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Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Deutscher Wetterdienst (DWD), Offenbach 63067, Germany
Anadolu University, Faculty of Engineering, Department of Civil Engineering., Eskisehir 26555, Turkey
Deltares, Operational Water Management Department, Delft 2600 MH, The Netherlands
Politecnico di Milano, Department of Civil and Environmental Engineering, P.zza L. da Vinci 32, Milano 20133, Italy
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading RG2 9AX, UK
Météo-France—CNRS, CNRM, UMR 3589, CEN, Saint Martin d’Hères F-38400, France
School of Science and Engineering, Reykjavik University; Reykjavik, 101, Iceland
UGA, CNRS, Institut des Géosciences de l’Environnement (IGE), UMR 5001, Grenoble 38041, France
Department of Agroecology and Biometeorology, Czech University of Life Sciences Prague, Kamycka 129, Prague 165 21, Czech Republic
Met Office, FitzRoy Road, Exeter, Devon EX1 3PB, UK
Norwegian Meteorological Institute, Oslo 0313, Norway
Finnish Meteorological Institute, Helsinki FI-00560, Finland
Author to whom correspondence should be addressed.
Geosciences 2018, 8(12), 489;
Received: 28 September 2018 / Revised: 30 November 2018 / Accepted: 7 December 2018 / Published: 14 December 2018
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments. View Full-Text
Keywords: COST Action ES1404; HarmoSnow; snow measurements; snow models; data assimilation; remote sensing COST Action ES1404; HarmoSnow; snow measurements; snow models; data assimilation; remote sensing
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Helmert, J.; Şensoy Şorman, A.; Alvarado Montero, R.; De Michele, C.; De Rosnay, P.; Dumont, M.; Finger, D.C.; Lange, M.; Picard, G.; Potopová, V.; Pullen, S.; Vikhamar-Schuler, D.; Arslan, A.N. Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey. Geosciences 2018, 8, 489.

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