Author:
Larue Fanny,Royer Alain,De Sève Danielle,Roy Alexandre,Cosme Emmanuel
Abstract
Abstract. Over northeastern Canada, the amount of water stored in a
snowpack, estimated by its snow water equivalent (SWE) amount, is a key
variable for hydrological applications. The limited number of weather
stations driving snowpack models over large and remote northern areas
generates great uncertainty in SWE evolution. A data assimilation (DA) scheme
was developed to improve SWE estimates by updating meteorological forcing
data and snowpack states with passive microwave (PMW) satellite observations
and without using any surface-based data. In this DA experiment, a particle
filter with a Sequential Importance
Resampling algorithm (SIR) was applied and an
inflation technique of the observation error matrix was developed to avoid
ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2)
brightness temperature (TB) observations were assimilated into a
chain of models composed of the Crocus multilayer snowpack model and
radiative transfer models. The microwave snow emission model (Dense Media
Radiative Transfer – Multi-Layer model, DMRT-ML), the vegetation transmissivity
model (ω-τopt), and atmospheric and soil radiative
transfer models were calibrated to simulate the contributions from the
snowpack, the vegetation, and the soil, respectively, at the top of the
atmosphere. DA experiments were performed for 12 stations where daily
continuous SWE measurements were acquired over 4 winters (2012–2016). Best
SWE estimates are obtained with the assimilation of the TBs at
11, 19, and 37 GHz in vertical polarizations. The overall SWE bias is reduced
by 68 % compared to the original SWE simulations, from 23.7 kg m−2
without assimilation to 7.5 kg m−2 with the assimilation of the three
frequencies. The overall SWE relative percentage of error (RPE) is 14.1 %
(19 % without assimilation) for sites with a fraction of forest cover
below 75 %, which is in the range of accuracy needed for hydrological
applications. This research opens the way for global applications to improve
SWE estimates over large and remote areas, even when vegetation contributions
are up to 50 % of the PMW signal.
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference101 articles.
1. Andreadis, K. M. and Lettenmaier, D. P.: Implications of representing
snowpack stratigraphy for the assimilation of passive microwave satellite
observations, J. Hydrometeorology, 13, 1493–1506, https://doi.org/10.1175/JHM-D-11-056.1, 2012.
2. Arakawa, A.: Adjustment mechanisms in atmospheric motions, J. Meteor. Soc. Japan, Special issue of collected papers, 75, 155–179, 1997.
3. Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on
particle filters for online nonlinear/non-Gaussian Bayesian tracking,
IEEE T. Signal Proces., 50, 174–188, 2002.
4. Brankart, J.-M., E. Cosme, C.-E. Testut, P. Brasseur, and J. Verron: Efficient Adaptive Error Parameterizations for Square Root or Ensemble Kalman
Filters: Application to the Control of Ocean Mesoscale Signals, Mon. Weather Rev., 138, 932–950, 2010.
5. Brown, R. and Tapsoba, D.: Improved mapping of snow water equivalent over
Quebec, 64th Eastern Snow Conference, St. John's, Newfoundland, Canada, 2007.
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