SystSystème de modélisation et d’assimilation de ème de modélisation et d’assimilation de surface à Environnement Canadasurface 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 météorologieEnvironnement Canada

Near surface soil moisture

(valid at 1200 UTC 22 October 2004)

m3m-3

Better soil moisture resulted in significant improvements for:

Precipitation bias (24-48h)

Low-level air Temp. Errors

bias

RMSbias

RMS

(K)

(hour)

(hPa)

(K)

Temp. Errors

• Low-level air temp. and humidity

• Diurnal cycle of the PBL

• Precipitation biases

Old OP model

New surfacescheme with soilmoisture assimilation (OI)

NOTE: mostly in summer

48-h integrations

Impact of Surface Processes on NWP: Impact of Surface Processes on NWP: Short-Range Regional Model (2001)Short-Range Regional Model (2001)

(Bélair et al.)

m3m-3

(valid at 0000 UTC 15 December 2001)

Near surface soil moisture

Has been implemented in the global forecasting system (31 October 2006).

ISBA + soil moisture

Control

Precipitation Threat Score (Day 4)- SHEF

120-h, Europe

Impact of Surface Processes on NWP: Impact of Surface Processes on NWP: Medium-Range Global Model (2006)Medium-Range Global Model (2006)

(Bélair et al.)

End-to-End Land Surface SystemEnd-to-End Land Surface System(under construction)(under construction)

LAND SURFACE DATABASES and HIGH-RES ANALYSES

GRID PARAMETERS

SURFACE FIELDSGENERATOR

LAND SURFACE DATA ASSIMILATION SYSTEM(CaLDAS)

OFF-LINE LAND SURFACEMODELING SYSTEM

(MEC)

LAND SURFACEMODELS

(in-line or off-line)

OBSERVATIONS

TRANSFER MODELS

ATMOSPHERICFORCING

DO

WN

SC

AL

ING

MO

DE

LS

DO

WN

SC

AL

ING

MO

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LS

LAND SURFACEINITIAL CONDITIONS

OUTPUTPROCESSOR MODELS or

CLIENTS

LAND SURFACEFORECASTS

orBOUNDARY CONDITIONSFOR ATMOSPHERIC AND

HYDROLOGY MODELS

3 compontents

• Fields generator• Assimilation and analyses• Models

FORECASTS

BESTESTIMATES

Land Surface DatabasesLand 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 LAIOptimal Interpolation Analysis of LAIUsing MODIS (Using MODIS (Gu, Bélair, Mahfouf, Deblonde, 2006Gu, Bélair, Mahfouf, Deblonde, 2006))

222

)(

c

ca

b

ba

o

oaa

LAILAILAILAILAILAILAIJ

ccooa LAILAILAI

22

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, and αc = 22

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o

αo =

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 DatabasesLand 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 Orography and Urban Areas for the VO-2010 External Surface Modeling System External Surface Modeling System

Topography and the Vancouver urban area

Computational grid: 1400 x 1800 (100 m)

Topography from SRTM (30 m)

Urban LULC from Lemonsu et al. (2007)

Example of Urban CoversExample of Urban Covers: Montreal : Montreal (Vector database, 44 classes)(Vector database, 44 classes)

RPN’s Surface Modeling SystemRPN’s Surface Modeling System

Sea ice

Soil and vegetation

Glaciers

Water

Urban

Soils and vegetation

Water

Urban covers

Glaciers

Sea ice

Snow

ISBA, CLASS, Force-Restore

Simple scheme with constant surface temperature (Lake model eventually)

TEB

Force-restore scheme (with snow), module from CLASS

3-layer model with snow on top

Very simple scheme over glaciers and sea ice; better in ISBA, CLASS and TEB, SNTHERM (IPY project)

External Land Surface System:External Land Surface System:Refining CMC’s Forecasts at the SurfaceRefining CMC’s Forecasts at the Surface

Grid

siz

e

Urban(200 m)

Local(2.5 km)

Regional(15 km)

Global(33 km)

1 day

2 days

10 days

ExternalSurfaceModel

With horizontal resolution as high as that of surface databases (e.g., 200 m)

MO

DE

L O

UT

PU

T

MO

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L O

UT

PU

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PU

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ATMOSPHERIC FORCING

10 days

Cost of the external surface modeling system is much less than an integration of the full atmospheric model

External Surface Modeling SystemExternal Surface Modeling System

TemperaturesSoil water contentSoil ice contentSnow characteristicsUrban surfaces wetness

INITIAL SURFACECONDITIONS

Near-surface air characteristics (temperature, humidity, winds)Surface pressureIncident radiation (solar and infrared)Precipitation (rain and snow)

ATMOSPHERIC FORCING from GEM (forecasts)

LAND SURFACECHARACTERISTICS

DOWNSCALING MODELS

EXTERNAL SURFACE MODEL

TopographyRoughnessLand/water fractionsSoil textureNatural cover typesUrban cover typesGlaciers

OUTPUTS

All surface prognostic variablesLow-level air temperature and humidityLow-level winds (from adaptation+roughness)Surface fluxes (coupling with atmos models)Surface runoff and drainage (coupling with hydro)

Low res forcing

High res forcing

Snow in Mountains:Snow in Mountains:The Effect of Downscaling Air TemperatureThe Effect of Downscaling Air Temperature

15 km

1 km

Orography

Snow Water Equivalent(kg/m2)

(GEM)

(MEC)

ALB

ALB

ALB

ALB

(Valid 0000 UTC 1 December 2006)

Urban Environment (UHI):Urban Environment (UHI):The Effect of High-Res DatabasesThe Effect of High-Res Databases

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

OKC urban area Near-surface air temperature

(K)

