Brown, R. D. and Brasnett, B.: Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/W9FOYWH0EQZ3, 2010.
Brown, R. D., Brasnett, B., and Robinson, D.: Gridded North American monthly snow depth and snow water equivalent for GCM evaluation, Atmos. Ocean, 41, 1–14, https://doi.org/10.3137/ao.410101, 2003.
Broxton, P. D., Zeng, X., and Dawson, N.: The impact of a low bias in snow water equivalent initialization on CFS seasonal forecasts, J. Climate, 30, 8657–8671, https://doi.org/10.1175/JCLI-D-17-0072.1, 2017.
Burke, E. J., Dankers, R., Jones, C. D., and Wiltshire, A. J.: A retrospective analysis of pan Arctic permafrost using the JULES land surface model, Clim. Dynam., 41, 1025–1038, https://doi.org/10.1007/s00382-012-1648-x, 2013.
Chen, M., Wang, W., and Kumar, A.: Prediction of monthly-mean temperature: The roles of atmospheric and land initial conditions and sea surface temperature, J. Climate, 23, 717–725, 2010.
Cho, E., Tuttle, S. E., and Jacobs, J. M.: Evaluating consistency of snow water equivalent retrievals from passiv
e microwave sensors over the north central US: SSM/I vs. SSMIS and AMSR-E vs. AMSR2, Remote Sens., 9, 465, https://doi.org/10.3390/rs9050465, 2017.
Cohen, J., Barlow, M., Kushner, P. J., and Saito, K.: Stratosphere–troposphere coupling and links with eurasian land surface variability, J. Climate, 20, 5335–5343, https://doi.org/10.1175/2007jcli1725.1, 2007.
Collow, A. B. M., Thomas, N. P., Bosilovich, M. G., Lim, Y. K., Schubert, S. D., and Koster, R. D.: Seasonal variability in the mechanisms behind the 2020 Siberian heatwaves, J. Climate, 35, 3075–3090, 2022.
Dawson, N., Broxton, P., and Zeng, X.: Evaluation of remotely sensed snow water equivalent and snow cover extent over the contiguous United States, J. Hydrometeorol., 19, 1777–1791, https://doi.org/10.1175/JHM-D-18-0007.1, 2018.
De Angelis, A. M., Schubert, S. D., Chang, Y., Lim, Y. K., Koster, R. D., Wang, H., and Marquardt Collow, A. B.: Dynamical Drivers of the Exceptional Warmth over Siberia during the Spring of 2020, J. Climate, 36, 4837–4861, 2023.
De Lannoy, G. J. M., Reichle, R. H., Houser, P. R., Arsenault, K. R., Verhoest, N. E. C., and Pauwels, V. R. N.: Satellite-scale snow water equivalent assimilation into a high-resolution land surface model, J. Hydrometeorol., 11, 352–369, https://doi.org/10.1175/2009JHM1192.1, 2010.
De Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J., and Isaksen, L.: Initialisation of land surface variables for numerical weather prediction, Surv. Geophys., 35, 607–621, 2014.
Dirmeyer, P. A.: The terrestrial segment of soil moisture–climate coupling, Geophys. Res. Lett., 38, L16702, https://doi.org/10.1029/2011GL048268, 2011. 2011.
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: The Second Global Soil Wetness Project (GSWP-2): Multi-model analysis and implications for our perception of the land surface, B. Am. Meteorol. Soc., 87, 1381–1397, 2006.
Dutra, E., Schär, C., Viterbo, P., and Miranda, P. M. A.: Land-atmosphere coupling associated with snow cover, Geophys. Res. Lett., 38, L15707, https://doi.org/10.1029/2011GL048435, 2011.
Dziubanski, D. J. and Franz, K. J.: Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model, J. Hydrol., 540, 26–39, https://doi.org/10.1016/j.jhydrol.2016.05.046, 2016.
