An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site

Open access


The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations.

Anderson, E.A, 1976. A point of energy and mass balance model of snow cover. NOAA Tech. Rep. NWS, 19, 150 p.

Andreadis, K.M., Lettenmaier, D.P., 2005. Assimilating remotely sensed snow observations into a macroscale hydrology model. Advances in Water Resources, 29.6, 872-886.

Avanzi, F., De Michele, C., Morin, S., Carmagnola, C.M., Ghezzi, A., Lejeune, Y., 2016. Model complexity and data requirements in snow hydrology: seeking a balance in practical applications. Hydrol. Process., 30, 2106-2118.

Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., Vitart, F., 2015. ERA-Interim/Land: a global land surface reanalysis data set. Hydrology and Earth System Sciences, 19, 1, 389-407.

Barnett, T.P., Adam, J.C., Lettenmaier, D.P., 2005. Potential impacts of a warming climate on water availability in snowdominated regions. Nature, 438, 7066, 303-309.

Bartelt, P., Lehning, M., 2002. A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model. Cold Regions Science and Technology, 35, 3, 123-145.

Boni, G., Castelli, F., Gabellani, S., Machiavello, G., Rudari, R., 2010. Assimilation of MODIS snow cover and real time snow depth point data in a snow dynamic model. In: Proc. Geoscience and Remote Sensing Symposium (IGARSS). IEEE International, pp. 1788-1791.

Boone, A., Etchevers, P., 2001. An intercomparison of three snow schemes of varying complexity coupled to the same land surface model: Local-scale evaluation at an Alpine site. Journal of Hydrometeorology, 2, 4, 374-394.

Boone, A., Habets, F., Noilhan, J., Clark, D., Dirmeyer, P., Fox, S., Gusev, Y., Haddeland, I., Koster, R., Lohmann, D., Mahanama, S., Mitchell, K., Nasonova, O., Niu, G.-Y., Pitman, A., Polcher,J., Shmakin, A. B., Tanaka, K., van den Hurk, B., Vérant, S., Verseghy, D., Viterbo, P., Yang, Z.-L., 2004. The Rhone-aggregation land surface scheme intercomparison project: An overview. Journal of Climate, 17, 1, 187-208.

Bowling, L.C., Lettenmaier, D.P., Nijssen, B., Graham, L.P., Clark, D.B., El Maayar, M., Essery, R., Goers, S., Gusev, Y.M., Habets, F., van den Hurk, B., Jin, J., Kahan, D., Lohmann, D., Ma, X., Mahanama, S., Mocko, D., Nasonova, O., Niu, G., Samuelsson, P., Shmakin, A.B., Takata, K., Verseghy, D., Viterbo, P., Xia, Y., Xue, Y., Tang, Z., 2003. Simulation of high-latitude hydrological processes in the Torne-Kalix basin: PILPS Phase 2 (e): 1: Experiment description and summary intercomparisons. Global and Planetary Change, 38, 1, 1-30.

Brasnett, B., 1999. A global analysis of snow depth for numerical weather prediction. Journal of Applied Meteorology, 38, 6, 726-740.

Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., Hahn, S., 2012. Assimilation of surface-and root-zone ASCAT soil moisture products into rainfall-runoff modeling. IEEE Transactions on Geoscience and Remote Sensing, 50, 7, 2542-2555.

Brun, E., Martin, Ε., Simon, V., Gendre, C., Coleou, C., 1989. An energy and mass model of snow cover suitable for operational avalanche forecasting. Journal of Glaciology, 35, 121, 333-342.

Caparrini, F., Castelli, F., Entekhabi, D., 2004. Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences. Journal of Hydrometeorology, 5, 1, 145-159.

Charrois, L., Cosme, E., Dumont, M., Lafaysse, M., Morin, S., Libois, Q., Picard, G., 2016. On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model. The Cryosphere, 10, 1021-1038.

Chen, F., Crow, W.T., Starks, P.J., Moriasi, D.N., 2011. Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture. Advances in Water Resources, 34, 4, 526-536.

