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AGU Fall Meeting 2008 Data assimilation techniques Initial State Forecast Analysis Observation Time t1t2t3 Ensemble Kalman filter Error covariance computed from ensemble of states Multiscale Ensemble Kalman filter Approximates model covariances by tree structure Represents large-scale covariance through local relationships between child-parent nodes Consistent spatial localization Similar updating to EnKF
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AGU Fall Meeting 2008
Multi-scale assimilation of remotely Multi-scale assimilation of remotely sensed snow observations for sensed snow observations for
hydrologic estimationhydrologic estimation
Kostas Andreadis, and Dennis Lettenmaier
Civil and Environmental Engineering, University of Washington
AGU Fall Meeting 2008
Motivation Long-term global passive microwave and visible
wavelength dataset In-situ measurements unable to capture large-
scale variability Number of issues with observations and models
(errors and spatial scaling) Data assimilation not “black box” Two-fold goal of examining a novel data
assimilation technique and evaluating remotely sensed snow observations in such a system
AGU Fall Meeting 2008
Data assimilation techniques
InitialState
ForecastAnalysis
Observation Timet1 t2 t3
Ensemble Kalman filter Error covariance
computed from ensemble of states
Multiscale Ensemble Kalman filter
Approximates model covariances by tree structure Represents large-scale covariance through local
relationships between child-parent nodes Consistent spatial localization Similar updating to EnKF
AGU Fall Meeting 2008
Upper Colorado river basin Synthetic twin experiment (10/2001 to
4/2002) Nominal precipitation/air temperature
used to generate true SWE and SCE Synthetic satellite observations (visible
and microwave) generated from truth Resampled P/T from climatology used
to represent model uncertainties EnKF and EnMKF assimilation using
resampled forcings
Experimental design
AGU Fall Meeting 2008
Model descriptions Variable Infiltration Capacity
snow hydrology model Subgrid variability in
topography and land cover Predicted SWE and SCE Dense Media Radiative
Transfer passive microwave emission model
Predicted TB a function of depth, grain size, density, temperature
Pp
pp p
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pp p
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p
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pp
p
p
p
p
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pP
p
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PpPp
p
Tsang et al. (2000)
AGU Fall Meeting 2008
Constructing the tree... Tree must be constructed based on physical
constraints (e.g. physiography) Structure could be dynamic or static Start at coarsest scale (root node), with
branches being populated according to: Distance Elevation Forest cover
Zhou et al. (2008)
AGU Fall Meeting 2008
Assimilation of TB – SWE maps
SWE differences (in mm) from truth at update times for three simulations
Truth Truth-Openloop Truth-EnKF Truth-EnMKF
29 Dec 2001
16 Feb 2002
AGU Fall Meeting 2008
The forested pixel problem... Forest cover can “mask” microwave emission Difficult to extract SWE information because TB
innovations are small
SWE Correlation of forested pixels with
closest non-forested pixel
TB innovation (K) of forested pixels
(>10%)
SWE update (mm) of forested pixels
(>10%)
AGU Fall Meeting 2008
Assimilation of SCE/TB – SWE maps
SWE differences from truth at update times for three simulations
Truth Truth-Openloop Truth-EnKF Truth-EnMKF
29 Dec 2001
16 Feb 2002
AGU Fall Meeting 2008
Assimilation of SCE – SWE maps
SWE differences (in mm) from truth at update times for three simulations
Truth Truth-Openloop Truth-EnKF Truth-EnMKF
29 Dec 2001
16 Feb 2002
AGU Fall Meeting 2008
Assimilation of TB – TB maps
36.5 GHz (Vertical Pol.) TB (in K) at update times for four simulations
Truth Openloop EnKF EnMKF
29 Dec 2001
16 Feb 2002
AGU Fall Meeting 2008
SWE Time series RMSEs of 12.6, 10.3 mm for EnKF, EnMKF
respectively versus 35.1 mm for Open-loop
TruthOpen-loop
EnKF
EnMKF
AGU Fall Meeting 2008
Conclusions Novel data assimilation technique Small differences between EnKF and EnMKF
(perhaps due to problem scale) Satellite retrievals are problematic (e.g. forest
cover), but assimilation seems to overcome some of those problems when combined
Other types of measurements (active microwave, melt state)
Improved forward models (e.g. multi-layer snow and microwave emission models)