Essery, R. L. H., Rutter, N., Pomeroy, J., Baxter, R., Stahli, M., Gustafsson, D., Barr, A., Bartlett, P., and Elder, K.: SNOWMIP2: an evaluation of forest snow process simulations, B. Am. Meteorol. Soc., 90, 1120–1135, 2009.
Foster, J. L., Sun, C., Walker, J. P., Kelly, R., Chang, A., Dong, J., and Powell, H.: Quantifying the uncertainty in passive microwave snow water equivalent observations, Remote Sens. Environ., 94, 187–203, 2005.
Gan, Y., Zhang, Y., Kongoli, C., Grassotti, C., Liu, Y., Lee, Y. K., and Seo, D. J.: Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States, Remote Sens. Environ., 254, 112280, https://doi.org/10.1016/j.rse.2020.112280, 2021.
Girotto, M., Musselman, K. N., and Essery, R. L. H.: Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow, Current Climate Change Reports, 6, 81–94, https://doi.org/10.1007/s40641-020-00159-7, 2020.
Gloege, L., Kornhuber, K., Skulovich, O., Pal, I., Zhou, S., Ciais, P., and Gentine, P.: Land-Atmosphere Cascade Fueled the 2020 Siberian Heatwave, AGU Advances, 3, e2021AV000619, https://doi.org/10.1029/2021AV000619, 2022.
Helfrich, S. R., McNamara, D., Ramsay, B. H., Baldwin, T., and Kasheta, T.: Enhancements to, and forthcoming developments in the interactive multisensor snow and ice mapping system (IMS), Hydrol. Process., 21, 1576–1586, https:// doi.org/10.1002/hyp.6720, 2007.
Helmert, J., Şensoy Şorman, A., Montero, R. A., De Michele, C., De Rosnay, P., Dumont, M., Finger, D., Lange, M., Picard, G., Potopová, V., Nitu, R., Roubin, R., Trofaier, A., Zehnter, H., Schöber, J., and Zappa, M.: Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey, Geoscience, 8, 489, https://doi.org/10.3390/geosciences8120489, 2018.
Imaoka, K.: AMSR2 Unified Level-3 SWE Data, NSIDC, NASA National Snow and Ice Data Center, Boulder, Colorado, USA [data set],
https://n5eil01u.ecs.nsidc.org/AMSA/AU_DySno.001/ (last access: 20 November 2024), 2010.
Imaoka, K., Kachi, M., Kasahara, M., Ito, N., Nakagawa, K., and Oki, T.: Instrument performance and calibration of AMSR-E and AMSR2, Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci., 38, 13–16, 2010.
Jeong, J. H., Linderholm, H. W., Woo, S. H., Folland, C., Kim, B. M., Kim, S. J., and Chen, D.: Impacts of snow initialization on subseasonal forecasts of surface air temperature for the cold season, J. Climate, 26, 1956–1972, 2013.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., and Endo, H.: Th
e JRA-55 reanalysis: general specifications and basic characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, 2015.
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., Gordon, C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C. H., Malyshev, S., McAvaney, B., Mitchell, K., Mocko, D., Oki, T., Oleson, K., Pitman, A., Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., and Yamada, T.: Regions of strong coupling between soil moisture and precipitation., Science, 305, 1138–1140, https://doi.org/10.1126/science.1100217, 2004.
Koster, R. D., Mahanama, S., Yamada, T., Balsamo, G., Berg, A., Boisserie, M., Dirmeyer, P., Doblas-Reyes, F., Drewitt, G., and Gordon, C.: The second phase of the global land–atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill, J. Hydrometeorol., 12, 805–822, 2011.
Kumar, S. V., Jasinski, M., Mocko, D. M., Rodell, M., Borak, J., Li, B., Beaudoing, H. K., and Peters-Lidard, C. D.: NCA-LDAS land analysis: development and performance of a multisensor, multivariate land data assimilation system for the national climate assessment, J. Hydrometeorol., 20, 1571–1593, https://doi.org/10.1175/JHM-D-17-0125.1, 2019.