Clark, M.P., Hay, L.E., 2004. Use of medium-range numerical weather prediction model output to produce forecasts of stream-flow. J. Hydrometeorol., 5, 15-32.

Clark, M.P., Slater, A.G., Barrett, A.P., Hay, L.E., McCabe, G.J., Rajagopalan, B., Leavesley, G.H., 2006. Assimilation of snow covered area information into hydrologic and landsurface models. Advances in Water Resources, 29, 8, 1209-1221.

Cressman, G.P., 1959. An operational objective analysis system. Mon. Wea. Rev., 87, 10, 367-374.

Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L. Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hòlm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.- J. , Park, B.-K., Peubey, C., deRosnay, P., Tavolato, C., Thépaut, J.-N., Vitart, F., 2011. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 656, 553-597.

De Lannoy, G.J.M., Reichle, R.H., Arsenault, K.R., Houser, P.R., Kumar, S., Verhoest, N.E.C., Pauwels, V.R.N., 2012. Multiscale assimilation of advanced microwave scanning radiometer- EOS snow water equivalent and moderate resolution imaging spectroradiometer snow cover fraction observations in northern Colorado. Water Resour. Res., 48, W01522. DOI: 10.1029/2011WR010588.

Dong, J., Walker, J.P., Houser, P.R., Sun, C., 2007. Scanning multichannel microwave radiometer snow water equivalent assimilation. Journal of Geophysical Research: Atmospheres, 112, D7.

Douville, H., Royer, J.-F., Mahfouf, J.-F., 1995. A new snow parameterization for the Meteo-France climate model Part I: validation in stand-alone experiments. Climate Dynamics, 12, 1, 21-35.

Drusch, M., Vasiljevic, D., Viterbo, P., 2004. ECMWF’s global snow analysis: assessment and revision based on satellite observations. J. Appl. Meteorol., 43, 1282-1294.

Dunne, S., Entekhabi, D., 2005. An ensemble‐based reanalysis approach to land data assimilation. Water Resour. Res., 41, 2.

Dunne, S., Entekhabi, D., 2006. Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plains 1997 field experiment. Water Resour. Res., 42, 1.

Durand, M., Margulis, S.A., 2006. Feasibility test of multifrequency radiometric data assimilation to estimate snow water equivalent. Journal of Hydrometeorology, 7, 3, 443-457.

Durand, M., Margulis, S.A., 2008. Effects of uncertainty magnitude and accuracy on assimilation of multiscale measurements for snowpack characterization. J. Geophys. Res. Atmos., 113, D02105.

Dutra, E., Balsamo, G., Viterbo, P., Miranda, P.M., Beljaars, A., Schär, C., Elder, K., 2010. An improved snow scheme for the ECMWF land surface model: description and offline validation. Journal of Hydrometeorology, 11, 4, 899-916.

Dutra, E., Viterbo, P., Miranda, P.M., Balsamo, G., 2012. Complexity of snow schemes in a climate model and its impact on surface energy and hydrology. Journal of Hydrometeorology, 13, 2, 521-538.

Endrizzi, S., Gruber, S., Dall'Amico, M., Rigon, R., 2014. GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects. Geoscientific Model Development, 7, 2831-2857.

Essery, R., Rutter, N., Pomeroy, J., Baxter, R., Stahli, M., Gustafsson, D., Barr, A., Bartlett, P., Elder, K., 2009. SNPWMIP2: An evaluation of forest snow process simulations. Bull. Amer. Met. Soc., 90, 1120-1135.

Essery, R., Morin, S., Lejeune, Y., Ménard, C.B., 2013. A comparison of 1701 snow models using observations from an Alpine site. Advances in Water Resources, 55, 131-148.

