
The analyzed state xa is thus given by:

x a  xb  K y  H (xb )

where K is the gain matrix:
K  B 1  HT R 1H  HT R 1
1
This formulation of the variational problem could be
easily converted to an Extended Kalman Filter
(Balsamo, Mahfouf, Bélair, Deblonde, 2006a, b)
1 October 2005
Satellite Observations of Soil Moisture
~40-50 km
spatial
resolution
hourly
L-band Tb
C-band Tb
6-hourly
IR Ts
T/H 2m
CMC’s Snow Analysis
Global Analysis (33 km)
Valid 5 December 2006
Strategy similar to CaPA
(statistical interpolation)
Background snow depth is given
by a simple off-line snow model
Reports from SYNOP and METAR
are used
(Brasnett)
When done on a 2.5-km grid, with
adaptation of temperature forcing for
the high-resolution orography, the
analysis seems more realistic in
mountainous regions
Surface Externalisée et la
Modélisation du Climat Régional
• La surface externalisée, à haute résolution, pourrait être
d’intérêt pour la modélisation du climat régional
• Plusieurs éléments météorologiques d’intérêts sont
associés à la surface (température de l’air, vent, phase
de la précipitation, couverture de neige, …)
• Première version du système de prévision sera avec
couplage 1-way, mais couplage 2-way est aussi possible
(déjà fait avec les modèles de glace marine)
• Caractéristiques de surface peuvent évoluer d’une
manière pronostique (e.g., modèle de végétation du
CCCma) ou de manière prescrite (prévision
d’urbanisation de la région Montréalaise).
Scope of the Project
Direct contributors: Bélair, Bilodeau, Charpentier, Fortin, Zadra, Chamberland, Yu, Doré,
Carrera, Lemay, Grunmann, Barscz, plus several PDFs soon coming on external funding.
Collaborators: Climate Research Division, National Water Research Institute, Canadian
Meteorological Centre – Ouranos ??
External funding: CRTI (CBRN Research and Technology Initiative), EPiCC
(Environmental Prediction in Canadian Cities), NAESI (National Agri-Environmental Standards
Initiative), GRIP (Government-Related Initiatives Program), IPY (International Polar Year,
TAWEPI and CFL), VO2010 (Vancouver Olympic Games of 2010)
Active projects: Surface geophysical fields, Canadian Precipitation Analysis (CaPA),
Canadian Land Data Assimilation System (CaLDAS), Canadian Land Surface Scheme
(CLASS), Atmospheric forcing and downscaling, Town Energy Balance, Momentum fluxes,
blowing snow, surface-atmosphere coupling, near-surface wind forecasting, operational
transfer of updates to ISBA
Objectives of the Land Surface Group/Project
• Improve the performance of existing and new
environmental prediction systems
(atmosphere, hydrology) by providing better
forcing data to them
• Improve analyses/forecasts of the land surface
state (soil wetness, snow on the ground) and
of near-surface atmospheric conditions
• Develop new products, such as snow
conditions (and avalanche?), conditions in
cities, blowing snow, low-level winds
Met-Surface:
Statistical Adaptation for Local Prediction
INITIALIZATION / ASSIMILATION
FORECAST
LAM1 km for day 1
Reg for day 2
Glb after
GEM
OBS
FORCING
Precip
Tair, qair
Wind
Cloud cover
OBS
SURFACE
VARIABLES
SCRIBE
Tsnow
Snow depth
Soil
moisture
Tsurf
LAND SURFACE MODEL + VAR ASSIMILATION
Assimilation window
UMOS
Radiation is calculated
from cloud cover and Tair
Precip
Tair, qair
Wind
Cloud cover
LAND SURFACE MODEL
Assimilation window
Forecast
Initial Conditions
Land Surface Models, Analyses, and
Assimilation in CMC’s Operational System
ANALYSES
TS
TS, ES, TP
For snow anal
Gaussian
1080x540
Gaussian
1080x540
TP
TM
TS, ES
Gaussian
1080x540
18 UTC
Reg-576x641
TS,ES
ASSIMILATION
SEQ. ASSIMILATION
SNOW
SD
Global 800x600
SEQ. ASSIMILATION
SD
Gaussian 1080x540
Regional 576x641
SD
SD
ISBA
fields
ISBA
fields
6-h
forecasts
ISBA
fields
SD
18-h
forecasts
PR
MODELS
ENSEMBLES
GLOBAL
REGIONAL
LOCAL
GEM and SEF
(ISBA, FR, glaciers, water)
GEM 800x600 uniform
(ISBA, glaciers, water)
GEM 576x641 variable
(ISBA, glaciers, water)
GEM-LAM East and West
(ISBA, glaciers, water)
GENESIS
DATABASES
Soil texture, orography,
vegetation, lakes, and glaciers
ISBA fields: Tsurf(1,2), Wsoil(1,2), wice, snow albedo, snow density, wsliq, wlveg
ISBA and
snow fields
Long-Term Vision for CMC’s Land Surface
Models, Analyses, and Assimilation
ANALYSES
TP
TS, ES, TP
Vegetation
TM-Lakes
GL-Lakes
External system’s
Grid (high-res)
OI
Satellite
High-res grid
Satellite
High-res grid
Satellite
CaPA
Model, surface
and satellite
data
TS,ES
ASSIMILATION
Canadian Land surface Data Assimilation System (CaLDAS)
2D-Var for soil moisture and surface temperature (screen-level + sat)
Snow mass and coverage (surface data + sat)
High-res global grids (same as external system)
Initial conditions
for land surface
schemes
LAND SURFACE MODELS
MESH
External Land Surface Modeling System (MEC – Environmental Community Model)
High-resolution grid over Canada (1 km or less) – Lower resolution grid over world (5 km or less)
(CLASS or ISBA, TEB, WATER - possibly LAKES, SNOW, GLACIERS, EOLE, blowing snow)
Forcings
2-way coupling
OTHER MODELS
ENSEMBLES
GLOBAL
REGIONAL
(FR, ISBA, CLASS,
WATER, GLACIERS)
DATABASES
Gen-Gen
Soil texture, orography,
vegetation, water bodies,
glaciers, and cities
LOCAL
HYDROLOGY