Multi-physics ensemble snow modelling in the western Himalaya

Combining multiple data sources with multi-physics simulation frameworks offers new potential to extend snow model inter-comparison efforts to the Himalaya. As such, this study evaluates the sensitivity of simulated regional snow cover and runoff dynamics to different snowpack process representations. The evaluation is based on a spatially distributed version of the Factorial Snowpack Model (FSM) set up for the Astore catchment in the upper Indus basin. The FSM multi-physics model was driven by climate fields from the High Asia Refined Analysis (HAR) dynamical downscaling product. Ensemble performance was evaluated primarily using MODIS remote sensing of snow-covered area, albedo and land surface temperature. In line with previous snow model inter-comparisons, no single FSM configuration performs best in all of the years simulated. However, the results demonstrate that performance variation in this case is at least partly related to inaccuracies in the sequencing of inter-annual variation in HAR climate inputs, not just FSM model limitations. Ensemble spread is dominated by interactions between parameterisations of albedo, snowpack hydrology and atmospheric stability effects on turbulent heat fluxes. The resulting ensemble structure is similar in different years, which leads to systematic divergence in ablation and mass balance at high elevations. While ensemble spread and errors are notably lower when viewed as anomalies, FSM configurations show important differences in their absolute sensitivity to climate variation. Comparison with observations suggests that a subset of the ensemble should be retained for climate change projections, namely those members including prognostic albedo and liquid water retention, refreezing and drainage processes

Multivariate data assimilation in snow modelling at Alpine sites

The knowledge of snowpack dynamics is of critical importance to several real-time applications such as agricultural production, water resource management, flood prevention, hydropower generation, especially in mountain basins. Snowpack state can be estimated by models or from observations, even though both these sources of information are affected by several errors

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

Abstract 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

On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model

International audienceThis paper examines the ability of optical re-flectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multi-layer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter , to represent simulation uncertainties. In snowpack mod-eling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates. Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45 % the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community

A performance assessment of gridded snow products in the Upper Euphrates

Snow observations are important in many mountain areas to quantify the water stored in snowpacks and to predicting runoff during the melting period. In this study we compare the performance of five different regional-scale gridded snow products to reproduce snow water equivalent (SWE) in the Upper Euphrates region (Karasu Basin, 10,275 km2), with observations from automatic weather stations in the catchment through Taylor diagrams. The products compared are the ERA5, ERA5-Land, MERRA-2, snow data from a dynamical downscaling of ERA-5 (period 2000-2018) and SWE generated from microwave satellite data (SWE-E(H13) period 2013-2015 product of the EUMETSAT H SAF project). The H13 product presented deficiencies in terms of not being able to reproduce the spatial and temporal variability of the snowpack. ERA-5 and, in particular, ERA-Land products, at 30 and 9 km grid size, respectively, showed good performance in reproducing snow evolution compared to four available observation sites. MERRA2 at 50 km resolution showed lower skills compared to the above-mentioned products. Resulting snow data from WRF at 10 km resolution did not show any improvement with respect to the global datasets. The impossibility of testing different configurations due to the lack of observations to compare and the computational constraints to test different parametrizations may be the reasons to explain the low performance although they remain speculative. All the gridded datasets showed good performance in reproducing snow duration over the basin, compared to remotely sensed data. Results highlight ERA-Land dataset as a very promising tool for regional snow studies in mountainous regions with limited observations, in a cost-effective way