Etchevers, P., Martin, E., Brown, R., Fierz, C., Lejeune, Y., Bazile, E., Boone, A., Dai, Y.J., Essery, R., Fernandez, A., Gusev, Y., Jordan, R., Koren, V., Kowalczyk, E., Nasonova, N.O., Pyles, R.D., Schlosser, A., Shmakin, A.B., Smirnova, T.G., Strasser, U., Verseghy, D., Yamazaki, T., Yang, Z.L., 2003. Validation of the energy budget of an alpine snowpack simulated by several snow models (SnowMIP project). Annals of Glaciology, 38, 1, 150-158.

Evensen, G., 1994. Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99, C5, 10143-10162.

Evensen, G., 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53, 4, 343-367.

Filippa, G., Cremonese, E., Galvagno, M., Migliavacca, M., Di Cella, U.M., Petey, M., Siniscalco, C., 2015. Five years of phenological monitoring in a mountain grassland: interannual patterns and evaluation of the sampling protocol. International journal of biometeorology, 59, 12, 1927-1937.

Froidurot, S., Zin, I., Hingray, B., Gautheron, A., 2014. Sensitivity of precipitation phase over the Swiss Alps to different meteorological variables. Journal of Hydrometeorology, 15, 2, 685-696.

Galvagno, M., Wohlfahrt, G., Cremonese, E., Rossini, M., Colombo, R., Filippa, G., Julitta, T., Manca, G., Siniscalco, C., Migliavacca, M., Morra di Cella, U., 2013. Phenology and carbon dioxide source/sink strength of a subalpine grassland in response to an exceptionally short snow season. Environmental Research Letters, 8, 2, 025008.

Gelb, A., 1974. Optimal linear filtering, in Applied Optimal Estimation, edited by A. Gelb, pp. 102-155, MIT Press, Cambridge, Mass.

Griessinger, N., Seibert, J., Magnusson, J., Jonas, T., 2016. Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments. Hydrology and Earth System Sciences, 20, 9, 3895-3905.

Gupta, H.V., Kling, H., Yilmaz, K.K., Martinez, G.F., 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377, 1, 80-91.

Hedstrom, N.R., Pomeroy, J.W., 1998. Accumulation of intercepted snow in the boreal forest: measurements and modelling. Hydrological Processes, 12, 1611-1625.

Huang, C., Newman, A.J., Clark, M.P., Wood, A.W., Zheng, X., 2017. Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States. Hydrology and Earth System Sciences, 21 1, 635-650.

Jordan, R., 1991. A one-dimensional temperature model for a snow cover: Technical documentation for SNTHERM. 89 (No. CRREL-SR-91-16). Cold Regions Research and Engineering Lab Hanover, NH.

Jimenez-Munoz, J.C., Sobrino, J.A., 2008. Split-window coefficients for land surface temperature retrieval from lowresolution thermal infrared sensors. IEEE geoscience and remote sensing letters, 5, 4, 806-809.

Kojima, K., 1967. Densification of a seasonal snow cover, in Physics of Snow and Ice, Proc. Int. Conf. Low Temp. Sci., vol. I, part 2, S.929-S.952.

Lehning M., Bartelt, P.B., Brown, R.L., Fierz, C., Satyawali, P., 2002. A physical SNOWPACK model for the Swiss Avalanche Warning Services. Part II: Snow Microstructure. Cold regions science and technology, 35, 3, 147-167.

Liston, G.E., Sturm, M., 1998. A snow-transport model for complex terrain. Journal of Glaciology, 44, 148, 498-516.

Liston, G.E., Pielke, R.A., Greene, E.M., 1999. Improving firstorder snow‐related deficiencies in a regional climate model. Journal of Geophysical Research: Atmospheres, 104(D16), 19559-19567.

Liston, G.E., Hiemstra, C.A., 2008. A simple data assimilation system for complex snow distributions (SnowAssim). Journal of Hydrometeorology, 9, 5, 989-1004.

Magnusson, J., Farinotti, D., Jonas, T., and Bavay, M., 2011. Quantitative evaluation of different hydrological modelling approaches in a partly glacierized Swiss watershed. Hydrol. Process., 25, 2071-2084.

Magnusson, J., Gustafsson, D., Hüsler, F., Jonas, T., 2014. Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods. Water Resources Research, 50, 10, 7816-7835.

Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., Jonas, T., 2015. Evaluating snow models with varying process representations for hydrological applications: Snow model evaluation. Water Resour. Res., 51, 2707-2723.

Malik, M.J., van der Velde, R., Vekerdy, Z., Su, Z., 2012. Assimilation of satellite-observed snow albedo in a land surface model. Journal of hydrometeorology, 13, 3, 1119-1130.

Mellor, M., 1964. Properties of snow, Cold Reg. Sci. Eng. Monogr., III-A1.

Miller, R.N., Ghil, M., Gauthiez, F., 1994. Advanced data assimilation in strongly nonlinear dynamical systems. Journal of the atmospheric sciences, 51, 8, 1037-1056.

Montzka, C., Pauwels, V., Franssen, H.J.H., Han, X., Vereecken, H., 2012. Multivariate and multiscale data assimilation in terrestrial systems: A review. Sensors, 12, 12, 16291-16333.

Moradkhani, H., 2008. Hydrologic remote sensing and land surface data assimilation. Sensors, 8, 5, 2986-3004.

Nijssen, B., Bowling, L.C., Lettenmaier, D.P., Clark, D.B., El Maayar, M., Essery, R.,Goers, S., Gusev, Y.M., Habets, F., van den Hurk, B., Jin, J., Kahan, D., Lohmann, D., Ma, X., Mahanama, S., Mocko, D., Nasonova, O., Niu, G., Samuelsson, P., Shmakin, A.B., Takata, K., Verseghy, D., Viterbo, P., Xia, Y., Xue, Y., Yang, Z., 2003. Simulation of high latitude hydrological processes in the Torne-Kalix basin: PILPS Phase 2 (e): 2: Comparison of model results with observations. Global and Planetary Change, 38, 1, 31-53.

Pan, M., Sheffield, J., Wood, E.F., Mitchell, K.E., Houser, P.R., Schaake, J.C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R.W.,

Pinker, R.T., Tarpley, J.D., 2003. Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. Journal of Geophysical Research: Atmospheres, 108, D22.

Rodell, M., Houser, P.R., 2004. Updating a land surface model with MODIS-derived snow cover. Journal of Hydrometeorology, 5, 6, 1064-1075.

Rutter, N., Essery, R., Pomeroy, J., Altimir, N., Andreadis, K., Baker, I., Barr, A., Bartlett, P., Boone, A., Deng, H., Douville, H., Dutra, E., Elder, K., Ellis, C., Feng, X., Gelfan, A., Goodbody, A., Gusev, Y., Gustafsson, D., Hellström, R., Hirabayashi, Y., Hirota, T., Jonas, T., Koren, V., Kuragina, A., Lettenmaier, D., Li, W.-P., Luce, C., Martin, E., Nasonova, O., Pumpanen, J., Pyles, R.D., Samuelsson, P., Sandells, M., Schädler, G., Shmakin, A., Smirnova, T.G., 27, Stähli, M., Stöckli, R., Strasser, U., Su, H., Suzuki, K., Takata, K., Tanaka, K., Thompson, E., Vesala, T., Viterbo, P., Wiltshire, A., Xia, K., Xue, Y., Yamazaki, T., 2009. Evaluation of forest snow processes models (SnowMIP2). Journal of Geophysical Research: Atmospheres, 114, D6.

Schlosser, C.A., Slater, A.G., Robock, A., Pitman, A.J., Vinnikov, K.Y., Henderson-Sellers, A., Speranskaya, N.A., Mitchell, K., and the PILPS2 contributors, 2000. Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS Phase 2 (d). Monthly Weather Review, 128, 2, 301-321.

Slater, A.G., Pitman, A.J., Desborough, C.E., 1998. The validation of a snow parameterization designed for use in general circulation models. International journal of climatology, 18, 6, 595-617.