(Lemonsu et al. 2007, in preparation)

Ancillary data– Vegetation types (USGS,

GLOBCOVER)– Soil texture (STATSGO,

FAO)– Water bodies– Cities (NLCD, CMC)– Topography (USGS,

SRTM, CDED, USGS-NED)

ANCILLARY DATA

OFF-LINE SURFACE MODELING SYSTEM

Land schemes ISBA, CLASSRegional - North America (1-2 km)Global (5-10 km)Integrated on CMC's supercomputer

Precipitation analyses - meteorological forecasts - Canadian Precipitation

Analysis (CaPA)

Radiation analysesBest estimates from models and

assimilation cycles

ATMOSPHERIC FORCING

Soil Moisture- low-level air characteristics- IR heating rates-C-band radiances

-L-band Tb and o (Hydros)

OBSERVATIONS

Vegetation From remote sensing:MODIS, AVHRR (NDVI)

Snow on groundFrom surface obs andremote-sensing:SSM/I, MODIS (visible, MW)

Freeze/thawFrom remote-sensing:QuikScat (Ku-band)Hydros (L-band)SSM/I (Microwave)

Transfer models (emission, backscatter, surface layer…)

Surface temperature From remote sensing:GOES, … (IR)

Canadian Land Surface Data Assimilation Canadian Land Surface Data Assimilation System (CaLDAS) – under constructionSystem (CaLDAS) – under construction

Downscaling

Meteorological Analyses - low-level winds, temps, and humidity

Variational Assimilation of Soil Moisture and Variational Assimilation of Soil Moisture and Surface Temperatures Surface Temperatures (Simplified 2D-Var)(Simplified 2D-Var)

Cost function

)()(2

1

2

1)( 11 xyRxyxxBxxx HHJ TbTb

Linear hypothesis

xHxxx HH

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

0)( xJ

The analyzed state xa is thus given by:

)( bba H xyKxx

where K is the gain matrix:

1111 RHHRHBK TT

This formulation of the variational problem could be easily converted to an Extended Kalman Filter

(Balsamo, Mahfouf, Bélair, Deblonde, 2006a, b)

15 July 2005

1 October 2005

Satellite Observations of Soil MoistureSatellite Observations of Soil Moisture

L-band TbL-band Tb C-band TbC-band Tb IR TsIR Ts T/H 2mT/H 2m

hourly 6-hourly

~40-50 km spatial resolution

CMC’s Snow AnalysisCMC’s Snow Analysis

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

Global Analysis (33 km)

Valid 5 December 2006

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

(Brasnett)

Surface Externalisée et la Surface Externalisée et la Modélisation du Climat RégionalModélisation 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 ProjectScope 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/ProjectObjectives 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

INITIALIZATION / ASSIMILATION FORECAST

OBSFORCING

OBSSURFACE

VARIABLES

PrecipTair, qair

WindCloud cover

TsnowSnow depthSoil moistureTsurf

Assimilation window Assimilation window Forecast

GEM

UMOS

SCRIBE

LAM1 km for day 1Reg for day 2Glb after

PrecipTair, qairWindCloud cover

Radiation is calculated from cloud cover and Tair

LAND SURFACE MODEL + VAR ASSIMILATION LAND SURFACE MODEL

Initial Conditions

Met-Surface:Met-Surface:Statistical Adaptation for Local PredictionStatistical Adaptation for Local Prediction

Land Surface Models, Analyses, and Land Surface Models, Analyses, and Assimilation in CMC’s Operational SystemAssimilation in CMC’s Operational System

ENSEMBLESGEM and SEF

(ISBA, FR, glaciers, water)

GLOBALGEM 800x600 uniform(ISBA, glaciers, water)

REGIONALGEM 576x641 variable(ISBA, glaciers, water)

LOCALGEM-LAM East and West

(ISBA, glaciers, water)

GENESISSoil texture, orography,

vegetation, lakes, and glaciers

TMGaussian1080x540

TSFor snow anal

Gaussian1080x540

TS, ES, TPGaussian1080x540

TS, ES18 UTC

Reg-576x641

SEQ. ASSIMILATIONGlobal 800x600

SEQ. ASSIMILATIONRegional 576x641

SNOWGaussian 1080x540

ISBAfields ISBA

fields

ISBAfields

SD SD

SD

SD

SD

6-hforecasts

18-hforecasts

ISBA andsnow fields

ISBA fields: Tsurf(1,2), Wsoil(1,2), wice, snow albedo, snow density, wsliq, wlveg

PR

ANALYSES

ASSIMILATION

MODELS

DATABASES

TP

TS,ES

Long-Term Vision for CMC’s Land Surface Long-Term Vision for CMC’s Land Surface Models, Analyses, and AssimilationModels, Analyses, and Assimilation

ENSEMBLES(FR, ISBA, CLASS,

WATER, GLACIERS)

GLOBAL REGIONAL LOCAL

Gen-GenSoil texture, orography,

vegetation, water bodies, glaciers, and cities

TM-LakesHigh-res grid

Satellite

TS, ES, TPExternal system’s

Grid (high-res)

ANALYSES

ASSIMILATION

OTHER MODELS

DATABASES

VegetationOI

Satellite

GL-LakesHigh-res grid

Satellite

Canadian Land surface Data Assimilation System (CaLDAS)

High-res global grids (same as external system)2D-Var for soil moisture and surface temperature (screen-level + sat)Snow mass and coverage (surface data + sat)

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)

LAND SURFACE MODELS

CaPAModel, surface

and satellitedata

HYDROLOGY

MESH

Initial conditions for land surface schemes

2-way coupling Forcings

TP

TS,ES