Station Assimilation

ROI-based assimilation of station snow depth and SWE observations.

Overview

openAMUNDSEN-DA supports ROI-based assimilation of in situ station snow observations:

• station_hs for snow depth
• station_swe for snow water equivalent
• each active station contributes one likelihood term,
• stations with lower uncertainty influence the weights more strongly,
• stations with higher uncertainty influence the weights more weakly,
• all active stations on one assimilation date are combined into one ROI-level member weighting.

Supported Variables

The following assimilation-event variables are supported:

data_assimilation:
  assimilation_events:
    - date: 2023-01-10
      variable: station_hs
    - date: 2023-03-09
      variable: station_swe

Required Inputs

Station observation CSVs

Station observations live under:

obs/stations/<station_id>.csv

Expected columns:

• time
• snow_depth
• swe

Typical layout:

time,snow_depth,swe
2022-10-01 00:00:00,0.10,15.1
2022-10-01 01:00:00,0.10,14.8
...

Notes:

• snow_depth is expected in m
• swe is expected in mm
• a station file may exist even if only one variable is later assimilated
• missing values are allowed, but the requested variable must exist for the station/date to become active

Station DA metadata

Station DA metadata live in:

obs/stations/stations_da_metadata.csv

Expected columns:

• station_id
• station_uncertainty_pct
• hs_sigma_abs_min
• swe_sigma_abs_min

Example:

station_id,station_uncertainty_pct,hs_sigma_abs_min,swe_sigma_abs_min
proviantdepot,20,0.05,15
latschbloder,80,0.20,

Notes:

• station_uncertainty_pct still falls back to the project-level default when the metadata value is empty.
• hs_sigma_abs_min is required for every active station_hs station.
• swe_sigma_abs_min is required for every active station_swe station.
• Missing required absolute sigma metadata is a hard configuration error.

Configuration

Project-level station DA settings live under:

data_assimilation:
  station:
    default_station_uncertainty_pct: 25
    min_station_uncertainty_pct: 10
    single_station_factor: 2.0

Meaning of the keys:

• default_station_uncertainty_pct: fallback when station_uncertainty_pct is empty in metadata
• min_station_uncertainty_pct: lower bound to avoid overconfident station influence
• single_station_factor: extra sigma inflation when only one active station is available for a date

The absolute sigma floor is configured per station in stations_da_metadata.csv:

• hs_sigma_abs_min in m for station_hs
• swe_sigma_abs_min in mm for station_swe

Effective Station Uncertainty

The station uncertainty used in DA is an effective uncertainty, not only sensor error.

It can absorb:

• instrument uncertainty
• local station quality and maintenance confidence
• siting concerns
• flat-field or sheltered-location concerns
• broad point-to-ROI representativeness concerns

Interpretation:

• lower station uncertainty -> narrower likelihood -> stronger update
• higher station uncertainty -> wider likelihood -> weaker update

Because percentage-only scaling can become unrealistically strict near zero snow, the implementation always combines the relative term with the station-wise absolute sigma floor from metadata.

Likelihood

For one assimilation date, each active station contributes a Gaussian log-likelihood term for every ensemble member.

Definitions:

• y_s = observed station value
• H_m,s = modeled point value at the same station for member m
• sigma_s = effective station sigma for station s

Residual:

residual_m,s = H_m,s - y_s

Station contribution:

log L_m,s ∝ -0.5 * (residual_m,s / sigma_s)^2

ROI-level member likelihood:

log L_m,total = sum over all active stations s of log L_m,s

The normalized particle-filter weights are then computed from these summed log-likelihoods.

Single-Station Handling

If only one active station is available on a DA date, the observation is still used, but more cautiously.

Version 1 policy:

• write a warning
• inflate the station sigma by single_station_factor

Effective single-station sigma:

sigma_eff = sigma_base * single_station_factor

This keeps a single station from speaking too loudly at ROI scale while still allowing it to contribute.

What Counts as an Active Station

A station is active on a DA date when:

• the station belongs to the ROI / setup station set,
• the required observation variable exists,
• the observation value is finite,
• the observation value is nonnegative,
• a unique nearest timestamp can be resolved,
• model point output exists for the station for all members.

Stations failing these checks are skipped and logged.

Outputs and Diagnostics

For station DA dates, the pipeline writes:

• weights_station_hs_YYYYMMDD.csv or weights_station_swe_YYYYMMDD.csv
• station_diagnostics_station_hs_YYYYMMDD.csv or station_diagnostics_station_swe_YYYYMMDD.csv
• one diagnostic PNG per station-DA date

The diagnostics CSV contains station-level details used for the update, such as:

• station_id
• member_id
• obs_value
• model_value
• sigma
• residual terms
• final member weights

The fraction time-series plot also marks station DA dates with vertical lines:

• HS for station_hs
• SWE for station_swe

Practical Interpretation

The station DA update does not force the ensemble mean directly to the station observation.

Instead, it reweights the members that already exist.

That means:

• if the ensemble already contains members close to the station observation, the posterior mean can move strongly toward the station
• if no ensemble member is close to the station observation, the DA can only move toward the best available members

This is standard particle-filter behavior and is especially important for strong, low-uncertainty station updates.

Related Documentation

• Configuration Reference
• Observation Processing
• Running
• Data Assimilation Methods