Assimilation of multiple types of snow observations through a large scale spatialized particle filter

Particle filtering is interesting for snow data assimilation because of its minimal assumptions. However, implementing a particle filter over a large spatial domain is challenging for many reasons. For instance, the number of required particles rises exponentially as the domain size increases. Another important issue when spatializing a particle filter for snow data assimilation is the creation of spatial discontinuities when resampling the particles at locations where snow observations are available. In this presentation, we will describe how we implemented a spatialized particle filter for snow data assimilation over a large portion of the province of Quebec, Canada (600 000 km² ). Two different types of snow observations where assimilated with this particle filter: sporadic manual snow surveys, which measure snow water equivalent directly, and continuous automated snow depth observations, which we converted to snow water equivalent using an ensemble of neural networks. We will then explain how a more frequent data assimilation can create unwanted discontinuities and break the spatial structure of the particles, and how we can remediate that by using an adaptation of the Schaake Shuffle reordering method. We will show that this solution significantly reduces the random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. We emphasize that the proposed spatialized particle framework could also eventually accommodate other types of data, such as citizen science data and gamma monitoring data. Overall, the proposed method allows to obtain improved spatial representation of snow water equivalent compared to the previous operational method used by the government of Quebec.