Data Assimilation Implementation

This page identifies software stages and artifacts. For equations, methodological motivation and scientific interpretation, use Wagner et al. (2026), the manuscript accompanying openAMUNDSEN-DA v0.9.

Event sequence

For each configured event, the project runner:

1. propagates the open loop and prior members with openAMUNDSEN;
2. evaluates the configured observation operator;
3. calculates and normalizes likelihood weights;
4. records effective sample size and the resampling decision;
5. materializes the posterior and applies configured rejuvenation; and
6. uses the posterior as the next step’s prior.

The current particle weights are scalar at the model-domain or subdomain scale. Independent subdomain execution is a decomposition strategy and does not add localization or exchange particles across boundaries.

Observation families

The event schema supports fractional snow covered area (scf), wet snow fraction (wet_snow), wet snow line altitude (wet_snow_line), station snow depth (station_hs) and station snow water equivalent (station_swe). Each family has an explicit operator and uncertainty configuration.

Technical artifacts

• per-event weight CSVs and plots record member likelihood response;
• effective sample size plots record degeneracy and resampling decisions;
• results/benchmark/ stores configured evaluation cases and scores;
• results/grids/da_output_grids.nc stores compact open-loop, ensemble and event-analysis fields; and
• results/run_manifest.json records hashes, stage state, provenance and outputs.

See Output Data for paths and Configuration for the event/uncertainty schema.