Kwon, Y., Yang, Z.-L., Hoar, T. J., and Toure, A. M.: Improving the radiance assimilation performance in estimating snow water storage across snow and land-cover types in North America, J. Hydrometeorol., 18, 651–668, https://doi.org/10.1175/JHM-D-16-0102.1, 2017.
Lee, Y. K., Kongoli, C., and Key, J.: An in-depth evaluation of heritage algorithms for snow cover and snow depth using AMSR-E and AMSR2 measurements, J. Atmos. Ocean. Tech., 32, 2319–2336, 2015.
Li, F. and Wang, H.: Autumn Eurasian snow depth, autumn Arctic sea ice cover and East Asian winter monsoon, Int. J. Climatol., 34, 3616–3625, 2014.
Li, F., Orsolini, Y. J., Keenlyside, N., Shen, M. L., Counillon, F., and Wang, Y. G.: Impact of snow initialization in subseasonal-to-seasonal winter forecasts with the Norwegian Climate Prediction Model, J. Geophys. Res.-Atmos., 124, 10033–10048, 2019.
Lim, S., Gim, H.-J., Lee, E., Lee, S., Lee, W. Y., Lee, Y. H., Cassardo, C., and Park, S. K.: Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a), Geosci. Model Dev., 15, 8541–8559, https://doi.org/10.5194/gmd-15-8541-2022, 2022.
Liu, Y., Peters-Lidard, C. D., Kumar, S. V., Arsenault, K. R., and Mocko, D. M.: Blending satellite-based snow depth products with in situ observations for streamflow predictions in the upper Colorado River basin, Water Resour. Res., 51, 1182–1202, https://doi.org/10.1002/2014WR016606, 2015.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C., Derksen, C., Mudryk, L., Moisander, M., Hiltunen, M., Smolander, T., Ikonen, J., Cohen, J., Salminen, M., Norberg, J., Veijola, K., and Venäläinen, P.: GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset, Sci. Data, 8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021.
Meng, J., Yang, R., Wei, H., Ek, M., Gayno, G., Xie, P., and Mitchell, K.: The land surface analysis in the NCEP climate forecast system reanalysis, J. Hydrometeorol., 13, 1621–1630, https://doi.org/10.1175/JHM-D-11-090.1, 2012.
Meyal, A. Y., Versteeg, R., Alper, E., Johnson, D., Rodzianko, A., Franklin, M., and Wainwright, H.: Automated cloud based long short-term memory neural network based SWE prediction, Front. Water, 2, 574917, https://doi.org/10.3389/frwa.2020.574917, 2020.
Miyoshi, T. and Yamane, S.: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution, Mon. Weather Rev., 135, 3841–3861, 2007.
Oaida, C. M., Reager, J. T., Andreadis, K. M., David, C. H., Levoe, S. R., Painter, T. H., Bormann, K. J., Trangsrud, A. R., Girotto, M., and Famiglietti, J. S.: A High-Resolution Data Assimilation Framework for Snow Water Equivalent Estimation across the Western United States and Validation with the Airborne Snow Observatory, J. Hydrometeorol., 20, 357–378, https://doi.org/10.1175/JHM-D-18-0009.1, 2019.
Orsolini, Y. J., Senan, R., Balsamo, G., Doblas-Reyes, F. J., Vitart, F., Weisheimer, A., Carrasco, A., and Benestad, R. E.: Impact of snow initialization on sub-seasonal forecasts, Clim. Dynam., 41, 1969-1982, 2013.
Orsolini, Y. J., Senan, R., Vitart, F., Weisheimer, A., Balsamo, G., and Doblas-Reyes, F.: Influence of the Eurasian snow on the negative North Atlantic Oscillation in subseasonal forecasts of the cold winter 2009/10, Clim. Dynam., 47, 1325–1334, https://doi.org/10.1007/s00382-015-2903-8, 2016.
