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

Jürgen Helmert,Patricia De Rosnay,David Finger,Vera Potopová,Rodolfo Alvarado Montero,Dagrun Vikhamar-Schuler,Aynur Şensoy Şorman,Samantha Pullen,Ghislain Picard,Carlo De Michele,Marie Dumont,Martin Lange,Ali Arslan

doi:10.3390/geosciences8120489

Abstract

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.

Highlights

As a major part of the cryosphere, snow is an important component of the hydrological cycle and with its unique physical properties, it is an essential environmental variable directly affecting theEarth’s energy balance
This paper aims to assess the current situation and understand the diversity of usage of snow observations in data assimilation (DA), forcing, monitoring, validation, or verification within numerical weather prediction (NWP), hydrology, snow and climate models
The literature review reveals the importance of conventional snow observations for DA and model calibration and validation, and the potential benefits of obtaining additional observations from national networks, according to the survey responses, concrete plans for using new or upcoming data sources of snow observations exist for use in snow models of NWP, hydrology or other modeling environments

Summary

Introduction

As a major part of the cryosphere, snow is an important component of the hydrological cycle and with its unique physical properties, it is an essential environmental variable directly affecting theEarth’s energy balance. Understanding the microstructural, macrophysical, thermal and optical properties of snowpack is essential [10] and there is a great need for accurate snow data at different spatial and temporal resolutions to address the challenges of changing snow conditions. Snow measurements are becoming increasingly important for freshwater management, mitigation of climate changes, adaptation to new climate conditions, and risk assessments such as avalanches, floods [11], and droughts [12]. The background error estimates areare done either by by distance weighting or taken as aas fixed value in. The background error estimates done either distance weighting or taken a fixed value most of theofNWP models, the former is more is commonly used compared to the latter. A few institutes workingworking on NWPon of ensembles anotherused method used inapplications.

Objectives

Results

Discussion

Conclusion