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Mesoscale Data AssimilationMesoscale Data Assimilationfor NACPfor NACP
Scott DenningDusanka Zupanski
Marek Uliasz
Evolving in-situ COEvolving in-situ CO22 Network Network
Figure 2: Continuous calibrated CO2 mixing ratio sites to be used for dataassimilation. Sites for which we currently have hourly data are in red. Sites forwhich we anticipate data soon are in blue.
Figure 3: Daily minimum CO2 mixing ratio for 6 observing sites in 2004.
• 13 sites in 2005, maybe 20 by 2007 (NOAA?)
• Big synoptic variations and spatial gradients!
COCO22 Modeling in GEOS4 Modeling in GEOS4• Offline coupling: SiB driven
by NDVI and GEOSsurface weather
• Hourly SiB fluxesprescribed as LBC
• Offline tracer transportusing PCTM driven byGEOS winds, convection,and turbulence
• Reasonable variability atseasonal, diurnal, andsynoptic time scales (r2 =.66)
340350360370380390
150 160 170 180
obs
sim
340350360370380390
180 190 200 210
340350360370380390
210 220 230 240
June 2004
July
August
1° x 1.25 ° grid
Measured NEE of COMeasured NEE of CO22 (WLEF) (WLEF)
• Coherent diurnal cycles, but …• Day-to-day variability of ~ factor of 2 due to passing
weather disturbances• How to specify temporal autocorrelation in inversions?
COCO22 Asssimilation Asssimilation
• Fine-scale variations (hourly, 20-km pixels)from weather forcing, NDVI as processedby forward model logic
• Multiplicative biases (10-day, 100-kmpixels) derived by inversion of hourly [CO2]
FCO2 (x, y,t) = !R (x, y)R(x, y,t) − !A (x, y)A(x, y,t))
Ck ,m = !R,i, jRi, j ,nCk ,m,i, j ,n* + !A,i, jAi, j ,nCk ,m,i, j ,n
*( )i, j ,n∑ ∆t f∆x∆y + CIN ,k ,m
where k = tower numberm = sampling timen = flux release timei, j = source grid cells
Data Assimilation Set-UpData Assimilation Set-UpDiscrete stochastic-dynamic model
Dusanka Zupanski, CIRA/[email protected]
Discrete stochastic observation model
111 )()( : −−− += kkkk wxGxMxM
w k-1 – model error (stochastic forcing)
M – non-linear dynamic (RAMS-SiB-BGC) model
G – model (matrix) reflecting the state dependence of model error
kkk xHy !+= )( :D
! k – measurement + representativeness error
H – non-linear observation operator (M M "" D D )
min]([]([21
][)(][21 11 =−−+−−= −−
obsT
obsbfT
b HHJ yxRyxxxxx ))P
(1) State estimate (optimal solution):
)()( 1bobs
TTba xyRPPxxx HHHH −++== −
(2) Estimate of the uncertainty of the solution:
TTaf GGQMMPP +=
Tji
jif MMMM )]()()][()([)( , xpxxpxP −+−+=ENSEMBLE KALMAN FILTER or EnsDA APPROACH
In EnsDA solution is defined in ensemble subspace (reduced rank problem) !
KALMAN FILTER APPROACH
MAXIMUM LIKELIHOOD ESTIMATE (VARIATIONAL APPROACH ):
MINIMUM VARIANCE ESTIMATE (KALMAN FILTER APPROACH ):
DATA ASSIMILATION EQUATIONS:DATA ASSIMILATION EQUATIONS:
Mesoscale Ensemble CDASMesoscale Ensemble CDAS(synthetic data experiment)(synthetic data experiment)
• SiB-RAMS weather, GPP,respiration, atmospheric[CO2]
• Sample modelatmosphere at 11 towerseach hour
• Estimate biases in GPPand resp every 10 days on100 km grid (5800 x3800 km domain)
• Smaller ensembles ~stable w/ covariancelocalization and5 ∆x smoothing
N=4408 N=100
$R N=4408 N=100$R
$GPP $GPP
Coupled Modeling and Assimilation SystemCoupled Modeling and Assimilation System
CSU RAMS
Radiation
Clouds
CO2 Transport and Mixing Ratio
Winds
Surface layerPrecipitationPBL
(T, q)
Eddy covariance H, LE, NEEObserved snow coverObserved soil moisture & T
Sat fire products
MODIS LAI/fPAR
MODIS land cover
Soil texture maps
LateralBoundaryConditions
Fossil fuel emissionsAir-sea gas flux
Biogeochemistry
Microbial poolsLitter pools
Slow soil C
RootsWoodLeaves
passive soil C
allocation autotrophic resp
heterotrophicresp
SiB3
Snow (0-5 layers)
Photosynthesis
Soil T & moisture (10 layers)
Canopy air spaceSfc TLeaf T
H LE NEE
CO2CO2
Ecosystem inventory data
Observed weatherObserved CO2 (timeseries, flasks, sat)Wind profilers, ceilometers
Input Data Evaluation Data
Ring of TowersRing of Towers
• inexpensiveinstrumentsdeployed onsix 75-mtowers in2004
• ~200 kmradius
• 1-minutedata May-August
Ring of Towers DataRing of Towers Datamid-day onlymid-day only June 9- July 5, 2004June 9- July 5, 2004
Mid-Day CO2: June 9 - July 5
350
355
360
365
370
375
380
385
1 3 5 7 9 11 13 15 17 19 21 23 25 27
brule
fence
redcliff
wittenberg
WLEF
LEF396
sylvania
5 ppm over 200 kmu ~ 10 m/s∆z ~ 1500 m~ 13 µmol m-2 s-1
Mid-Cont Intensive Plans?Mid-Cont Intensive Plans?
• Technical capability to do high-resolutionnested-grid simulations or assimmilationsfor the MCI– E.g., 10 km weather, NEE, CO2 over MCI domain
during growing season– cloud-resolving (1-km) for ~ a week
• We are not funded to do this, but willpropose it to DOE TCO
• Detailed comparison to towers, fluxaircraft, MINT airborne data
Footprints from RUC-13Footprints from RUC-13
% ∆x = 13 km,∆t = 1 hr
• Archiving u, v, w,cloud flux, TKEevery hour forwhole domain
• LPDM forNACP towers
• Infl functiongeneration viaweb interfaceavailable to all by2007
Process-Based Fossil Fuel EmissionsProcess-Based Fossil Fuel Emissions(K. Gurney, PI)(K. Gurney, PI)
• Combine inventory data and process attributes to constructdetailed space and time dependent emissions of fossil fuel CO2
and CO
• Database/model has three classes of inputs:– Point sources (e.g., power plants, smelters)– Mobile sources (vehicle emissions)– Area sources (e.g., residential sources)
• Resolution: 36 km, hourly downscale from roads, points, lights, pop’n?
• Optimize parameters in emissions model(s) using transport inRAMS and observed [CO]
Modules
Area source
Point source
Vehicle
Biogenic
nonroad