Pullen, S., Jones, C., and Rooney, G.: Using satellite-derived snow cover data to implement a snow analysis in the met office NWP model, J. Appl. Meteorol., 50, 958–973, https://doi.org/10.1175/2010JAMC2527.1, 2011.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.: Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, https://doi.org/10.1038/s41586-020-2258-0, 2020.
Ramsay, B. H.: The interactive multisensor snow and ice mapping system, Hydrol. Process., 12, 1537–1546, 1998a.
Ramsay, B. H.: IMS Snow Cover Data, NOAA, National Snow and Ice Data Center, Boulder, Colorado, USA [data set],
https://noaadata.apps.nsidc.org/NOAA/G02156/ (last access: 20 November 2024), 1998b.
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv. Water Resour. 31, 1411–1418, 2008.
Reichle, R. H. and Koster, R. D.: Bias reduction in short records of satellite soil moisture, Geophys. Res. Lett., 31, L19501, https://doi.org/10.1029/2004GL020938, 2004.
Reichle, R. H., Koster, D., De Lannoy, G. J. M., Forman, B. A., Liu, Q., Mahanama, S. P. P., and Toure, A. M.: Assessment and Enhancement of MERRA Land Surface Hydrology Estimates, J. Climate, 24, 6322–6338, https://doi.org/10.1175/JCLI-D-10-05033.1, 2011.
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P., Koster, R. D., and De Lannoy, G. J.: Assessment of MERRA-2 land surface hydrology estimates, J. Climate, 30, 2937–2960, 2017.
Seo, E., Lee, M. I., Jeong, J. H., Koster, R. D., Schubert, S. D., Kim, H. M., Kim, D. H., Kang H. S., Kim, H. K., MacLachlan, C., and Scaife, A. A.: Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events, Clim. Dynam., 52, 1695–1709, 2019.
Seo, E., Lee, M. I., Schubert, S. D., Koster, R. D., and Kang, H. S.: Investigation of the 2016 Eurasia heat wave as an event of the recent warming, Environ. Res. Lett., 15, 114018, https://doi.org/10.1088/1748-9326/abbbae, 2020.
Seo, E., Lee, M. I., and Reichle, R. H.: Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter, Remote Sens. Environ., 253, 112222, https://doi.org/10.1016/j.rse.2020.112222, 2021.
Smyth, E. J., Raleigh, M. S., and Small, E. E.: Improving SWE Estimation With Data Assimilation: The Influence of Snow Depth Observation Timing and Uncertainty, Water Resour. Res., 56, e2019WR026853, https://doi.org/10.1029/2019WR026853, 2020.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating snow water equivalent using snow depth data and climate classes, J. Hydrometeorol., 11, 1380–1394, 2010.
Su, H., Yang, Z.-L., Dickinson, R. E., Wilson, C. R., and Niu, G.-Y.: Multisensor snow data assimilation at the continental scale: The value of gravity recovery and climate experiment terrestrial water storage information, J. Geophys. Res., 115, D10104, https://doi.org/10.1029/2009JD013035, 2010.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Karna, J. P., Koskinen, J., and Bojkov, B.: Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Remote Sens. Environ., 115, 3517–3529, 2011.
Toure, A. M., Luojus, K., Rodell, M., Beaudoing, H., and Getirana, A.: Evaluation of simulated snow and snowmelt timing in the Community Land Model using satellite-based products and streamflow observations, J. Adv. Model. Earth Sy., 10, 2933–2951, 2018.
U.S. National Ice Center: IMS daily Northern Hemisphere snow and ice analysis at 1 km, 4 km, and 24 km resolutions, version 3, NSIDC: National Snow and Ice Data Center, Boulder, Colorado, USA [data set], https://doi.org/10.7265/N52R3PMC, 2008.
You, Y., Huang, C., Gu, J., Li, H., Hao, X., and Hou, J.: Assessing snow simulation performance of typical combination schemes within Noah-MP in northern Xinjiang, China. J. Hydrol. 581, 124380, https://doi.org/10.1016/j.jhydrol.2019.124380, 2020.
