Advanced

Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods

Magnusson, Jan; Gustafsson, David; Hüsler, Fabia and Jonas, Tobias (2014) In Water Resources Research 50(10). p.7816-7835
Abstract
In alpine and high-latitude regions, water resource decision making often requires large-scale estimates of snow amounts and melt rates. Such estimates are available through distributed snow models which in some situations can be improved by assimilation of remote sensing observations. However, in regions with frequent cloud cover, complex topography, or large snow amounts satellite observations may feature information of limited quality. In this study, we examine whether assimilation of snow water equivalent (SWE) data from ground observations can improve model simulations in a region largely lacking reliable remote sensing observations. We combine the model output with the point data using three-dimensional sequential data assimilation... (More)
In alpine and high-latitude regions, water resource decision making often requires large-scale estimates of snow amounts and melt rates. Such estimates are available through distributed snow models which in some situations can be improved by assimilation of remote sensing observations. However, in regions with frequent cloud cover, complex topography, or large snow amounts satellite observations may feature information of limited quality. In this study, we examine whether assimilation of snow water equivalent (SWE) data from ground observations can improve model simulations in a region largely lacking reliable remote sensing observations. We combine the model output with the point data using three-dimensional sequential data assimilation methods, the ensemble Kalman filter, and statistical interpolation. The filter performance was assessed by comparing the simulation results against observed SWE and snow-covered fraction. We find that a method which assimilates fluxes (snowfall and melt rates computed from SWE) showed higher model performance than a control simulation not utilizing the filter algorithms. However, an alternative approach for updating the model results using the SWE data directly did not show a significantly higher performance than the control simulation. The results show that three-dimensional data assimilation methods can be useful for transferring information from point snow observations to the distributed snow model. Key Points Evaluating methods for assimilating snow observations into distributed models Assimilation can improve model skill also at locations without observations Assimilation of fluxes appears more successful than assimilation of states (Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
distributed snow modeling, data assimilation, ensemble Kalman filter, optimal interpolation, point observations
in
Water Resources Research
volume
50
issue
10
pages
7816 - 7835
publisher
American Geophysical Union
external identifiers
  • Scopus:84911383648
ISSN
0043-1397
DOI
10.1002/2014WR015302
project
MERGE
language
English
LU publication?
no
id
bcf5ead3-e0be-45ed-90a1-891d964d18df (old id 7515579)
date added to LUP
2015-07-08 14:46:38
date last changed
2016-11-27 04:28:13
@misc{bcf5ead3-e0be-45ed-90a1-891d964d18df,
  abstract     = {In alpine and high-latitude regions, water resource decision making often requires large-scale estimates of snow amounts and melt rates. Such estimates are available through distributed snow models which in some situations can be improved by assimilation of remote sensing observations. However, in regions with frequent cloud cover, complex topography, or large snow amounts satellite observations may feature information of limited quality. In this study, we examine whether assimilation of snow water equivalent (SWE) data from ground observations can improve model simulations in a region largely lacking reliable remote sensing observations. We combine the model output with the point data using three-dimensional sequential data assimilation methods, the ensemble Kalman filter, and statistical interpolation. The filter performance was assessed by comparing the simulation results against observed SWE and snow-covered fraction. We find that a method which assimilates fluxes (snowfall and melt rates computed from SWE) showed higher model performance than a control simulation not utilizing the filter algorithms. However, an alternative approach for updating the model results using the SWE data directly did not show a significantly higher performance than the control simulation. The results show that three-dimensional data assimilation methods can be useful for transferring information from point snow observations to the distributed snow model. Key Points Evaluating methods for assimilating snow observations into distributed models Assimilation can improve model skill also at locations without observations Assimilation of fluxes appears more successful than assimilation of states},
  author       = {Magnusson, Jan and Gustafsson, David and Hüsler, Fabia and Jonas, Tobias},
  issn         = {0043-1397},
  keyword      = {distributed snow modeling,data assimilation,ensemble Kalman filter,optimal interpolation,point observations},
  language     = {eng},
  number       = {10},
  pages        = {7816--7835},
  publisher    = {ARRAY(0x7bd65a0)},
  series       = {Water Resources Research},
  title        = {Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods},
  url          = {http://dx.doi.org/10.1002/2014WR015302},
  volume       = {50},
  year         = {2014},
}