Large-scale snowpack estimation using ensemble data assimilation methodologies, satellite observations and synthetic datasets

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Title: Large-scale snowpack estimation using ensemble data assimilation methodologies, satellite observations and synthetic datasets
Author: Su, Hua
Abstract: This work focuses on a series of studies that contribute to the development and test of advanced large -scale snow data assimilation methodologies . Compared to the existing snow data assimilation methods and strategies , which are limited in the domain size and landscape coverage , the number of satellite sensors , and the accuracy and reliability of the product , the present work covers the continental domain , compares single - and multi -sensor data assimilations , and explores uncertainties in parameter and model structure . In the first study a continental -scale snow water equivalent (SWE ) data assimilation experiment is presented , which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS ) snow cover fraction (SCF ) data to Community Land Model (CLM ) estimates via the ensemble Kalman filter (EnKF ) . The greatest improvements of the EnKF approach are centered in the mountainous West , the northern Great Plains , and the west and east coast regions , with the magnitude of corrections (compared to the use of model only ) greater than one standard deviation (calculated from SWE climatology ) at given areas . Relatively poor performance of the EnKF , however , is found in the boreal forest region . In the second study , snowpack related parameter and model structure errors were explicitly considered through a group of synthetic EnKF simulations which integrate synthetic datasets with model estimates . The inclusion of a new parameter estimation scheme augments the EnKF performance , for example , increasing the Nash -Sutcliffe efficiency of season -long SWE estimates from 0 .22 (without parameter estimation ) to 0 .96 . In this study , the model structure error is found to significantly impact the robustness of parameter estimation . In the third study , a multi -sensor snow data assimilation system over North America was developed and evaluated . It integrates both Gravity Recovery and Climate Experiment (GRACE ) Terrestrial water storage (TWS ) and MODIS SCF information into CLM using the ensemble Kalman filter (EnKF ) and smoother (EnKS ) . This GRACE /MODIS data assimilation run achieves a significantly better performance over the MODIS only run in Saint Lawrence , Fraser , Mackenzie , Churchill & Nelson , and Yukon river basins . These improvements demonstrate the value of integrating complementary information for continental -scale snow estimation .
URI: http : / /hdl .handle .net /2152 /7679
Date: 2010-06-03

Citation

Large-scale snowpack estimation using ensemble data assimilation methodologies, satellite observations and synthetic datasets. Doctoral dissertation, The University of Texas at Austin. Available electronically from http : / /hdl .handle .net /2152 /7679 .

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