agu fall meeting 2008 multi-scale assimilation of remotely sensed snow observations for hydrologic...

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


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


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


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


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

pp

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p

p

p

p

pp

p

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p

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pP

p

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PpPp

p

Tsang et al. (2000)


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)


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


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%)


Assimilation of SCE/TB – SWE maps

SWE differences from truth at update times for three simulations


29 Dec 2001

16 Feb 2002


Assimilation of SCE – SWE maps

SWE differences (in mm) from truth at update times for three simulations


29 Dec 2001

16 Feb 2002


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


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


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)

agu fall meeting 2008 multi-scale assimilation of remotely sensed snow observations for hydrologic...

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