Slater, A.G., Schlosser, C.A., Desborough, C.E., Pitman, A.J., Henderson-Sellers, A., Robock, A., Vinnikov, K.Y., Mitchell, K., Boone, A., Braden, H., Chen, F., Cox, P.M., de Rosnay, P., Dickinson, R.E., Dai,Y.-J., Duan ,Q., Entin, J., Etchevers, P., Gedney, N., Gusev, Y.M., Habets, F., Kim, J., Koren, V., Kowalczyk, E.A., Nasonova, O.N., Noilhan, J., Schaake, S., Shmakin, A.B., Smirnova, T.G., Verseghy, D., Wetzel, P., Xue, Y., Yang, Z.-L., Zeng, Q., 2001. The representation of snow in land surface schemes: Results from PILPS 2 (d). Journal of Hydrometeorology, 2, 1, 7-25.

Slater, A.G., Clark, M.P., 2006. Snow data assimilation via an ensemble Kalman filter. Journal of Hydrometeorology, 7, 3, 478-493.

Stauffer, D.R., Seaman, N.L., 1990. Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Monthly Weather Review, 118, 6, 1250-1277.

Stigter, E.E., Wanders, N., Saloranta, T.M., Shea, J.M., Bierkens, M.F.P., Immerzeel, W.W., 2017. Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment, Cryosph., 1647-1664.

Su, H., Yang, Z.L., Niu, G.Y., Dickinson, R.E., 2008. Enhancing the estimation of continental‐scale snow water equivalent by assimilating MODIS snow cover with the ensemble Kalman filter. Journal of Geophysical Research: Atmospheres, 113, D8.

Su, H., Yang, Z.L., Dickinson, R.E., Wilson, C.R., Niu, G.Y., 2010. Multisensor snow data assimilation at the continental scale: The value of gravity recovery and climate experiment terrestrial water storage information. Journal of Geophysical Research: Atmospheres, 115, D10.

Sun, C., Walker, J.P., Houser, P.R., 2004. A methodology for snow data assimilation in a land surface model. Journal of Geophysical Research: Atmospheres, 109, D8.

Verseghy, D.L., 1991. CLASS-A Canadian land surface scheme for GCMs. I. Soil model. International Journal of Climatology, 11, 2, 111-133.

Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., Willemet, J.M., 2012. The detailed snowpack scheme Crocus and its implementation in SURFEX v7. 2. Geoscientific Model Development, 5, 773-791.

Wächter, A., Biegler, L.T., 2006. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 106, 1, 25-57.

Winstral, A., Marks, D., 2014. Long‐term snow distribution observations in a mountain catchment: Assessing variability, time stability, and the representativeness of an index site. Water Resources Research, 50, 1, 293-305.

Wiscombe, W.J., Warren, S.G., 1980. A model for the spectral albedo of snow. I: Pure snow. Journal of the Atmospheric Sciences, 37, 12, 2712-2733.

Wood, A.W., Hopson, T., Newman, A., Brekke, L., Arnold, J., Clark, M., 2016. Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology, 17, 2, 651-668.

Zappa, M., Pos, F., Strasser, U., Warmerdam, P., Gurtz, J., 2003. Seasonal water balance of an Alpine catchment as evaluated by different methods for spatially distributed snowmelt modelling. Hydrology Research, 34, 3, 179-202.

Zhang, T., 2005. Influence of the seasonal snow cover on the ground thermal regime: An overview. Reviews of Geophysics, 43, 4.

Yang, Z.L., Dickinson, R.E., Robock, A., Vinnikov, K.Y., 1997. Validation of the snow submodel of the biosphere- atmosphere transfer scheme with Russian snow cover and meteorological observational data. Journal of Climate, 10, 2, 353-373.

Journal of Hydrology and Hydromechanics

The Journal of Institute of Hydrology SAS Bratislava and Institute of Hydrodynamics CAS Prague

Journal Information

IMPACT FACTOR 2018: 2,023
5-year IMPACT FACTOR: 2,048

CiteScore 2018: 2.07

SCImago Journal Rank (SJR) 2018: 0.713
Source Normalized Impact per Paper (SNIP) 2018: 1.228


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 5056 5056 28
PDF Downloads 302 302 24